Automatic cost calculation and safety matching platform based on order drawings and specifications based on AI analysis

Abstract

We provide an information processing device, a quotation acquisition method, a program, and a recording medium that provide a BtoB quotation platform for the manufacturing industry that utilizes AI technology. [Solution] The information processing device automatically analyzes order drawings and specifications using artificial intelligence, estimates the part shape, materials, processing process, and labor hours to calculate the cost, automatically determines whether manufacturing is possible by comparing it with a factory's equipment capacity database, matches with an appropriate factory, safely manages confidential drawings with comprehensive security functions such as encryption, access control, and watermarking, and provides a highly reliable trading environment with price validity evaluation and a user evaluation system.

Description

[Background technology]

[0001] This invention relates to the technical field of B2B (Business-to-Business) transaction platforms in the manufacturing industry. Specifically, it relates to a system, device, method, program, and recording medium that utilizes artificial intelligence (AI) technology to automatically analyze order drawings and specifications, estimate material costs, processing costs, and labor hours, calculate costs, and provide a secure matching function between purchasers and contractors. The platform of the present invention innovatively automates the traditional manual quotation process, contributing to improved efficiency and productivity throughout the manufacturing industry. Specifically, automatic recognition technology for drawing data reduces human error and dramatically improves quotation accuracy. Furthermore, transparency of transaction history using blockchain technology promotes the building of trust between purchasers and contractors. Furthermore, real-time supply and demand matching enables optimal allocation of manufacturing resources, improving supply chain efficiency throughout the industry. This invention supports the digital transformation of small and medium-sized manufacturers and contributes to strengthening their global competitiveness. In particular, as the shortage of skilled engineers becomes more serious, AI-based support for technology transfer is positioned as an important function.

[0002] More specifically, this technology accepts design information in various formats, including CAD data, PDF drawings, text specifications, 3D model data, point cloud data, and hologram data, as input for various manufactured products, including but not limited to metalworking parts, building components, medical equipment components, electronic components, and automotive components. It automatically estimates the part's shape, materials, processing steps, and required labor hours using artificial intelligence technology, and automatically calculates the cost price based on these estimates. The system of the present invention comprehensively understands complex manufacturing requirements and proposes the optimal manufacturing method. For example, if multiple manufacturing methods (e.g., cutting, casting, 3D printing) are available for the same part, the system compares and analyzes the cost, quality, and delivery time of each method to recommend the method that best meets the client's needs. It also incorporates evaluations from the perspectives of environmental impact and sustainability, calculating carbon footprints and assessing recyclability. Furthermore, it supports global supply chains and realizes comprehensive cost calculations that take into account exchange rate fluctuations and geopolitical risks. This technology embodies the concept of Industry 4.0, and by linking with smart factories, it also makes it possible to grasp production status in real time and dynamically adjust costs.

[0003] The present invention also relates to platform technology that integrates various security and quality assurance functions, such as secure management of confidential design information, NDA (Non-Disclosure Agreement) integration, access control, price validation, and user evaluation systems. These functions are realized via all current and future communication methods, including, but not limited to, wired, wireless, Internet, intranet, cloud, edge, satellite, optical, quantum, 5G, 6G, Wi-Fi, Bluetooth, NFC, LoRaWAN, millimeter-wave, terahertz, molecular, DNA, EEG, neural interface, holographic, and space-time communication. The security architecture of the present invention is based on a zero-trust model, verifies all access, and controls access based on the principle of least privilege. Furthermore, an AI-driven anomaly detection system detects and automatically responds to unauthorized access and information leak attempts in real time. The encryption technology employs post-quantum cryptography, which is resistant to quantum computers, and is designed to be able to withstand future threats. Additionally, distributed ledger technology ensures that all transactions and information access records are stored in an immutable form, ensuring the integrity of the audit trail.

[0004] Traditionally, the manufacturing industry's parts ordering process typically involved a purchaser preparing manufacturing drawings and specifications, sending them to multiple factories and suppliers, and requesting quotes. Each factory then manually reviewed and analyzed the drawings, selecting materials, determining processing steps, calculating labor hours, and finally calculating a price quote. This traditional process was fraught with inefficiencies and challenges. First, the level of understanding of the drawings varied depending on the engineer's experience and knowledge level, leading to different interpretations of the same drawing at different factories. This resulted in significant variations in quoted prices, making it difficult for purchasers to determine a fair price. Furthermore, when parts required complex shapes or special processing, inexperienced engineers struggled to accurately estimate labor hours, resulting in problems such as unprofitable orders and delayed delivery. Furthermore, because the quotation process itself was highly personal, quotation responses were delayed when the person in charge was unavailable, leading to lost business opportunities. Paper-based information management was also common, making it difficult to search and analyze past quotation data, limiting the use of knowledge from similar projects and the development of pricing strategies. This traditional quotation process presented the following challenges: First, drawing analysis and process analysis required a significant amount of time and effort from specialized engineers, often taking days or even weeks to respond to quotes, hindering rapid ordering decisions. Furthermore, quote accuracy and pricing depended heavily on the experience and subjectivity of the person in charge, resulting in large price differences between factories even for the same drawing. This issue was particularly prevalent in small and medium-sized manufacturers, who placed a heavy burden on responding to numerous quote requests with limited human resources. It was not uncommon for quote preparation to take one to two hours for simple parts and more than half a day for complex parts, during which time engineers were unable to focus on other production activities, resulting in lost opportunities. Furthermore, quote accuracy issues directly led to poor profitability and quality issues after receiving orders, seriously affecting a company's profitability and reliability. Pricing was often based on rule of thumb without a full understanding of market trends and competitor trends, making it difficult to offer competitive prices. Furthermore, while globalization has led to an increase in quote requests from overseas, language barriers and differences in business practices often prevented appropriate responses. [Prior art documents] [Patent documents]

[0005] Patent Publication No. 2019-102065

[0006] Although blueprints and specifications often contain important corporate design know-how and confidential information, security measures for sending and receiving such information via email and file-sharing services have traditionally been insufficient, raising concerns about the risk of information leaks. Problems such as unauthorized copying, secondary use, and disclosure of blueprint data to competitors have become particularly serious. Cases of industrial espionage and internal fraud have led to the leaking of important design information, resulting in the production of counterfeit products and technology theft. Furthermore, as the use of cloud storage services has increased, data storage locations and management permissions have become unclear, raising compliance issues. Strict information management is particularly required for medical devices and aerospace-related parts, but existing file-sharing methods make it difficult to obtain access logs and track users. Furthermore, with the increasing sophistication of cyberattacks, traditional password protection and file encryption alone are no longer sufficient, necessitating the need for more advanced security measures. International transactions also require compliance with various countries' data protection regulations (e.g., GDPR, CCPA), and the risk of sanctions due to compliance violations poses a significant threat to companies.

[0007] In recent years, online B2B matching platforms have become widespread, providing services that connect buyers and sellers online. However, these existing platforms primarily provide communication between companies and basic transaction management functions, without fully implementing advanced features such as automatic cost calculation from drawings or automatic determination of whether or not a product can be manufactured. Many existing platforms merely provide company information databases and inquiry forms, and the actual technical matching and quotation preparation are still manual. Furthermore, evaluations of registered companies' technical capabilities and equipment capabilities are subjective, making it difficult for buyers to select the appropriate seller. Pricing transparency is also lacking, and even when obtaining competitive bids, the pricing rationale of each company is unclear, often resulting in a simple price comparison. Furthermore, because these platforms are not industry-specific, they lack an understanding of manufacturing-specific requirements (e.g., tolerances, surface treatments, heat treatments), making them unsuitable for the exchange of detailed specifications. Transaction security is also an issue, and there are insufficient guarantees against prepayment fraud and quality defects.

[0008] Furthermore, advances in artificial intelligence (AI) technology have improved the accuracy of current image recognition and natural language processing, leading to increasing efforts to apply these technologies to manufacturing. In the future, more advanced AI technologies, such as artificial general intelligence (AGI), artificial superintelligence (ASI), quantum AI, biological AI, hybrid AI, swarm intelligence, collective intelligence, and distributed intelligence, are expected to be realized. Furthermore, the integration of AI with human intelligence augmentation technologies, such as brain-computer interfaces, neural implants, mind-reading technology, emotion recognition technology, creativity augmentation technology, intuition augmentation technology, and cognitive augmentation technology, is expected to create new possibilities that go beyond the limits of conventional AI. While current AI technology has demonstrated superior performance in certain tasks, it still lags behind human experts in complex decision-making and creative problem-solving in manufacturing. However, advances in deep learning have dramatically improved the ability to understand drawings, achieving accuracy comparable to that of skilled engineers in 3D shape recognition and processing method estimation. In the future, AI may be able to understand design intent and propose improvements. By combining it with quantum computing, the ability to solve combinatorial optimization problems will be dramatically improved, making it possible to achieve complex production scheduling and resource allocation optimization.

[0009] However, these advanced technologies have yet to be integrated into a comprehensive analysis system that can accommodate a wide variety of manufacturing drawings (2D, 3D, 4D, nD, hand-drawn, CAD data, point cloud data, hologram data, virtual reality data, augmented reality data, mixed reality data, EEG pattern data, emotional pattern data, etc.), manufacturing-specific terminology, multilingual support, cultural differences, regional regulations, international standards, industry standards, company-specific standards, historical changes, technological advances, and material innovations. Manufacturing drawings vary in notation across industries and companies, and the same processing instructions can be expressed in a variety of ways. For example, the notation of surface roughness can be expressed using multiple standards, such as Ra, Rz, and Rmax, making it difficult to interpret them in a consistent manner. Furthermore, there is a great deal of tacit knowledge not captured in drawings, and "common sense" tolerances and processing methods vary significantly across regions and industries. Furthermore, the emergence of new materials increasingly exacerbates conventional processing practices. Although progress is being made in unifying international standards, individual national standards still remain, and these differences must be absorbed in global transactions. Language is also a serious issue, and not only is translation of technical terms required, but interpretation based on an understanding of the cultural context is also required.

[0010] Furthermore, communications technology is expected to evolve from current 5G to future 6G and 7G, as well as to revolutionary technologies such as quantum communication, optical communication, terahertz communication, holographic communication, EEG communication, DNA communication, molecular communication, and space-time communication. However, the technological foundation for manufacturing platforms compatible with these diverse communication methods is not yet fully developed. Next-generation communication technologies not only offer faster communication speeds but also possess characteristics such as ultra-low latency, ultra-high reliability, and massive simultaneous connections, potentially revolutionizing integration with IoT devices on the manufacturing floor. For example, 6G communications will enable holographic communication to project 3D drawings into space from remote locations, enabling multiple engineers to simultaneously review the drawings. Quantum communication will theoretically enable eavesdropping-proof communication, revolutionizing the transmission and reception of highly confidential design information. Furthermore, advances in EEG communication technology are expected to enable designers to directly communicate their intentions to systems, enabling more intuitive design and manufacturing instructions. However, an architecture for integrating these technologies and enabling a gradual transition while maintaining compatibility with existing systems has yet to be established.

[0011] Taking into account the problems of the prior art described in the Background Art section above, the present invention aims to solve the following problems. These are structural issues facing the manufacturing industry, and are difficult to solve through individual measures; they require system-wide innovation. Addressing these issues is particularly urgent due to changes in the external environment, such as intensifying global competition, the aging of skilled engineers, and increasingly complex supply chains. Furthermore, the need for non-face-to-face work, brought to light by the COVID-19 pandemic, is accelerating the demand for digitalization and automation. Furthermore, from the perspectives of SDGs and ESG investment, there is a growing demand for transparency and sustainability in manufacturing processes, and these societal demands must also be met. The present invention aims to provide an integrated solution to these complex challenges. It is intended not only to improve operational efficiency but also to promote the transformation of the manufacturing industry's business model itself and serve as a foundation for new value creation.

[0012] The first challenge is to provide technology that can automatically and accurately estimate material types, processing processes, and required labor hours from order drawings and specifications, and quickly calculate costs based on these estimates. Traditional manual analysis is time-consuming and cost-intensive, necessitating automation using AI technology. However, this is merely simple automation, and there is room for further technological improvement, such as improving estimation accuracy and supporting diverse drawing formats. The essence of this challenge lies in how to digitize human tacit knowledge and rules of thumb and train AI. Drawing interpretation requires consideration of not only the information depicted but also undescribed information (industry common sense, processing constraints, etc.). For example, even if there is no instruction to chamfer sharp corners, chamfering is still necessary in actual processing to avoid stress concentrations. AI must be able to make such judgments automatically. Furthermore, cost calculation requires the construction of more realistic cost calculation models that consider complex factors such as setup time, defect rates, equipment depreciation, and overhead costs, rather than simply integrating material costs and processing time.

[0013] The second challenge is to create a database of information on factory and manufacturer equipment capacity, processing technology, and available materials, and then compare it with the required specifications of the ordered parts to automatically determine whether a part can be manufactured. This allows purchasers to efficiently select capable factories and contractors to avoid unnecessary work on projects outside their own scope. However, further improvements are needed, including more sophisticated evaluation algorithms and more flexible evaluation criteria. Determining whether a part can be manufactured is insufficient based solely on equipment specifications; qualitative factors such as processing know-how, past performance, and the skill level of the technician must also be considered. For example, even with the same 5-axis machining center, the complexity of the shapes that can be machined varies greatly depending on the operator's skill level. Furthermore, factors such as the availability of materials and the availability of outsourced post-processing (heat treatment, surface treatment, etc.) are important factors in determining whether a part can be manufactured comprehensively. Furthermore, a real-time evaluation system must be developed that takes into account dynamic factors such as fluctuations in capacity during peak and off-peak periods and the availability of rush orders.

[0014] The third challenge is providing comprehensive security features for securely sharing and managing highly confidential design drawings and specifications. Specifically, multi-layered security measures, such as encryption, access control, watermarking, viewing log management, and NDA collaboration, are required. However, these technologies are merely combinations of known security technologies, and security enhancements tailored to the unique needs of the manufacturing industry are required. A unique aspect of security in the manufacturing industry is the long-term value of design data. Once leaked, design information can be misused for long periods of time to manufacture counterfeit products, requiring stricter management than standard data protection. Furthermore, as supply chains become more complex, information needs to be shared among multiple companies in stages, making it important to set appropriate access permissions at each stage. Furthermore, the spread of 3D printing technology has made it possible to manufacture products directly from digital data, making the data itself as valuable as the product itself. In this environment, the application of more advanced security technologies, such as blockchain-based tamper prevention and AI-based access detection, is essential.

[0015] The fourth challenge is to develop a function that objectively evaluates the validity of calculated quoted prices and presents a fair price range by comparing them with past transaction records and market rates. This will prevent unreasonably high prices and low-price orders and create a fair trading environment. However, there is room for improvement, including but not limited to refining the price evaluation algorithm and methods for reflecting market trends. Evaluating price validity requires more than simple statistical comparisons; multidimensional analysis is required, taking into account various factors such as part complexity, required precision, delivery time, and lot size. External factors such as regional differences, seasonal fluctuations, and fluctuations in raw material prices must also be taken into account. Furthermore, flexibility is required to respond to price-disrupting changes caused by innovation and the introduction of new technologies. For example, advances in 3D printing technology could significantly reduce the cost of complex-shaped parts that were previously expensive. A comprehensive price evaluation system that predicts such technological innovations and incorporates future price trends is needed. Another important challenge is to develop a balanced information disclosure mechanism that ensures price transparency while protecting corporate trade secrets.

[0016] The fifth challenge is to build trust among platform users by providing a rating system based on transaction history, a review function, and a function to detect and remove malicious users. However, some aspects of the current system, such as ensuring the fairness of the rating system and preventing rating manipulation, are merely implemented as simple rating functions, and more advanced trust evaluation technology is needed. Trust evaluation in B2B manufacturing transactions is unique and differs from rating systems on general e-commerce sites. It requires multifaceted evaluation, including long-term business relationships, technical expertise, and consistent quality. Furthermore, because a single failure can have a significant impact on a company's credibility, it is important to design a fair and constructive rating system. Furthermore, it is necessary to balance providing opportunities for new entrants with the advantages of established companies. To prevent rating manipulation and fraud, tamper-proof functions using blockchain technology and AI-based detection of unnatural rating patterns are also needed. Furthermore, in international transactions with different cultural backgrounds, it is necessary to standardize rating standards while taking into consideration local business practices.

[0017] To solve the above problems, the present invention provides the following means. However, these means are merely examples of the present invention and are not limited thereto. Various modifications and improvements are possible within the scope of the technical concept of the present invention. The means of the present invention are not simply a combination of individual technical elements, but are designed to organically link each other and create synergistic effects. Furthermore, based on the concept of open innovation, they are designed to be scalable so that they can easily link with external technologies and services. Furthermore, they are designed to be continuously updated in response to technological advances and changes in the market environment. Each of the means described below not only functions independently, but also operates in an integrated manner to form a comprehensive platform that accelerates the digital transformation of the manufacturing industry.

[0018] The first aspect of the present invention provides a drawing and specification analysis unit using an artificial intelligence analysis engine. This analysis unit extracts geometric and physical features, such as the outer shape, internal structure, dimensions, hole locations, surface roughness, and material density distribution, from 2D drawings, 3D drawings, 4D drawings (including time axes), nD drawings, virtual reality drawings, augmented reality drawings, mixed reality drawings, hologram drawings, etc., using currently known image recognition technologies and visual recognition technologies that may be developed in the future. It also obtains surface information, volume, material properties, stress distribution, temperature distribution, magnetic field distribution, and electric field distribution from 3D CAD data, point cloud data, mesh data, voxel data, NURBS data, parametric data, feature-based data, etc., and extracts material specifications, surface treatment requirements, tolerance information, functional requirements, performance requirements, aesthetic requirements, and sensory requirements from text specifications, audio specifications, video specifications, electroencephalogram patterns, emotion patterns, etc., using a variety of analysis technologies, including but not limited to natural language processing, voice recognition, image analysis, electroencephalogram analysis, and emotion analysis. This analysis engine utilizes transfer learning and few-shot learning technologies to achieve highly accurate recognition even with limited training data. It also incorporates explainable AI (XAI) technology to present the basis for analysis results in a form that humans can understand, enabling collaborative work with engineers. Furthermore, its continuous learning function automatically adapts to new drawing formats and notation methods, ensuring it is always up to date with the latest manufacturing technologies.

[0019] The second aspect of the present invention is to provide a manufacturing feasibility determination unit linked to a factory equipment database. This determination unit determines the specifications of the equipment (conventional machine tools, CNC machine tools, 3D printers, 4D printers, molecular assemblers, nanomanipulators, robot arms, collaborative robots, autonomous robots, swarm robots, soft robots, biorobots, quantum processing machines, laser processing machines, electron beam processing machines, ion beam processing machines, plasma processing machines, ultrasonic processing machines, chemical processing equipment, bioprocessing equipment, etc.) owned by each factory, manufacturing base, virtual factory, automated factory, unmanned factory, space factory, undersea factory, mobile factory, etc., the specifications of the materials that can be processed (metals, resins, ceramics, composite materials, nanomaterials, biomaterials, smart materials, metamaterials, artificial muscles, self-repairing materials, shape memory materials, phase change materials, electronic materials, optical materials, magnetic materials, superconducting materials, etc.), the specifications of the equipment ... Information on the material (e.g., conductive materials, quantum materials, biodegradable materials, recycled materials), compatibility tolerances, production capacity, quality control capability, environmental compatibility, sustainability, carbon neutral compatibility, etc. is compiled into a database using current and future data management technologies, including but not limited to relational databases, NoSQL databases, graph databases, time series databases, distributed databases, blockchain databases, quantum databases, DNA databases, holographic storage, etc., and the results of the analysis are compared with multidimensional matching, probabilistic matching, fuzzy matching, semantic matching, ontology-based matching, etc. to determine whether the material is "compatible," "compatible with conditions," "difficult to compatible," "requires technological development," "requires capital investment," etc.

[0020] The third aspect of the present invention is to provide a multifaceted cost calculation unit. This calculation unit calculates direct material costs, indirect material costs, material loss costs, material management costs, material quality costs, etc. based on estimated material type, quantity, quality, supplier, procurement time, inventory status, price fluctuations, supply and demand balance, etc., and calculates machining costs, manual labor costs, assembly costs, inspection costs, testing costs, certification costs, quality assurance costs, etc. based on estimated processing steps, man-hours, equipment utilization rates, worker skills, quality requirements, precision requirements, surface treatment requirements, heat treatment requirements, inspection requirements, etc., and calculates total costs by adding design costs, development costs, tool costs, jig costs, mold costs, setup costs, transportation costs, insurance costs, customs duties, environmental costs, carbon offset costs, waste disposal costs, recycling costs, life cycle costs, etc. as needed. To reflect real-time material price fluctuations, the cost calculation unit connects to commodity exchange APIs to obtain the latest market information. It is also possible to calculate prices in multiple currencies, taking into account exchange rate fluctuation risks. Furthermore, machine learning is used to predict the probability of hidden costs and unexpected additional expenses from past performance data, providing a more realistic cost estimate. Detailed cost breakdowns are visualized, allowing clients to verify the validity of each cost element. The "what-if" analysis function also makes it possible to simulate in real time the impact of specification and quantity changes on costs.

[0021] A fourth aspect of the present invention is to provide a comprehensive security management unit. This management unit is capable of implementing a variety of authentication methods, including data encryption (symmetric encryption, asymmetric encryption, hybrid encryption, stream encryption, block encryption, elliptic curve encryption, lattice encryption, multivariate encryption, code-based encryption, post-quantum encryption, quantum key distribution, quantum cryptography, DNA encryption, chaos encryption, biometric encryption, neuro-encryption, etc.), multi-factor authentication (password, PIN, biometric authentication, behavioral authentication, voice authentication, face authentication, iris authentication, fingerprint authentication, vein authentication, DNA authentication, brainwave authentication, heart rate authentication, gait authentication, keystroke authentication, mouse operation authentication, location information authentication, time information authentication, device authentication, certificate authentication, token authentication, one-time password, push authentication, risk-based authentication, adaptive authentication, context authentication, etc.), and authentication methods. It provides comprehensive security functions such as authentication, authorization, and authorization mechanisms (e.g., zero trust authentication), role-based access control (RBAC), attribute-based access control (ABAC), mandatory access control (MAC), discretionary access control (DAC), rule-based access control, time-based access control, location-based access control, device-based access control, risk-based access control, AI-based access control, quantum access control, etc.), and drawing watermarking (visible watermarking, invisible watermarking, robust watermarking, fragile watermarking, zero watermarking, blind watermarking, quasi-blind watermarking, multiple watermarking, holographic watermarking, quantum watermarking, blockchain watermarking, DNA watermarking, steganography, fingerprinting, etc.).

[0022] The fifth aspect of the present invention provides a price validity evaluation unit and a user evaluation management unit. The price validity evaluation unit performs multidimensional comparisons of the calculated estimated price with similar past cases, industry average prices, international market prices, forecast prices, AI-estimated prices, crowdsourcing prices, blockchain-recorded prices, decentralized price information, real-time market prices, fixed-term prices, option prices, etc., and calculates validity scores, confidence intervals, risk levels, forecast accuracy, market position, etc. using statistical analysis, machine learning analysis, deep learning analysis, time series analysis, regression analysis, Bayesian analysis, Monte Carlo analysis, sensitivity analysis, scenario analysis, stress testing, risk analysis, uncertainty analysis, fuzzy analysis, neural network analysis, genetic algorithm analysis, particle swarm optimization analysis, quantum computing analysis, etc. This evaluation system learns industry-specific price formation mechanisms and achieves context-aware price evaluation that goes beyond simple statistical comparisons. For example, it understands that the pricing logic differs between prototypes and mass-produced products, and applies appropriate evaluation criteria for each. It also takes into account price differences due to regional differences and company size to provide a fair evaluation. The evaluation results are presented along with a reliability rating and can be used as an objective basis for price negotiations. Furthermore, the system also has a function for predicting future price trends, helping to formulate mid- to long-term procurement strategies.

[0023] The present invention provides the following effects. However, these effects are merely examples of the present invention, and different effects may be obtained depending on the embodiment and operating conditions. The effects of the present invention are not limited to direct improvements in business efficiency, but are wide-ranging, including transformation of manufacturing business models, strengthening competitiveness, and realization of sustainable growth. Furthermore, these effects are interrelated and create synergies, creating value that exceeds the simple sum of the individual effects. Furthermore, the introduction of the present invention is expected to revitalize the entire manufacturing ecosystem and accelerate the creation of innovation. The effects listed below have a variety of time frames, ranging from those realized in the short term to those that become apparent in the medium to long term.

[0024] The first benefit is a significant reduction in the time from order placement to quotation response. The quotation process, which previously took days or weeks, can now be completed in minutes or hours thanks to AI automated analysis. However, the reduction in time varies depending on the complexity of the drawings and the factory's response status, and a uniform time reduction is not guaranteed. This time reduction not only reduces work hours but also leads to increased business opportunities. Rapid quotation responses speed up client decision-making and increase the probability of winning. Furthermore, engineers' time, which was previously spent creating quotation, can be redirected to more value-added tasks. Furthermore, 24 / 7 automated quotation response allows for immediate response to inquiries from overseas, even in different time zones, which is expected to expand global business. Standardizing the quotation process eliminates personal dependencies and enables the continuous provision of quotation of consistent quality.

[0025] The second benefit is improved estimate accuracy and standardization. Compared to conventional methods that relied on subjective manual judgment, objective analysis by AI ensures consistent estimate quality. However, because the accuracy of AI analysis depends on the quality and quantity of training data, continuous data accumulation and learning are important, and other methods of improving accuracy must also be considered. Improved estimate accuracy directly leads to improved profitability after an order is received. Labor-hour estimates, which previously relied on experience and intuition, can now be made more accurate through statistical predictions based on past performance data. This significantly reduces the risk of receiving orders at a loss and improves corporate profitability. Furthermore, by clarifying the basis for estimates, constructive discussions based on objective data become possible in price negotiations with clients. Specifically, by combining statistical quality control techniques with machine learning algorithms, it is possible to improve the standard deviation of estimation errors from ±30% with conventional methods to within ±10%. Drawing analysis using deep learning models quantifies and systematizes the tacit knowledge of experienced engineers, allowing even junior engineers with less than five years of experience to achieve the same level of estimation accuracy as experts. Furthermore, by analyzing the discrepancy between past estimates and actual manufacturing costs, the system identifies systematic error factors (such as fluctuations in material prices, underestimations of process time, and misinterpretation of quality requirements) and implements a self-learning function that continuously corrects them. In quality control, the concept of statistical process control (SPC) is applied to the estimation process to visualize and monitor variations in estimation accuracy, automating early detection of anomalies and causal analysis. Furthermore, the benchmarking function allows companies to conduct comparative analyses against competitors and industry standards, creating an environment where they can objectively understand their own estimation accuracy positioning.

[0026] The third benefit is improved security of confidential information. Multi-layered security features significantly reduce the risk of leaking drawings, allowing online transactions to be conducted with peace of mind. However, security is not absolute, and measures must be continually strengthened to respond to new threats. The security framework employs a zero-trust architecture and builds a multi-step authentication system based on the principle of "never trust, always verify." Specifically, end-to-end encryption (E2EE) ensures data remains encrypted throughout the entire process, from transmission to storage and processing, preventing even server administrators from accessing plaintext data. The encryption method uses post-quantum cryptography recommended by the National Institute of Standards and Technology (NIST), ensuring resistance to future quantum computer attacks. Access control combines multi-factor authentication, which comprehensively analyzes time, location, device, and behavioral patterns, with machine learning-based anomalous access detection to reduce the probability of unauthorized access to less than 0.01%. Additionally, a tampering detection system utilizing blockchain technology ensures data integrity and ensures the reliability of audit trails. In the event of a data breach, the automated incident response system (SOAR) instantly notifies relevant parties, identifies the scope of impact, and implements containment measures to minimize damage.

[0027] The fourth effect is improved transparency and fairness in transactions. The price validity evaluation function prevents unfair pricing, and the user rating system makes it easier to select trustworthy trading partners. However, the effectiveness of the evaluation system varies depending on how it is operated and the standards set, so these functions are not perfect. As a mechanism for ensuring transparency, the system provides a visualization dashboard of the price formation process and publishes anonymized statistical information on the breakdown of cost components (material costs, processing costs, administrative costs, profit margins, etc.). This allows purchasers to compare prices with market rates, and contractors to objectively evaluate their own price competitiveness. To ensure fairness, the AI-driven price monitoring system automatically detects abnormal price fluctuations and unnatural bidding patterns and issues alerts for transactions suspected of collusion or price manipulation. To improve the reliability of the evaluation system, blockchain-based evaluation record management prevents evaluation tampering, and natural language processing-based sentiment analysis automatically identifies fake and unfair reviews. In addition, the evaluator reliability scoring function quantifies and weights the reliability of evaluators based on past evaluation accuracy and transaction performance. For dispute resolution, the AI ​​mediation system references data from similar cases and proposes objective solutions, supporting prompt and fair dispute resolution. The transaction transparency report function regularly generates and publishes statistical information on market trends, price trends, quality indicators, satisfaction indicators, and other factors, contributing to the healthy development of the entire industry.

[0028] Hereinafter, embodiments for carrying out the present invention will be described in detail. However, the embodiments shown below are merely examples of the present invention, and various modifications and improvements are possible within the scope of the technical concept of the present invention, and the present invention is not limited to these embodiments. The implementation architecture of this invention is based on a cloud-native microservice design, enabling container orchestration on Kubernetes, inter-service communication control using a service mesh (Istio), and unified API endpoint management using an API gateway (Kong, Ambassador, etc.). Each functional module is implemented as an independent service, achieving high availability (99.99%), horizontal scalability (processing 1 million requests per second), and fault isolation (Circuit Breaker Pattern). For the data infrastructure, a big data platform is built that integrates HDFS, Apache Spark, Apache Kafka, Elasticsearch, etc., enabling both real-time streaming processing and large-scale batch processing. For the AI / ML infrastructure, an MLOps pipeline utilizing TensorFlow, PyTorch, MLflow, Kubeflow, etc. automates the continuous integration and continuous deployment (CI / CD) of models. For security design, DevSecOps practices are used to incorporate security into the entire development lifecycle, and automated security testing such as SAST, DAST, and IAST is performed. In compliance with international standards, we will establish a governance system that meets the requirements of ISO / IEC 27001, SOC 2 Type II, GDPR, CCPA, etc.

[0029] In at least one embodiment, a B2B quotation platform system with basic AI analysis functions is provided. This system has a configuration in which an orderer terminal, a contractor terminal, and a server device are connected via a network. The server device is equipped with a drawing and specification analysis unit, a manufacturing feasibility determination unit, and a cost calculation unit. The detailed system architecture design adopted a three-tier architecture (presentation layer, business logic layer, and data access layer) to ensure separation of concerns and maintainability. The presentation layer utilized responsive web design to provide optimal usability on PCs, tablets, and smartphones, and PWA (Progressive Web Apps) technology enabled offline functionality. The business logic layer utilized web frameworks such as Spring Boot, Express.js, and Django to support both RESTful and GraphQL APIs. The data access layer utilized databases such as PostgreSQL, MongoDB, and Redis, depending on the application, to achieve large-scale data processing through database sharding and high availability through replication. WebSocket and Server-Sent Events (SSE) were used for real-time communication, enabling instant notification of quotation progress. A content delivery network (CDN) also enabled low-latency distribution of drawing data to users worldwide. The ELK stack (Elasticsearch, Logstash, and Kibana) was implemented for log management, providing comprehensive system monitoring and performance analysis.

[0030] The Drawing and Specification Analysis Unit has the ability to use artificial intelligence technology to extract part shapes from design information in various formats, including, but not limited to, 2D drawings, 3D drawings, CAD data, hand-drawn drawings, photographic images, video data, audio data, text data, binary data, etc. Specifically, it analyzes drawing images using current and future artificial intelligence technologies, including, but not limited to, deep learning-based image recognition models, convolutional neural networks (CNNs), recurrent neural networks (RNNs), transformer models, generative adversarial networks (GANs), variational autoencoders (VAEs), graph neural networks (GNNs), attention mechanisms, vision transformers (ViTs), BERT, GPT, LLaMA, etc., to identify geometric elements, including lines, arcs, dimension lines, notes, symbols, patterns, etc., and then infer the part's 3D shape, material properties, processing requirements, etc., from the combination of these elements. However, artificial intelligence technology is advancing every day, and the methods listed here are only a few examples. Any information processing technology, including new artificial intelligence technology, quantum computing technology, biocomputing technology, neuromorphic technology, etc., that will be developed in the future, can also be applied. In the technical implementation of drawing analysis, the computer vision pipeline consists of four stages: preprocessing, feature extraction, pattern recognition, and postprocessing. In the preprocessing stage, noise removal is performed using a Gaussian filter, brightness normalization is performed using histogram equalization, and geometric correction is performed using projective transformation. Feature extraction combines feature descriptors such as edge detection (Canny, Sobel, Laplacian), corner detection (Harris, FAST), SIFT, SURF, and ORB. In pattern recognition, drawing elements are identified with high accuracy using the latest object detection algorithms such as YOLO v8 and Detectron2, and region segmentation is performed using semantic segmentation (DeepLab, U-Net, etc.). Furthermore, text information from drawings is extracted using OCR technology (Tesseract, PaddleOCR, etc.), and specification requirements are understood using natural language processing. For 3D shape reconstruction, a 3D model is generated from a 2D drawing using technologies such as Structure from Motion (SfM) and Multi-View Stereo (MVS), and shape analysis is performed using point cloud processing (PointNet++) and mesh processing (MeshCNN).

[0031] The manufacturing feasibility determination unit compares the equipment information (machining machine type, processable sizes, compatible materials, etc.) registered in the factory database with the estimation results from the analysis unit, and automatically determines whether each factory can handle the request. The determination results are output along with a certainty factor, allowing the purchaser to prioritize the factory that is most likely to be able to handle the request. However, the determination criteria and the method for calculating the certainty factor differ depending on the implementation, and in some cases these methods are the only ones used. The detailed implementation of the manufacturability algorithm employs a multi-stage evaluation process. In the first stage, basic requirements (material compatibility, size constraints, precision requirements, etc.) are matched to eliminate factories that do not meet the necessary requirements. In the second stage, the capability compatibility is evaluated by calculating the probability of success based on each factory's past performance data. Bayesian inference is used to calculate the posterior probability from the prior probability (factory's basic capability) and likelihood (success rate for similar projects), and a judgment is made with a confidence interval. In the third stage, a comprehensive evaluation is performed using multi-criteria decision analysis (MCDA) that integrates technical compatibility, cost competitiveness, delivery capability, quality stability, and risk factors. Techniques such as the Analytic Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) are used to optimize the weighting of each evaluation axis. Ensemble methods such as Random Forest, Gradient Boosting, and Support Vector Machine are applied to the machine learning model to achieve both predictive accuracy and interpretability. Continual learning is also used to incrementally learn new performance data and continuously improve judgment accuracy.

[0032] At least one embodiment provides a system with enhanced 3D CAD data analysis capabilities. This embodiment supports standard 3D file formats such as STEP, IGES, and STL, and has the ability to directly extract shape information, volume, surface area, and other data from CAD data. In the technical implementation of 3D CAD data processing, we built a CAD I / O library that integrates CAD data geometric kernels (ACIS, Parasolid, Open CASCADE, etc.) to support a variety of CAD formats. We implemented a geometry processing engine that unifiedly processes 3D representation formats such as B-Rep (Boundary Representation), CSG (Constructive Solid Geometry), mesh, and point clouds. Shape analysis involves curvature analysis based on differential geometry, shape feature extraction using topological data analysis (TDA), and compact shape representation using Fourier shape descriptors. The manufacturability analysis (DFM: Design for Manufacturing) function automatically detects difficult-to-manufacture elements such as undercuts, thin-walled sections, and aspect ratios and calculates a manufacturability score. Material usage optimization maximizes material yield through nesting algorithms and 3D packing problem solutions. Furthermore, as preprocessing for finite element analysis (FEA), automatic mesh generation, boundary condition setting, and material property assignment are performed to automate simulation preparation for structural, thermal, and fluid analysis. Point cloud data processing implements alignment using the ICP (Iterative Closest Point) algorithm, plane detection using RANSAC, and region segmentation using Region Growing.

[0033] The 3D model analysis unit extracts surface information from the 3D model using mesh analysis technology, estimates the processing direction from the normal vector of each surface, and automatically detects undercut shapes and difficult-to-process areas. It also calculates the amount of material used with high precision using volume calculations and estimates the surface treatment cost using surface area calculations. However, the 3D analysis method is not limited to this, and other methods such as point cloud processing and voxel analysis can also be applied. To advance 3D geometric analysis, we implement precise shape analysis using computational geometry algorithms. Mesh quality evaluation detects inappropriate elements using quality metrics such as aspect ratio, distortion, and interior angles, and automatically repairs them using Delaunay triangulation and the Advancing Front method. Normal vector calculation generates a smooth normal field by weighted averaging of vertex and face normals, and evaluates machining tool accessibility. Visibility analysis determines the tool's approachability to each surface and calculates the optimal machining posture for 5-axis machining centers. Volume calculation achieves highly accurate volume measurement by combining the Monte Carlo method, application of the divergence theorem, and voxelization techniques. Surface area calculation achieves both accuracy and computational efficiency by combining approximate calculations using discretized curved surfaces with analytical calculations. Material optimization automatically generates design proposals that reduce weight while maintaining structural performance using topology optimization algorithms (SIMP method, ESO method, etc.). In addition, as part of a 3D printing suitability evaluation, a manufacturability evaluation will be conducted that takes into account constraints specific to 3D additive manufacturing, such as overhang angle, areas requiring support material, and optimization of the modeling direction.

[0034] At least one embodiment provides a system with a specification analysis function that utilizes natural language processing. In this embodiment, the system is equipped with a function to automatically extract information such as material specifications, surface treatment requirements, and quality standards from text-based specifications. The implementation of natural language processing (NLP) technology utilizes a large-scale language model (LLM) based on a multi-layer Transformer architecture. The preprocessing stage performs language analysis, including normalization, tokenization, part-of-speech tagging, syntactic analysis, and semantic role labeling. Named Entity Recognition (NER) accurately identifies technical specification elements such as material names, standard numbers, dimensional values, and quality requirements. Relation Extraction extracts dependencies between specification elements (e.g., material-surface treatment, dimension-tolerance) and structures them as a knowledge graph. Intent Recognition classifies the requirement level of the specification (e.g., mandatory, recommended, optional) and outputs prioritized requirements specifications. For multilingual support, technologies such as neural machine translation, cross-lingual information retrieval, and multilingual named entity recognition are used to unify processing of technical documents in English, Chinese, German, and other languages. Furthermore, the system connects with external knowledge sources such as technical terminology dictionaries, technical standards databases, and material properties databases to perform context-specific semantic interpretation. For ambiguity resolution, technologies such as Word Sense Disambiguation and Coreference Resolution are used to ensure appropriate interpretation of pronoun references and polysemous words.

[0035] The natural language processor uses current and future natural language processing technologies, including but not limited to large-scale language models, Transformer architectures, BERT, GPT, T5, RoBERTa, DeBERTa, ELECTRA, ALBERT, DistilBERT, ChatGPT, Claude, Gemini, LLaMA, PaLM, Chinchilla, Gopher, Jurassic, OPT, BLOOM, GLM, ERNIE, context embedding, semantic embedding, knowledge graph embedding, multimodal learning, cross-modal learning, zero-shot learning, few-shot learning, in-context learning, chain of sorts, thought trees, iterative refinement, self-correcting learning, reinforcement learning with human feedback (RLHF), constitutional AI, red teaming, etc. to decompose specification text at the word, sentence, paragraph, and document levels, and to identify material names, standard numbers, numerical information, quality requirements, functional requirements, etc. using functions such as named entity recognition, relationship extraction, sentiment analysis, intent estimation, summary generation, question answering, and dialogue generation. Furthermore, implicit requirements, design intentions, constraints, optimization goals, etc. are inferred from contexts such as "stainless steel is used to improve corrosion resistance" using intent analysis, context understanding, inference, common sense inference, causal inference, analogical inference, etc. However, natural language processing technology is rapidly developing, and these methods are just a few examples, and it is also possible to introduce any language processing technology developed in the future, including language understanding technology, multilingual processing technology, multimodal language technology, neural language interface technology, etc. In optimizing the performance of language models, domain adaptation is used to tune the model to specialize in the terminology, technical expressions, and writing styles unique to the manufacturing industry. Technologies such as few-shot learning and in-context learning are used to implement the ability to quickly adapt to new specification formats from a small number of examples. Knowledge distillation transfers knowledge from large-scale models to lightweight models, enabling high-speed inference on edge devices. Inference enhancement technologies such as prompt engineering, chain-of-thought, and tree of thoughts enable logical interpretation of complex technical specifications. Furthermore, the Retrieval-Augmented Generation (RAG) architecture links with external technical databases (material standards, JIS standards, ISO standards, etc.) to interpret specifications based on the latest technical information. For quality assurance, uncertainty quantification is used to quantify the reliability of predictions, and a system is built to encourage human experts to verify low-confidence extraction results.

[0036] At least one embodiment provides a system with enhanced security features. This embodiment employs advanced encryption technologies such as end-to-end encryption, zero-knowledge proofs, and homomorphic encryption, and implements a function for performing analysis processing on drawing data while keeping it encrypted. To implement enhanced security technologies, we build a multi-layer defense system using an optimal combination of cryptographic primitives. For end-to-end encryption, we use protocols that provide forward secrecy, such as Signal Protocol and Double Ratchet Algorithm, to ensure the security of past communication history. For zero-knowledge proof systems, we use technologies such as zk-SNARKs, zk-STARKs, and Bulletproofs to prove the validity of computation results without disclosing confidential information. For homomorphic encryption implementation, we use schemes such as BGV, BFV, and CKKS depending on the application, enabling AI inference processing in an encrypted state. Multi-party computation (MPC) allows multiple factories to perform collaborative computations without sharing confidential information, enabling secure information exchange with competitors. We utilize hardware-assisted security features such as hardware security modules (HSMs), trusted execution environments (TEEs), Intel SGX, and ARM TrustZone to securely generate, store, and use cryptographic keys. In addition, post-quantum cryptography technologies such as quantum key distribution (QKD), lattice cryptography, and homogeneous mapping cryptography will ensure resistance to future quantum computing threats.

[0037] The encryption processor encrypts the drawing data upon upload using current and future encryption technologies, including, but not limited to, AES-128, AES-192, AES-256, RSA, Elliptic Curve Cryptography (ECC), Diffie-Hellman key exchange, hash functions (e.g., SHA-256, SHA-3, Blake2), digital signatures (e.g., DSA, ECDSA), homomorphic encryption, multi-party computation (MPC), secret sharing, zero-knowledge proofs, fully homomorphic encryption (FHE), lattice cryptography, multivariate cryptography, code-based cryptography, homogeneous map cryptography, quantum key distribution (QKD), quantum cryptography, post-quantum cryptography, lightweight cryptography, stream ciphers, block ciphers, symmetric cryptography, asymmetric cryptography, hybrid cryptography, authenticated encryption, message authentication codes (MAC), digital watermarking, steganography, chaos cryptography, DNA cryptography, biometric cryptography, etc., and manages the encryption keys in a secure environment, including a key management system, hardware security module (HSM), trusted execution environment (TEE), secure enclave, quantum secure communications, etc. Using homomorphic encryption processing with new-generation computing technologies, including but not limited to quantum computing, DNA computing, optical computing, neuromorphic computing, membrane computing, and molecular computing, artificial intelligence processing, machine learning processing, statistical processing, optimization processing, etc. are performed without decrypting the data, and only the analysis results are decrypted and provided. This creates a system in which even server administrators, cloud providers, and third parties cannot view the original drawing contents. However, as encryption and computing technologies are constantly evolving and there are constraints such as computational costs, communication costs, and storage costs, it is necessary to select the optimal technology that takes into account the balance between practicality, efficiency, security level, etc., and other security methods, privacy protection technologies, anonymization technologies, etc., in addition to these technologies, can also be considered. In the operational management of the encryption system, key lifecycle management (generation, distribution, renewal, expiration, and disposal) is automated to minimize the risk of key leakage or unauthorized use. Encryption performance is optimized by utilizing hardware acceleration functions such as AES-NI and SHA-NI to minimize encryption processing overhead. Additionally, a dynamic encryption algorithm selection function automatically selects the optimal encryption method based on the data confidentiality level, processing requirements, and performance constraints. In terms of auditing and compliance, the system provides cryptographic implementations that comply with standards such as FIPS 140-2, Common Criteria, and SOC 2, and the soundness of the encryption system is verified through regular security audits.

[0038] At least one embodiment provides a transaction history management system that utilizes blockchain technology. In this embodiment, all transaction processes, from quotation requests to contract signing, manufacturing completion, and acceptance inspection, are recorded on the blockchain, creating a transaction history that cannot be tampered with. In terms of the technical details of blockchain implementation, the choice of consensus algorithm is optimized depending on the application. For private blockchains, PBFT (Practical Byzantine Fault Tolerance) is used, for consortium blockchains, Raft is used, and for public blockchains, Proof of Stake (PoS) is used as the basis, with emphasis on energy efficiency and processing performance. For smart contract development, languages ​​such as Solidity, Vyper, and Rust are used to implement complex business logic such as automatic evaluation of quotation conditions, milestone-based payments, and automatic refunds based on quality conditions. Off-chain solutions (Lightning Network, State Channels, Plasma, etc.) are used to improve transaction processing capacity, enabling thousands of transactions per second. To ensure interoperability, cross-chain protocols such as Polkadot and Cosmos enable data sharing between different blockchains. To protect privacy, zero-knowledge proofs (technology used by Zcash, Monero, etc.) are implemented to prove the existence of transactions while concealing detailed information. In addition, by linking with IPFS (InterPlanetary File System), large amounts of drawing data can be stored in distributed storage, and efficient data management can be achieved by recording only hash values ​​on the blockchain.

[0039] The blockchain management unit records each transaction step on the blockchain as a transaction and automatically executes contract terms using smart contracts. For example, it implements a mechanism whereby payment is automatically made once production completion is confirmed. However, blockchain technology has issues with processing speed and scalability, and implementations must take these constraints into account, so it is not necessarily limited to this technology. In advanced smart contract implementation, external data (e.g., manufacturing completion certificates, quality inspection results, delivery confirmations) are securely imported into the blockchain using oracles (Chainlink, Band Protocol, etc.). To accommodate increasingly complex conditional branching, contract logic is designed using formal methods such as decision trees and finite state machines, and conditions are verified for incompleteness and contradictions in advance. To diversify payment terms, flexible payment schemes such as milestone-based payments, quality-based adjustments, and performance bonuses are implemented. For dispute resolution, collaboration with decentralized arbitration systems such as Aragon Court and Kleros automates fair dispute resolution by a third party. Upgradability (Upgradeable Smart Contracts) also accommodates changes to contract terms and the addition of new features. For gas optimization, transaction costs are reduced through batch processing, efficient use of state variables, and optimization of function execution paths. For security audits, static analysis tools such as MythX and Slither are combined with professional audits to thoroughly verify the vulnerabilities of smart contracts. Additionally, we will implement defenses against known attack patterns such as flashloan attacks, reentrancy attacks, and front-running to ensure the safety of funds.

[0040] At least one embodiment provides a system with a function for responding to real-time price fluctuations. In this embodiment, a function is implemented to monitor market fluctuations in material prices in real time and automatically reflect these fluctuations in estimated prices. In implementing a real-time price monitoring system, we adopted an event-driven architecture to enable instant processing of price fluctuation events. To diversify data sources, we built a data pipeline that integrated commodity exchange APIs (LME, COMEX, SHFE, etc.), financial data providers (Bloomberg, Reuters, Yahoo Finance, etc.), government statistical data (Ministry of Economy, Trade and Industry, Statistics Bureau, Ministry of Internal Affairs and Communications, etc.), and industry association data. For streaming data processing, we processed tens of thousands of price updates per second using messaging systems such as Apache Kafka and Apache Pulsar, and performed complex price analysis using real-time processing engines such as Apache Storm and Apache Flink. For price forecasting, we forecast short-, medium-, and long-term price trends using time-series deep learning models such as LSTM, GRU, and Transformer, and built a hybrid forecasting system that combined them with statistical models such as ARIMA, GARCH, and VAR. For risk management, we quantified price fluctuation risk using risk indicators such as VaR (Value at Risk) and CVaR (Conditional Value at Risk) to automatically propose hedging strategies. In addition, the circuit breaker function detects abnormal price fluctuations and automatically halts trading and issues emergency notifications.

[0041] The price monitoring department is involved in real-time data processing, streaming data processing, batch processing, edge computing, fog computing, cloud computing, distributed computing, parallel computing, GPU computing, TPU computing, FPGA computing, and ASIC. The Company uses current and future computing technologies, including but not limited to, quantum computing, analog computing, optical computing, etc., to periodically, continuously, and in real time obtain various market factors, such as price information from commodity exchanges, price lists from material manufacturers, exchange rates, interest rates, inflation rates, supply and demand balances, geopolitical risks, climate change, natural disasters, pandemics, technological innovations, and policy changes, and analyzes price fluctuation patterns, periodicities, trends, seasonality, abnormal values, outliers, structural changes, etc., using current and future data analysis technologies and artificial intelligence technologies, including but not limited to, time series analysis, regression analysis, principal component analysis, factor analysis, cluster analysis, discriminant analysis, decision tree analysis, random forest, gradient boosting, support vector machine, Bayesian network, hidden Markov model, state space model, ARIMA model, GARCH model, machine learning, deep learning, reinforcement learning, transfer learning, meta-learning, federated learning, automated machine learning (AutoML), neural architecture search (NAS), etc. When sudden price fluctuations, market manipulation, bubbles, crashes, etc. are detected, existing quoted prices are automatically updated using anomaly detection through machine learning, statistical testing, threshold monitoring, pattern matching, etc., and notifications are sent to relevant parties via a variety of communication methods including email, SMS, push notifications, voice calls, chatbots, AR / VR notifications, IoT device notifications, etc. However, price fluctuation predictions, market analysis, economic forecasts, etc. are inherently uncertain, complex, and nonlinear, making perfect predictions and absolute accuracy difficult, and these functions only provide reference information and decision-making support, and actual trading decisions, investment decisions, risk management, etc. require final human judgment, expert advice, and the gathering of diverse information. To enhance price fluctuation analysis, feature engineering using machine learning is used to automatically discover potential factors affecting price fluctuations. Technical analysis applies traditional indicators such as moving averages, RSI, MACD, Bollinger bands, and Fibonacci retracements, as well as advanced methods such as fractal analysis, wavelet transform, and chaos theory. Fundamental analysis quantifies the impact of macroeconomic indicators, corporate performance, geopolitical events, and natural disasters, and incorporates them into price models. It also implements functions to predict market sentiment and policy trends through social media analysis, news sentiment analysis, and policy document analysis. For real-time processing, latency optimization enables a response time of less than 100 milliseconds from price fluctuation detection to notification, providing high-frequency trading (HFT)-level reaction speeds.

[0042] In at least one embodiment, an international transaction support system with multilingual capabilities is provided. In this embodiment, functions are implemented to automatically translate drawing notes, specifications, user interfaces, etc., facilitating transactions with overseas factories. The technical implementation of the multilingual system utilizes a neural machine translation (NMT) engine at its core, leveraging large-scale language models such as Transformer, BERT, and GPT to achieve high-quality translation. Domain adaptation improves translation accuracy for technical terms, specialized expressions, abbreviations, and other terms specific to the manufacturing industry. Multilingual pre-trained models such as multilingual BERT, XLM-R, and mBERT streamline knowledge transfer between languages. For resource-limited language support, technologies such as zero-shot translation and few-shot learning enable practical translation even for language pairs with limited training data. For technical document translation, consistent translation is provided using specialized dictionaries, bilingual corpora, and translation memories (TM). For drawing translation, OCR technology is used to extract text from drawings and position the translated text while preserving the layout. Furthermore, integration with CAT (Computer-Assisted Translation) tools streamlines the quality improvement process by human translators. For quality evaluation, a quality assurance system is established that combines automatic evaluation metrics such as BLEU, METEOR, and BERTScore with quality checks by human evaluators.

[0043] The multilingual processing unit uses neural machine translation technology to translate technical documents with high accuracy, accurately converting specialized terminology and abbreviations specific to the manufacturing industry. It also automatically converts units (inches to millimeters) and conforms to standards (JIS to ASTM) while taking cultural differences into account. However, translation accuracy varies depending on the language pair and field of expertise, and manual confirmation is recommended for important information. Multilingual support methods other than these translation functions are also being considered. To improve translation quality, fine-tuning is performed using a large-scale parallel corpus from the manufacturing industry to build domain-specific translation models. To ensure consistency in technical terminology, constrained translation is integrated with a terminology database to prevent mistranslations of specialized terminology. To improve contextual understanding, document-level translation takes the entire document context into account in translation, preserving the continuity of pronoun references and specialized concepts. The quality estimation function automatically evaluates the reliability of translations, prompting human translators to review low-reliability translations. Multilingual pivot translation provides indirect translation via English for language pairs without direct translation resources. Additionally, the speech translation function supports real-time speech translation during international video conferences, facilitating communication across language barriers. To support region-specific standards, the system connects with international standard databases such as ISO, JIS, ANSI, DIN, and BS to automatically convert standard numbers and specifications.

[0044] In at least one embodiment, a 3D drawing confirmation system utilizing AR (Augmented Reality) technology is provided. In this embodiment, a function is implemented to superimpose a 3D model on the actual work site via the camera of a smartphone or tablet device, helping users understand the contents of the drawing. In implementing AR technology, the SLAM (Simultaneous Localization and Mapping) algorithm estimates the device's position and orientation in real time, enabling stable placement of 3D objects. Techniques such as Visual-Inertial SLAM and RGB-D SLAM are used to accommodate various environmental conditions. 3D rendering engines such as Unity, Unreal Engine, OpenGL ES, and Vulkan are used to achieve high-quality 3D display. Occlusion handling accurately depicts the front-to-back relationship between real and virtual objects, providing an immersive AR experience. Interaction functions enable intuitive 3D model manipulation using gesture recognition, voice commands, and touch controls. Multi-user AR allows multiple users to share AR experiences in the same space, supporting collaborative design reviews. Cloud Anchors enable sharing of AR spaces across different devices. WebAR technology also provides AR experiences from a web browser without the need for a dedicated app, lowering the barrier to adoption. Performance optimization includes techniques such as Level of Detail (LOD), frustum culling, and occlusion culling to achieve smooth AR display on mobile devices.

[0045] The AR display unit acquires the device's position and orientation information, constructs a 3D spatial coordinate system, and places a virtual 3D model in real space. Users can rotate and enlarge the 3D model using their fingers or touch operations to check the part shape from different angles. It also provides a function to overlay dimensional information and material information on the 3D model. However, AR technology depends on device performance and environmental conditions, and stable operation is not guaranteed in all situations, and in some cases, it is just a display technology. To enhance the AR user interface, we will build an intuitive operation system that integrates multimodal interaction (gestures, voice, gaze, touch, etc.). For gesture recognition, we will utilize libraries such as MediaPipe and OpenPose to achieve precise tracking of finger movements. For voice commands, we will implement a voice recognition engine that supports manufacturing terminology to understand specialized instructions such as "display cross-section view" and "highlight tolerance information." Haptic feedback (tactile feedback) will provide a sense of contact with the 3D model, creating a more realistic operating experience. For information display optimization, we will use eye tracking to detect the user's area of ​​focus and display relevant information at the appropriate time. Furthermore, Contextual UI will personalize displayed information according to the work process and skill level, improving work efficiency. For collaborative work support, we will implement a function that allows remote experts to add annotations and instructions to the AR space, promoting knowledge sharing with on-site workers.

[0046] At least one embodiment provides a system with a quality control function for AI learning data. In this embodiment, the system automatically evaluates the quality of the drawing data and transaction performance data used for learning, and implements a function to eliminate low-quality data to improve the performance of the AI ​​model. In implementing data quality management technology, we build a multidimensional quality assessment framework to quantitatively evaluate quality dimensions such as completeness, accuracy, consistency, timeliness, validity, and uniqueness. Statistical quality control monitors time-series changes in data quality using methods such as control charts and process capability analysis to detect quality degradation early. Anomaly detection detects outliers and evaluates data reliability using algorithms such as isolation forest, one-class SVM, and local outlier factor. Data profiling automatically detects quality issues such as missing value rates, duplication rates, data type inconsistencies, and range violations. Quality prediction using machine learning models predicts future quality degradation based on past quality patterns, enabling preventative quality control. Privacy-preserving data quality assessment in a federated learning environment also enables quality management in a distributed environment without data sharing. Data lineage management tracks quality propagation from the data source to end use and supports root cause analysis of quality issues.

[0047] The data quality control unit quantitatively evaluates the resolution, noise level, missing information, etc. of drawings and calculates a quality score. It also checks the consistency of transaction data (validity of price and specifications, etc.) and detects and removes outliers. It also analyzes data bias and automatically adjusts the balance of the learning dataset. However, the definition of data quality is somewhat subjective, making complete automation difficult, and other approaches besides these quality control methods can also be applied. For automated quality control, we adopt a hybrid approach that combines rule-based quality checks and machine learning-based quality evaluation. The rule-based approach defines explicit quality rules based on industry standards, internal criteria, and legal and regulatory requirements to reliably detect quality violations. The machine learning approach learns from past high-quality data patterns to predict and evaluate the quality of new data. Data augmentation technology synthetically generates missing quality data and increases the diversity of the training dataset. Active learning prioritizes data with high quality evaluation uncertainty and presents it to human experts for efficient quality labeling. Explainable AI technology also visualizes the basis for quality evaluation, helping to understand and improve quality issues. Continual learning incrementally learns new quality patterns to prevent the quality evaluation model from becoming obsolete. Automated quality improvement involves automatically performing preprocessing such as data cleaning, normalization, and imputation to improve quality.

[0048] At least one embodiment provides a manufacturing planning support system that integrates a predictive maintenance function. In this embodiment, the system analyzes the operating status and maintenance history of factory equipment, predicts equipment failures, and reflects the results in the manufacturing schedule. The technical implementation of predictive maintenance involves high-frequency collection of IoT sensor data (vibration, temperature, acoustics, current, hydraulic pressure, etc.) and real-time preprocessing using edge computing. Time-series data analysis involves learning equipment deterioration patterns using deep learning models such as LSTM, GRU, and Transformer to predict the probability of failure. A hybrid approach that combines physics-based and data-driven models enables highly accurate predictions even with limited data. Digital twin technology is used to build a virtual model of the equipment, and failure simulations are performed in conjunction with actual operating data. Failure mode and effects analysis (FMEA) is used to evaluate the impact of failures on each equipment component and identify priority monitoring targets. Survival analysis is also used to statistically estimate equipment lifespan and develop optimal maintenance plans. For anomaly detection, unsupervised learning, which learns only from normal data, is used to build a monitoring system capable of detecting unknown failure patterns. An optimization algorithm is used to simultaneously optimize production scheduling and maintenance plans, taking prediction results into account.

[0049] The predictive analysis unit performs time-series analysis of equipment sensor data (vibration, temperature, current values, etc.) to detect abnormal patterns. It uses machine learning to learn signs of failure and calculates the probability of failure and the expected time of failure. These prediction results are reflected in the manufacturing plan, and a production schedule is automatically generated that avoids equipment with a high risk of failure. However, prediction accuracy varies depending on the type of equipment and usage conditions, making 100% prediction difficult, and these prediction functions may only play a supporting role. To improve failure prediction algorithms, multimodal learning is used to integrate and analyze different sensor data (vibration, acoustics, thermal images, electrical signals, etc.) to identify failure patterns that are difficult to detect with a single sensor. Transfer learning shortens the learning period by applying failure patterns from similar equipment to new equipment. Few-shot learning efficiently learns from a small number of failure cases to improve the accuracy of predicting rare failures. Uncertainty quantification calculates confidence intervals for predictions to support risk-based decision-making. Explainable AI technology identifies and visualizes failure causes, promoting understanding among maintenance workers. Reinforcement learning also enables dynamic optimization of maintenance strategies, achieving the optimal balance between equipment availability and costs. To continuously improve prediction accuracy, online learning is used to sequentially learn new operating data and adapt to environmental changes and the progression of deterioration. For alert generation, a hierarchical warning system (caution, warning, emergency) enables appropriate escalation.

[0050] At least one embodiment provides an environmentally friendly quotation system with a carbon footprint calculation function. In this embodiment, the system automatically calculates the amount of CO2 emissions generated in the manufacturing process and implements a function to support factory selection that takes environmental impact into consideration. To implement carbon footprint calculation technology, we will build a comprehensive environmental impact assessment system based on the life cycle assessment (LCA) methodology. Using an LCA framework compliant with ISO 14040 / 14044 standards, we will calculate CO2 emissions throughout the entire process, from raw material procurement (cradle) to product disposal (grave). The emission factor database will integrate standard databases such as IDEA (Japan), Ecoinvent (international), and USEEIO (US), and apply region- and industry-specific emission intensity factors. To calculate energy consumption, we will model the machine tool's power consumption profile, operating pattern, and load factor in detail, and perform precise calculations that reflect actual manufacturing conditions. To take renewable energy utilization rates into account, we will apply carbon intensity that reflects each factory's power procurement contract (green power, RE100, etc.). To calculate transportation emissions, we will perform detailed calculations that take into account logistics route optimization, transportation method selection (truck, rail, ship, air), loading efficiency, etc. Furthermore, we will support the realization of net-zero manufacturing by supporting carbon offsets and carbon credit trading. Emissions forecasting using machine learning predicts future emissions based on past energy consumption patterns and assesses the effects of capital investment and process improvements in advance. The process is highly compatible with quality control systems such as ISO9001, and can be positioned as part of company-wide quality improvement activities.

[0051] The environmental impact calculation unit estimates CO2 emissions at each stage, including material procurement, processing, and transportation, and calculates a comprehensive environmental impact score. It also takes into account the factory's energy source (e.g., renewable energy ratio) and location (transport distance). Clients can select factories by comparing both price and environmental impact. This calculation unit collects energy consumption data (electricity, gas, oil, etc.) for each manufacturing process and applies regional and fuel-specific emission factors to calculate accurate CO2 emissions. It also performs a cradle-to-grave analysis, taking into account emissions from the mining, refining, and transportation stages of raw materials, as well as emissions from the manufacturing and disposal of manufacturing equipment. It also includes negative emissions from forests, CCS (carbon capture and storage), and carbon offsets to assess net environmental impact. Factors such as a factory's environmental management system certification status, energy-saving equipment installation status, and waste reduction record are also reflected in the environmental impact score, creating a system that promotes the selection of environmentally conscious manufacturers. However, since calculating environmental impact involves many assumptions, accurate figures are difficult to obtain; these functions only provide approximate values.

[0052] At least one embodiment provides a system for automatically generating drawing descriptions using speech recognition technology. This embodiment implements a function that provides audible explanations of drawing contents to assist visually impaired users and those with drawing comprehension difficulties. This function aims to bridge the digital divide, improve accessibility, and achieve inclusive design. Specifically, the system utilizes the latest speech recognition engines, such as Whisper, Speech-to-Text, Google Speech API, and Azure Speech Services, to convert users' voice questions into text with high accuracy. Multilingual support (Japanese, English, Chinese, Korean, Thai, Vietnamese, etc.) also eliminates language barriers in international manufacturing operations. To improve speech recognition accuracy, the system builds a manufacturing-specific terminology dictionary, technical specification glossary, material name database, and other features. By combining these with context understanding, the system can appropriately respond to even complex technical questions. Furthermore, noise cancellation, acoustic model optimization, and speaker adaptive learning enable stable speech recognition even in noisy factory environments, creating a highly practical system.

[0053] The speech generation unit automatically generates explanatory text based on the results of drawing analysis, such as "This part is cylindrical with a diameter of 50 mm and has a 10 mm through hole in the center," and outputs it using text-to-speech technology. It also provides a dialogue function that responds to user voice questions such as "What is this part made of?" This speech generation system integrates advanced speech synthesis technologies, including Amazon Polly, Google Text-to-Speech, Azure Cognitive Services, and NVIDIA Riva, to produce natural, easy-to-listen speech. Customization is possible based on the user's hearing characteristics using speech speed, volume, and pitch adjustment functions. Furthermore, support for SSML (Speech Synthesis Markup Language) enables speech output optimized for technical document reading, including emphasis on important figures, appropriate pauses, and accurate pronunciation of technical terms. Multilingual speech synthesis is also supported, accurately reproducing the pronunciation rules, accents, and intonation of each country. Barrier-free features include screen reader integration for the visually impaired and subtitle display for the hearing impaired, ensuring comprehensive accessibility. However, the accuracy of speech recognition and speech synthesis is affected by environmental noise and dialects, and complete understanding cannot be guaranteed. Therefore, other support methods beyond these speech technologies are also possible.

[0054] At least one embodiment provides a defect rate reduction support system with a quality prediction function. This embodiment implements a function that analyzes past manufacturing performance and quality data to predict the manufacturing quality of new parts in advance and recommend factories with low defect rates. This function aims to enhance quality control in the manufacturing industry, achieve preventive quality assurance, and reduce overall quality costs. The quality prediction algorithm combines traditional statistical quality control (SQC) methods with cutting-edge AI technologies such as machine learning, deep learning, and reinforcement learning. Specifically, the system comprehensively analyzes design factors such as the geometric complexity of the part, the mechanical properties of the material (tensile strength, hardness, ductility, toughness, fatigue strength, etc.), surface roughness requirements, dimensional tolerance requirements, and shape tolerance requirements, as well as manufacturing factors such as each factory's processing equipment accuracy, worker skill level, quality control system, environmental conditions (temperature, humidity, vibration, etc.), and maintenance management status. It also automatically performs failure mode analysis (FMEA) using past quality data to identify potential quality risks in advance. Furthermore, by linking with real-time quality monitoring data, quality fluctuations during manufacturing can be detected and an adaptive quality prediction model can be constructed. Prediction results are presented along with confidence intervals, enabling quantitative evaluation of quality risks.

[0055] The quality prediction department comprehensively analyzes factors such as the complexity of part geometry, material properties, processing tolerances, and factory technical level to calculate a manufacturing quality risk score. It uses machine learning to learn from factories that have previously experienced high levels of defects with similar parts, as well as patterns that are prone to problems under specific processing conditions, to build a predictive model. This prediction system builds an advanced prediction engine that integrates traditional quality engineering methods such as statistical process control (SPC), design of experiments (DOE), and quality function deployment (QFD) with cutting-edge machine learning technologies such as deep learning, ensemble learning, and Bayesian optimization. The training data for the quality prediction model uses a large-scale dataset that includes manufacturing performance over the past 10 years, quality inspection results, customer complaint information, process capability indices (Cp, Cpk), defect rate trends, and cause-specific defect analysis results. It also utilizes external knowledge sources such as materials science databases, processing technology databases, and equipment specification databases to combine theoretical predictions based on physical laws with statistical predictions. To improve prediction accuracy, active learning methods are used to identify important data points and efficiently collect additional data. Furthermore, explainable AI (XAI) technology visualizes the basis for predictions and provides specific suggestions for quality improvement activities. However, because quality is affected by many factors, perfect prediction is difficult, and these prediction functions only provide reference information. For actual quality assurance, an appropriate inspection system and continuous improvement activities are essential.

[0056] At least one embodiment provides an automated contract system with smart contract functionality. This embodiment automates the entire process, from quotation approval to contract conclusion and payment, reducing manual administrative work. This system is based on blockchain technology, encryption technology, and digital signature technology, enabling tamper-proof and highly transparent contract management. Specifically, contract terms are automatically executed using smart contracts built on enterprise blockchain platforms such as Ethereum, Hyperledger Fabric, and Corda. Standard clauses for international commercial contracts (ICC rules, UNCITRAL rules, national commercial laws, etc.) are incorporated into contract templates to ensure legal validity. Furthermore, multi-signature functionality automates the step-by-step approval process involving multiple approvers, and timestamp functionality strengthens the evidential value of the contract conclusion time. For payment processing, the system supports a variety of payment methods, including bank API integration, cryptocurrency payments, and CBDC (central bank digital currency) payments, and implements a mechanism for automatically executing payments upon confirmation of production completion. Furthermore, integration with IoT sensors enables real-time monitoring of production progress and automatic triggering of interim payments, supporting cash flow optimization.

[0057] The contract automation department automatically converts quotation details into digital contracts and concludes them with electronic signatures. When certain conditions, such as completion of manufacturing or acceptance inspection, are met, payment processing is automatically executed according to pre-defined conditions. In the event of a dispute, the system works with arbitration institutions to quickly resolve the dispute. This automated system uses an automatic contract generation engine to automatically create comprehensive contracts that include quotation specifications, delivery dates, quality standards, payment terms, liability clauses, intellectual property clauses, and confidentiality clauses. PKI (public key infrastructure) technology is used for electronic signatures, ensuring legal validity while streamlining the signing process. In addition, the contract performance monitoring system tracks manufacturing progress, quality inspection results, and delivery date compliance in real time, providing early detection of contract violations and automatic alerts. Payment automation also enables secure and rapid fund transfers through integration with interbank payment systems (SWIFT (registered trademark)), Real Time Gross Settlement System (RTGS), and Automated Clearing House (ACH). As a dispute resolution mechanism, integration with an ODR (Online Dispute Resolution) platform will automate step-by-step resolution procedures such as AI mediation, expert panels, and arbitration tribunals, thereby shortening the dispute resolution period and reducing costs. However, as human judgment is sometimes required to interpret contractual content and respond to exceptional circumstances, complete automation is difficult, and flexible responses beyond these automated functions are also necessary.

[0058] At least one embodiment provides a supply chain management system with a risk analysis function. This embodiment implements a function to analyze external risks, such as natural disasters, political situations, and economic conditions, and evaluate the stability of the supply chain. This system comprehensively monitors and analyzes various risk factors in the global manufacturing industry, helping to ensure business continuity and build a resilient supply chain. Risk analysis targets multidimensional risk factors, such as geopolitical risks (trade wars, sanctions, regime changes, etc.), natural disaster risks (earthquakes, typhoons, floods, droughts, fires, etc.), economic risks (exchange rate fluctuations, inflation, interest rate fluctuations, business fluctuations, etc.), social risks (labor disputes, social unrest, pandemics, etc.), technological risks (cyberattacks, system failures, technological changes, etc.), and environmental risks (climate change, environmental regulations, resource depletion, etc.). The probability of occurrence, impact, and duration of each risk are quantitatively evaluated, and priority is visualized using a risk matrix. The system also analyzes the correlations, chain reactions, and cumulative effects of multiple risks to assess systemic risk. To monitor risks, we will utilize a variety of information sources, including satellite data, weather data, economic indicators, news analysis, social media analysis, government announcements, and reports from international organizations, and build an early warning system using AI technology.

[0059] The Risk Analysis Department collects and analyzes external information, such as weather data, earthquake information, political news, and currency fluctuations, to quantify the location risk of each factory. It predicts the probability and duration of supply disruptions based on past disaster history and recovery time data. Clients can mitigate risk by distributing orders to multiple factories. This analysis department utilizes advanced analytical techniques, including machine learning, deep learning, time series analysis, anomaly detection, and pattern recognition, to automatically extract risk factors that could affect the manufacturing industry from massive amounts of external data. Specifically, it uses natural language processing technology to extract potential risk information from text data such as news articles, government reports, academic papers, and industry reports, and detects changes in market sentiment through sentiment analysis. It also analyzes satellite images to grasp disaster damage in real time and visualize damage to transportation networks, factory operation status, and the availability of logistics routes. Economic data analysis predicts economic trends based on macroeconomic indicators, such as GDP growth rate, unemployment rate, inflation rate, interest rate trends, stock index, and commodity futures prices, and quantifies the impact on the manufacturing industry. Furthermore, complex network theory is applied to analyze supply chain structures to identify single points of failure (SPOF), evaluate network robustness, and optimize alternative routes. The risk prediction model combines probabilistic methods, scenario analysis, Monte Carlo simulation, stress testing, etc. to provide prediction results that take uncertainty into account. However, changes in the external environment are difficult to predict, and risk analysis is merely a probabilistic estimate and does not guarantee that an actual event will occur. Therefore, other risk assessment methods, not limited to these analytical methods, can also be applied.

[0060] At least one embodiment of the present invention provides a system with intellectual property rights protection capabilities. This embodiment implements a function to automatically detect patent technologies and designs contained in drawings and provide advance warning of intellectual property rights infringement risks. This function addresses the increasing intellectual property rights disputes and rising risk of patent litigation in the global manufacturing industry. The intellectual property rights database integrates patent, utility model, design, and trademark data published by patent offices around the world (e.g., USPTO, EPO, JPO, CNIPA, KIPO), creating a comprehensive, regularly updated database. The search engine implements multifaceted search functions, including shape similarity search, function similarity search, technology classification search, inventor search, and applicant search, in addition to conventional keyword search. Furthermore, AI image recognition technology is used to extract visual features from drawings and quantitatively evaluate their similarity to existing designs and patent drawings. Furthermore, natural language processing technology is used to analyze the contents of technical specifications and automatically determine their correspondence with patent claims. The intellectual property rights infringement risk assessment calculates a comprehensive risk score that takes into account the patent's validity, scope of rights, geographical scope, remaining term, etc., and presents countermeasures such as measures to avoid infringement, obtaining licenses, and design changes.

[0061] The IP Search Unit compares uploaded drawings with existing patent and design databases to calculate similarity. If a high similarity is detected, a warning is displayed along with the relevant intellectual property information. It also detects novel design elements and recommends patent applications. This search system integrates with the databases of the world's five major patent offices (IP5: USPTO, EPO, JPO, CNIPA, and KIPO) as well as international classification systems such as the WIPO Global Brand Database, the International Classification for Designs (Locarno Classification), the International Patent Classification (IPC), and the Cooperative Patent Classification (CPC), enabling comprehensive intellectual property searches. The search algorithm employs advanced information retrieval techniques, including vector space modeling, latent semantic analysis (LSA), topic modeling (LDA), and deep learning feature extraction. It also uses 3D shape recognition technology to extract three-dimensional design features, detecting three-dimensional design infringement risks that are difficult to detect using traditional 2D drawing searches. To improve the accuracy of search results, the system implements features such as weighting by technical field, applicant credibility assessment, citation relationship analysis, and integrated display of family patents. The novelty assessment function automates the analysis of differences from existing technologies, quantitative assessment of inventive step, and determination of industrial applicability, thereby objectively evaluating the feasibility of patent applications. The patent portfolio analysis function also provides strategic information such as competitors' patent strategies, technological development trends, and identification of white space technological areas. Rights management functions, such as managing the validity period of intellectual property rights, annuity payment management, and rights transfer history tracking, are also integrated to create a comprehensive intellectual property management platform. However, determining intellectual property rights requires specialized legal judgment, and automated search results are only for reference; the final decision must be left to a patent attorney or other expert. Therefore, other intellectual property protection methods beyond these search functions are also possible.

[0062] At least one embodiment provides a system with a manufacturing process optimization function. In this embodiment, a function is implemented to analyze part shapes and factory equipment and automatically propose optimal machining sequences and tool selection. This optimization system comprehensively achieves productivity improvement, quality improvement, and cost reduction in the manufacturing industry. The optimization algorithm is a hybrid optimization engine that combines various optimization methods, such as linear programming, integer programming, dynamic programming, genetic algorithms, simulated annealing, particle swarm optimization, tabu search, and ant colony optimization. Manufacturing process analysis automates tool path generation, cutting condition optimization, and machining time calculation through integration with a computer-aided manufacturing (CAM) system. Furthermore, finite element method (FEM) analysis is used to predict stress distribution, temperature distribution, and deformation during machining, optimizing machining accuracy and tool life. The factory equipment database integrates various machine tool specifications (maximum machining size, spindle speed, feed rate, tool change time, etc.), tool libraries (detailed specifications for cutting tools, grinding tools, special tools, etc.), and jig and fixture information to achieve realistic optimization that takes equipment capacity constraints into account. In addition, digital twin technology will be used to create a virtual factory environment, allowing for simulation and optimization of manufacturing processes in advance.

[0063] The process optimization department considers the geometric constraints, material properties, and precision requirements of each part to enumerate possible machining routes. It evaluates factors such as machining time, tool wear, and quality risks for each route to select the optimal process overall. It also proposes efficiency improvements, such as simultaneous machining of multiple parts and shortening setup time. This optimization process uses multi-objective optimization techniques to analyze the trade-offs between conflicting goals (such as minimizing cost and maximizing quality) and presents a Pareto-optimal solution set. Machining route generation simultaneously considers geometric constraints (tool interference, undercut, accessibility, etc.), physical constraints (cutting force, processing heat, vibration, etc.), and quality constraints (surface roughness, dimensional accuracy, shape accuracy, etc.). Tool selection optimization performs combinatorial optimization of tool materials (high-speed steel, cemented carbide, ceramic, CBN, diamond, etc.), tool shapes (end mills, drills, reamers, taps, etc.), and coatings (TiN, TiAlN, DLC, etc.). Machining condition optimization involves multivariate optimization of parameters such as cutting speed, feed rate, cutting depth, and coolant conditions to achieve the best balance between productivity and tool life. Setup optimization is formulated as a job shop scheduling problem to generate an optimal schedule that simultaneously minimizes setup time, maximizes equipment utilization, and meets delivery deadlines. It also reduces the number of setups by performing continuous machining with the same tool or group machining of parts with similar shapes. By linking with a quality prediction model, the impact of changing machining conditions on quality can be evaluated in advance, achieving optimization that takes quality risks into account. However, optimization is the result of calculations under given conditions, and unexpected problems may occur in actual manufacturing sites. Therefore, these optimization suggestions are for reference only, and the final decision must be made by a manufacturing site expert.

[0064] At least one embodiment provides a delivery time reduction system with an inventory integration function. This embodiment implements a function that monitors the factory's material inventory status in real time and proposes alternative designs using in-stock materials. This function integrates the concepts of just-in-time (JIT) production, lean manufacturing, and supply chain optimization to simultaneously achieve effective inventory utilization and delivery time reduction. Integration with the inventory management system integrates various technologies, such as an enterprise resource planning (ERP) system, a warehouse management system (WMS), RFID, barcodes, and IoT sensors. Material inventory data includes detailed information such as material type, specifications, dimensions, quantity, arrival date, quality certificate, storage location, and expiration date. Furthermore, EDI (Electronic Data Interchange) integration with suppliers provides real-time information on the expected arrival of ordered materials and the progress of materials in production. The alternative material recommendation engine automatically extracts alternative materials that meet performance requirements by analyzing the similarities between the mechanical, chemical, and physical properties of materials. The system also recommends the optimal alternative material based on a comprehensive evaluation that takes into account material cost, processability, availability, and other factors. The design change proposal function works in conjunction with the CAD system to automatically generate design change proposals suitable for alternative materials, and verify safety through strength calculations, stress analysis, etc.

[0065] The Inventory Management Collaboration Department connects with each factory's inventory management system via API to obtain real-time information on material type, quantity, and expected arrival date. When materials required for ordered parts are out of stock, the system proposes alternative materials that meet performance requirements and automatically generates corresponding design change proposals. This shortens material procurement time and overall delivery time. This system integrates with MRP (Material Requirements Planning) and MRPII (Manufacturing Resource Planning) to achieve advanced inventory optimization, including material demand forecasting, reorder point management, and safety stock management. To improve inventory data accuracy, automatic identification systems using RFID technology, QR Code (registered trademark), and NFC are implemented to automatically record inventory receipts and deliveries and streamline inventory work. IoT sensors also monitor material storage environments (temperature, humidity, vibration, etc.) and integrate material quality maintenance and management. When recommending alternative materials, candidate materials that meet required specifications are extracted by comparing them with a materials database (mechanical properties, chemical composition, thermal properties, electrical properties, etc.). Performance data, such as past substitution performance, customer approval history, and quality issue history, are also taken into account to achieve highly practical alternative proposals. Automatic design change generation works in conjunction with parametric CAD to automatically adjust design parameters (plate thickness, dimensions, joining method, etc.) when materials are changed. Additionally, finite element analysis quantitatively evaluates changes in strength and rigidity, confirming the impact on performance in advance. Quantifying the effect of shortened delivery times accurately calculates the effect of shortening delivery times by adopting alternative materials through a total lead time analysis that integrates material procurement lead time, manufacturing lead time, and logistics lead time. Furthermore, cost impact analysis comprehensively evaluates fluctuations in material costs, processing costs, logistics costs, etc., visualizing the trade-off between cost and delivery time. However, material changes may affect performance and quality, and alternative proposals are only for reference. The final material selection is the responsibility of the client. Other delivery time shortening methods beyond these proposal functions are also possible.

[0066] At least one embodiment provides a system with a collaborative design function. In this embodiment, a function is implemented that allows the purchaser and the contractor to jointly consider design changes and achieve an optimal design that takes manufacturability into consideration. This collaborative design system integrates the concepts of DFM (Design for Manufacturing), DFA (Design for Assembly), and DFX (Design for X) to realize concurrent engineering that simultaneously optimizes manufacturability, cost, quality, environmental friendliness, and other factors from the design stage. The collaborative design platform is equipped with a cloud-based integrated CAD system, real-time collaborative editing functions, a version control system, and change history tracking functions. In addition, a 3D collaborative review function utilizing VR / AR technology allows geographically separated designers and manufacturing engineers to conduct design reviews in a virtual space. The design evaluation function automatically calculates multifaceted evaluation indicators such as manufacturing cost, manufacturing time, quality risk, and environmental impact, quantitatively visualizing the impact of design changes. Furthermore, an AI design support function automatically generates design improvement proposals using past design examples, manufacturing performance, and quality data. The knowledge management system stores design know-how, manufacturing know-how, trouble cases, improvement cases, etc. in a database, promoting knowledge sharing between designers and manufacturing engineers.

[0067] The collaborative design support function allows contractors to add manufacturing improvement proposals in the form of comments to the initial design uploaded by the client. It automatically calculates the impact of design changes on cost and quality and generates a comparison report of the changes before and after the change. The version control function tracks design change history and allows users to revert to previous versions as needed. This support system incorporates the concept of Product Lifecycle Management (PLM) into design change management, enabling systematic management of engineering change requests (ECRs), engineering change proposals (ECPs), and engineering change orders (ECOs). The comment function allows for the exchange of opinions in a variety of formats, including text, audio, video, and 3D annotations, streamlining complex technical discussions. In addition, the automatic translation function supports multilingual collaborative design, enabling design collaboration across global manufacturing networks. The impact assessment system performs a chain analysis of the impact of design changes on each stage, including manufacturing processes, material procurement, quality control, inspection processes, logistics, and service, and generates a comprehensive change impact assessment report. It also predicts change risks and presents countermeasures by searching past cases of similar design changes. The approval workflow function automates a step-by-step approval process based on the importance, scope of impact, cost impact, etc. of the design change, ensuring approval from the appropriate decision maker. By linking with simulation, it automatically executes performance predictions, manufacturing simulations, quality predictions, etc. after design changes to verify the effects of the changes in advance. Furthermore, by linking with design optimization algorithms, it also automatically generates optimal design proposals through multi-objective optimization. However, design changes require complex engineering judgment, and the results of automatic calculations are only approximate values, so detailed verification is the designer's responsibility. Therefore, other collaborative methods other than these support functions can also be applied.

[0068] At least one embodiment of the present invention provides a system with an energy efficiency optimization function. This embodiment implements a function to predict energy consumption in manufacturing processes and propose energy-saving machining methods. This function responds to societal demands, such as achieving carbon neutrality, promoting ESG management, reducing energy costs, and complying with environmental regulations. Energy consumption prediction involves detailed analysis of the power consumption characteristics of various manufacturing equipment (machine tools, heating furnaces, drying furnaces, compressors, cooling equipment, etc.), correlations with machining conditions, and equipment operation patterns. Furthermore, a factory-wide energy flow analysis quantifies direct energy consumption, indirect energy consumption, energy loss, recoverable energy, and other factors. Energy-saving proposals include specific improvement proposals, such as optimization of machining conditions (optimization of cutting speed, feed rate, spindle speed, etc.), optimization of tool selection (selection of low-friction tools and high-efficiency tools), machining method changes (switching from conventional machining to high-efficiency machining), and integrated equipment operation (improving efficiency through synchronized operation of multiple equipment). Capital investment proposals are also made, including the use of renewable energy, energy recovery systems, and upgrading to high-efficiency equipment. By linking with an energy management system (EMS), it also supports operational optimization such as real-time energy monitoring, demand control, and peak cutting control.

[0069] The Energy Analysis Department analyzes the power consumption, processing time, and equipment utilization rate of each processing step to calculate total energy consumption. It proposes energy reduction measures, such as using high-efficiency tools, optimizing processing conditions, and integrating equipment operations. It also recommends environmentally friendly manufacturing methods, taking into account the factory's renewable energy usage rate. This energy analysis system visualizes energy consumption at each stage of the manufacturing process through detailed energy accounting. Specifically, it classifies energy consumption into standby power, processing power, auxiliary equipment power (lighting, air conditioning, ventilation, etc.), and transportation power, clarifying the breakdown of energy consumption. It also performs energy cost analysis, taking into account power rate structures such as time-of-use, seasonal, and demand charges. The quantitative effects of energy-saving proposals are accurately calculated, including CO2 reductions, energy cost savings, and payback periods, to evaluate the economic viability of the proposals. Furthermore, it conducts comparative analyses with other factories using energy efficiency indicators (energy consumption per unit of product, energy efficiency per equipment utilization rate, etc.). When proposing equipment upgrades, the company quantitatively evaluates the effects of introducing high-efficiency motors, inverter controls, LED lighting, high-efficiency air conditioning, and exhaust heat recovery systems, and then formulates an upgrade plan with high return on investment. It also evaluates the indirect energy-saving effects of factory layout optimization, such as shortening transport distances, streamlining material flow, and optimizing equipment placement. Linking with energy monitoring systems also provides functions such as understanding energy usage in real time, early detection of abnormal consumption, and optimal operation through automatic control. However, since many variables are involved in predicting energy consumption, there is a possibility that it will differ from actual consumption. Therefore, these analysis results are only estimates, and detailed energy-saving measures must be considered by experts at each factory.

[0070] At least one embodiment provides a system with education and training support functions. In this embodiment, the system provides learning content for manufacturing engineers and designers and implements functions to support technical skill improvement. This education support system aims to solve issues related to skill transfer, human resource development, and technology standardization in the manufacturing industry. The learning content covers a wide range of fields, including basic machining techniques, advanced manufacturing techniques, quality control methods, safety management, and environmental management. It also utilizes the latest educational technologies, such as microlearning, adaptive learning, gamification, and VR / AR training, to provide an effective learning experience. Individual learning progress management involves detailed analysis of learning history, level of understanding, proficiency, and areas of weakness, automatically generating an individually optimized learning path. Furthermore, a skills assessment system conducts periodic skill measurements and provides objective skill certification. In collaboration with in-house education, the system supports systematic human resource development through integration with in-house training programs, on-the-job training (OJT), mentoring systems, and other systems. Furthermore, to contribute to industry-wide technology standardization, a knowledge sharing platform for manufacturing technology best practices, standard operating procedures, quality standards, and other information is also established. By collaborating with international technical certification systems (ISO skills certification, national skills tests, etc.), we will also be able to achieve globally recognized skills certification.

[0071] The Learning Support Department provides anonymized learning materials from past transactions, creating an environment where engineers can learn about estimating concepts and selecting manufacturing processes. It also offers interactive learning features, such as virtual factory tours using virtual reality (VR) technology and processing experiences using simulation functions. It also evaluates engineers' skill levels and automatically generates personalized learning plans. This learning support system systematically organizes and stores vast amounts of technical information, know-how, and case studies using a knowledge management system (KMS). Learning content is created using 3DCG, animation, interactive simulation, and other technologies to develop materials that visually explain complex manufacturing processes. Multilingual support is also provided to support international skills transfer and technical cooperation. Skill assessment measures overall skill level through multifaceted evaluations, including written tests, practical exams, project evaluations, and peer reviews. Furthermore, the concept of Continuing Professional Development (CPD) is introduced to support engineers' lifelong learning. Learning analytics analyzes learners' behavioral patterns, comprehension, and learning speed to maximize educational effectiveness. In addition, the social learning function encourages learners to share knowledge and learn from each other. For companies, it also provides HR strategy support functions such as measuring ROI on human resource development, skill gap analysis, and succession planning. As a contribution to industry standardization, it also works on standardizing manufacturing technology, formulating skills certification standards, and international cooperation. However, learning effectiveness varies from person to person, and not all users will experience the same results. These learning systems only play a supplementary role, and practical experience is important for actual technical acquisition. Furthermore, rapid advances in technology require continuous updating of learning content, and these educational functions are merely basic support; individual instruction, specialized training, practical projects, and other aspects are essential for improving advanced technical skills.

[0072] At least one embodiment provides a system with an anomaly detection function. This embodiment implements a function for automatically detecting quoted prices and transaction patterns that significantly deviate from normal, enabling early detection of fraudulent transactions and abnormal market trends. This anomaly detection system applies fraud detection technology developed in the financial industry, machine learning anomaly detection methods, and statistical quality control concepts to transaction management in the manufacturing industry. The anomaly detection algorithm employs an ensemble method that combines statistical methods (e.g., 3σ method, interquartile range method, Mahalanobis distance), machine learning methods (e.g., one-class SVM, isolation forest, local outlier factor), and deep learning methods (e.g., autoencoder, GAN, LSTM). Detection targets include price anomalies (e.g., significant deviations from market prices, abnormal price fluctuations), transaction pattern anomalies (e.g., abnormal transaction frequency, transaction time, transaction volume), behavior pattern anomalies (e.g., unusual access patterns, operation patterns), and quality anomalies (e.g., abnormal quality fluctuations, increased complaints). In addition, by linking with external data, it achieves highly accurate anomaly detection that takes into account contextual information such as market trends, economic indicators, industry trends, and competitive situations. Detection results are classified by importance and managed in stages, such as anomalies requiring emergency response, anomalies requiring careful monitoring, and anomalies at the reference information level. Furthermore, explainable AI technology visualizes the basis for anomaly detection, supporting the judgment of human experts.

[0073] The anomaly detection unit uses statistical techniques and machine learning to learn normal trading patterns and detect abnormal transactions that deviate from these. For example, it automatically detects quoted prices that deviate significantly from market prices, large volumes of transactions by specific users, and unnatural evaluation patterns. Detected anomalies are notified to administrators, who can suspend trading or conduct further investigations as necessary. This detection system continuously monitors large volumes of trading data using real-time streaming processing to detect anomalies early. The machine learning model automatically learns normal patterns using unsupervised learning, creating an adaptive system that can handle new abnormal patterns. Time-series anomaly detection also monitors temporal changes in trading patterns and performs precise anomaly detection, taking seasonality, periodicity, trends, and other factors into account. To improve detection accuracy, it also employs advanced detection methods such as multidimensional anomaly detection, correlation anomaly detection, and cluster anomaly detection. Furthermore, domain knowledge is utilized to predefine abnormal patterns specific to the manufacturing industry (such as last-minute demand following a sudden rise in material prices or concentrated trading before and after long holidays) and implement detection rules that take industry characteristics into account. To reduce false positives, detection accuracy is continuously improved through a voting system using multiple algorithms, confidence score calculation, and feedback learning by human experts. Dynamic adjustment of detection sensitivity also enables adaptive monitoring in response to changes in market conditions. Visualization of detection results provides an interface that allows administrators to intuitively grasp the situation through dashboards, alerts, reports, and more. Analysis of detection history also supports trend analysis of abnormal patterns and the formulation of measures to prevent recurrence. However, anomaly detection carries the risk of false positives (determining normal transactions as abnormal) and oversights (determining abnormal transactions as normal), and detection accuracy is not 100%. These detection functions are merely functional, and the final decision requires human confirmation.

[0074] At least one embodiment provides a system with a regulatory compliance support function. In this embodiment, a function is implemented to automatically check compliance with various manufacturing-related regulations (safety standards, environmental regulations, quality standards, etc.). This regulatory compliance system addresses the increasingly complex, stricter, and more frequent regulatory changes in the global manufacturing industry. A comprehensive regulatory database is built, including target regulations such as safety regulations (Occupational Safety and Health Act, Machinery Safety Standards, CE / UL Marking, etc.), environmental regulations (RoHS Directive, REACH Regulation, Waste Management Act, etc.), quality regulations (ISO quality standards, Medical Device Regulations, Food Safety Regulations, etc.), trade regulations (Export Control Regulations, Customs Act, Rules of Origin, etc.), and intellectual property regulations (Patent Act, Trademark Act, Copyright Act, etc.). The regulatory compliance check automatically compares product specifications, material composition, manufacturing processes, quality control systems, environmental management systems, etc. with various regulatory requirements to evaluate compliance. Furthermore, an automatic regulatory change monitoring function continuously collects and analyzes the latest information on the establishment of new regulations, amendments to existing regulations, and enforcement dates, supporting proactive response. It also handles complex international regulatory environments such as mutual recognition of international regulations, regulatory harmonization, and technical trade measures (TBT), automatically determining regulatory requirements for each export destination country. It also provides comprehensive compliance support, including calculating regulatory compliance costs, supporting certification acquisition, and assisting with negotiations with regulatory authorities.

[0075] The Regulatory Checking Department identifies applicable regulations based on a part's intended use, materials, manufacturing method, etc., and evaluates compliance with design specifications. For example, it checks standard requirements such as ISO 13485 and FDA guidelines for medical device parts, and IATF 16949 for automotive parts. If non-conformances are detected, it proposes corrections or alternative specifications. This regulatory check system uses artificial intelligence (AI)-based natural language processing technology to automatically extract requirements from complex regulatory documents and convert them into a structured rule base. To address ambiguity in regulatory interpretation, it also provides an interpretation support function that references past certification cases, regulatory agency decisions, and industry guidelines. Regulatory compliance assessments employ a risk-based approach, including product risk analysis, hazard analysis, and risk assessment, to prioritize regulatory requirements. It also comprehensively checks regulatory requirements throughout the entire product lifecycle (design, manufacturing, sales, use, and disposal) to ensure compliance at each stage. In response to international regulations, it manages regulatory requirements from multiple countries in an integrated manner and supports the development of unified specifications based on the most stringent requirements. We also support the use of international cooperative frameworks such as mutual recognition agreements (MRAs) and simplified conformity assessment procedures. In response to regulatory changes, we enable rapid response to regulatory changes through automatic updates to the legal database, impact analysis, and transition plan formulation. Furthermore, we provide procedural support for prior consultation with regulatory authorities, approval applications, and review responses. In compliance management, we ensure continuous regulatory compliance through continuous monitoring of regulatory compliance status, internal audit support, corrective action management, and other measures. However, because laws and regulations are complex and frequently revised, it is difficult to fully comply with them. Therefore, these checking functions only provide reference information, and the final determination of legal compliance must be made by experts or certification bodies.

[0076] At least one embodiment provides a system with a supplier development support function. In this embodiment, a function is implemented to support manufacturers seeking to enter the manufacturing industry in improving their technical capabilities and establishing a quality control system. The system aims to expand sustainable supply chains by gradually resolving various barriers to new entrants (technical, financial, reliability, knowledge, etc.) to the manufacturing industry. Specifically, the system begins with an analysis of the current situation of prospective entrants, and then conducts a multifaceted technical assessment, quality system evaluation, financial soundness check, market understanding survey, etc. to clearly understand each supplier's unique strengths and challenges. Based on this, an individualized development program is developed, adopting a comprehensive approach to support gradual capability improvement. Furthermore, the system provides learning opportunities from both theoretical and practical perspectives by combining a mentoring system with existing high-quality suppliers, technical guidance from industry experts, and on-site improvement support from quality control consultants.

[0077] The Supplier Development Department evaluates the current capabilities of prospective suppliers and identifies technical shortcomings and quality control items requiring improvement. It supports gradual capacity development by assisting in the development of improvement plans, introducing technical instructors, and recommending capital investments. Suppliers who reach a certain level are issued a certificate, creating an environment in which clients can do business with confidence. This development process systematically supports compliance with international standards (e.g., ISO 9001, ISO 14001, OHSAS 18001), production technology improvements (introduction of lean manufacturing, Six Sigma, TPM, etc.), digitalization (IoT, AI, big data utilization, etc.), and human resource development (engineer training, quality control training, safety training, etc.). Furthermore, it creates a continuous learning environment by sharing industry best practices, providing opportunities to tour advanced factories, hosting technology exchange events, and creating opportunities to participate in joint research and development projects. Furthermore, it aims to visualize and optimize the development effects through progress management, regular capability assessments, improvement monitoring, and performance measurement. However, improving technical skills and quality control capabilities requires time and continuous effort, and dramatic improvements cannot be expected in a short period of time. These support functions only play a supporting role, and the ultimate responsibility lies with each individual company.

[0078] At least one embodiment provides a system with digital twin functionality. This embodiment implements a function that reproduces the entire manufacturing process in a digital space and executes a virtual manufacturing simulation. Digital twin technology is an innovative approach that realizes comprehensive value creation, such as optimizing manufacturing efficiency, enabling predictive maintenance, improving quality, and reducing costs, by linking real-world manufacturing systems with their virtual replicas. This technology significantly reduces physical prototyping and experiments, and enables optimization at the design stage, pre-verification of manufacturing processes, optimization of equipment layout, and improvement of logistics flows in a virtual space. Furthermore, by linking with real-time data, the actual manufacturing situation is reflected in the virtual space, providing advanced manufacturing management functions such as budget / actual management, anomaly detection, and dynamic adjustment of production plans. Furthermore, by incorporating machine learning and AI technologies, the system also implements a function that applies optimization models learned from past data to the virtual space to predict and improve future manufacturing performance.

[0079] The Digital Twin Construction Department creates 3D models of factory equipment layout, worker traffic patterns, material flow, and other factors, and recreates the entire manufacturing process on a computer. When manufacturing new parts, simulations are performed in digital space before actual production begins to identify problems and optimize the process. By linking with real-time data, the actual manufacturing situation can be reflected in the digital twin, enabling budget-to-actual management and the identification of areas for improvement. The construction process comprehensively involves accurate on-site surveying using 3D scanning technology, integration with CAD data, implementation of a physical simulation engine, and the creation of an automatic sensor data acquisition system. In addition, detailed models of the operating characteristics of manufacturing equipment, the physical properties of materials, worker behavior patterns, and environmental conditions (temperature, humidity, vibration, etc.) are also created to create a virtual environment that closely resembles reality. Furthermore, probabilistic factors (equipment failure, material quality variations, work time fluctuations, etc.) are incorporated to achieve robust simulations that take uncertainty into account. Digital twins are not simply visualization tools; they function as intelligent manufacturing systems that integrate optimization algorithms, machine learning models, predictive analysis functions, and other functions, supporting continuous improvement activities. However, the accuracy of digital twins is limited compared to the complexity of the real world, and it is difficult to accurately reproduce all phenomena. These simulation results are only approximate, and unexpected problems may occur in actual manufacturing.

[0080] At least one embodiment provides a user satisfaction improvement system with a sentiment analysis function. This embodiment implements a function that uses natural language processing to analyze user comments and feedback during the transaction process and quantify satisfaction and stress factors. This function aims to enhance customer relationship management (CRM) by quantifying qualitative emotional aspects in addition to traditional quantitative evaluation indicators (price, delivery time, quality, etc.), thereby achieving a more comprehensive evaluation of customer satisfaction. The natural language processing technology utilizes the latest large-scale language models, deep learning architectures, sentiment dictionaries, context analysis technologies, etc. to achieve highly accurate emotion recognition that is compatible with multiple languages ​​and cultures. The system also provides advanced analytical functions such as tracking emotional changes over time, analyzing sentiment trends by customer segment, and comparing sentiment scores with competitors. Furthermore, the system integrates value-added services based on sentiment analysis results, such as an automated response system, personalized improvement suggestions, and proactive customer support, to support continuous improvement of the customer experience.

[0081] The sentiment analysis unit extracts emotional expressions from text data such as chat history, review comments, and inquiry details, and classifies them as positive, negative, or neutral. It identifies factors causing dissatisfaction (price, delivery time, quality, communication, etc.) and generates improvement suggestions. It also analyzes the behavioral patterns of satisfied users to extract best practices for building good business relationships. This analysis process utilizes advanced natural language understanding technology to accurately grasp context and intent, conducting multi-layered sentiment analysis at the word, sentence, document, and dialogue levels. It also builds a manufacturing-specific sentiment analysis model that takes into account industry-specific terminology, idiomatic expressions, and implicit expectations, enabling more precise sentiment evaluation. It also learns differences in emotional expressions based on individual attributes (company size, industry, region, transaction history, etc.) to provide personalized sentiment analysis. The analysis results are visualized in a dashboard format and provided in a form that can be used to understand sentiment trends, detect problems early, and measure improvement effects. It also provides practical functions based on sentiment analysis results, such as automatic alerts, identifying priority cases, and optimizing customer success activities. However, because the interpretation of emotions is subjective and varies greatly depending on the context and cultural background, the results of automated analysis are only for reference, and important decisions require human confirmation. Therefore, other methods for improving satisfaction beyond these analytical functions can also be considered.

[0082] At least one embodiment provides a system with an automatic scheduling function. This embodiment implements a function for efficiently scheduling multiple manufacturing requests, maximizing factory utilization rates while meeting delivery deadlines. This function employs advanced algorithms that integrate operations research, mathematical optimization, and AI technology to solve complex production planning problems in the manufacturing industry. Scheduling problems are complex combinatorial optimization problems that require simultaneous consideration of numerous constraints (equipment capacity, material procurement, personnel allocation, quality requirements, delivery time constraints, etc.) and multi-objective optimization (cost minimization, delivery time reduction, quality improvement, utilization rate maximization, etc.). The system provides advanced features such as a dynamic scheduling function that responds to real-time changes in constraints (urgent requests, equipment failures, material delays, etc.), comparison of optimization results across multiple scenarios, robust optimization that takes uncertainty into account, and adaptive scheduling that learns from past performance data. It also achieves holistic scheduling that integrates human aspects such as workload leveling, skill matching, and motivation maintenance.

[0083] The scheduling optimization unit generates an optimal production schedule using optimization methods such as genetic algorithms and simulated annealing, taking into account the manufacturing time, priority, delivery date constraints, and equipment constraints for each part. It dynamically responds to plan changes due to emergency requests or equipment failures, automatically rescheduling, and also has the ability to continuously improve the accuracy of manufacturing time predictions based on past performance data. The optimization process utilizes multi-objective optimization algorithms (NSGA-II, SPEA2, MOEA / D, etc.) to appropriately balance the trade-offs between conflicting objective functions (cost vs. delivery date, quality vs. efficiency, etc.). It also incorporates machine learning techniques (reinforcement learning, deep learning, ensemble learning, etc.) to achieve adaptive optimization that learns from past successful patterns. Furthermore, it employs stochastic optimization and robust optimization methods that take into account uncertainties and probabilistic factors (demand fluctuations, failure rates, work time variations, etc.) to generate a robust schedule that can respond to realistic fluctuations. Optimization results are presented in intuitive visualization formats such as Gantt charts, resource utilization graphs, and KPI achievement rates to support understanding of the plan and decision-making. In addition, the "what-if" scenario analysis function allows for advance assessment of the impact of various condition changes, supporting risk management and strategic planning. However, since many unforeseen factors occur on the manufacturing floor, perfect scheduling is impossible, and these optimization results are merely theoretical values; flexible adjustments are required in actual operations, so other scheduling methods, not just automation functions, can also be used in combination.

[0084] At least one embodiment provides a system with a crowdsourcing function. This embodiment implements a function that exposes complex design challenges and technical problems to a wide range of experts and solicits solutions through crowdsourcing. This function revolutionizes problem-solving approaches that rely on traditional in-house resources or limited external consultants, realizing problem-solving through collective intelligence leveraging a global knowledge network. The system provides comprehensive functions such as anonymization and standardization of problems, matching to appropriate experts, automatic evaluation of proposal quality, intellectual property protection, and optimization of reward distribution. It also builds an efficient and effective crowdsourcing platform with functions such as a database of past solutions, expert profiling, problem categorization, and difficulty assessment. Furthermore, it integrates advanced functions such as problem-solving support using AI technology, automatic proposal integration, and optimal solution selection support, providing an innovative problem-solving environment that combines human creativity with AI processing power. It builds a network of experts from diverse fields of expertise (mechanical engineering, materials engineering, electrical engineering, chemical engineering, information engineering, design engineering, etc.) to promote the creation of innovative cross-disciplinary solutions.

[0085] The Crowdsourcing Management Department anonymizes and publishes difficult technical problems and solicits solutions from engineers worldwide. It automatically evaluates proposed solutions and predicts their feasibility and effectiveness. It rewards outstanding proposers and encourages continued participation. It also analyzes each engineer's expertise and capabilities based on their past proposals to match them with appropriate problems. The management process includes appropriate anonymization of problems (removal of confidential information, extraction of essential elements, standardization of technical specifications, etc.), distribution to a global expert network, multilingual support (machine translation, technical dictionaries, cultural adaptation, etc.), and schedule management that takes time zones into account. The proposal evaluation system also sets multifaceted evaluation criteria, including technical validity, feasibility, originality, economic viability, and ease of implementation, to achieve objective evaluation by combining AI technology and expert review. Furthermore, it properly manages intellectual property rights, concludes NDAs with proposers, and handles legal aspects such as the allocation of rights to deliverables, creating a safe and fair crowdsourcing environment. The reward system will create a sustainable ecosystem through fair reward calculations based on the quality of proposals and the degree of contribution, a tiered payment system, and long-term partnership building. However, with crowdsourcing, there is a large variation in quality, and expert judgment is required to verify the content of proposals, so these external uses are merely supplementary measures, and the client must bear the final responsibility.

[0086] At least one embodiment of the present invention provides a system with a sustainability assessment function. This embodiment implements a function to evaluate the sustainability of the entire manufacturing process and calculate a comprehensive sustainability score from environmental, social, and economic perspectives. This function builds a comprehensive assessment system that integrates life cycle assessment (LCA), social impact assessment, and economic analysis to address contemporary social issues such as expanding ESG (Environment, Social, Governance) investment, addressing the SDGs (Sustainable Development Goals), achieving carbon neutrality, and promoting a circular economy. Environmental aspects include quantitative assessments of greenhouse gas emissions, water consumption, waste generation, chemical substance use, impact on biodiversity, and land use changes. Social aspects include assessments of working conditions, human rights protection, community contributions, supply chain transparency, and consumer safety. Economic aspects include comprehensive analysis of long-term economic value creation, innovation creation, risk management, and other factors. The system also provides functions such as compliance with international sustainability standards (GRI, SASB, TCFD, etc.), comparison with industry benchmarks, and identification and proposal of improvement opportunities.

[0087] The Sustainability Assessment Department analyzes the entire life cycle of a product, from raw material procurement to manufacturing, use, and disposal, quantifying environmental impacts (e.g., CO2 emissions, resource consumption, waste generation), social impacts (e.g., working conditions, contributions to local communities, human rights issues), and economic benefits (e.g., job creation, technological innovation, and improved competitiveness). We also evaluate the impact on corporate value from an ESG investment perspective and provide reference information for investment decisions. The assessment process is based on life cycle assessment (LCA) methods in accordance with ISO 14040 / 14044, and utilizes integrated specialized assessment methods such as cradle-to-cradle analysis, water footprint analysis, and carbon footprint analysis. We also ensure traceability throughout the supply chain and accurately calculate greenhouse gas emissions for Scope 1 (direct emissions), Scope 2 (indirect emissions), and Scope 3 (other indirect emissions). The social impact assessment evaluates factors such as occupational safety and health, child labor prevention, fair wages, and gender equality based on international standards such as the ILO Convention, the United Nations Global Compact, and the United Nations Guiding Principles on Business and Human Rights. In addition to traditional financial indicators, the economic evaluation will employ new value assessment methods such as social return on investment (SROI), economic value added (EVA), and value creation models based on integrated reporting. It will also quantify contributions to a circular economy, such as circular economy indicators, resource efficiency, and zero waste rates, to support the transition to a sustainable business model. However, sustainability evaluation criteria are diverse, there are no unified indicators, and these evaluation results are only one perspective; multifaceted consideration is required, and other approaches, not limited to evaluation methods, can also be applied.

[0088] At least one embodiment provides a system with a virtual factory tour function. This embodiment implements a function that uses VR technology to virtually tour the inside of a factory and visually confirm the manufacturing process and quality control system. This function overcomes the constraints of physical factory tours (geographical, time, security, cost, etc.) and enables efficient factory evaluation and selection within global manufacturing networks. The VR content utilizes cutting-edge technologies, such as high-resolution photography using 360-degree cameras, precise spatial reproduction using 3D modeling technology, and dynamic display using real-time rendering technology. It also visualizes in detail the operating mechanisms of manufacturing equipment, quality control processes, safety measures, environmental initiatives, etc., and supports intuitive understanding through audio guides, subtitles, interactive operations, etc. Furthermore, quantitative data such as the factory's production capacity, technical features, quality control system, and environmental response status are integrated and displayed in the VR space, providing a comprehensive factory evaluation experience that combines sensory understanding and logical analysis. Multilingual support, accessibility support, and compatibility with a variety of VR devices also enable a global and inclusive usage environment.

[0089] The VR Factory Tour section converts footage of the factory interior captured with a 360-degree camera into VR content that can be viewed on a head-mounted display or web browser. Visitors can virtually check the operating status of manufacturing equipment, the skill level of workers, and the implementation of quality control. Audio guides and explanatory text are also provided to provide detailed explanations of equipment specifications and technical features. The VR content is created using advanced imaging technologies, including 3D spatial reconstruction using photogrammetry, point cloud data processing, texture mapping, and lighting optimization, to create a realistic virtual experience. It also records changes in the manufacturing process over time, differences in operating conditions depending on the season or time of day, and changes in work content on different product lines, supporting a comprehensive understanding of the factory. Interactive features include movement within the virtual space, changing the viewpoint, displaying equipment details, displaying work procedures step by step, and displaying quality data in real time, providing an active learning experience. Furthermore, features such as note-taking within the VR space, evaluation item checking, and information sharing with other visitors support efficient factory evaluation activities. As a multi-sensory experience, it also integrates haptic feedback, sound effects, temperature sensations, etc. to create a more immersive experience. It also provides business support functions such as factory tour history management, comparative analysis, and automatic report generation, aiming to streamline the factory selection process. However, VR content is limited to the situation at the time of shooting and does not reflect real-time factory conditions, so it is not a complete substitute for an actual factory tour; these virtual tour functions merely provide supplementary information.

[0090] At least one embodiment of the present invention provides a system with a technology trend analysis function. This embodiment implements a function for analyzing the latest trends in manufacturing and materials technologies and predicting future technological innovations. This function provides strategic information essential for companies to maintain competitive advantage and promote innovation in a rapidly changing technological environment. The technology trend analysis integrates and analyzes a variety of information sources, including academic paper databases (IEEE Xplore, ScienceDirect, SpringerLink, etc.), patent databases (USPTO, EPO, JPO, WIPO, etc.), industry reports, technology exhibition information, startup trends, government research and development policies, and international standardization trends. Large-scale data analysis utilizing AI technologies (natural language processing, machine learning, deep learning, etc.) tracks the entire technology lifecycle from the nascent to mature stages, and detects patterns of technological convergence and divergence, cross-disciplinary technology integration, and signs of disruptive innovation at an early stage. Furthermore, technology forecasting methods (Delphi method, scenario analysis, technology roadmapping, etc.) are combined to forecast the direction of medium- to long-term technological development and support strategic decision-making.

[0091] The Technology Trends Analysis Department extracts technological trends from sources such as academic papers, patent information, industry news, and technology exhibition reports to predict the maturity and timing of commercialization of emerging technologies. It continuously monitors progress in technological fields such as 3D printing, nanomaterials, IoT, and robotics and analyzes their impact on the manufacturing industry. It identifies components and processes likely to be affected by technological innovations and promotes early countermeasure development. The analysis process utilizes advanced analytical methods such as large-scale literature analysis using text mining technology, impact assessment using citation network analysis, technology relevance mapping using co-occurrence analysis, and technology development trajectory tracking using time series analysis. Furthermore, technology maturity assessment employs multifaceted evaluation indices such as Technology Readiness Level (TRL), Market Readiness Level (MRL), and Investment Readiness Level (IRL) to objectively evaluate the feasibility of technologies for commercialization. Furthermore, technology forecasting applies theoretical frameworks such as the S-curve model, hype cycle model, and technology substitution model to identify technology growth patterns and future projections. Technology impact assessments support strategic decisions regarding technology introduction by comprehensively analyzing factors such as applicability to manufacturing processes, substitution with existing technologies, cost-effectiveness, regulatory and standardization compliance, and market acceptability. They also evaluate comparative analysis with competing technologies, the possibility of technology integration, and trends in ecosystem formation, providing multifaceted information necessary for technology strategy planning. However, because technological developments are difficult to predict and are greatly influenced by social and economic conditions, these predictions are merely reference information, and important decisions such as investment decisions require careful consideration. Other information gathering methods, not limited to forecasting methods, should also be used in conjunction with these methods.

[0092] At least one embodiment provides a system with an automatic quotation generation function. This embodiment implements a function that automatically generates a standard quotation format based on AI analysis results, reducing the administrative burden on factories. This function aims to streamline, standardize, and improve the quality of quotation creation operations in the manufacturing industry, revolutionizing the traditional manual quotation creation process. The automatic generation system structures a variety of information, such as cost calculation results, manufacturing specifications, delivery date information, quality requirements, and special conditions, and automatically arranges them in an industry-standard quotation layout. It also provides advanced functions such as quotation customization according to client company requirements, multilingual support, display in different currencies, and compliance verification with legal requirements. Furthermore, it achieves continuous improvement and optimization by learning from past quotation templates, analyzing best practices, and incorporating customer feedback. To improve the reliability of quotations, it also integrates quality assurance functions such as content consistency checks, automatic detection of calculation errors, legal compliance confirmation, and conformance verification with industry standards. It also automates the entire business process, including quotation distribution, tracking, update management, and approval workflow, enabling end-to-end quotation management.

[0093] The quotation generation unit integrates cost calculation results, manufacturing specifications, delivery date information, etc., and automatically arranges them in an industry-standard quotation layout. It also provides a function to adjust the level of detail of the quotation (approximate, detailed, ultra-detailed, etc.) according to customer requests and display only the necessary items. It also has the ability to learn from past quotation templates to generate customized quotation documents that reflect the characteristics of each factory. The generation process implements advanced document generation functions such as automatic layout of structured data, dynamic section configuration, conditional display / hide control of items, automatic application of calculation formulas, and automatic unit conversion. To improve the visual quality of quotation documents, it also provides functions such as professional design templates, automatic insertion of graphic elements, integration of branding elements, and optimization of readability. The quality assurance function ensures the generation of high-quality quotation documents by verifying numerical calculations, checking item consistency, verifying required information, conforming to formatting standards, and automatically detecting typos. It meets modern business requirements by supporting a variety of output formats (PDF, Excel, Word, XML, etc.), integrating electronic signatures, and implementing security features (watermarks, password protection, etc.). Furthermore, transparency and accountability are ensured through quotation history management, version control, change history tracking, and approval process recording. The customer collaboration function supports smooth communication by sharing quotations, commenting, processing revision requests, and notifying approval status. However, because the contents of quotations carry legal responsibility, the automatically generated results are merely drafts, and final content review and approval require human judgment. Therefore, other efficiency techniques, not limited to these automation functions, can also be applied.

[0094] At least one embodiment of the present invention provides a system with a competitive analysis function. This embodiment implements a function for analyzing competitors' market trends and evaluating a company's competitive advantage. This function is an important business intelligence function for supporting strategic decision-making in an increasingly competitive environment. Competitive analysis involves an integrated analysis of a variety of information sources, including public information (e.g., corporate websites, financial reports, press releases, industry reports), market research data, customer feedback, and the opinions of industry experts. Advanced analytical functions are provided, including automated data collection using AI technology, information extraction using natural language processing, pattern recognition using machine learning, and future trend forecasting using predictive analysis. The system also conducts a multifaceted evaluation of competitors' products and services, pricing strategies, technological capabilities, market share, customer satisfaction, financial status, and management strategies, and performs a detailed comparative analysis with the company's own competitors. Furthermore, the system provides strategic insights, such as analyzing industry-wide trends, monitoring new entrants, assessing the threat of alternative technologies, and identifying market opportunities, to support the formulation of medium- to long-term competitive strategies.

[0095] The Competitive Analysis Department analyzes competitors' technological capabilities, pricing levels, service offerings, etc. from publicly available information (websites, press releases, financial reports, etc.) and generates comparative reports. It visualizes competitive positioning using indicators such as market share, customer satisfaction, and technological innovation, and identifies areas for improvement. It also monitors competitors' new technology adoption and capital investment trends to detect early signs of market change. The analysis process systematically involves automated data collection using web scraping technology, information extraction using natural language processing, customer response analysis using sentiment analysis, and management status evaluation using financial analysis. It also builds comprehensive competitive profiles by analyzing competitors' product portfolios, pricing strategies, marketing strategies, and technological development directions. Market positioning analysis involves competitive mapping along strategic axes such as price vs. quality, innovation vs. stability, and service vs. cost, allowing for an objective assessment of a company's relative positioning. We also analyze competitors' strengths and weaknesses, identify success factors, and identify failure patterns to extract strategic implications and provide insights that can be used in your own strategy planning. Time-series analysis tracks dynamic changes in competitors' performance, strategic shifts, market responses, and other factors to predict future competitive developments. Furthermore, we apply strategic analysis frameworks such as SWOT analysis, five forces analysis, and value chain analysis to provide structured competitive analysis. However, competitive analysis is merely speculation based on publicly available information and may differ from the actual competitive situation. These analysis results are provided only as reference information, and strategic decisions require careful consideration. Other approaches, not just evaluation methods, may also be applicable.

[0096] At least one embodiment provides a system with disaster response capabilities. This embodiment implements a function that predicts the impact of natural and man-made disasters on the manufacturing industry and supports the formulation of business continuity plans (BCPs). This function aims to comprehensively assess risk and strengthen resilience in response to increasing disaster risks due to intensifying climate change, increasing geopolitical risks, and increasingly complex supply chains. Disaster risk analysis comprehensively evaluates not only natural disaster risks such as earthquakes, tsunamis, typhoons, floods, droughts, and forest fires, but also man-made disaster risks such as terrorism, war, cyberattacks, pandemics, and political unrest. In addition to direct physical damage, indirect impacts such as supply chain disruptions, logistics network paralysis, power outages, communication infrastructure failures, and human resource shortages are also analyzed in detail. Risk assessment utilizes advanced analytical methods such as probabilistic risk assessment (PRA), scenario-based analysis, Monte Carlo simulation, and system dynamics to achieve quantitative risk assessment. We also conduct spatial risk analysis that integrates geographic information systems (GIS), meteorological data, geological data, demographic data, etc., to provide risk assessments that take into account regional characteristics.

[0097] The Disaster Impact Analysis Department evaluates natural disaster risks, such as earthquakes, typhoons, and floods, based on geographical conditions and quantifies each factory's vulnerability to disasters. It predicts the probability and duration of supply chain interruptions based on past disaster cases and recovery time data. It automatically generates countermeasures, such as selecting alternative factories, distributing inventory, and establishing emergency communication systems, to support the formulation of BCPs. The analysis methodology integrates engineering techniques, such as utilizing hazard maps, analyzing ground data, assessing the vulnerability of building structures, and analyzing the dependency of lifelines, with social science techniques, such as economic impact assessments, social impact assessments, and environmental impact assessments. In addition, when setting disaster scenarios, the department considers not only single disasters but also complex disaster patterns, such as compound disasters (earthquake + tsunami, typhoon + flood, etc.), chain disasters (earthquake → fire → explosion, etc.), and widespread disasters (simultaneous damage to multiple regions), to conduct realistic risk assessments. To predict recovery times, it uses machine learning to analyze past disaster statistics, insurance data, recovery case studies, and other data to build a predictive model that takes into account the scale of the disaster, the extent of the damage, and the availability of recovery resources. The business continuity strategy combines four basic strategies—risk avoidance, risk mitigation, risk transfer, and risk acceptance—to propose the optimal combination of measures, taking cost-effectiveness into consideration. It also provides practical functions such as emergency decision-making support, disaster information collection and distribution, stakeholder communication and coordination, and prioritization of recovery activities. Furthermore, it integrates continuous improvement functions, such as regular training plans, review and update of measures, and response to new threats, to support dynamic BCP management. However, because the scale and impact of disasters are difficult to predict and unexpected events may occur, these predictions are only for reference. Expert knowledge is required to formulate an actual BCP, and continuous review is important for disaster response.

[0098] At least one embodiment of the present invention provides a system with a cost structure visualization function. This embodiment implements a function for detailed analysis of calculated cost breakdowns and identifying potential cost reduction opportunities. This function aims to achieve transparency and optimization of manufacturing costs by building an advanced cost analysis system that integrates cost management methods such as Activity-Based Costing (ABC), Target Costing, and Value Engineering. In addition to the traditional classification of direct material costs, direct labor costs, and manufacturing overhead, the cost structure analysis hierarchically analyzes detailed cost elements such as design costs, procurement costs, quality costs, logistics costs, inventory costs, equipment depreciation costs, energy costs, and environmental costs. Furthermore, analytical methods such as value stream mapping, process costing, and life cycle costing are utilized to systematically identify cost generation mechanisms and improvement opportunities. Furthermore, the system objectively evaluates a company's cost levels through benchmarking, competitive comparisons, and comparisons with industry standards, supporting the setting of improvement goals and strategic decision-making. The visualization function clearly displays complex cost structures using intuitive graphical representations such as Sankey diagrams, treemaps, and waterfall graphs.

[0099] The Cost Analysis Department hierarchically breaks down cost elements, such as material costs, processing costs, and administrative costs, and visualizes the percentage of each element in total costs using pie and bar charts. It identifies abnormally high cost elements by comparing them with past results for similar parts and the industry average. It quantitatively presents cost reduction proposals through material changes, process improvements, and volume effects, and predicts the reduction effects. The analysis process utilizes management accounting techniques such as identifying cost drivers, optimizing cost allocation, separating variable and fixed costs, marginal profit analysis, and break-even analysis to promote a fundamental understanding of cost structures. It also analyzes cost trends over time, taking into account seasonality and eliminating the effects of inflation, thereby achieving accurate cost evaluation. It identifies improvement opportunities by extracting critical cost elements using Pareto analysis, predicting improvement effects using what-if analysis, evaluating risk factors using sensitivity analysis, and searching for optimal solutions using optimization algorithms. It also supports practical cost reduction activities by linking with improvement activities on the manufacturing floor (Kaizen, 5S, TPM, etc.). The evaluation of cost reduction proposals involves comprehensively quantifying the reduction effect, assessing feasibility, calculating the payback period, analyzing the impact on quality and delivery time, and assessing risks to support decision-making. Furthermore, cost reduction progress management, effect measurement, and support for the PDCA cycle of continuous improvement promote sustainable cost improvement activities. However, because cost structure is determined by complex factors, simple comparative analysis can overlook elements, and the results of these analyses are merely estimates, so detailed cost reduction studies require specialized knowledge from the manufacturing site.

[0100] At least one embodiment of the present invention provides a system with a design automation function. This embodiment implements a function that automatically generates an optimal design that meets required specifications while taking into account manufacturing and cost constraints. This function transforms the traditional trial-and-error design process into a scientific and quantitative process through an innovative approach that combines design engineering and AI technology. Design automation utilizes mathematical optimization methods such as topology optimization, shape optimization, sizing optimization, and material optimization, as well as AI technologies such as machine learning, deep learning, reinforcement learning, genetic algorithms, and swarm intelligence optimization. Furthermore, by linking with simulation technologies such as the finite element method (FEM), computational fluid dynamics (CFD), and multibody dynamics (MBD), the system precisely evaluates the performance of design proposals and achieves optimization based on the laws of physics. Regarding design constraints, a variety of constraints, including geometric constraints, manufacturing constraints, material constraints, strength constraints, vibration constraints, thermal constraints, electromagnetic constraints, cost constraints, and environmental constraints, are simultaneously considered to generate feasible design solutions. In addition, multi-objective optimization appropriately balances the trade-offs between conflicting design goals (weight reduction vs. strength improvement, cost reduction vs. performance improvement, etc.) and presents Pareto-optimal solutions. Furthermore, design support AI that has learned from a design knowledge database, past design cases, and failure cases generates practical design proposals that also integrate empirical knowledge.

[0101] The design optimization module evaluates the performance of design proposals using simulation techniques such as strength calculations, thermal analysis, and fluid analysis, and optimizes the shape using genetic algorithms and topology optimization. Objective functions, such as minimizing weight and maximizing strength, are optimized while satisfying manufacturing constraints (minimum machining diameter, draft angle, etc.) and economic constraints (target cost, delivery date, etc.). The relationship between performance and cost resulting from design changes is visualized using a Pareto chart to support designer decision-making. This optimization process combines various optimization techniques, including multi-objective optimization algorithms, particle swarm optimization, differential evolution, artificial bee colony optimization, and simulated annealing, to find the true global optimum without falling into local optima. Advanced optimization techniques, such as robust optimization that takes uncertainty into account, reliability-based optimization that considers reliability, and constrained optimization that simultaneously satisfies multiple constraints, are also applied. Sensitivity analysis of design parameters identifies important parameters that have a significant impact on the design, prompting designers to focus their efforts. However, design optimization is based on theoretical values ​​based on computational models and may differ from actual performance. These automated design results are only for reference, and the final design decision must be made by the designer.

[0102] At least one embodiment of the present invention provides a system with a customer behavior analysis function. This embodiment implements a function that analyzes the behavioral patterns of platform users and provides individually customized services. This customer behavior analysis function collects and analyzes multifaceted user behavior data, such as website clickstream analysis, quote request pattern analysis, search keyword analysis, dwell time analysis, and abandonment rate analysis. A machine learning algorithm automatically estimates characteristics such as user preferences, areas of interest, budget range, delivery date requirements, and quality requirements to build a personal profile. Based on the analysis results, a recommendation engine proposes optimal factories, sets price alerts, provides priority notifications for new features, and suggests related parts. The system also clusters users with similar behavioral patterns to support the development of segmented marketing strategies. Aiming to improve user satisfaction and promote platform usage, the system quantifies the effects of interface improvements using an A / B testing function, thereby continuously improving usability. Furthermore, the system also provides a function that uses a behavior prediction model to estimate future quote requests, sales forecasts, cancellation risk, and other factors to support the formulation of business plans.

[0103] The Behavioral Analysis Department collects and analyzes user behavior data, such as login times, viewed pages, search keywords, and quote request patterns, to identify individual preferences and trends. It uses machine learning to cluster similar users and optimize services based on personas. It provides personalized user experiences, including factory recommendations, price alert settings, and priority notification of new features. This behavioral analysis uses real-time streaming processing to instantly detect changes in user behavior and dynamically update recommendations. It uses a time-series prediction model based on deep learning to predict future user behavior and provide proactive services. It also uses natural language processing technology to perform sentiment and intent analysis on text data such as user feedback, review comments, and inquiries, quantifying satisfaction and requests for improvement. It anonymizes and pseudonyms behavioral data to protect privacy while conducting analysis and comply with relevant laws and regulations, such as the GDPR and the Personal Information Protection Act. Furthermore, it detects anomalies in user behavior to support early detection of fraudulent use, security breaches, and system failures, thereby maintaining the safety and reliability of the platform. However, behavioral analysis is subject to restrictions from the perspective of privacy protection, and there are limits to the accuracy of the analysis. These personalization features only provide reference information, and the final decision must be made by the user themselves.

[0104] At least one embodiment provides a system with a standardization promotion function. This embodiment implements a function for promoting the standardization of part design and manufacturing processes, thereby reducing costs and improving quality. This standardization promotion function is an important function that contributes to improving efficiency and competitiveness throughout the manufacturing industry. The system uses statistical analysis of vast amounts of past design data to extract patterns of frequently used part shapes, materials, tolerances, surface treatments, etc., and identifies specifications that could become industry standards. Machine learning algorithms automatically detect common design elements and continuously update standardization candidates. The system also has a function for checking consistency with international standards (ISO, JIS, ASTM, DIN, etc.), industry standards, and in-house standards, and evaluating the degree of compliance. The system quantifies the benefits of standardization (cost reduction rate, quality improvement degree, lead time reduction effect, etc.) and clarifies the return on investment of standardization investment. Furthermore, the system comprehensively supports organizational standardization activities, including creating a standardization promotion roadmap, supporting the operation of standardization committees, and providing standardization education content. Considering the balance with customization, the system clearly distinguishes between areas that should be standardized and areas that should be differentiated, promoting strategic standardization.

[0105] The Standardization Analysis Department extracts frequently used part shapes, materials, tolerances, etc. from past design data and builds a standard parts library. It recommends the use of standard parts when creating new designs, and proposes standard-compliant design proposals when custom parts are required. It also promotes standardization of manufacturing processes between factories, helping to reduce quality variation and establish a mutual backup system between factories. This analysis process combines data mining techniques, statistical analysis, and machine learning to extract significant patterns from large amounts of design data. It uses clustering analysis to identify groups of parts with similar functions and evaluate their potential for consolidation. It also determines standardization priorities based on multiple indicators, such as frequency of use, procurement cost, inventory turnover, and quality performance. It analyzes the ripple effects of standardization by quantitatively assessing the effects of supplier consolidation through parts integration, unit cost reductions through mass production, reduced design labor, and quality stabilization. Furthermore, it automates the PDCA cycle, including standardization progress management, effect measurement, issue identification, and improvement proposals, to support ongoing standardization activities. Taking international market expansion into consideration, the analysis also includes regional standardization requirements, cultural differences, and local procurement constraints. However, standardization also has an aspect of limiting design freedom, and it is difficult to meet all requirements with standard products. Promoting standardization is only one approach, and it is important to strike an appropriate balance with individual optimization.

[0106] At least one embodiment provides a system with contract management functionality. This embodiment implements a function for centrally managing all contract processes from order placement to delivery and monitoring contract performance. This contract management functionality provides comprehensive contract lifecycle management suited to the digital contract era, streamlining contract work and reducing risk. The system provides integrated functions such as automatic generation of contract templates, standardization of contract terms, integration with electronic signatures, automated contract approval workflows, contract expiration management, renewal notifications, and contract change management. Using artificial intelligence technology, the system automatically reviews contracts, detects risk clauses, compares and analyzes similar contracts, and proposes optimized contract terms. It also analyzes past contract performance from a contract database to support benchmarking of contract terms, planning negotiation strategies, and predicting contract success rates. Blockchain technology is also used to provide reliability enhancement functions such as preventing contract tampering, verifying performance, and supporting dispute resolution. Furthermore, governance functions such as compliance checks, contract risk assessment, audit support, and report generation support companies' contract management systems. Multilingual support also streamlines contract management in international transactions.

[0107] The Contract Management Department automatically tracks each stage of the process, including quotation approval, purchase order issuance, production start, progress report, inspection, and payment, and visualizes contract performance status on a dashboard. It detects risks of contract non-performance, such as delivery delays and quality issues, and sends warning notifications to relevant parties. It also automates exception handling, such as managing changes to contract terms, processing additional orders, and handling returns and exchanges. This management process utilizes IoT sensors and RFID tags to monitor production progress, logistics status, inventory levels, and other factors in real time, ensuring transparency of contract performance. Machine learning is used to predict risks such as delays, quality risks, and payment risks based on past contract data, and to propose preventive measures. It also provides functions such as damage calculation, penalty calculation, and insurance claim support in the event of a contract breach. Furthermore, contract performance analysis supports supplier evaluation, optimization of contract terms, and strengthening of negotiating power. Security is ensured through contract data encryption, access control, audit log management, and other features, thoroughly protecting confidential information. Compliance with relevant laws and regulations, such as the Electronic Bookkeeping Act, Subcontract Act, and Antimonopoly Act, is also automated to strengthen compliance. It also meets the unique requirements of international transactions, such as multi-currency support, currency hedging, and international payments. However, legal expertise is required to interpret contractual content and resolve disputes, and there are limits to automated processing by the system. These management functions merely improve the efficiency of administrative processing, and important decisions require human confirmation.

[0108] At least one embodiment provides a system with an engineer skill management function. This embodiment implements a function to quantify the skill levels of engineers working in the manufacturing industry and support appropriate task assignment. This engineer skill management function is a strategic human resources management system that balances the optimal utilization of human resources with the development of engineers' capabilities. The system integrates multifaceted data, such as engineers' qualifications, years of experience, past project performance, quality evaluations, efficiency indicators, and learning histories, to build a comprehensive skill profile. It uses artificial intelligence technology to analyze engineers' potential, growth potential, and areas of aptitude, and proposes career development tailored to their individual characteristics. It also visualizes the skill distribution throughout the organization and supports the identification of skill gaps, optimization of personnel allocation, and formulation of recruitment plans. It also provides integrated human resource development functions, such as knowledge sharing among engineers, mentoring programs, and on-the-job training management. Furthermore, it responds to changing technological trends and continuously promotes the improvement of an organization's technical capabilities by predicting required skills, recommending training programs, and supporting qualification acquisition. It also has a function that matches the required skills of a project with the skills possessed by engineers and automatically proposes optimal team formation.

[0109] The Skills Assessment Department comprehensively analyzes engineers' years of experience, qualification status, past manufacturing performance, quality evaluations, etc., and quantifies their skill levels by technology field. It estimates the required skill level for new projects, recommends suitable candidates, and identifies missing skills. It also recommends training programs for engineers and provides functions to support planned skill development. This evaluation process performs multifaceted evaluations that combine quantitative evaluations (productivity indicators, quality indicators, efficiency indicators, cost indicators, etc.) with qualitative evaluations (creativity, problem-solving ability, communication skills, leadership, etc.). It integrates multiple perspectives, such as 360-degree evaluations, peer reviews, self-evaluations, and supervisor evaluations, to ensure objectivity and fairness. Machine learning is used to continuously improve the evaluation model based on past evaluation data, thereby increasing evaluation accuracy. The validity of evaluations is also verified by comparing with industry benchmarks, competitors, and international standards. To increase engineers' motivation, gamification elements are introduced to foster a sense of accomplishment and a competitive spirit for skill improvement. It also provides a mechanism for aligning individual career goals with the strategic goals of the organization and building a win-win relationship.However, the skills of engineers are difficult to quantify, and experience and intuitive judgment are also important, so they cannot be fully expressed by numerical evaluation alone.These evaluation results are only reference information, and actual work assignments are largely the judgment of on-site managers.

[0110] At least one embodiment provides a system with raw material traceability functionality. This embodiment implements a function that tracks the entire flow of raw materials used in a product, from the source of procurement to the final product, and supports rapid cause investigation when quality issues arise. This traceability function is an important system that meets strict quality requirements, such as food safety, pharmaceutical regulations, and automotive parts quality control. Blockchain technology is used to achieve tamper-proof record management, ensuring transparency and reliability throughout the supply chain. The system automatically collects detailed records of all processes, from raw material mining and cultivation to refining, processing, distribution, manufacturing, inspection, and shipping, and manages them as digital certificates. Identification technologies such as QR codes (registered trademark), RFID tags, and NFC tags enable rapid information access on-site. AI image recognition also automates material appearance inspection, quality assessment, and foreign matter contamination detection, reducing human error and improving inspection accuracy. Comprehensive quality control functions, including supplier management, quality audits, certification management, and risk assessment, are also provided. We help strengthen your competitiveness in the global market by assisting you in complying with international quality standards (ISO9001, ISO22000, HACCP, etc.).

[0111] The Traceability Management Department uses blockchain technology to record raw material lot numbers, manufacturer information, quality inspection results, and other information, creating unalterable historical information. Processing conditions, workers, and inspection results for each manufacturing process are also recorded, enabling traceability from the final product back to the raw materials. When a quality issue occurs, the impact scope and cause analysis are quickly performed, and, if necessary, recall targets are automatically identified. This management system uses an IoT sensor network to monitor environmental conditions such as temperature, humidity, pressure, and vibration in real time and comprehensively record factors affecting quality. Machine learning is used to detect signs of quality abnormalities and achieve preventive quality control. Quality control methods such as statistical process control (SPC) and statistical quality control (SQC) are also automatically applied to stabilize quality. Data sharing with suppliers enables integrated management of consistent quality information from the raw materials stage. Furthermore, the automatic generation of quality certificates, component analysis reports, inspection reports, and other documents in response to customer requests streamlines the provision of quality evidence. Automatically checking compliance with international quality standards, environmental regulations, labor standards, and other standards strengthens compliance. However, traceability systems rely on the accuracy of records, and any input errors or omissions will undermine their reliability. These tracking functions are not perfect, and investigation of significant quality issues requires detailed analysis by experts.

[0112] At least one embodiment provides a system with a manufacturing cost forecasting function. This embodiment implements a function to forecast long-term manufacturing cost trends, taking into account future material price fluctuations, rising labor costs, equipment depreciation, and other factors. This cost forecasting function is an advanced analytical system that supports important management decision-making, such as strategic business planning, investment decisions, pricing, and contract negotiations. The system continuously monitors a variety of external factors, including macroeconomic indicators, commodity market conditions, exchange rates, interest rates, inflation rates, labor market trends, technological innovation trends, policy changes, and geopolitical risks, and analyzes the impact of these interactions on costs. Machine learning and AI technologies are used to elucidate complex causal relationships and build highly accurate forecasting models. Scenario analysis also provides forecasts based on multiple scenarios, including optimistic, standard, and pessimistic, to support risk management and strategy planning. Sensitivity analysis also identifies the factors that most affect costs and clarifies key management items. Forecast results are provided for multiple time frames, such as quarterly, annual, and five-year periods, to support decision-making from short-, medium-, and long-term perspectives. Quantifying uncertainty clearly displays forecast accuracy and confidence intervals, facilitating risk-informed decision-making.

[0113] The cost forecasting unit analyzes historical price fluctuation data, economic indicators, industry trends, etc., and calculates future cost trends using a time-series forecasting model. It provides forecast results under multiple scenarios (optimistic, realistic, and pessimistic), taking into account factors such as seasonal fluctuations, business cycles, and technological innovations. This information can be used to set price adjustment clauses in long-term contracts and determine the timing of capital investments. This forecasting process performs advanced analysis combining econometric, statistical, and machine learning techniques. Various forecasting methods, such as ARIMA, GARCH, state space models, Bayesian networks, deep learning, and reinforcement learning, are applied to improve forecast accuracy. Information from external data sources (commodity exchanges, economic statistics, industry reports, news analysis, social media analysis, etc.) is also integrated to achieve comprehensive forecasts. For performance evaluation of forecasting models, accuracy is quantified using statistical indicators such as MAPE, RMSE, and MAE, and model improvement is continuously implemented. Furthermore, ensemble learning combines multiple forecasting models to overcome the limitations of single models. Anomaly detection enables early detection of abnormal market conditions outside the scope of the forecasting model and monitors the validity of forecasts. However, future predictions are inherently uncertain and may fluctuate significantly due to unexpected external factors (disasters, changes in political situations, etc.). These prediction results are for reference only, and important decision-making requires a comprehensive review of multiple sources of information.

[0114] At least one embodiment provides a system with a manufacturing quality prediction function. This embodiment implements a function that predicts the quality level of the final product in advance based on design specifications, material properties, manufacturing conditions, etc., and supports the prevention of quality issues. This quality prediction function fundamentally transforms the concept of quality control in the manufacturing industry, realizing a paradigm shift from the traditional focus on quality inspection in downstream processes to a focus on building quality into upstream processes. The system integrates knowledge from materials science, process engineering, and quality engineering to build a quality prediction model based on physical phenomena. Machine learning is used to learn the complex interactions of factors that affect quality from massive amounts of past manufacturing data, achieving highly accurate predictions. Digital twin technology also performs manufacturing simulations in virtual space, enabling quality predictions under various conditions. Real-time data integration monitors the quality status during manufacturing and supports dynamic quality control. Comprehensive quality control support functions are also provided, such as optimizing manufacturing conditions based on quality prediction results, proposing process improvements, and optimizing inspection plans. Integration with preventive maintenance enables comprehensive quality predictions that take into account the impact of equipment conditions on quality.

[0115] The Quality Prediction Department uses machine learning to learn the correlation between past manufacturing data and quality results to predict the quality risks of new products. It comprehensively evaluates factors such as the mechanical properties of materials, the stability of processing conditions, and the skill level of workers to quantify the probability of defects and quality variation. Based on the prediction results, it proposes optimization of manufacturing conditions and additional inspection items, enabling preventative quality control. This prediction system enhances established quality control methods such as statistical quality control (SQC), design of experiments (DOE), the Taguchi method, and six sigma with AI technology to achieve more precise quality predictions. It combines statistical methods such as multivariate analysis, regression analysis, discriminant analysis, and principal component analysis with machine learning methods such as neural networks, support vector machines, random forests, and gradient boosting to elucidate nonlinear and complex quality relationships. It also analyzes time-series changes in quality characteristics to support the prediction and countermeasures of quality drift. Quantifying uncertainty clarifies the reliability of predictions and enables risk-based quality control. Furthermore, analyzing quality costs quantifies the cost-effectiveness of quality improvement investments to support management decisions. However, because product quality is affected by many factors, perfect prediction is difficult, and these prediction functions only provide reference information. For actual quality assurance, an appropriate inspection system and continuous improvement activities are essential.

[0116] At least one embodiment provides a system with a supply chain optimization function. This embodiment implements a function that supports supply chain design that integrates management of multiple factories, logistics providers, and inventory locations, and simultaneously minimizes total costs and shortens delivery times. This supply chain optimization function is a strategic system for efficiently managing complex supply networks in modern, globalized manufacturing and establishing a competitive advantage. The system achieves overall optimization by integrating demand forecasting, production planning, procurement planning, logistics planning, and inventory planning. It utilizes mathematical optimization methods such as linear programming, integer programming, dynamic programming, and stochastic programming, as well as metaheuristics (e.g., genetic algorithms, particle swarm optimization, and ant colony optimization) to efficiently solve large-scale, complex optimization problems. It also applies advanced optimization techniques such as robust optimization that takes into account uncertainties (demand fluctuations, supply constraints, disaster risk, etc.) and optimization that considers risk diversification. Real-time data integration dynamically responds to changes in the supply chain situation, achieving continuous optimization. Furthermore, it incorporates multifaceted evaluation indicators, such as sustainability, social responsibility, and environmental impact, into the optimization process to support supply chain design that complies with ESG management.

[0117] The Supply Chain Optimization Department uses mathematical optimization techniques to calculate the optimal supply chain configuration, taking into account demand forecasts, production capacity, transportation costs, inventory costs, and other constraints. It also takes into account constraints such as geographical constraints, risk diversification, and sustainability. It evaluates robustness against demand fluctuations and supply constraints and proposes a flexible supply chain design that can accommodate multiple scenarios. This optimization process employs an integrated approach that simultaneously optimizes multiple decisions, including network design, inventory placement, production allocation, transportation routes, and procurement strategies. Hierarchical optimization maintains consistency across strategic (facility placement, supply contracts), tactical (production planning, inventory planning), and operational (scheduling, delivery planning) levels. Dynamic optimization also responds to changing conditions over time, achieving continuous optimization. Sensitivity analysis evaluates the stability of the optimal solution and confirms its robustness against parameter variations. Furthermore, what-if analysis compares optimization results under various assumptions to support strategic decision-making. Multi-objective optimization simultaneously considers conflicting objectives such as cost, delivery time, quality, risk, and environmental impact to provide a balanced solution. However, supply chain optimization is a complex combinatorial problem, and there are trade-offs between calculation time and solution quality. These optimization results are merely theoretical values, and many adjustments are required for actual operation.

[0118] At least one embodiment provides a system with technology transfer support functions. In this embodiment, a function is implemented to facilitate technology transfer from factories with advanced manufacturing technologies to factories seeking to acquire the technology. This technology transfer support function is a comprehensive technology transfer platform aimed at eliminating technological gaps in the manufacturing industry, global technology dissemination, and promoting innovation. The system provides integrated support for all processes, including visualization of technology providers' technology portfolios, analysis of technology acquirers' needs, optimal matching, support for technology transfer agreements, transfer process management, and effectiveness measurement. A knowledge management system streamlines the systematic management, search, and sharing of technical documents, promoting the conversion of tacit knowledge into explicit knowledge. Advanced technology transfer methods, such as remote technical instruction using VR / AR technology, technical understanding support using 3D simulations, and online training systems, are also provided. Furthermore, continuous improvement of technology transfer projects is supported through technology transfer effectiveness measurement, ROI calculation, success factor analysis, and other methods. Integrated risk management functions, such as intellectual property rights protection, confidentiality management of technical information, and compliance with competition law, are also provided to ensure safe and effective technology transfer. In international technology transfer, we also support efforts to address cultural differences, language differences, and regulatory differences.

[0119] The Technology Transfer Support Department matches technology providers with technology acquirers and manages the entire process, from signing technology transfer agreements to actual technical training. It ensures efficient technology transfer through technical document translation, remote training system provision, progress management, and effectiveness measurement. It also establishes an ongoing support system after technology transfer to support the technology's establishment and development. This support process applies a systematic technology transfer methodology that includes technology assessment, technology gap analysis, transfer plan formulation, execution management, and evaluation and improvement. It provides a customized transfer approach tailored to the characteristics of the technology (e.g., manufacturing technology, management technology, product technology), maximizing the effectiveness of the transfer. It also uses a technology transfer maturity model to visualize the progress of the transfer process and ensure appropriate milestone management. It also supports the development of technology support personnel through collaboration with human resource development programs. Furthermore, it promotes continuous information exchange, mutual learning, and joint improvement after the transfer by forming a technology transfer community. It builds a knowledge base of technology transfer success stories, failures, and best practices to help improve the success rate of future technology transfer projects. However, technology transfer is subject to intellectual property rights and competitive constraints, and not all technologies are transferable. These support functions merely serve as intermediaries, and the success of actual technology transfer depends on the efforts and compatibility between the parties involved.

[0120] At least one embodiment provides a system with energy management functionality. This embodiment implements functionality for monitoring and analyzing energy consumption in manufacturing processes and quantifying the effectiveness of energy-saving measures. This energy management functionality is a core system for green manufacturing, meeting diverse requirements such as achieving carbon neutrality, reducing energy costs, and complying with environmental regulations. The system offers comprehensive functionality, including real-time energy monitoring using smart meters and IoT sensors, analyzing energy usage patterns, automatically detecting energy-saving opportunities, proposing optimal operating conditions, and continuously improving energy efficiency. The system uses machine learning to learn energy consumption patterns and automatically detects abnormal consumption, predicts consumption, and proposes optimizations. The system also offers advanced energy management functionality, such as analyzing the effectiveness of renewable energy deployment, optimizing the operation of energy storage systems, and supporting participation in demand response. By linking with energy management systems (EMS), building management systems (BMS), manufacturing execution systems (MES), and other systems, the system achieves integrated energy optimization throughout the entire factory. Furthermore, the system also provides integrated environmental management support functions, such as support for complying with energy management standards such as ISO 50001, carbon footprint calculations, and environmental report creation.

[0121] The Energy Management Department monitors the power consumption, operating hours, load factor, and other data of each manufacturing facility in real time and calculates energy usage efficiency. It compares data with past data to detect abnormal consumption patterns and identify areas for equipment degradation and optimization of operating conditions. It also calculates the effectiveness of introducing renewable energy and the payback period for energy-saving equipment, supporting the formulation of energy strategies. This management system visualizes energy flow within the factory through energy flow analysis and identifies energy loss sources. It applies advanced energy analysis methods, such as pinch analysis and exergy analysis, to quantify areas for improvement relative to the theoretical optimum. It also monitors changes in energy efficiency in response to fluctuations in production volume by managing energy intensity (per product, per sales, etc.). Energy analysis across multiple axes, such as by equipment, process, product, and time, enables detailed energy management. Furthermore, it integrates external information, such as weather data, production plans, and electricity rates, to improve the accuracy of energy optimization. It automates the PDCA cycle, including setting energy reduction targets, managing progress, and measuring results, to support continuous energy improvement. However, energy management involves a trade-off with manufacturing quality and productivity, and simply pursuing energy conservation may result in a decrease in overall efficiency. These analysis results are only one perspective, and a comprehensive judgment is required.

[0122] At least one embodiment provides a system with a virtual prototyping function. This embodiment implements a function for evaluating product performance and performing design verification through computer simulation before producing a physical prototype. This virtual prototyping function is an innovative design verification system that shortens product development time, reduces development costs, and improves design quality. The system is based on computer-aided engineering (CAE) technology and provides integrated multi-physics simulations, including structural analysis, fluid analysis, thermal analysis, electromagnetic field analysis, acoustic analysis, and crash analysis. It utilizes high-performance computing (HPC), cloud computing, GPU parallel processing, and other technologies to quickly execute large-scale, complex simulations. Digital twin technology also links real-world products with virtual-world models to enable real-time performance monitoring and optimization. Furthermore, immersive design reviews using VR / AR technology, collaborative design support, an intuitive operation interface, and other features maximize designer creativity. Advanced design methods, such as uncertainty analysis, reliability analysis, and robust design, are also integrated to support practical and robust product design. Multidisciplinary coupled analysis enables comprehensive performance evaluation that takes complex interactions into account.

[0123] The Virtual Prototyping Department integrates various simulation technologies, including structural analysis using the finite element method, fluid analysis, thermal analysis, and electromagnetic field analysis, to evaluate product performance from multiple perspectives. Probabilistic analysis, taking into account uncertainties in material properties, manufacturing tolerances, and operating environments, is used to evaluate the robustness of designs. Simulation results are visualized in 3D to facilitate intuitive understanding. This analysis process maximizes analysis efficiency through automated mesh generation, automatic boundary condition setting, and automated convergence assessment. High-precision analysis methods, such as adaptive mesh refinement, h-adaptation, and p-adaptation, are applied to optimize the balance between computational cost and accuracy. Furthermore, surrogate models are used for fast approximate analysis to efficiently perform sensitivity analysis, optimization, and statistical analysis of design parameters. Multi-scale analysis integrates hierarchical analyses from microstructure to macroperformance, enabling consistent performance evaluation from the material level to the product level. Furthermore, correlation analysis with experimental data verifies the validity of the simulation model and promotes continuous accuracy improvement. Quantifying the uncertainty in simulation results clearly demonstrates the reliability of predictions and supports risk-based design decisions. However, the accuracy of the simulation depends on the assumptions made in the analytical model, and it is difficult to completely reproduce actual phenomena. Therefore, these virtual test results are only for reference, and for important products, verification through physical testing is essential.

[0124] At least one embodiment provides a system with an automatic work instruction generation function. This embodiment implements a function that automatically generates detailed work instructions for workers based on manufacturing drawings and process plans, supporting efficiency improvements on the manufacturing floor. This automatic work instruction generation function is a next-generation manufacturing support system that solves multifaceted challenges in the manufacturing industry, such as converting tacit knowledge into explicit knowledge, standardizing work, stabilizing quality, and transferring skills. The system automatically extracts work procedures from 3D CAD data and generates customized work instructions that take into account the worker's skill level, the equipment used, safety requirements, and other factors. Augmented and virtual reality (AR / VR) technology provides visual and intuitive work guidance, promoting worker understanding and improving work efficiency. Voice recognition and synthesis technology also provides functions such as hands-free confirmation of work instructions and voice-based work recording. Integration with IoT sensors enables real-time monitoring of work progress, automatic inspection at quality checkpoints, and automatic measurement of work time. Furthermore, analysis of work performance data provides continuous improvement of work procedures, identification of bottleneck processes, and support for worker skill improvement. Multilingual support also supports standardization of work instructions at international manufacturing sites. The AI ​​also has the ability to learn the work know-how of skilled workers, making it more efficient to pass on skills to the next generation.

[0125] The work instruction generation unit combines drawing information with standard operating procedures to automatically create part-specific work instructions. It automates the optimization of work procedures, tool selection, and quality checkpoint settings, standardizing the knowledge of experienced workers. It also has a function to adjust the level of detail in the instructions based on the worker's skill level, generating instructions optimized for each individual. This generation process systematically manages past work cases, best practices, and trouble cases using a knowledge-based system to improve the quality of work instructions. Natural language processing technology automatically interprets text information such as drawing notes and specification requirements and reflects them in the work instructions. It also automatically performs safety risk assessments and incorporates work safety precautions, protective equipment selection, and emergency response measures into the work instructions. Automatic work time estimation ensures consistency with production plans and proposes realistic work schedules. It also integrates quality control functions such as automatically setting inspection items according to quality requirements, applying statistical quality control methods, and responding to non-conforming products. The effectiveness of work instructions is continuously improved through comprehension tests, proficiency assessments, and feedback collection. Multi-sensory support (visual, auditory, tactile) allows for work instructions that are suited to various learning styles, promoting worker understanding. However, in manufacturing, tacit knowledge not recorded on drawings is important, and automatically generated instructions alone may not be sufficient. These standardization functions are merely basic support, and on-site ingenuity and improvement activities are essential for improving quality and efficiency.

[0126] At least one embodiment provides a system with a failure prediction function. This embodiment implements a function that predicts equipment failures in advance based on status monitoring data from manufacturing equipment and supports the optimization of preventive maintenance. This failure prediction system is based on an IoT sensor network and collects multifaceted data, including vibration analysis, acoustic analysis, temperature monitoring, current monitoring, lubricant analysis, and visual inspection using image recognition. The collected data is combined with real-time preprocessing using edge computing and advanced cloud-based analytical processing to achieve predictions over a wide time scale, from millisecond-level anomaly detection to long-term trend analysis. Machine learning algorithms include LSTM (Long Short-Term Memory), which is specialized for time-series data, an autoencoder model that excels at anomaly detection, and multimodal learning, which captures correlations between multiple sensor data. This allows for the construction of individually optimized models that take into account differences between individual pieces of equipment and their operating environments. Furthermore, by linking with digital twin technology, the system also provides a function that performs virtual simulations of equipment operation and pre-evaluates the risk of failure under various operating conditions.

[0127] The failure prediction unit uses machine learning to analyze equipment status data acquired from vibration sensors, temperature sensors, current sensors, and other sensors to detect precursor patterns of failure. It identifies precursor signals for each type of failure through correlation analysis with past failure history, and calculates the probability of failure and the expected timing of failure. Based on the prediction results, it formulates an optimal maintenance plan to minimize the impact of planned shutdowns on production. Further detailed implementation integrates advanced signal processing technologies, such as frequency analysis using FFT (fast Fourier transform), time-frequency analysis using wavelet transform, bearing deterioration detection using envelope analysis, lubrication system monitoring using oil analysis, and heat distribution analysis using infrared thermography. To improve prediction accuracy, it performs an integrated analysis of equipment manufacturer technical specifications, past maintenance history, part replacement records, operating parameter history, and environmental data (temperature, humidity, vibration, electromagnetic noise, etc.), and combines statistical methods such as multivariate analysis, principal component analysis, and independent component analysis. Furthermore, to improve the reliability of failure prediction, ensemble learning using multiple prediction models, uncertainty quantification using Bayesian statistics, risk assessment using Monte Carlo simulation, etc. are implemented, and the prediction results are provided along with confidence intervals. However, equipment failures occur due to complex factors, and it is impossible to predict all failure patterns, so these prediction functions only provide supplementary information, and it is important to combine them with the judgment of experienced maintenance engineers.

[0128] At least one embodiment provides a system with a continuous improvement support function. This embodiment implements a function to analyze various data from manufacturing processes, automatically detect improvement opportunities, and support continuous improvement activities. This improvement support system adopts a hybrid approach that integrates improvement methods such as the Toyota Production System (TPS), lean manufacturing, Six Sigma, and total quality management (TQM), and automatically selects the optimal improvement method based on the characteristics of the manufacturing site. Data collection is seamlessly integrated with existing systems such as manufacturing execution systems (MES), quality management systems (QMS), enterprise resource planning systems (ERP), and computerized maintenance management systems (CMMS), and multifaceted KPIs (key performance indicators) such as productivity indicators, quality indicators, cost indicators, on-time delivery rates, overall equipment effectiveness (OEE), and customer satisfaction are monitored in real time. The analysis engine combines traditional methods such as statistical process control (SPC), design of experiments (DOE), analysis of variance (ANOVA), regression analysis, and time series analysis with advanced technologies such as anomaly detection using machine learning, pattern discovery using clustering, and causal relationship identification using association analysis. To generate improvement proposals, past improvement cases are compiled into a knowledge base, and a function is implemented to automatically recommend solutions to similar problems using natural language processing.

[0129] The Improvement Opportunity Analysis Department utilizes current and future artificial intelligence, cognitive science, and brain science technologies, including but not limited to artificial intelligence, machine learning, deep learning, reinforcement learning, meta-learning, transfer learning, lifelong learning, curriculum learning, self-supervised learning, contrastive learning, multi-task learning, multimodal learning, cross-modal learning, federated learning, continuous learning, online learning, active learning, semi-supervised learning, weakly supervised learning, zero-shot learning, few-shot learning, in-context learning, prompted learning, chain of sorts, thought trees, iterative reasoning, self-correcting learning, reinforcement learning with human feedback (RLHF), constitutional AI, and red teaming, in addition to traditional statistical methods, to continuously monitor and analyze multidimensional indicators such as production efficiency, quality standards, cost structure, customer satisfaction, employee satisfaction, environmental impact, social impact, innovation level, sustainability, resilience, agility, diversity, and inclusion, and identify areas for improvement, hidden problems, potential risks, new opportunities, creative solutions, and disruptive innovations. As a more detailed analytical function, the system visualizes the interdependencies between manufacturing processes using dynamic network analysis, automatically identifying bottleneck processes, optimizing process balances, and discovering opportunities to shorten lead times. It also quantifies the causal relationship between product quality and process parameters using structural equation modeling (SEM), Granger causality testing, Bayesian networks, and other methods, generating proposals for optimizing process conditions to improve quality. In cost analysis, it integrates detailed cost calculations using Activity-Based Costing (ABC), visualization of waste using value stream mapping, and identification of constraints using Theory of Constraints (TOC), to propose specific measures for improving profit margins. In customer value analysis, it elucidates the latent structure of customer needs using Kano analysis, conjoint analysis, customer journey mapping, and other methods, identifying opportunities to increase added value.

[0130] At least one embodiment provides an integrated post-design quotation platform system. In this embodiment, when a user completes design work and submits design information, such as final design drawings, 3D CAD data, specifications, and BOMs (bills of materials), to the platform, the system automatically executes comprehensive quotation services based on various manufacturing conditions and vendor characteristics. The core of this system is a multimodal data integration processing engine that unifies various design data formats, such as 2D drawings, 3D CAD data, point cloud data, mesh data, parametric data, and assembly data, to automatically extract manufacturing requirements, such as shape features, material properties, tolerance requirements, surface treatment specifications, and heat treatment conditions. Furthermore, the system implements a design intent estimation AI that goes beyond simple shape analysis to estimate part functions, operating environments, stress distribution, fatigue characteristics, and other factors, and proposes optimal manufacturing methods, material selection, and quality control requirements. To improve quotation accuracy, the system uses machine learning to analyze over one million past quotation and manufacturing performance data, building highly accurate prediction models based on statistical information, such as manufacturing costs, quality risks, and delivery performance for similar parts. Furthermore, a real-time market price linkage system automatically reflects fluctuations in raw material prices, exchange rates, logistics costs, energy prices, etc., and always provides quotes based on the latest market conditions.When selecting a supplier, a multi-dimensional evaluation algorithm comprehensively evaluates technical capabilities, quality track record, delivery deadline compliance rate, price competitiveness, geographical conditions, environmental responsiveness, financial stability, etc., and automatically extracts candidate manufacturers that best meet the required specifications.

[0131] The design information reception unit automatically analyzes the specified manufacturing conditions from the uploaded design data. If a material has been specified in advance (e.g., explicit specification of "use SUS304 stainless steel" or "aluminum alloy A6061"), a quote will be generated assuming manufacturing using the specified material. If no material is specified, the AI ​​material recommendation engine will comprehensively evaluate the part's use, strength requirements, environmental conditions, cost constraints, etc., propose multiple optimal material candidates (e.g., first recommendation, second recommendation, cost-oriented, performance-oriented, etc.), and generate parallel quotes for each material. This design information analysis process uses natural language processing technology to automatically extract material requirements, surface treatment requirements, tolerance requirements, quality standards, etc. from the specification text, and integrates them with geometric analysis of CAD data to achieve comprehensive manufacturing requirements definition. The material recommendation system compares the results of simulations such as stress analysis, fatigue analysis, and thermal analysis using the finite element method (FEA) with property information stored in a materials database, such as mechanical properties, physical properties, chemical properties, processability, weldability, corrosion resistance, and wear resistance, to select the optimal material that meets the usage environment (temperature, humidity, corrosion, wear, fatigue, etc.) and required performance (strength, rigidity, lightness, conductivity, insulation, etc.). It also takes into account environmental impact assessments using life cycle assessments (LCA), supply chain risk assessments, and cost fluctuation risk assessments, supporting material selection that balances sustainability and economic efficiency. For weight calculations, the system performs high-precision calculations, such as detailed volume calculations of 3D CAD data, accurate volume calculations of hollow and thin-walled structures, and consideration of the density distribution of composite materials, to accurately estimate material usage.

[0132] The vendor profiling engine analyzes and evaluates the characteristics of each registered manufacturer across multiple dimensions. Using past order data, it calculates scores for factors such as "large-lot production capability" (monthly production capacity, facility size, personnel structure, etc.), "small-lot flexibility" (minimum lot size, setup efficiency, individualized response capabilities, etc.), "prototype development capability" (engineer skills, facility precision, development speed, etc.), "high-precision processing capability" (facility precision, quality control system, technical certification, etc.), "short-term delivery capability" (priority production system, inventory management, logistics efficiency, etc.), "cost competitiveness" (price level, efficiency, economies of scale, etc.), "technological innovation" (introduction of new technologies, R&D investment, patent ownership, etc.), "quality stability" (defect rate, complaint rate, certifications, etc.), "environmental awareness" (ISO14001, carbon neutrality, waste reduction, etc.), and "international response capability" (export performance, multilingual support, compliance with international standards, etc.). This evaluation system conducts a multifaceted evaluation that integrates quantitative indicators (order history, production capacity, equipment specifications, quality data, etc.) and qualitative indicators (engineer skills, organizational culture, innovativeness, customer responsiveness, etc.). The quantitative evaluation involves statistical analysis of objective data such as the number of orders received over the past three years, average lot size, delivery delay rate, quality defect rate, and price competitiveness index to quantify each supplier's capabilities. The qualitative evaluation uses natural language processing to analyze information such as customer reviews, third-party certifications, industry ratings, technical paper publications, and patent applications to evaluate technical capabilities, reliability, innovativeness, etc. The financial soundness evaluation also analyzes financial statements such as balance sheets, income statements, and cash flow statements to evaluate management stability, growth potential, investment capacity, etc., and determine the long-term viability of business transactions. Geographical factors include factory location, logistics network, disaster risk, and power supply stability, and evaluate the robustness of the supply chain.

[0133] The smart matching algorithm performs multidimensional matching of the required specifications of new parts (materials, dimensions, tolerances, quantity, delivery time, quality, etc.) with the characteristic scores of each supplier to automatically select the optimal supplier candidates. For prototypes, the algorithm prioritizes "prototype development capabilities," "technological innovation capabilities," and "ability to respond to short delivery times." For large-lot production, the algorithm prioritizes "large-lot production capabilities," "cost competitiveness," and "quality stability." For high-precision parts, the algorithm prioritizes "high-precision processing capabilities," "quality stability," and "technical certification." The matching results are presented along with a compatibility score, allowing users to select a supplier based on objective data. At the core of this matching system is a deep learning model using a multilayer neural network, which learns optimal supplier selection patterns from past successes and failures. The training data includes part specifications, selected suppliers, manufacturing results (quality, delivery time, cost, etc.), and customer satisfaction, and an optimized matching model is built from over 100,000 pieces of actual data. Furthermore, a reinforcement learning algorithm continuously improves matching accuracy, enabling highly accurate recommendations even for new suppliers and new part types. In the matching evaluation, in addition to deterministic evaluation, a probabilistic evaluation using Monte Carlo simulation is performed, providing a comprehensive judgment that also includes risk assessment. Multiple vendor candidates are proposed, including not only optimal solutions but also suboptimal solutions, providing the user with the options necessary for their final decision. Furthermore, the system takes into account real-time information such as market trends, the competitive situation, and the vendor's current order status, and makes realistic recommendations that also include the actual likelihood of receiving an order.

[0134] The dynamic pricing proposal system automatically generates individually optimized pricing proposals for each identified potential supplier. It analyzes each supplier's areas of expertise, current order status, capacity utilization rate, past price trends, market competition, seasonal factors, and other factors in real time to calculate multiple pricing options, including standard price, competitive bid price, value-added price, and rush price. It also automatically generates detailed estimates that include tiered pricing based on quantity (economies of scale), price adjustments based on delivery date (rush fee, standard rate, marginal delivery discount), quality options (standard quality, high quality, ultra-high quality), and additional services (surface treatment, heat treatment, inspection, packaging, delivery, etc.), and presents them to designers in an easy-to-understand comparison table format. This pricing calculation system uses a machine learning-based price prediction model to analyze multiple factors, including past price data, market trends, raw material price fluctuations, exchange rate fluctuations, and supply-demand balances, to estimate the optimal price range. The prediction model employs ensemble learning, which combines multiple algorithms, including time series analysis, regression analysis, decision trees, random forests, gradient boosting, and neural networks, to improve prediction accuracy. The system also implements a function that uses a game-theoretic approach to predict competitors' pricing strategies and proposes optimal pricing strategies. To ensure transparency in price proposals, a detailed breakdown of price components (material costs, processing costs, administrative costs, profits, etc.) is displayed, clearly indicating the basis for price validity. Furthermore, a price negotiation support function automatically generates alternative proposals (material changes, tolerance relaxation, quantity adjustments, etc.) according to the user's budget constraints and requirements, supporting cost optimization.

[0135] Through a transparent price adjustment mechanism, a portion of the quotation information presented to users (statistical information such as market price range, competitive situation, and supply-demand balance) is shared with each manufacturer in an anonymized form. However, specific quotation prices and company names of other companies are kept confidential, and information is provided only to the extent that it does not impede fair competition. This provides an environment in which manufacturers can objectively understand their own price positioning and independently consider price revisions, changes to terms, value-added proposals, and technical improvement proposals as necessary. This transparent price formation process creates a healthy market environment that balances fair prices for purchasers and fair profits for contractors. This system uses blockchain technology for decentralized price information management to prevent data tampering and ensure transparency. Price information is recorded on the blockchain in encrypted form, allowing only authorized stakeholders to access it. Zero-knowledge proof technology also enables the system to prove the validity and competitiveness of price ranges without disclosing specific price figures. To ensure the fairness of market prices, the system will incorporate a price audit function by an independent third party and establish a mechanism to prevent fraudulent activities such as collusion and price manipulation. The price trend analysis will also take into account external factors such as macroeconomic indicators, industry trends, technological innovation, and regulatory changes, and provide a function to predict medium- to long-term price trends. Furthermore, an international price comparison function will visualize price deviations with overseas markets, helping to improve international competitiveness. However, price determination will be left entirely to the independent management judgment of each business operator, and the system will be designed not to encourage price control or collusion, and will strictly comply with relevant laws and regulations such as the Antimonopoly Act.

[0136] At least one embodiment provides an integrated manufacturing platform for the printing and publishing industries. This embodiment significantly expands upon existing systems focused on metal processing and machine part manufacturing, implementing a set of functions specialized for quotation, ordering, and production management for graphic-related products such as printed materials, publications, packaging, and promotional materials. This printing industry-specific system supports a variety of printing technologies, including desktop publishing (DTP), computer-to-plate (CTP), digital printing, offset printing, gravure printing, flexographic printing, silkscreen printing, large-format inkjet printing, and 3D printing, and proposes optimal manufacturing processes taking into account the characteristics of each printing method. It also supports a wide range of ink types, including CMYK, RGB, spot colors (Pantone, DIC, etc.), fluorescent colors, metallic colors, UV-curable inks, water-based inks, solvent-based inks, and food-safe inks, as well as a variety of substrates, including fine paper, coated paper, matte paper, art paper, paperboard, corrugated board, plastic, metal, and fabric. For print quality control, we will integrate color management systems that comply with international and domestic standards such as ISO12647, G7, and Japan Color, and quantitatively manage quality indicators such as color reproduction, density control, register accuracy, and gloss. Furthermore, to support environmentally friendly printing, we will also integrate eco-printing options such as FSC-certified paper, recycled paper, vegetable ink, waterless printing, and carbon-neutral printing, supporting sustainable printing production. For post-processing, we will support a variety of processing techniques such as cutting, folding, binding, lamination, UV printing, foil stamping, embossing, die-cutting, and perforation, building a system that can meet complex finishing requirements.

[0137] The print data analysis engine automatically analyzes native files created with major design software such as Adobe Creative Suite (Illustrator, Photoshop, InDesign, Acrobat, etc.), CorelDRAW, and QuarkXPress, as well as standard print formats (PDF / X, EPS, AI, PSD, TIFF, JPEG, etc.). It extracts detailed technical specifications directly related to print production, such as color information (RGB, CMYK, spot colors (Pantone, DIC, etc.), gold and silver), resolution (72 dpi, 150 dpi, 300 dpi, 600 dpi, etc.), image quality, vector / raster mix, font embedding, transparency effect usage, overprint / knockout settings, trapping settings, bleed / margin settings, etc.) and other design complexity (number of colors, gradients, special effects, number of layers, etc.) and calculates a printability score. This analysis process utilizes deep learning-based image recognition technology to automatically detect even the most minute elements that affect printability (line thickness, character size, color boundaries, gradation smoothness, etc.). The preflight function also detects potential printing issues (low-resolution images, out-of-gamut colors, inappropriate fonts, insufficient bleed, etc.) in advance and generates improvement suggestions. Color management automatically performs accurate color space conversion using ICC profiles, optimal distribution of spot and process colors, and color correction that takes into account the characteristics of the printing press. Furthermore, from the perspective of optimizing printing costs, the system also provides cost-saving advice such as suggestions for reducing the number of colors, suggesting alternative colors for spot colors, and suggesting changes to printing methods. Data processing features include high-speed processing of large files, simultaneous analysis of multiple files through batch processing, and load balancing through cloud processing, achieving practical processing speeds.

[0138] The automatic printability diagnostic system comprehensively checks extracted design data from the perspective of print quality. It automatically detects issues such as insufficient image resolution (e.g., using 72 dpi web-based data for commercial printing), color gamut overruns (e.g., color loss when converting from the RGB gamut to the CMYK gamut), font problems (e.g., failure to outline, use of fonts incompatible for printing), insufficient bleed, inadequate registration marks and guides, problems with mixing spot and process colors, printability of thin lines and small letters, and compatibility of paper quality and printing method. It then proactively presents risk and countermeasures for quality issues. For detected issues, it also offers specific solutions, such as automatic correction suggestions, alternative design suggestions, and suggestions for changing the printing method. For more detailed diagnostic functionality, it integrates with a database of printing press characteristics to simulate print results on specific presses. This allows for advance prediction of actual print results, including halftone dot reproducibility, color reproduction range, register accuracy, and print speed, helping to prevent quality issues. In addition, the optimization proposal function for combining paper type and printing conditions optimizes the combination of paper characteristics such as coated paper, high-quality paper, and matte paper with printing methods such as offset printing and digital printing. It also considers ink drying, fixation, gloss, and durability to propose optimal printing specifications for the final application. Furthermore, the post-processing suitability evaluation function pre-evaluates the risk of problems occurring in downstream processes such as folding, binding, and surface finishing, achieving total quality assurance. Color management also includes a function that visualizes the difference between the monitor display color and the actual printed color, and specifically suggests the need for color proofing and the direction of color adjustment.

[0139] The printing materials and specifications selection advisor automatically selects and proposes the optimal specifications from among paper type (high-quality paper, coated paper, matte paper, art paper, Japanese paper, recycled paper, synthetic paper, waterproof paper, light-resistant paper, scented paper, antibacterial paper, etc.), paper thickness and weight (various standards such as ream weight, grammage, and thickness in μm), paper size (standard and special sizes such as A, B, 46, 56, 69, and Hatron), surface treatment (gloss PP, matte PP, gloss varnish, matte varnish, UV printing, foil stamping, embossing, debossing, etc.), binding specifications (saddle stitching, perfect binding, hardback binding, spiral binding, ring binding, etc.), special processing (die cutting, window cutting, perforation, folding, lamination, etc.), taking into comprehensive consideration the end use of the printed material (commercial printing, publication printing, package printing, outdoor advertising, in-store POP tools, etc.), required quality level, durability requirements, environmental requirements, budget constraints, etc. This selection process utilizes machine learning to analyze past cases, customer satisfaction data, and quality complaint histories to provide reliable recommendations based on proven results. From a cost-optimization perspective, the system also proposes multiple material and specification combinations that achieve comparable quality, providing options tailored to your budget. In response to environmentally conscious printing, the system proactively proposes eco-friendly material options, such as FSC-certified materials, carbon-neutral materials, recyclable materials, and biodegradable materials. Furthermore, a durability evaluation function that takes into account the environment in which the printed material will be used (indoors and outdoors, temperature and humidity, light, abrasion, etc.) helps select materials suitable for long-term use. The compatibility evaluation of paper and ink also includes a function that analyzes physicochemical properties such as penetration, color development, drying, and fixation to suggest optimal combinations.

[0140] The printing company characteristics database provides detailed profiling of each printing company's areas of expertise (offset printing, digital printing, gravure printing, flexographic printing, silkscreen printing, large-format inkjet printing, 3D printing, etc.), details of equipment owned (printer manufacturer and model, number of colors, maximum print size, print speed, post-processing equipment, etc.), quality control system (ISO9001, ISO12647, G7 certification, JAPAN COLOR certification, etc.), special technology capabilities (UV printing, scent printing, 3D printing, holographic printing, phosphorescent printing, thermo-indicating printing, etc.), lot size range (on-demand single copy support, small lot specialty, medium lot standard, large lot support, extra-large lot support, etc.), delivery date support capabilities (same-day finishing, next-day finishing, short delivery time support, standard delivery time, long-term planning support, etc.), and ancillary services (planning and design, photography, design revisions, proofreading, delivery, inventory management, etc.), enabling precise matching with the required specifications of printed materials. This database is built by conducting detailed interviews with each printing company, conducting on-site inspections of equipment specifications, verifying quality certification status, and analyzing past production performance to maintain accurate and up-to-date information. To keep up with the rapid advances in printing technology, we also continuously track the introduction of new technologies, equipment upgrade history, and the progress of technician skill development, thereby keeping the database up-to-date. Quality evaluation involves detailed analysis of past print samples, color measurements, print quality evaluations, and customer satisfaction surveys to objectively evaluate each printing company's capabilities. Numerical evaluation indicators are developed for key quality characteristics, such as color reproduction accuracy, register accuracy, print unevenness, and post-processing accuracy, enabling quantitative comparisons. Real-time order availability assessments are also implemented, taking into account market trends, such as seasonal fluctuations specific to the printing industry (such as peak periods like the New Year holidays and fiscal year-end and fiscal year-end), paper price fluctuations, and fluctuations in printing demand. Risk management capabilities, such as disaster response capabilities, BCP systems, and information security measures, are also included in the evaluation criteria to assess the continuity of stable printing production.

[0141] The printing cost calculation system accurately reflects the complex cost structure unique to the printing industry. It calculates detailed itemized pricing, including base costs (initial costs such as plate costs, plate making costs, CTP costs, and printing plate costs), paper costs (paper unit price x usage + loss rate), ink costs (unit prices by type, such as CMYK process ink, spot color ink, and UV-curable ink), printing labor costs (press operation costs, labor costs, electricity costs, etc.), post-processing costs (process costs for cutting, folding, collating, binding, surface treatment, and special processing), inspection and quality control costs, packaging and delivery costs, and administrative overhead. It also provides realistic pricing information that reflects dramatic price fluctuations based on lot size (e.g., effects of plate cost dispersion, improved paper handling efficiency, and improved work efficiency), price fluctuations based on delivery date (e.g., express rates, standard rates, and flexible delivery discounts), seasonal factors (e.g., charges for busy periods such as the New Year holidays and fiscal year-end), and quality options (e.g., color proofing, machine proofing, and on-site inspection fees). This cost calculation system uses machine learning to analyze extensive data from the printing industry to continuously improve the accuracy of its cost prediction model. It dynamically calculates prices, taking into account fluctuations in raw material prices (paper, ink, printing plates, etc.), energy prices, labor costs, and capital equipment depreciation over time. It also quantifies the complexity of printed materials (number of colors, image ratio, frequency of special effects, etc.) and implements cost adjustments based on the difficulty of printing. Furthermore, it provides detailed models of the characteristics of each printing method (e.g., plate costs and lot effects for offset printing, and the variable cost structure for digital printing) to help select the optimal printing method. It also includes the cost impact of environmentally friendly printing (e.g., premiums for FSC-certified paper, price differences for environmentally friendly inks, carbon offset costs) in its calculations, helping to evaluate the economic viability of sustainable printing production. However, due to the large price fluctuations in the printing industry caused by external factors such as fluctuations in paper prices, ink prices, currency fluctuations, and supply-demand balance fluctuations, the calculated prices are only a reference value, and the final price is determined through direct negotiations with each printing company.

[0142] The Manufacturer Equipment Registration System provides a comprehensive registration platform that creates a detailed database of all production equipment owned by each manufacturing company. Equipment eligible for registration includes CNC lathes (vertical, horizontal, and hybrid), machining centers (3-axis, 4-axis, and 5-axis, gantry, horizontal, and vertical), milling machines (general-purpose, NC, and CNC), drill presses (benchtop, vertical, radial, and deep hole), grinding machines (flat, cylindrical, internal, centerless, and tool), EDM machines (wire cut and die-sinker), laser processing machines (CO2, fiber, and YAG), plasma cutting machines, water jet cutting machines, presses (hydraulic, mechanical, and servo), and ball mills. This includes machines (press brakes, roll benders), welding machines (TIG, MIG, arc, spot, laser, electron beam), painting equipment (electrostatic coating, dip coating, spray coating), heat treatment furnaces (hardening, tempering, annealing, carburizing, nitriding), surface treatment equipment (plating, anodizing, blasting, barrel polishing), inspection equipment (3D measuring machines, image measuring machines, surface roughness testers, hardness testers, material testing machines), transport equipment (cranes, forklifts, AGVs, robots), etc. For each piece of equipment, the manufacturer name, model name, manufacturing year, detailed specifications (machinable size, precision, capacity, accessories), operating status, maintenance history, certification status, etc. are registered and used as basic data for optimal matching using AI analysis.

[0143] The Processing Capability Detailed Registration System will systematically register the processing technologies, materials, precision, size ranges, etc. that each manufacturer can handle. In the processing technology field, detailed technology classifications and support levels will be registered, including cutting (turning, milling, drilling, threading, gear cutting, broaching, reaming, tapping), grinding (surface grinding, cylindrical grinding, internal grinding, centerless grinding, tool grinding, creep feed grinding), special processing (electrical discharge machining, electrolytic machining, laser processing, water jet machining, ultrasonic processing, chemical etching), plastic processing (pressing, deep drawing, bending, drawing, shearing, forging, rolling), joining (various welding, brazing, soldering, adhesive bonding, mechanical bonding), surface processing (plating, anodizing, painting, blasting, buffing, lapping, honing), heat treatment (hardening, tempering, annealing, thermal refining, carburizing, nitriding, induction hardening), and assembly (mechanical assembly, electrical assembly, wiring, adjustment, inspection). The supported materials include steel materials (carbon steel, alloy steel, stainless steel, tool steel, cast iron), non-ferrous metals (aluminum alloys, copper alloys, titanium alloys, magnesium alloys, nickel alloys), resin materials (general-purpose plastics, engineering plastics, super engineering plastics, thermosetting resins), ceramics, composite materials, etc., and the system registers their capabilities and processing history, allowing users to select the most suitable manufacturer based on the material characteristics.

[0144] In the precision machining capability and tolerance management system, each manufacturer registers detailed technical specifications such as the machining accuracy, tolerance range, and surface quality they can achieve. For dimensional tolerances, specific ranges are registered, including IT grades (IT5 to IT16), hole tolerances (H5, H6, H7, etc.), shaft tolerances (f6, g6, h6, k6, m6, etc.), and fit tolerances (gap, intermediate, tightness), and feasibility is verified by comparing them with past machining performance data. For geometric tolerances, the measurable range and guaranteed accuracy are registered for each item, including straightness, flatness, roundness, cylindricity, line profile, surface profile, parallelism, squareness, inclination, position, coaxiality, symmetry, circular runout, and total runout. Regarding surface quality, detailed records are kept of control items and measurement equipment such as surface roughness (Ra, Rz, Rmax), surface properties (processing marks, waviness, scratches, corrosion, discoloration), hardness (HRC, HRB, HV, HB), residual stress, and grain size.In addition, comprehensive precision processing capabilities are evaluated, including the company's track record in meeting special precision requirements (optical components, medical equipment, aerospace components, etc.), the availability of clean room facilities, quality control system (certification status such as ISO9001, AS9100, ISO13485), and inspection equipment specifications (measurement accuracy of coordinate measuring machines, temperature control status, calibration history), to achieve optimal matching according to the demands of high-precision component manufacturing.

[0145] The production capacity management system dynamically manages detailed production information for each manufacturer, including monthly and daily production capacity, equipment utilization rates, personnel structure, and shift operations. Equipment-specific capacity management integrates and manages each machine's theoretical processing capacity (number of pieces processed per hour, continuous operation time, setup time, and tool change time), actual operation performance (operation rate, defect rate, setup efficiency, and maintenance downtime), and future operation plans (schedules for currently ordered projects, regular maintenance plans, and equipment upgrade plans). For personnel structure, detailed information is registered, including the number of skilled workers (experts, mid-level engineers, new employees), skill level (nationally certified, in-house certified, undergoing on-the-job training), work schedule (day shift, two-shift, three-shift, flextime), overtime availability, and holiday work capacity, quantifying the ability to respond to emergencies and short-term deliveries. The system automatically selects the manufacturer best suited to the orderer's quantity requirements by clarifying the minimum order quantity (from 1 unit, 10 units or more, 100 units or more, etc.), maximum quantity (upper limit considering equipment capacity, personnel structure, and material procurement ability), and optimal production quantity (recommended range based on a comprehensive evaluation of cost efficiency, quality stability, and delivery date certainty) for the lot handling range.It also implements mid- to long-term capacity forecasts that take into account seasonal fluctuations (year-end and New Year holidays, fiscal year-end, summer holidays, etc.), order fluctuations due to market trends, capital investment plans, etc., to support the formulation of stable production plans.

[0146] The engineer skills and talent database systematically manages the skills, years of experience, qualifications, and areas of expertise of each manufacturer's engineers. Regarding skills qualifications, the database manages the number of nationally certified employees by skill field and grade, including machining technicians (1st, 2nd, 3rd, and special grades), welding technicians (manual welding, semi-automatic welding, TIG welding, and various materials), mold manufacturing technicians, finishing technicians, machine inspection technicians, and machine maintenance technicians. The database also quantifies overall skill levels, including skills evaluations based on the in-house certification system, on-the-job training progress, external training attendance history, and participation in skills competitions. In specialized technology fields, the database registers the number and proficiency levels of employees with specialized skills such as precision machining, hard-to-cut material machining, thin-wall machining, deep-hole machining, multi-axis simultaneous machining, micromachining, large-part machining, and complex-shape machining, clarifying the availability of personnel capable of handling specialized machining requests. Regarding the ability to adapt to new technologies, the company evaluates CAD / CAM operation skills, CNC programming ability, measuring equipment operation skills, quality control methods, participation in improvement activities, utilization of the proposal system, etc., to quantify the ability to adapt to technological innovation. Furthermore, the company also records the progress of multi-skill development, cross-training attendance history, rotation experience, leadership training participation, and other personnel development status, visualizing organizational efforts to strengthen technical capabilities. This personnel information is used as an important evaluation factor when matching projects with high technical difficulty or projects that apply new technologies.

[0147] The equipment operation schedule management system provides detailed management of each manufacturer's equipment, including real-time operation status, future operation schedules, and order availability dates. The equipment-specific schedule visualizes current processing orders (part name, quantity, processing content, expected completion date and time), pending orders (waiting for setup, waiting for materials, waiting for drawing confirmation, waiting for quality confirmation), and future order-planned orders (contracted, quote submitted, under negotiation) in chronological order, accurately calculating when new orders can be accepted. It also integrates and manages schedules for periodic maintenance work (daily inspections, weekly maintenance, monthly maintenance, annual overhauls), extraordinary maintenance work (failure repair, cutting tool replacement, calibration work, modification work), and equipment upgrade work (introduction of new models, layout changes, addition of auxiliary equipment), predicting their impact on production plans in advance. The operation rate optimization function automatically balances loads between equipment, optimizes personnel allocation, minimizes setup time, and considers the possibility of parallel processing, helping to improve overall production efficiency. Regarding emergency response capabilities, the system quantifies the ability to respond to short delivery requests and sudden specification changes by evaluating overtime availability, ability to work on holidays, possibility of transferring work to other facilities, possibility of outsourcing to subcontract factories, etc. Furthermore, it also implements a function to analyze seasonal fluctuation patterns, fluctuations by day of the week, fluctuations by time of day, etc. from past operation performance data and predict future operation status with high accuracy, thereby supporting the formulation of long-term order plans.

[0148] The quality control and certification system registration comprehensively manages each manufacturer's quality control system, certification status, inspection equipment, quality track record, etc. Regarding quality management systems, the registration records include detailed certification status, expiration dates, and inspection history for ISO 9001 (quality management system), ISO 14001 (environmental management system), ISO 45001 (occupational health and safety management system), IATF 16949 (automotive industry quality management system), AS 9100 (aerospace industry quality management system), ISO 13485 (medical device quality management system), etc. Regarding inspection equipment, the registration records manage the ownership status and calibration history of 3D measuring machines (measurement accuracy, measurement range, temperature control status), image measuring machines, surface roughness testers, hardness testers, material testing machines, non-destructive testing equipment (ultrasonic testing, magnetic particle testing, penetrant testing, X-ray testing), dimensional measuring instruments (micrometers, vernier calipers, height gauges, gauges, etc.). Quality performance data includes information such as the defect rate over the past three years, the number and details of customer complaints, the implementation status of corrective measures, the effectiveness of preventive measures, internal audit results, and management review results, and is used to objectively evaluate quality stability. Regarding the status of response to special quality requirements, clean room management (cleanliness level, temperature and humidity control, particle measurement), traceability management (lot management, process records, inspection reports), statistical process control (SPC), and the use of design of experiments (DOE) are also recorded, clarifying the ability to respond to projects requiring high quality. This quality information is used as an important selection criterion when matching projects with industries requiring high quality, such as medical devices, aerospace, and automobiles.

[0149] The Specialized Equipment and Special Technology Registration System allows for detailed registration of specialized equipment and technologies for special processing and advanced technologies that are difficult to achieve with general-purpose machines. For ultra-precision processing equipment, the specifications and precision ranges of ultra-precision lathes, grinding machines, diamond processing machines, ion beam processing equipment, atomic layer etching equipment, and other equipment that achieve nano-level processing accuracy are registered. For large-scale processing equipment, details are recorded for gantry machining centers, large lathes, heavy-cutting machines, large presses, and other equipment, including maximum processing size, maximum processing weight, floor load capacity, and overhead crane capacity. For special material processing equipment, material-specific equipment and processing know-how are registered, including titanium alloy machines, Inconel processing machines, ceramic processing machines, CFRP processing machines, and carbide material processing machines. For multi-tasking machines, the specifications and usage history of integrated multi-processing equipment such as turning / milling machines, laser / cutting machines, and 3D printer / cutting machines are recorded. For specialized measurement and inspection equipment, we register details of advanced measuring equipment and measurable items, such as ultra-high precision 3D measuring machines, non-contact measuring machines, X-ray CT scanners, electron microscopes, and surface analysis equipment. We also register special environment equipment, such as clean rooms, temperature-controlled rooms, vibration isolation tables, and electromagnetically shielded rooms, to meet processing requirements in special environments. This specialized equipment information is a crucial factor in matching special requirements that are difficult for general manufacturers to meet.

[0150] The material procurement and inventory management system provides detailed management of each manufacturer's material procurement capabilities, inventory management system, supplier network, and other information. For available materials, the system records the availability and procurement lead times for steel materials (various grades, including ordinary steel, specialty steel, stainless steel, tool steel, and cast iron), non-ferrous metals (aluminum alloys, copper alloys, titanium alloys, magnesium alloys, and nickel-based superalloys), resin materials (general-purpose plastics, engineering plastics, and super engineering plastics), ceramics, and composite materials. The inventory management system records the types and stock levels of regularly stocked materials, as well as the implementation of inventory optimization methods such as safety stock settings, reorder point management, ABC analysis for priority classification, and turnover management. For material quality management, the system records detailed information such as incoming inspection systems (chemical composition analysis, mechanical property testing, dimensional inspection, and visual inspection), material certification management, traceability management, and non-conforming material handling procedures. For supplier management, the system evaluates business relationships with major material trading companies and manufacturers, credit terms, emergency procurement capabilities, and the availability of alternative suppliers to minimize material procurement risks. In handling special materials, we will record the procurement history and procurement routes for rare metals, high-purity materials, special specification materials, imported materials, etc., and clarify the possibility of responding to projects requiring special materials. We will also comprehensively evaluate material procurement capabilities, including measures to deal with material price fluctuations, the status of hedging transactions, price negotiation power, and the status of obtaining volume discounts, and use this as a source of cost competitiveness.

[0151] The manufacturing performance and portfolio management system systematically classifies and manages each manufacturer's past manufacturing performance by industry, product, and technology, and uses it as the basis for evaluating technological capabilities through AI analysis. Industry-specific performance records include detailed records of manufacturing performance and technological accumulation in the automotive industry (engine parts, body parts, suspension parts, electrical components), aerospace industry (structural parts, engine parts, accessories, electronic components), medical device industry (surgical instruments, diagnostic equipment parts, implants, catheters), semiconductor manufacturing equipment (chamber parts, transport mechanisms, precision positioning parts), machine tools (spindles, feed mechanisms, control parts), and industrial machinery (hydraulic parts, pneumatic parts, rotating machinery parts). Product complexity assessment quantifies the number of parts, assembly processes, required precision level, number of material types, number of surface treatment types, etc. to objectively evaluate technical difficulty. Technical feature analysis identifies track records of special technology applications such as thin-wall machining, deep-hole drilling, complex shape machining, high-precision machining, difficult-to-cut material machining, large part machining, and micro-part machining, clarifying technical strengths. Regarding quality performance, data such as the quality achievement status, defect rate, customer satisfaction, and repeat order rate for each project are accumulated and used for reliability evaluation.In addition, detailed manufacturing data such as past drawing data, processing conditions, tools used, processing time, and quality measurement data are analyzed using machine learning, and used to improve the accuracy of automatic quotes for similar projects and propose optimal processing conditions.This performance data is an important factor in evaluating the technical suitability of new projects and determining the possibility of receiving orders.

[0152] The AI ​​matching and project receiving system performs multidimensional matching of AI analysis of drawings and specifications uploaded by clients with manufacturers' registered information, automatically delivering projects with high compatibility. Matching evaluation criteria include technical compatibility (machinability, material compatibility, precision achievability, surface treatment compatibility), production capacity compatibility (capacity, lot size compatibility, delivery date compatibility), quality requirement compatibility (quality control system, certification acquisition, performance evaluation), and cost competitiveness (price level, efficiency, material procurement ability), and these are quantified as a compatibility score. For project delivery, detailed project information, including analyzed drawing data, estimated processing conditions, rough estimate information, delivery date requirements, and quality requirements, is automatically delivered to manufacturers whose compatibility score exceeds a threshold. Project information display features include a 3D drawing viewer, 2D drawing display, specification highlighting, automatic extraction of processing points, and reference to similar past projects, providing an environment where manufacturers can quickly and accurately understand project details. The technical issue extraction function visualizes processing precautions, quality control points, cost drivers, risk factors, etc. identified through AI analysis, supporting manufacturers' order acceptance decisions. It also provides information on the purchaser's past transaction performance, payment history, evaluation information, etc., enabling transaction risk assessment. It also provides information on the urgency of the project, the competitive situation, estimated order probability, etc., supporting manufacturers' sales strategy formulation. With this information, manufacturers can efficiently acquire projects that best suit their technical capabilities, while significantly reducing the time and cost required for traditional sales activities.

[0153] The automated quotation approval and adjustment system provides comprehensive functionality for manufacturers to review, adjust, and approve initial quotation data generated by AI analysis. The AI-generated quotation validation function compares the proposed processing method, process sequence, tools used, processing time, material usage, surface treatment specifications, and other factors with the manufacturer's actual processing know-how to verify their validity. Processing condition optimization considers the characteristics of the manufacturer's equipment, tool inventory status, worker skill level, and past performance on similar projects to propose modifications to more efficient and reliable processing conditions. The cost adjustment function allows detailed adjustments to each cost element, such as material costs, processing costs, administrative costs, and profit margins, reflecting the manufacturer's unique cost structure, procurement conditions, and efficiency. Quality assurance level adjustment sets quality assurance details and additional costs that take into account the manufacturer's quality control system, inspection equipment, and certification level in relation to the required quality standards. The delivery date adjustment function comprehensively evaluates current order status, equipment operation plans, personnel allocation, material procurement lead times, and other factors to set a feasible delivery date. Risk assessment and countermeasures quantify technical risks, quality risks, delivery risk, cost risks, etc., and reflect risk countermeasure costs in the estimate as necessary. In addition, the value-added proposal function allows for the creation of highly competitive estimates that include added value such as proposals for design improvements, cost reductions, quality improvements, and delivery time reductions. Through these adjustments, the AI-generated estimates are refined into realistic and competitive estimates that are tailored to the actual conditions at the manufacturing site.

[0154] The order acceptance / approval flow system systemizes the manufacturer's decision-making process for submitted orders, supporting efficient and reliable decision-making. Technical feasibility assessment quantitatively evaluates feasibility by comparing the required processing precision, surface quality, material properties, and shape complexity with the company's technical capabilities, equipment capacity, and past performance. Production plan compatibility assessment evaluates the consistency of the requested delivery date, production volume, quality requirements, etc. with the company's production plan, equipment operation status, and personnel allocation, and makes a comprehensive judgment, including the impact of the order on existing orders. Profitability assessment analyzes the estimated price, estimated cost, expected profit rate, capital turnover, etc., to evaluate compatibility with the company's profit standards. Risk assessment comprehensively evaluates technical risks (new technology, high level of difficulty, lack of past experience), quality risks (strict requirements, special inspection, long-term warranty), delivery risk (short delivery date, possibility of change, external factors), and transaction risks (new customers, payment terms, contract terms), etc., to calculate the overall risk level. The approval flow setting automatically sets the approval route (on-site judgment, technical department approval, management approval) according to the project size, technical difficulty, risk level, etc., supporting quick and appropriate decision-making. The competitive situation analysis evaluates the participation of other companies in the same project, estimated competitive strength, probability of winning, etc., and supports the formulation of order strategies. The system also provides a function to consider alternative order formats such as partial orders, joint orders, and outsourcing, enabling flexible order responses. These functions enable manufacturers to optimally allocate limited management resources and achieve order activities that balance profitability and growth.

[0155] The contract conclusion and transaction management system centrally manages the entire transaction process, from contract procedures after order approval to manufacturing completion and delivery. The electronic contract function uses electronic signatures to quickly conclude contract documents at each stage, such as quotation approval, purchase order issuance, order confirmation issuance, manufacturing drawing approval, and specification change confirmation, streamlining administrative processes while ensuring legal effectiveness. Manufacturing progress management visualizes process progress, quality inspection results, and delivery date compliance status in real time, simultaneously reporting progress to the client and managing internal manufacturing. Quality control records electronically manage inspection records, measurement data, inspection reports, etc. at each stage, such as material acceptance inspection, in-process inspection, and final inspection, ensuring traceability. The change management function automates technical reviews, cost impact assessments, and delivery date impact assessments for various change requests, including design changes, specification changes, quantity changes, and delivery date changes, streamlining the change approval process. Payment management automates cash management, including partial invoicing based on progress, final invoicing at completion inspection, payment due date management, and payment confirmation, optimizing cash flow. Customer satisfaction management involves collecting and analyzing evaluations of delivery deadlines, quality achievement, communication, after-sales service, etc., in order to continuously strengthen customer relationships. In addition, the evaluation and review function after a transaction is completed allows for the accumulation of manufacturing performance data, the identification of areas for improvement, and the sharing of knowledge, thereby improving the quality of future projects and reducing costs.

[0156] The sales cost reduction and efficiency improvement system dramatically reduces the significant costs and time required for traditional sales activities in the manufacturing industry. Traditional sales cost analysis analyzes detailed cost structures, including sales personnel costs (sales representative salaries, travel expenses, entertainment expenses, and communication costs), exhibition participation costs (exhibition fees, booth setup costs, catalog production costs, and personnel dispatch costs), advertising and promotion costs (trade magazine advertising, website creation and management, and brochure production), and proposal activity costs (time spent on creating quotes, technical review time, and presentation preparation), and quantifies the reduction effects. The automatic matching effect achieves a 10x or greater improvement in sales efficiency through highly accurate prospect extraction using AI matching, compared to inefficient sales methods such as cold calling, referral sales, and exhibition sales. The quotation creation efficiency improvement system reduces the time required for traditional manual quotation creation (interpreting drawings, considering processing methods, estimating costs, and creating quotes) by more than 90% through AI automatic analysis and estimation, enabling a significant increase in the number of quotations handled. In terms of streamlining the sales negotiation process, only proposals whose technical compatibility has been confirmed in advance are sent, reducing wasted sales negotiation time and enabling sales activities to focus on proposals with a high probability of winning.In terms of reducing marketing costs, instead of the traditional method of advertising to an unspecified number of people, the automatic sending of proposals that match the company's technical capabilities significantly reduces advertising costs while maximizing the effectiveness of targeted marketing.In addition, by reallocating sales personnel, it is possible to concentrate human resources on value-added creation activities such as new technology development, quality improvement, and improved customer service.

[0157] The past performance AI analysis and technical capability evaluation system uses advanced AI technology to analyze past manufacturing drawings, processing conditions, quality data, manufacturing performance, etc. uploaded by manufacturers, enabling objective evaluation of technical capabilities and improved accuracy in optimal project matching. Drawing analysis AI uses deep learning to automatically extract shape features, dimensional accuracy, tolerance requirements, surface treatment, material properties, etc. from past manufacturing drawings and quantify the technical areas that can be handled. Processing condition analysis extracts each manufacturer's processing know-how and technical features from detailed data such as tools used, cutting conditions, processing sequence, and setup methods, and generates optimization proposals for similar projects. Quality performance analysis evaluates quality stability and technical reliability based on past quality measurement data, defect rates, customer evaluations, etc., and determines suitability for projects requiring high quality. Technological progress trend analysis evaluates the status of technological capability improvement, the effects of new technology introduction, and capital investment effects based on changes in manufacturing performance over time, and uses this information to predict future technological capabilities. Special technology extraction automatically identifies technological differentiating factors such as uncommon processing methods, special material compatibility, and the use of unique jigs and tools, clarifying competitive advantages in niche markets. Benchmark analysis visualizes competitive positioning by comparing technological capabilities with those of competitors, analyzing differences from the industry average, and objectively evaluating technical strengths and weaknesses. It also provides strategic advice based on AI analysis results, such as proposals for improving technological capabilities, capital investments, and human resource development, to help manufacturers continuously strengthen their competitiveness. These analysis results serve as important foundational data for improving the accuracy of matching new projects and properly assessing manufacturers' technical capabilities.

[0158] The real-time factory operation monitoring system utilizes IoT technology and AI analysis to monitor each manufacturer's factory operation status 24 hours a day, 365 days a year, enabling optimal project distribution and production plan optimization. Equipment operation monitoring involves installing IoT sensors on each piece of manufacturing equipment to collect and analyze operational status (operating, stopped, undergoing maintenance, in preparation), processing progress (processing start time, estimated completion time, actual time), and equipment status (vibration, temperature, current value, hydraulic pressure, etc.) in real time. Production performance management automatically collects data such as production quantity, quality results, defect occurrence status, worker allocation, and material consumption to visualize production efficiency and cost performance. Preventive maintenance management detects anomalies in equipment status data to detect signs of failure early and minimize unexpected equipment downtime through planned maintenance work. Personnel allocation optimization automatically proposes optimal personnel allocation based on a comprehensive evaluation of each worker's skills, experience, current workload, health status, etc. Material inventory monitoring uses RFID tags and barcodes to manage the incoming and outgoing of materials, inventory quantity, quality status, expiration dates, etc. in real time, minimizing the risk of stockouts. Energy management monitors utility consumption such as electricity consumption, gas consumption, and compressed air consumption to achieve energy-saving operation and reduce environmental impact. Safety management monitors worker location information, entry into hazardous areas, and whether protective equipment is being worn, in order to prevent industrial accidents. This real-time data is used as an important basis for determining the possibility of receiving new orders, improving the accuracy of delivery date responses, and optimizing production plans, directly linking to improving the competitiveness of manufacturers.

[0159] The customized quotation and proposal system provides the ability to create value-added proposals and differentiated quotes that leverage each manufacturer's unique technical capabilities, equipment characteristics, and know-how in addition to standard AI-generated quotes. Technical improvement proposals differentiate from competitors by proposing more efficient processing methods, process shortening, and quality improvement proposals based on the manufacturer's experience and know-how in response to AI-analyzed processing methods. Cost-reduction proposals address customer cost-reduction needs through proposals for material changes (cheaper materials with equivalent performance), tolerance relaxation (tolerance adjustments that do not affect functionality), and design changes (modifications to shapes that are easier to manufacture). Quality improvement proposals achieve high added value through proposals such as additional quality assurance beyond the required quality, long-term quality assurance, additional surface treatments for quality improvement, and optimized heat treatment conditions. Delivery time reduction proposals offer emergency response services that shorten standard delivery times through methods such as prioritized production, overtime, equipment priority allocation, and parallel processing. Environmentally conscious proposals address customer CSR demands by proposing environmentally conscious manufacturing, such as the use of environmentally friendly materials, energy-saving processing methods, waste reduction, and recycling. In after-sales service proposals, we propose long-term support systems such as regular inspections, maintenance, repairs, improvements, and spare part supplies, aiming to build long-term relationships with customers. These proposal functions allow us to move away from simple price competition and realize differentiated competition based on technological capabilities and added value, thereby improving both the manufacturer's profitability and customer satisfaction.

[0160] The multi-site / subcontract factory network management system integrates management of the entire network, including the headquarters factory, affiliated companies, subcontractors, and other subcontractors, maximizing our ability to handle large or complex projects. Site-specific capacity management accurately tracks each site's equipment specifications, technical capabilities, production capacity, quality levels, cost structure, and other factors, automatically selecting the site that best meets the project requirements. Distributed process management divides complex products into multiple processes and assigns the optimal site to each process, creating an overall optimized production system. Unified quality management ensures consistent quality regardless of the site by unifying quality standards, standardizing inspection methods, and sharing quality information across all sites. Logistics optimization comprehensively optimizes the transportation of parts and products between sites, inventory allocation, and delivery date adjustments, simultaneously shortening lead times and reducing costs. Risk diversification ensures business continuity by continuing production at alternative sites in the event of a site's malfunction due to disasters, breakdowns, strikes, or other factors. Complementary technological capabilities combine the technical strengths of each site to handle highly challenging projects that would be difficult to handle at a single site. Cost optimization allows for highly competitive estimates through optimal cost allocation that takes advantage of differences between bases in labor costs, material costs, logistics costs, and management costs. The information sharing platform uses a cloud-based integrated information system to share technical information, quality information, progress information, cost information, and more in real time, strengthening collaboration across the entire network. These functions enable manufacturers to handle large-scale, complex projects that exceed the capabilities of individual factories, helping them expand their business and strengthen their competitiveness.

[0161] The continuous improvement and optimization system uses AI to analyze big data such as accumulated manufacturing performance, customer evaluations, and market trends, providing comprehensive improvement proposals that continuously enhance manufacturers' competitiveness. Manufacturing process optimization learns optimal processing conditions from data such as past processing conditions, tool usage history, and quality results, improving processing efficiency and quality for new projects. Capital investment optimization analyzes market trends, order trends, technology trends, and the competitive landscape to propose capital investment plans that maximize ROI and strengthen competitiveness. Human resource development optimization develops optimal human resource development plans for individuals and organizations based on technical acquisition status, project response performance, and skill gap analysis, supporting the improvement of technical capabilities. Quality improvement identifies key areas for quality improvement based on defect patterns, customer complaint analysis, and competitor quality comparisons, and proposes specific improvement measures. Cost improvement identifies cost reduction opportunities based on cost structure analysis, competitive cost comparisons, and efficiency evaluations, and proposes measures to improve profitability. Customer satisfaction improvement proposes measures to strengthen customer relationships based on analysis of customer evaluations, repeat customer rates, and referral rates, supporting long-term business expansion. In market adaptation, we propose adaptation measures to changes in the external environment, such as technological trends, changes in demand, and regulatory changes, and support the creation of a sustainable competitive advantage. In innovation promotion, we identify innovation opportunities, such as opportunities to introduce new technologies, enter new markets, and develop new services, and support business innovation. These improvement proposals enable manufacturers to maintain and improve their competitiveness at the forefront of the market, creating an environment in which they can achieve sustainable growth. By promoting digital transformation, we support the shift from traditional management reliant on experience and intuition to data-driven, scientific management, creating new competitive advantages for the manufacturing industry in the era of Industry 4.0.

[0162] The surface treatment detailed management and registration system categorizes and manages the surface treatment technologies available from each manufacturer in great detail, enabling precise matching with required specifications. For plating processes, technology classifications such as electroplating (chrome plating, nickel plating, zinc plating, tin plating, copper plating, silver plating, gold plating, platinum plating), electroless plating (electroless nickel, electroless copper, electroless gold), alloy plating (nickel-chrome, zinc-nickel, tin-lead, tin-silver) and other detailed specifications are registered, including the film thickness range (in μm), adhesion strength, corrosion resistance level, hardness (HV value), surface roughness (Ra value), color tone (value in Lab* color space), gloss level and other details for each treatment. For anodizing, detailed management is performed for each type of anodizing, such as sulfate anodizing (clear, black, various colors), hard anodizing, and chromate anodizing, including film thickness (5 μm to 100 μm), hardness (HV200 to HV500), abrasion resistance, insulation, number of dyeable colors (compatible with RAL color samples), and whether or not sealing is performed (steam sealing, nickel sealing).For painting, each type of paint, such as powder coating (epoxy, polyester, hybrid), liquid coating (urethane, epoxy, acrylic, fluorine), and electrodeposition coating (cationic, anionic), is registered along with quality control items such as film thickness control accuracy (within ±5 μm), adhesion strength (cross-cut test, cross-hatch test), weather resistance (QUV test, salt spray test time), color difference control (ΔE value), gloss (60° specular gloss), and hardness (pencil hardness, DuPont impact value).

[0163] The precision tolerance and geometric tolerance management system manages each manufacturer's achievable machining accuracy in great detail and accurately evaluates their ability to meet high-precision requirements. Dimensional tolerance management involves registering the range of tolerances available for each IT grade (IT01, IT0, IT1-IT18) by machining method (turning, milling, grinding, EDM, laser machining, etc.) and verifying feasibility by comparing it with actual data. Specifically, for each grade (IT5 grade (±0.004 mm), IT6 grade (±0.006 mm), IT7 grade (±0.010 mm), etc.), the available dimension range (φ1 mm to φ500 mm), material compatibility (carbon steel, stainless steel, aluminum alloy, titanium alloy, etc.), machining length limit (L / D ratio), etc. are quantified and managed. For geometric tolerances, the measurable range and guaranteed accuracy are registered in detail for each geometric characteristic (straightness, flatness, roundness, cylindricity, line profile, surface profile, parallelism, squareness, inclination, position, coaxiality, symmetry, circular runout, and total runout) based on ISO 5459. For example, the feasibility of meeting high-precision requirements such as roundness of 0.001 mm, coaxiality of 0.005 mm, and positional tolerance of φ0.01 mm is objectively evaluated based on the specifications and past performance of the measuring equipment used (roundness measuring machine, coordinate measuring machine, shape analyzer). Furthermore, the ability to meet high-level tolerance specifications such as composite tolerance, maximum material tolerance (MMC), minimum material tolerance (LMC), and projection tolerance band is also managed, clarifying the feasibility of meeting ultra-high-precision requirements in industries such as aerospace, medical equipment, and precision machinery.

[0164] The surface quality and roughness management system precisely controls the surface quality levels achievable by each manufacturer by processing method and material, and quantifies and evaluates their ability to meet surface quality requirements. Surface roughness management involves registering detailed achievable ranges for each processing method for parameters such as arithmetic mean roughness (Ra), maximum height roughness (Rz), ten-point mean roughness (Rzjis), root mean square roughness (Rq), skewness (Rsk), and kurtosis (Rku). For cutting processes, detailed records are kept of the tools used, cutting conditions, and actual results for each level: roughing (Ra 6.3 μm - 25 μm), medium finishing (Ra 1.6 μm - 6.3 μm), finishing (Ra 0.4 μm - 1.6 μm), precision finishing (Ra 0.1 μm - 0.4 μm), and ultra-precision finishing (Ra 0.025 μm - 0.1 μm). In grinding, the surface quality achievement capability for each grinding method, such as surface grinding (Ra 0.2 μm to 1.6 μm), cylindrical grinding (Ra 0.1 μm to 0.8 μm), internal grinding (Ra 0.2 μm to 1.6 μm), and centerless grinding (Ra 0.1 μm to 0.4 μm), is managed in relation to the type of grinding wheel and grinding conditions. In special processing, detailed control is performed on surface roughness (Ra 1.0 μm to 12.5 μm) in electrical discharge machining, heat-affected layer management in laser processing, and surface quality in water jet processing. Changes in surface quality after surface treatment (changes in roughness before and after plating, changes in surface properties due to anodizing, etc.) are also taken into consideration to accurately evaluate the surface quality assurance capability of the final product.

[0165] The material-specific precision management system manages machining accuracy by taking into account the physical and chemical properties of various materials and evaluates the ability to address material-specific machining challenges. For difficult-to-cut materials, detailed data is compi...

Claims

1. (Amended) An information processing device in a trading platform for the manufacturing industry, in which a purchaser uploads design information and automatically obtains quotes from multiple manufacturers, comprising: (a) a design information analysis unit that extracts or determines feature and material information from uploaded drawings, photographs, or 3D scan data; (b) a material selection unit that has a function of estimating manufacturing materials based on the extracted feature and material information or a function of accepting material selection by the purchaser; (c) an estimate generation unit that calculates the required processing steps and labor hours based on the features and selected materials, and generates an estimate for manufacturing based on material costs, processing costs, and labor hours; and (d) a function of calculating the prototype sample production price and mass production price, and displaying them in a comparable format.

2. (Amended) An information processing device in a trading platform for the manufacturing industry, in which a purchaser uploads design information and automatically obtains quotes from multiple manufacturers, comprising: (a) a design information analysis unit that extracts or determines feature and material information from uploaded drawings, photographs, or 3D scan data; (b) a material selection unit that has a function of estimating manufacturing materials based on the extracted feature and material information or a function of accepting a material selection by the purchaser; and (c) an estimate generation unit that calculates the required processing steps and labor hours based on the features and the selected material, and generates an estimate for manufacturing based on material costs, processing costs, and labor hours, wherein the material selection unit comprehensively evaluates the use, strength requirements, environmental conditions, and cost constraints of the part to recommend the optimal material, and presents the estimate results for the recommended material and alternative materials in a format that allows them to be compared.

3. (Amended) An information processing method in a trading platform for the manufacturing industry that automatically obtains quotations from multiple manufacturers from design drawings uploaded by a purchaser, the information processing method comprising: (a) a step of extracting geometric features and material information from the uploaded design drawings; (b) a step of estimating manufacturing-suitable materials based on the extracted geometric features and material information; (c) a step of calculating the required processing steps and labor hours based on the geometric features and manufacturing-suitable materials, and generating a manufacturing estimate based on material costs, processing costs, and labor hours; and (d) a step of calculating a sample production price taking into account initial costs, small-lot procurement material costs, and low-proficiency processing costs, calculating a mass production price taking into account learning effects, economies of scale in quantity, and equipment depreciation cost variance, and displaying the sample price and the mass production price in a comparable format.

4. (Amended) An information processing method in a trading platform for the manufacturing industry that automatically obtains quotations from multiple manufacturers based on design drawings uploaded by a purchaser, the information processing method comprising: (a) a step of extracting geometric features and material information from the uploaded design drawings; (b) a step of estimating suitable materials for manufacturing based on the extracted geometric features and material information; (c) a step of calculating the required processing steps and labor hours based on the geometric features and suitable materials for manufacturing, and generating a manufacturing estimate based on material costs, processing costs, and labor hours; and (d) a step of comprehensively evaluating the use, strength requirements, environmental conditions, and cost constraints of the part to recommend the optimal material, and presenting the estimate results for the recommended material and alternative materials in a comparable format.