Online customized manufacturing closed-loop platform system and method based on AI generation design and multi-dimensional factory matching

Abstract

The invention discloses a global user and manufacturer oriented end-to-end online customization manufacturing platform system and a method thereof, which realize the whole process from creative input to finished product delivery based on ai-driven design generation and global manufacturing resource intelligent scheduling. The platform comprises an AI design generation module, a CAD modeling and optimization module, a manufacturing rule verification module, a material intelligent recommendation module, a global factory matching and pricing module, a tax and logistics estimation module, a one-key ordering and delivery module, an intellectual property protection module and a user and factory authentication mechanism module. And a multi-part structure decomposition and remote part division type cost optimization scheduling module. A user can generate a product drawing through language or image input, a platform automatically outputs a CAD model and performs structure optimization and manufacturability verification, and materials are recommended in combination with a purpose scene. The system intelligently matches global manufacturers based on factors such as features, manufacturing difficulty, regions, tax and productivity, estimates the tax and logistics cost, and provides a one-key ordering path.

Description

Technical Field

[0001] The present invention relates to the fields of intelligent manufacturing and artificial intelligence applications, and in particular to an online platform system and method thereof for non-professional users that enables zero-threshold product design, manufacturing, and delivery. Background Art

[0002] Most existing manufacturing platforms, such as Xometry and Protolabs, only support quoting and ordering after users upload complete CAD models. These platforms are typically targeted at professional users with engineering design capabilities, relying heavily on manual design, process evaluation, and factory matching. These platforms present challenges such as high barriers to entry, long customization cycles, and low levels of automation. For complex manufacturing scenarios encompassing lightweight consumer goods, consumer electronics housings, medical device components, and precision metal parts, there is a lack of an integrated, zero-threshold platform open to the general public, enabling a complete "idea → design → manufacturing → delivery" solution from natural language or images, generating designs and automatically completing global factory matching, pricing, order placement, and delivery. Summary of the Invention

[0003] The present invention provides an end-to-end online customized manufacturing platform, covering the following modules: 1) AI Design Generation Module: This module receives user input of natural language descriptions or product images, uses generative algorithms to output product sketches, and automatically generates 3D CAD models based on a parametric template library. 2) CAD conversion and optimization module: used to perform operations such as surface cleaning, structural correction, and chamfering on the initial CAD model, and output the model in a STEP or IGES format compatible with the manufacturing process; 3) Process rule verification module (DFM engine): used to perform design manufacturability checks on CAD models, including wall thickness verification, draft angle detection, and minimum feature extraction, and provide optimization suggestions or automatically correct parameters; 4) Multi-dimensional factory matching and pricing module: This module calculates matching scores based on factors such as model characteristics, material type, process requirements, factory capacity, equipment type, historical yield, quotation records, region, and tax policies. It then screens several candidate manufacturers and provides a total quote for each solution (including processing fees, tariffs, and logistics). 5) Tariff and Logistics Estimation Module: This module maps product materials and geometric features to HS codes and estimates comprehensive costs based on the destination country's import and export regulations, tariff schedules, and real-time logistics APIs. 6) One-click ordering and delivery module: used to push the final selected CAD model, order parameters, and quotation plan to the selected factory ERP / MES system via a standard interface, automatically completing the ordering, progress tracking, and warehouse delivery processes.

[0004] 7) Material Selection Module: After the CAD model is generated, it automatically recommends the optimal manufacturing material based on product morphology, application scenarios, mechanical requirements, surface treatment requirements, and user preferences, and performs performance comparison and cost analysis of different material options. The material selection module includes: • Structural matching submodule: Analyzes the geometric features of the CAD model (such as minimum wall thickness, support span, connection method) to eliminate unsuitable material types; • Usage reasoning submodule: Combines the usage keywords entered by the user (such as "outdoor use", "anti-fall", "microwaveable") and calls the rule library to perform material screening; • Material database management submodule: maintains the property database including plastics (ABS, PP, PC), metals (aluminum alloy, stainless steel), composite materials, etc.; • User preference input interface: allows users to prioritize factors such as environmental protection, low cost, flexibility, and transparency; • Cost and performance comparison engine: Outputs a “recommended material list” among qualified materials and provides a comprehensive score based on material unit price, processing restrictions, recyclability, tax rate, etc.

[0005] 8) Intellectual Property Protection Module: Automatically compares public graphic databases to generate original designs and block the ordering process for infringing designs; 9) Factory service empowerment module: Provides connected factories with capabilities such as order recommendations, production scheduling suggestions, process standard push, and performance score feedback.

[0006] This system also includes the following options: • User interaction module: displays sketches, rendering previews, model parameters and quotation details in a graphical interface; • Template learning module: The system can continuously expand the parametric design library based on historical orders to improve AI design accuracy; • Security and traceability module: Add digital signatures to design plans and transaction processes, and record all data operation logs.

[0007] 10) Global factory access verification and due diligence module This module is used to conduct structured risk control and compliance verification when global manufacturers access the platform, ensuring they have real qualifications, controllable production capacity and stable delivery capabilities. It includes: • Cross-border identity verification submodule: supports uploading business licenses, VAT registration numbers, ISO / CE certifications, legal person IDs, and factory photos / videos in multiple formats; connects to global corporate registration databases (such as EU VIES, US D&B, and UAE MOA) to complete identity verification; • Capacity structure investigation submodule: Evaluate its process capability and production rhythm through equipment list, production line parameters, daily processing volume and production plan API connection; • International credit scoring submodule: Introduces multi-platform data sources (such as Alibaba 1688, Made-in-China, D&B, Trustpilot, etc.) to simultaneously analyze historical transaction behaviors; • Trial order mechanism for entry: Global factories are required to complete small batches of standard parts sample trial orders. The platform will record delivery time, quality and logistics consistency for admission scoring; • Compliance tracking submodule: Generates a “Factory Trusted ID”, records data and comments on each global order, and supports international traceability compliance audits.

[0008] 11) Global user authentication and transaction security module This module is used to complete risk grading, prepayment guarantee, and transaction identity confirmation before global users place orders, in order to protect the legitimate rights and interests of the supply side: • Partitioned real-name authentication submodule: Based on the user's location, different identity authentication services are called (such as China's "Network Security Real Person", US SSN verification, EU electronic passport identification, etc.) to ensure that account behavior can be traced; • First-order risk control and tiered prepayment mechanism: Dynamically set the prepayment ratio based on the order amount, IP region, and category risk level (e.g., orders from high-risk regions or first large orders require a payment of ≥80%); • Multi-currency payment escrow interface: The platform connects to Stripe, PayPal, Wise, Alipay, etc., to achieve multi-currency fund freezing and phased loan release; • Credit growth and anti-fraud system: records users’ performance in different regions to form a globally unified credit score (supports IP fraud detection, VPN identification, malicious arbitration record marking, etc.).

[0009] 12) Parts splitting and distributed manufacturing optimization module It is used to automatically parse the complex assembly (Assembly) in the user's design into multiple independently manufacturable parts, and match each part with the most suitable supplier (which may be located in different countries / regions). Finally, it comprehensively calculates the overall optimal solution that includes part manufacturing costs, cross-border logistics, assembly service fees, tariff burden, etc.

[0010] Submodules include: • Structural disassembly submodule: automatically identifies detachable structures based on CAD model hierarchy and interface features, and extracts the bill of materials (BOM); • Part process mapping submodule: determines the appropriate process (such as injection molding, CNC, sheet metal, etc.) based on the geometric characteristics, material properties and tolerance requirements of each part; • Region allocation and scoring submodule: calls the global manufacturing database to match the optimal region and quotation for each part, taking into account unit cost, tariffs, logistics routes, delivery cycle, etc.; • Assembly resource integration submodule: Integrate the final finished product delivery based on the delivery location, customer requirements and the platform’s available assembly service points (such as regional integration centers); • Total cost and delivery time optimization calculation submodule: outputs recommended paths and structural decomposition suggestions that globally minimize total costs and maximize controllable delivery time.

[0011] Beneficial effects Compared with the prior art, the present invention has the following beneficial effects: 1. Significantly lowers the threshold for custom manufacturing: This solution is truly designed for everyday people, makers, and end-users, enabling customized products with zero design knowledge. Users don't need CAD, manufacturing, or engineering backgrounds; they can generate manufacturing-grade models using only text or images, truly achieving "zero-threshold design + automated manufacturing." 2. Adapting to diverse manufacturing needs and covering multiple industry scenarios, the platform supports customization of low-, medium-, and high-complexity products, ranging from daily necessities and plastic accessories to high-precision metal structural parts, consumer electronics housings, and medical device casings. It accommodates a variety of processes, including CNC, injection molding, five-axis, sheet metal, and 3D printing. This complete closed-loop process of AI-generated design → DFM verification → factory matching → cost estimation → order execution is AI-driven throughout the entire process, significantly reducing manufacturing trial-and-error costs and labor reliance. 3. Support global user and factory collaboration to build a flexible manufacturing network. Users and manufacturers come from different countries around the world. The system supports cross-border real-name authentication, HS code mapping, tariff and logistics estimation, currency custody and segmented lending, solving global manufacturing trust and delivery issues.

[0012] 4. Intelligent material and supply chain decision-making enhances manufacturing success and transparency. The platform recommends a variety of materials based on application and structure, and compares performance and cost. It also provides real-time evaluation of global factories' delivery time, yield rate, equipment capacity, and tax burden, improving supply chain decision-making efficiency. Intelligent material matching, multi-dimensional factory evaluation, and one-click customs clearance and quotation provide high efficiency and transparency. 5. Highly automated manufacturing process: Through DFM verification and automatic conversion, the system can output STEP files without manual intervention, enabling rapid production. 6. Matchmaking efficiency and cost visualization advantages: Based on algorithmic scoring and full-chain cost estimates, users can transparently compare prices and select the optimal manufacturing path; 7. Strong global flexible manufacturing chain collaboration capabilities: Connecting global manufacturing nodes through APIs effectively responds to delivery pressure, geopolitical risks, or production capacity bottlenecks. Through systematic factory management and algorithm optimization matching, it significantly improves production efficiency and reduces supply chain friction.

[0013] 8. Significantly lowers the threshold for customized manufacturing, making it particularly suitable for small-batch, diversified flexible manufacturing trends, and is particularly suitable for the maker economy, C2M (customer-to-manufacturing), and cross-border customized consumption scenarios.

[0014] 9. Intellectual property protection and anti-counterfeiting security mechanisms are built in. The system has built-in original identification and counterfeit detection mechanisms, automatically comparing public graphic databases, generating originality signatures and evidence, effectively reducing the risk of infringement and piracy.

[0015] 10. Empower manufacturing plants to access digital manufacturing networks and optimize order-taking capabilities and structure. Establish a dual-end access mechanism for trusted factories and trusted users. Factories must pass global business certification, trial orders, and a quality scoring system for access. Users must undergo cross-border Know Your Customer (KYC) and advance payment guarantee mechanisms to ensure the authenticity, traceability, and closed-loop risk control of bilateral transactions.

[0016] 11. Activating global production capacity and small-batch customization potential, this platform standardizes access to fragmented global manufacturing capabilities, enabling makers, small businesses, and brands to customize personalized products with a lower threshold, accelerating the development of new consumption and new industries.

[0017] 12. Support distributed intelligent manufacturing optimization of complex structural parts, match the best global resources at the part level and comprehensively consider assembly, taxes and logistics to achieve the optimal cost solution for the entire chain

[0018] Brief description of the embodiments Example 1: Generate desktop storage boxes from natural language and complete online customized manufacturing The user enters the following natural language description through the platform interface: "I want a desktop storage box that can hold two mobile phones, with dimensions of 20 cm long, 10 cm wide, and 5 cm high, made of white plastic." The system performs the following steps: 1. AI design generation module: • Natural language is parsed into keywords (e.g., “storage box,” “size 20×10×5cm,” “two mobile phones,” “white,” “plastic”); • The system calls the trained generative design model and automatically generates a 3D sketch based on the parametric template of the "storage box" category; • The system completes modeling based on the input dimensional parameters and outputs a preliminary three-dimensional model (.stl format).

[0019] 2. CAD optimization and conversion module: • Automatic wall thickness checking and repair of preliminary models; • Automatically add chamfers and draft angles; • Finally, a STEP format model that complies with industrial manufacturing standards is generated.

[0020] 3. Process rule verification module: • The system determines that the structure is suitable for injection molding process; • Automatically check whether all parameters meet the minimum wall thickness requirements (e.g. wall thickness ≥ 1.2mm); • All rules pass and the system records the “manufacturable” flag.

[0021] 4. Multi-dimensional factory matching and pricing module: • The system automatically scores the platforms’ connected plastics manufacturers in Dongguan, China, injection molding factories in Zhejiang, and a 3D printing supplier in Vietnam; • Evaluation criteria include: current capacity availability, unit processing cost, minimum order quantity (MOQ), past yield rate, average delivery cycle, etc.; • The Dongguan factory scored the highest, with the system giving a unit cost of ¥4.20 and a delivery cycle of 4 working days.

[0022] 5. Tariff and logistics estimation module: • Based on the model material and function, the system automatically maps the HS Code to “3924.90.9090”; • The system identifies the customer's destination as Germany, and finds that the corresponding tariff is 6.5%, VAT is 19%, and the DHL express fee is ¥48; • Give a total price estimate: manufacturing fee ¥4.20 + customs duty and VAT ¥3.45 + logistics fee ¥48 = ¥55.65.

[0023] 6. One-click ordering and delivery module: • After the user confirms the Dongguan plan, click "One-click Order"; • The platform sends the design files and order parameters to the factory ERP via API; • The system automatically synchronizes manufacturing status, including mold preparation, injection molding start, quality inspection, and delivery; • Users can check the order status in real time through the platform and can expect to receive the finished product within 6 days.

[0024] Supplementary Example (added to Example 1) In the first embodiment, after the system completes the CAD model conversion, it automatically enters the material selection module: 1. Automatic identification of material requirements • The user did not actively specify the material, but the platform identified the product as a desktop storage item and suggested materials such as ABS, PP, and PLA; • When the user clicks “Durable and drop-resistant,” the system excludes PLA and retains ABS and PP.

[0025] 2. Performance Comparison Analysis • The system scores ABS and PP in terms of “heat resistance”, “impact resistance”, “surface gloss” and “recycling level”; • The recommended material is: PP (recommendation index 92), unit material cost ¥1.80 / kg.

[0026] 3. Matching of related process solutions • Material selection is fed back to the factory matching module, and the system will only retain factories that support PP injection molding for subsequent scoring and matching.

[0027] Example 2: Automatic reverse reconstruction of user-uploaded product photos A user uploaded a photo of a uniquely shaped headphone stand, and the platform prompted, "Please add a 45-degree side view." After the user complied, the system responded: 1. Call multi-view reconstruction model to generate triangular mesh; 2. Automatically identify facets and generate parametric contours; 3. Output the manufacturable CAD model. The subsequent process is the same as that of the first embodiment.

[0028] Example 3: Platform-assisted manufacturing plants perform intelligent order matching and process standard optimization After a medium-sized injection molding company successfully connected to the platform's factory-side system, the platform system automatically started the following enabling service process: 1. Capacity gap identification and order push The platform analyzed the factory's production records and order delivery data over the past 30 days and identified that its medium-sized injection molding machine production line had an idle production capacity section of approximately 32%, making it suitable for accepting orders for products without complex in-mold structures, weighing no more than 150g, and made of PP or ABS.

[0029] 2. Intelligent order matching and matching recommendations The system screened over 240 custom orders received in the past week and identified eight matching tasks that met the aforementioned criteria. Based on factors such as product type, quote flexibility, and response speed, the platform pushed these eight orders to the factory's backend, along with information such as projected profit margins, unit production time, and mold reuse recommendations.

[0030] 3. Process parameter coordination and optimization suggestions The factory selected three of the orders for quotation, and the system automatically provided CAD model analysis results, including: recommended injection pressure (e.g., 85MPa), draft angle parameters (above 2°), recommended glue feed position and shrinkage compensation rate (PP=1.6%), which were convenient for mold engineers to directly reference, significantly shortening mold trial and machine adjustment time.

[0031] 4. Order priority recommendation and credit score update Due to the factory's high on-time delivery rate (97.8%) and low defective rate (1.2%), the platform's rating system assigned it a "High Responsiveness" label. This rating system automatically prioritizes the factory at the top of the matching recommendation list, increasing its order acceptance rate.

[0032] 5. Continuous feedback and factory image optimization After the transaction is completed, the platform continues to collect indicators such as delivery time achievement rate, user ratings, and quotation accuracy, and updates its factory profile to automatically match orders with higher suitability in the next round of matching.

[0033] Example 4 (Global Factory) A Mexican injection molding factory submitted an application for registration through the platform, uploading its business license, VAT number, factory images, and ISO 9001 certificate. The platform then verified its credentials using the Mexican SAT database and the Dun & Bradstreet global credit database.

[0034] The platform assigned a trial order of standard plastic hooks from Canada. The factory completed the delivery on time and submitted a quality inspection video online through the platform. The factory received a score of 94, officially opening access to global orders.

[0035] Example 5 (Global Users) A customer from a high-risk region used the platform to customize a small aluminum alloy part. The platform identified the customer's IP address as belonging to a newly registered high-risk region and initiated the Know Your Customer (KYC) process. The customer uploaded their ID, which was verified by the system. The platform set a 70% down payment requirement. The user completed a payment freeze through the Wise payment system, and the platform then sent the design files to the factory. The platform then recorded their performance and generated a global credit ID, which influenced future order matching priorities.

[0036] Example 6: Image Input Generation Model + Anti-Counterfeit Design Blocking A user uploaded an online image of a headphone stand and supplemented it with a side-view image. The platform's AI system generated a corresponding 3D model, but the IP recognition subsystem detected an 88% similarity to a design from a renowned international brand. The platform halted the subsequent order process and prompted the user to make changes. The user selected a recommended, differentiated design sketch and successfully placed the order. The system generated a signature for the design and recorded it as proof of originality.

[0037] Example 7: High-precision electronic housing customization An American hardware designer uploaded a set of sketches and specified the material preference as "aluminum alloy, anodized." The platform automatically completed the modeling, generating a CAD model of the structure using a hybrid process (CNC, sandblasting, and engraving), and automatically verified wall thickness and draft angles. The matching algorithm generated a shortlist of candidates, including a five-axis shop in Shizuoka, Japan, and a CNC shop in Germany. The user ultimately selected the Japanese shop, and delivery was completed within seven days.

[0038] Embodiment 8: The user uploaded a set of overall CAD models of a medical-grade portable instrument. The system recognized that the assembly consists of seven main components, including the shell (injection molding), back panel (aluminum sheet metal), key panel (CNC), PCB fixing slot (ABS), lens cover (PA+glass fiber), etc.

[0039] 1. The system automatically disassembles the structure and extracts seven independently manufactured parts; 2. Match the processing technology of each part to the factories in the database that can undertake the project: • China: Lowest price for housing and PCB slot (injection molding); • Poland: Lower quotes and shorter delivery times for key panel CNC processing; • Vietnam: Backplane sheet metal bending has the best cost performance; 3. The platform calls the global logistics and customs clearance engine to determine when the package is delivered to the user's location (Israel). • Option A: All components are assembled in China and then shipped; • Option B: All components are shipped directly to the local platform warehouse in Israel for assembly; The system determines that Plan B can save about 18% in total tax and logistics costs, and recommends users to adopt it; 4. After the user confirms the order, they pay the prepayment in installments. The platform automatically dispatches manufacturers and coordinates the assembly schedule.

Claims

1. An online customized manufacturing platform system based on artificial intelligence design and multi-dimensional factory matching, characterized by: include: An AI design generation module, which receives user input of natural language descriptions or product images and generates parametric 3D product models based on generative algorithms. CAD conversion and optimization module, used to convert the model into a manufacturable CAD file format and perform facet correction, chamfer addition and structure verification; A process rule verification module is used to perform wall thickness detection, draft angle analysis, and minimum feature size determination on the CAD model, and generate a process manufacturability report; A multi-dimensional factory matching and pricing module, which combines product model characteristics, processing requirements, factory capacity status, quotation records, delivery forecasts, region, and tariffs to generate factory scores and output a list of recommended manufacturers. A tariff and logistics estimation module automatically maps HS codes based on product structure and materials, queries target country tax rates and logistics plans, and generates total cost estimates. A one-click ordering and delivery module that sends order data and manufacturing documents to the target factory's ERP or MES system and provides production progress and logistics tracking. Material selection module, used to filter and recommend materials in the material database based on model shape, user application requirements and performance preferences materials and compare costs; An intellectual property protection module, which compares the generated design with public databases to identify infringement risks and generate an original logo; The factory service empowerment module is used to provide order recommendations, production scheduling suggestions, process standard push, and performance score feedback to connected factories.

2. Global factory access verification module, used to access global industrial and commercial and credit data sources to complete manufacturer qualification due diligence; A global user transaction security module, which implements real-name authentication, currency custody, prepayment mechanisms for performance, and user credit scoring; The component splitting and distributed manufacturing optimization module is used to automatically disassemble the assembly structure into multiple parts and match the optimal manufacturing resources in different regions for each part, ultimately outputting the assembly path and the optimal overall cost solution.

3. A customized manufacturing method based on artificial intelligence and global manufacturing collaboration, characterized in that: The steps include: (1) Receive natural language description or product image input by the user; (2) Calling the generative algorithm to construct the product sketch and generate the parametric CAD model; (3) performing structural optimization and manufacturing process verification on the model and outputting a manufacturable format file; (4) Automatically recommend materials for the model and display the performance and cost comparison of each material; (5) Extract process features and disassemble components of single-body models or multi-part structures; (6) Based on the structural characteristics, processing requirements and regional parameters of each part, the global manufacturer database is used for scoring and matching; (7) Call the logistics and tariff engine to estimate the total cost, delivery time and risk score under different manufacturing paths; (8) Compare multiple distributed manufacturing + assembly paths and output the solution with optimal overall cost or controllable delivery time; (9) Show the user the complete design plan, the manufacturing location of each part, the assembly process and the payment plan; (10) After the user completes real-name authentication and risk control assessment, they pay the escrow advance and the platform executes manufacturing scheduling; (11) The system tracks the execution status of each manufacturer and coordinates assembly or joint shipment in appropriate areas until final delivery.

4. The method according to claim 1, wherein when performing part disassembly in step (5), the system can identify mechanical interfaces, hinges, snaps or boundaries of different materials to achieve logical independence determination of parts.

5. The method according to claim 1, wherein the global manufacturer rating factor in step (6) comprises: Unit processing quotation, delivery time, historical yield rate, equipment adaptability, regional taxation and logistics efficiency.

6. The method according to claim 1, wherein the tariff estimation in step (7) is based on HS code matching and calling the export and entry tax rate database and real-time freight rate data of various countries.

7. The method according to claim 1, wherein the manufacturing path optimization model in step (8) supports multi-objective switching such as minimizing cost, shortening delivery time, and optimizing carbon footprint.

8. The method according to claim 1, wherein the escrow payment in step (10) supports phased release, intelligent currency identification and payment locking mechanism for high-risk areas.

9. The method according to claim 1 further comprises the steps of originality identification and right confirmation, wherein the system generates a signature for the design model and Bound to the user ID to form a copyright certificate.

10. The system according to claim 1, wherein the AI design generation module adopts a well-trained text-geometry bimodal model to support template combination, language style recognition and functional keyword extraction.

11. The system according to claim 1, wherein the CAD optimization module can automatically process chamfers, slots, ribs and wall thickness reconstruction based on predefined rules, and adapt to various process paths such as injection molding CNC and 3D printing.

12. The system according to claim 1, wherein the process rule verification module includes at least the following verifications: minimum wall thickness threshold judgment, chamfer angle calculation, draft direction detection, overcut area warning and bottom residual material analysis.

13. The system of claim 1, wherein the material selection module supports Based on product functional semantics and morphological constraints and recommended materials for terminal use environment reasoning, including environmental protection grade, FDA certification, Flame retardant grade and other property indicators.

14. The system of claim 1, wherein the global manufacturer rating system Factor weights can be automatically updated by the platform's machine learning model and adaptively tuned based on historical performance and buyer feedback.

15. The system according to claim 1, wherein the platform automatically assigns a trial order task to each newly connected manufacturer and determines the maximum order size and authority level that the manufacturer can undertake based on its trial production performance.

16. The system according to claim 1, wherein the payment guarantee mechanism supports docking with third-party cross-border payment platforms such as PayPal, Stripe, Wise and RMB clearing channels, and has currency identification and risk freezing functions.

17. The system according to claim 1, wherein the intellectual property protection module identifies counterfeiting risks based on image embedding vectors and local feature matching mechanisms, and automatically terminates the order path when the risk exceeds a set threshold.

18. The system according to claim 1, wherein the parts splitting and distributed manufacturing optimization module further supports delegating each subtask to nodes in different countries and merging them at a local or neutral assembly site for final delivery.

19. The system according to claim 1, wherein the assembly optimization module automatically recommends assembly countries or warehouses based on customs clearance complexity, logistics path cross-cost and regional regulatory accessibility.