System and method for automated configuration to order and quote to order

Abstract

To provide computerized systems and methods for automating Configure to Order (CTO) and Quote to Order (QTO) processes.SOLUTION: Methods include receiving user inputs for desired product configurations, retrieving corresponding data from a bill of materials database, and calculating optimized pricing through intelligent rules based on real-time market data. Validation step includes generating real-time reports and reducing errors.SELECTED DRAWING: Figure 9

Description

[Technical Field]

[0001] (CROSS-REFERENCE TO RELATED APPLICATIONS) This application is a continuation-in-part (CIP) of U.S. Patent Application No. 18 / 341,714, filed June 26, 2023, and U.S. Patent Application No. 18 / 349,836, filed July 10, 2023. This application also claims the benefit of U.S. Provisional Application No. 63 / 513,073, filed July 11, 2023, U.S. Provisional Application No. 63 / 513,078, filed July 11, 2023, U.S. Provisional Application No. 63 / 515,075, filed July 21, 2023, and U.S. Provisional Application No. 63 / 515,076, filed July 21, 2023. Each of these applications is incorporated herein by reference in its entirety.

[0002] background Traditional ordering processes in distribution and supply chain platforms are plagued by inefficiencies, delays, and inaccuracies. In traditional environments, multiple systems and vendors typically perform each activity independently, from creating bills of materials to registering transactions, applying pricing, generating quotes, and issuing orders. This approach increases the likelihood of inefficiencies and errors.

[0003] Enterprise resource planning (ERP) systems have served as the workhorse in managing business processes, including distribution and supply chains. These systems act as a central repository where different departments, such as finance, human resources, and inventory management, can access and share real-time data. While comprehensive, ERP systems present several challenges in today's complex distribution and supply chain environments. One of the main challenges is data fragmentation. Data silos across different departments or separate ERP systems make it difficult to achieve real-time visibility. Users lack a comprehensive view of key distribution and supply chain metrics, which negatively impacts the decision-making process.

[0004] Furthermore, ERP systems often do not offer effective data integration capabilities. Traditional ERP systems are not designed for efficient integration with external systems or between different modules within the same ERP suite. This design results in tedious, error-prone manual processes for transferring data between systems, negatively impacting the flow of information throughout the supply chain. When information exists in different formats across systems, data inconsistencies arise, preventing accurate data analysis and leading to uninformed decision-making.

[0005] Data inconsistencies present another challenge. When data exists in different formats or units across departments or ERPs, standardizing this data for meaningful analysis can be a tedious process. Businesses often rely on time-consuming manual processes to convert and validate data, further delaying decision-making. Additionally, legacy ERP systems often lack the ability to effectively handle large amounts of data. These systems struggle to provide timely insights for operational improvement, which is particularly problematic for businesses dealing with complex and expanding distribution and supply chain networks.

[0006] Data security is another concern, especially considering the sensitivity of supply chain data, which includes customer details, pricing, and contracts. Ensuring compliance with global regulations on data security and governance adds another layer of complexity. Traditional ERP systems often lack robust security capabilities that are flexible enough to adapt to ever-evolving cybersecurity threats and compliance requirements.

[0007] Finally, consumer expectations for faster service and real-time information are putting additional pressure on legacy systems. In the age of digital transformation and e-commerce, customers expect instant quotes and quick order fulfillment. Legacy processes often take 6-72 hours, a timeframe that is not competitive in today's fast-paced marketplace.

[0008] Shortcomings in existing technologies not only create operational inefficiencies but also result in a poor customer experience. For example, delays in quote generation or order processing can result in lost sales opportunities. Additionally, inaccuracies in pricing or inventory levels can lead to customer dissatisfaction and potential business loss. Conventional systems and methods lack an integrated, efficient, and responsive approach to the ordering process. Summary of the Invention

[0009] Automated configure-to-order (CTO) and quote-to-order (QTO) processes aim to address the aforementioned deficiencies in the distribution industry by providing a unified platform experience. This platform consolidates various activities and systems into a single interface, allowing users to streamline the entire process. It reduces the time required for activities such as bill of materials creation, deal registration, pricing application, quote creation, and ordering. Therefore, there is a strong need for technology solutions that can effectively integrate, streamline, and accelerate these complex processes while ensuring data security and compliance.

[0010] In the global distribution industry, challenges such as inefficient distribution management, SKU management, and the transition to a direct-to-consumer model require innovative solutions. Traditional distribution methods are becoming increasingly inadequate, especially due to changing consumer expectations and regulations. This invention addresses these challenges by integrating a comprehensive set of functionality focused on distribution management, supply chain management, and customer visibility into one platform.

[0011] According to some embodiments, the CTO and QTO modules can be integrated with a real-time data mesh (RTDM) and a single pane of glass user interface (SPoG UI). The CTO module uses algorithms to optimize user selection based on real-time inventory and customization options. It also employs recursive algorithms to generate a bill of materials. The QTO module verifies user authorizations and calculates applicable discounts. Both modules employ validation algorithms to check for errors and inconsistencies.

[0012] In a non-limiting example, the configuration builder in the CTO module employs a decision tree algorithm using entropy minimization techniques to provide the user with compatible choices. Alternative embodiments employ machine learning models such as neural networks for more adaptive selection. The pricing engine predicts costs using a multivariate linear regression model, while another embodiment uses more advanced machine learning models such as random forests.

[0013] In one embodiment, the CTO module interacts with RTDM and the SPoG UI 705. Upon receiving a user request, the configuration builder can fetch real-time inventory from RTDM. The module can utilize a decision tree algorithm with entropy minimization for selection optimization. Alternatively, a machine learning model such as a neural network can refine the user's selection. A bill of materials (BOM) generator can use a recursive algorithm to list components in a hierarchical structure. In some embodiments, a depth-first search (DFS) algorithm can traverse this structure and create the complete BOM. A pricing engine can perform cost prediction. In a non-limiting example, the pricing engine can employ a multivariable linear regression model for cost prediction. Variables can include base price, quantity discounts, and special terms. An alternative option can implement a random forest algorithm for more complex variable relationships. An error-checking integrator can apply validation algorithms ranging from basic bounds checking to advanced anomaly detection. In some embodiments, a support vector machine (SVM) can classify configurations as valid or invalid based on historical data.

[0014] In some embodiments, the QTO module can initiate a QTO request via the SPoG UI. An authorization checker can verify user permissions against role-based access control policies in AAML 715. A pricing aggregator can query RTDM for current pricing data. A discount calculator can apply discounts. In a non-limiting example, the discount calculator can apply discounts using a weighted scoring algorithm. In some embodiments, a quote template filler can populate the quote template using a string replacement algorithm based on the KMP algorithm. An error checking integrator can review quotes for errors using rules predefined in AAML.

[0015] Additionally or alternatively, the error-checking integrators in both modules can use a set of validation algorithms. These can be support vector machines trained on historical data. Real-time data can be fetched from the CRM system via RTDM and / or through additional or alternative processes, such as OAuth 2.0 secure API calls, to ensure synchronization. SQL queries pull account-specific data, such as customization limits or previously negotiated pricing terms, directly from the CRM database via RTDM.

[0016] The embodiments disclosed herein integrate multiple systems, automate processes, and validate data configurations based on intelligent rules, enabling complex tasks to be performed efficiently without specialized knowledge, saving time and minimizing errors. Furthermore, the present invention is adaptable and configurable to meet evolving market and customer demands, thereby maintaining the relevance and sustainability of the distribution model. Thus, the present invention provides an efficient, integrated, and adaptable solution for automating CTO and QTO processes in the distribution industry.

[0017] Single Pane of Glass Single Pane of Glass (SPoG) can provide a comprehensive solution designed to address these multifaceted challenges. It can be configured to provide a holistic, user-friendly, and efficient platform that streamlines the distribution process.

[0018] According to some embodiments, SPoG can be configured to address supply chain and distribution management by improving visibility and control over supply chain processes. Through real-time tracking and analytics, SPoG can deliver valuable insight into inventory levels and product status, ensuring supply chain and distribution management processes are handled efficiently.

[0019] According to some embodiments, SPoG can consolidate multiple touchpoints into a single platform and emulate a direct consumer channel within a distribution platform. This consolidation provides a unified direct channel for consumers to interact with distributors, significantly reducing supply chain complexity and improving the overall customer experience.

[0020] SPoG provides innovative solutions for improved inventory management through advanced forecasting capabilities. These predictive analytics can highlight demand trends and guide companies to manage their inventory more efficiently, reducing the risk of stockouts or overstocks.

[0021] According to some embodiments, the SPoG may include a global compliance database that is updated in real time, allowing distributors to stay abreast of the latest international regulations. This feature significantly reduces the burden of manual tracking and ensures smooth, compliant cross-border transactions.

[0022] According to some embodiments, SPoG consolidates data from various OEMs into a single platform to streamline SKU management and product localization. This not only ensures data consistency but also significantly reduces the chance for errors. Furthermore, it provides the ability to efficiently manage and distribute localized SKUs, thereby meeting the needs and requirements of specific markets.

[0023] According to some embodiments, SPoG is a highly configurable and user-friendly platform whose intuitive interface allows users to easily access and purchase technology, thereby meeting the expectations of a new generation of technology buyers.

[0024] Additionally, SPoG's advanced analytical capabilities provide valuable insights that can drive strategy and decision-making. Trends can be tracked and analyzed in real time, enabling companies to stay ahead of the curve and adapt to changing market conditions.

[0025] SPoG's flexibility and scalability make it a future-proof solution, able to adapt to changing business needs and allowing companies to scale operations up or down as needed without making major changes to their infrastructure.

[0026] SPoG's innovative approach to solving distribution industry challenges is an invaluable tool. By increasing supply chain visibility, streamlining inventory management, ensuring compliance, simplifying SKU management, and delivering a superior customer experience, it offers a comprehensive solution to the complex problems that have long plagued the distribution industry. Through implementation, distributors can expect to see increased efficiency, reduced errors, and improved customer satisfaction, leading to sustainable growth in an ever-evolving global marketplace.

[0027] Real-Time Data Mesh (RTDM) According to some embodiments, the platform may include an implementation of Real-Time Data Mesh (RTDM). RTDS provides an innovative solution to address these challenges. RTDM is a distributed data architecture that enables real-time data availability across multiple sources and touchpoints. This feature improves supply chain visibility, enables efficient management, and allows distributors to handle disruptions more effectively.

[0028] RTDM's predictive analytics capabilities provide a solution for efficient inventory control. By providing insight into demand trends, it helps companies manage their inventory and reduces the risk of overstocking or running out of stock.

[0029] RTDM's global compliance database is updated in real time, ensuring distributors are up to date with international regulations, significantly reducing the burden of manual tracking and enabling cross-border transactions.

[0030] RTDM also simplifies SKU management and localization by consolidating data from various OEMs, ensuring data consistency and reducing the chance of errors. Its ability to manage and distribute localized SKUs efficiently aligns with specific market needs.

[0031] RTDM enhances the customer experience with its intuitive interface, making technology easier to access and purchase, and meeting the expectations of a new generation of technology buyers.

[0032] Benefits of SPoG and RTDM Integration Integrating the SPoG platform with RTDM offers numerous benefits. First, it provides a comprehensive solution to a long-standing problem in the distribution industry. RTDM's capabilities enable SPoG to improve supply chain visibility, streamline inventory management, ensure compliance, simplify SKU management, and deliver a superior customer experience.

[0033] The real-time tracking and analytics provided by RTDM improves SPoG's ability to effectively manage its supply chain and inventory, providing accurate, current information that enables distributors to make informed decisions quickly.

[0034] Integrating SPoG with RTDM also ensures data consistency and reduces errors in SKU management, and by providing a centralized platform for managing data from various OEMs, it simplifies product localization and helps meet market needs.

[0035] RTDM's global compliance database is integrated with SPoG to facilitate compliant cross-border transactions and reduce the burden of manual tracking, saving significant time and resources.

[0036] In some embodiments, a distribution platform incorporates SPoG and RTDM to provide an improved, comprehensive distribution system that can leverage the advantages of the distribution model, address its existing challenges, and position it for sustained growth in a constantly evolving global marketplace. [Brief explanation of the drawings]

[0037] [Figure 1] 1 illustrates one embodiment of an operating environment for a distribution platform, referred to herein as a system. [Figure 2]2 illustrates one embodiment of an operating environment for a distribution platform built with the elements introduced in FIG. 1. [Figure 3] 1 illustrates one embodiment of a system for distribution management. [Figure 4] 1 illustrates a system for automated configure-to-order (CTO) and quote-to-order (QTO) processes, according to one embodiment. [Figure 5] 1 illustrates an RTDM module according to one embodiment. [Figure 6] 1 illustrates a SPoG UI according to one embodiment. [Figure 7] 1 illustrates a system for automated configure-to-order (CTO) and quote-to-order (QTO) processes, according to one embodiment. [Figure 8] FIG. 1 is a flow diagram of a method for automating configure-to-order (CTO) and quote-to-order (QTO) processes according to some embodiments of the present disclosure. [Figure 9] FIG. 1 is a flow diagram for automating a configure-to-order (CTO) process according to some embodiments of the present disclosure. [Figure 10] FIG. 1 is a flow diagram for automating a quote-to-order (QTO) process according to some embodiments of the present disclosure. [Figure 11] FIG. 2 is a block diagram of exemplary components of a device according to some embodiments of the present disclosure. [Figures 12A-12Q] 1 illustrates various screens and functionality of the SPoG UI, according to some embodiments. DETAILED DESCRIPTION OF THE INVENTION

[0038] The present embodiments may be implemented in hardware, firmware, software, or a combination thereof. The present embodiments may also be implemented as instructions stored on a machine-readable medium, which may be read and executed by one or more processors. A machine-readable medium may include a mechanism for storing or transmitting information in a form readable by a machine (e.g., a computing device). For example, a machine-readable medium may include read-only memory (ROM), random access memory (RAM), magnetic disk storage media, optical storage media, flash memory devices, and others. Furthermore, firmware, software, routines, and instructions may be described herein as performing particular actions. However, it should be understood that such description is merely for convenience and that such actions are actually the results obtained by a computing device, processor, controller, or other device executing the firmware, software, routines, instructions, etc.

[0039] It should be understood that the acts shown in the example methods are not exhaustive and that other acts may similarly be performed before, after, or between any of the acts shown. In some embodiments of the present disclosure, acts may be performed in a different order and / or varied.

[0040] 1 illustrates a distribution platform operating environment 100, referred to in this embodiment as system 110. System 110 operates within the context of an information technology (IT) distribution model and serves the needs of various users, such as customers 120, end customers 130, vendors 140, resellers 150, and other entities involved in the distribution process. This operating environment encompasses a wide range of characteristics and dynamics that contribute to the success and efficiency of the distribution platform.

[0041] Customers 120 within the operating environment of system 110 represent businesses or individuals seeking IT solutions to meet their specific needs. These customers may require a diverse range of IT products, such as hardware components, software applications, network equipment, or cloud-based services. System 110 provides customers with a user-friendly interface, allowing them to browse, search, and select the most suitable IT solution based on their requirements. Through system 110, customers also have access to real-time data and analytics, enabling them to make informed decisions and optimize their IT infrastructure.

[0042] End customers 130 can be the ultimate beneficiaries of the IT solutions provided by system 110. End customers may include businesses or individuals who utilize IT products and services to improve their operations, productivity, or daily activities. End customers rely on system 110 to access a wide range of IT solutions and are ensured to have access to the latest technologies and innovations on the market. System 110 allows end customers to track their orders, receive delivery status updates, and access customer support services, thereby improving their overall experience.

[0043] Vendors 140 play a key role within the operating environment of system 110. These vendors include manufacturers, distributors, and suppliers that offer a diverse range of IT products and services. System 110 serves as a centralized platform for vendors to showcase their offerings, manage inventory, and facilitate transactions with customers and resellers. Vendors leverage system 110 to streamline supply chain operations, manage pricing and promotions, and gain insight into customer preferences and market trends. By integrating with system 110, vendors can expand their reach, access new markets, and improve their overall visibility and competitiveness.

[0044] Resellers 150 can be intermediaries in the distribution model that bridge the gap between vendors and customers. Resellers play a vital role in the IT distribution ecosystem by connecting customers to the right IT solutions from various vendors. Resellers may include retailers, value-added resellers (VARs), system integrators, or managed service providers. System 110 enables resellers to access a comprehensive catalog of IT solutions, manage their sales pipeline, and provide value-added services to customers. By leveraging system 110, resellers can improve their customer relationships, optimize their product offerings, and increase revenue streams.

[0045] Within the operating environment of the system 110, there may be various dynamics and characteristics that contribute to its effectiveness. These dynamics include real-time data exchange, integration with existing enterprise systems, scalability, and flexibility. The system 110 ensures that relevant data is exchangeable between users in real time, enabling accurate decision-making and timely action. Integration with existing enterprise systems, such as enterprise resource planning (ERP) systems, customer relationship management (CRM) systems, and warehouse management systems, enables communication and interoperability, eliminating data silos and enabling end-to-end visibility.

[0046] System 110 provides scalability and flexibility to accommodate the growing demands of IT distribution models, whether they involve a growing customer base, an increasing number of vendors, or a broader range of IT products and services. System 110 can be configured to handle large-scale data processing, storage, and analysis, ensuring it can support the evolving needs of the distribution platform. Additionally, system 110 leverages a technology stack that includes .NET, Java, and other suitable technologies, providing a solid foundation for its operation.

[0047] In summary, the operating environment of system 110 within the IT distribution model encompasses customers 120, end customers 130, vendors 140, resellers 150, and other entities involved in the distribution process. System 110 serves as a centralized platform that facilitates efficient collaboration, communication, and transaction processes among these users. By leveraging real-time data exchange, integration, scalability, and flexibility, system 110 enables users to optimize operations, improve customer experience, and drive business success within the IT distribution ecosystem.

[0048] Figure 2 illustrates a distribution platform operating environment 200 built with the elements introduced in Figure 1. Within this operating environment, integration points 210 facilitate data flow and connectivity between various customer systems 220, vendor systems 240, reseller systems 260, and other entities involved in the distribution process. This diagram illustrates the interconnectivity and mechanisms that enable efficient collaboration and data-driven decision-making. The operating environment is configured to utilize advanced artificial intelligence (AI) and machine learning (ML) techniques to automate the CTO and QTO processes and integrate, process, and analyze data. In this optional configuration, AI algorithms can be applied to the CTO process for real-time inventory management, customization options, and user selection optimization. Machine learning models, such as neural networks and decision trees, can be employed to provide users with more refined and personalized options. Similarly, the QTO process can use ML-based algorithms for real-time pricing lookup, discount application, and quote generation. Advanced analytics in the form of ensemble learning or reinforcement learning can be implemented to continuously optimize both the CTO and QTO processes.

[0049] The operating environment 200 can include the system 110 as a distribution platform that serves as a central hub for managing and facilitating the distribution process. The system 110 can be configured to function and operate as a bridge between the customer system 220, the vendor system 240, the reseller system 260, and other entities in the ecosystem. Communications, data exchange, and transaction processes can be integrated to provide a unified, streamlined experience for users. Additionally, the operating environment 200 can include one or more integration points 210 to ensure smooth data flow and connectivity. These integration points include the following:

[0050] Customer System Integration: Integration point 210 can enable system 110 to connect with customer system 220, enabling efficient data exchange and synchronization. Customer system 220 can include various entities, such as customer system 221, customer system 222, and customer system 223. These systems represent internal systems utilized by customers, such as enterprise resource planning (ERP) or customer relationship management (CRM) systems. Integration with customer system 220 allows customers to access real-time inventory information, pricing details, order tracking, and other relevant data, improving customer visibility and decision-making capabilities. The application of CTO and QTO processes ensures that customer system 220 can participate in real-time product configuration and quoting processes through integration with system 110. Specifically, the CTO module of system 110 can receive configuration requests from customer system 220, fetch real-time inventory and customization options from a proprietary database, and return an optimized bill of materials (BOM). Similarly, the QTO module can receive pricing inquiries, verify user permissions in customer system 220, calculate applicable discounts, and return dynamically generated quotes. This data exchange can occur via APIs or direct module calls and can be routed through additional security or analytics modules as needed. Thus, integration with customer system 220 provides automated, real-time solutions to both product configuration and pricing tasks, thereby improving the customer's operational efficiency and decision-making capabilities.

[0051] Trading System Integration: Integration points 210 can enable system 110 to connect to trading system 230, allowing for efficient data exchange and synchronization. Trading system 230 can include various entities such as trading system 231, trading system 233, trading system 233, etc. Integration with trading system 220 allows customers to access real-time inventory information, pricing details, order tracking, and other relevant data, improving visibility and decision-making capabilities.

[0052] Vendor System Integration: Integration point 210 facilitates connectivity between system 110 and vendor system 240. Vendor system 240 may include entities representing inventory management systems, pricing systems, and product catalogs employed by vendors, such as vendor system 241, vendor system 242, and vendor system 243. Integration with vendor system 240 ensures that vendors can efficiently update their product offerings, manage pricing and promotions, and receive real-time order notifications and fulfillment details. CTO and QTO processes and components within system 110 enable vendor system 240 to automate and optimize various aspects of product configuration and quoting. For example, the CTO process within system 110 uses decision tree algorithms or machine learning models to request specific inventory or customization options from vendor systems. This helps vendors align their stock or manufacturing processes with real-time market demand. Additionally, modules employ recursive algorithms to compile bills of materials (BOMs), which can then be communicated back to vendor systems for inventory allocation or assembly.

[0053] The QTO process looks up current pricing data, verifies user permissions, and algorithmically calculates applicable discounts. More advanced machine learning models, such as multivariate linear regression models or random forests, can be used to predict costs based on vendor-supplied data. Quotes generated by the QTO process can then be sent to vendor systems for approval, adjustment, or record-keeping.

[0054] In a non-limiting example, a CTO process can request real-time inventory data (e.g., via vendor system 241), while a QTO process can query the vendor data for the latest volume discount information. Real-time data exchange ensures that vendor inventory and pricing systems can be optimally utilized and that end users have access to the most current and beneficial options. This integration significantly improves data accuracy and operational efficiency for both system 110 and vendor system 240.

[0055] Reseller System Integration: Integration point 210 provides the ability for reseller systems 260 to connect with system 110. Reseller systems 260 may encompass entities representing the sales systems, customer management systems, and service delivery platforms employed by the reseller, such as reseller system 261, reseller system 262, and reseller system 263. Integration with reseller systems 260 enables the reseller to access current product information, manage customer accounts, track sales performance, and provide value-added services to customers.

[0056] Other Entity System Integration: Integration point 210 further enables connectivity with other entities involved in the distribution process. These entities may include entities such as entity system 271, entity system 272, and entity system 273. Integration with these systems ensures communication and data exchange, facilitating collaboration and an efficient distribution process. In some embodiments, integration of the CTO and QTO process with other entity systems can ensure that entity system 271 is involved in the real-time product configuration and quoting process via system 110.

[0057] Integration point 210 also enables connectivity with systems of record 280 for additional data management and integration. Representing systems of record 280 can represent enterprise resource planning (ERP) systems or customer relationship management (CRM) systems, including both legacy ERP systems (e.g., SAP, Impulse, META, I-SCALA, etc.) as well as future systems. Systems of record can include one or more storage repositories of critical legacy business data. This facilitates integrated data exchange and synchronization between the distribution platform, system 110, and the ERP, enabling real-time updates and ensuring the availability of accurate, up-to-date information. Integration point 210 establishes connectivity between systems of record 280 and the distribution platform, enabling stakeholders to leverage the rich data stored in the ERP for efficient collaboration, data-driven decision-making, and streamlined distribution processes. These systems represent internal systems utilized by customers, vendors, and others.

[0058] Integration points 210 within operating environment 200 can be facilitated through standardized protocols, APIs, and data connectors. These mechanisms ensure compatibility, interoperability, and secure data transfer between the distribution platform and connected systems. System 110 employs industry-standard protocols, such as RESTful APIs, SOAP, or GraphQL, to establish communication channels and enable data exchange.

[0059] In some embodiments, the system 110 may incorporate authentication and authorization mechanisms to ensure secure access and data integrity. Technologies such as OAuth or JSON Web Token (JWT) may be employed to authenticate users, authorize data access, and maintain the integrity and confidentiality of exchanged information.

[0060] In some embodiments, integration points 210 and data flow within operating environment 200 enable users to operate within a connected ecosystem. Data generated at various stages of the distribution process, including customer orders, inventory updates, shipping details, and sales analytics, flows between customer systems 220, vendor systems 240, reseller systems 260, and other entities. This data exchange facilitates real-time visibility, enables data-driven decision-making, and improves operational efficiency across the distribution platform.

[0061] In some embodiments, system 110 utilizes advanced technologies such as Typescript, NodeJS, ReactJS, .NET Core, C#, and other suitable technologies to support integration points 210 and enable communication within operating environment 200. These technologies provide a solid foundation for system 110, ensuring scalability, flexibility, and efficient data processing capabilities. Furthermore, integration points 210 may also employ algorithms, data analytics, and machine learning techniques to derive valuable insights, optimize distribution processes, and personalize customer experiences. Integration points 210 and the data flow within operating environment 200 enable users to operate within a connected ecosystem. Data generated at various touchpoints, including customer orders, inventory updates, pricing changes, or delivery status, flows between different entities, systems, and components. The integrated data can be processed, harmonized, and made available to relevant users in real time through system 110. This real-time access to accurate and current information enables users to make informed decisions, optimize supply chain operations, and improve customer experiences.

[0062] 2 may include conventional, well-known elements that are only briefly described herein. For example, each of the customer systems, such as customer system 220, may include a desktop personal computer, a workstation, a laptop, a PDA, a mobile phone, or any Wireless Access Protocol (WAP)-enabled device, or any other computing device capable of interfacing directly or indirectly with the Internet or other network connection. Each of the customer systems is typically capable of running an HTTP client, such as Microsoft's Edge browser, Google's Chrome browser, Opera's browser, or a WAP-enabled browser for mobile devices, enabling the customer systems to access, process, and display information, pages, and applications available from the distribution platform over a network.

[0063] Additionally, each customer system may typically be equipped with a user interface device, such as a keyboard, mouse, trackball, touchpad, touchscreen, pen, or similar device for interacting with the graphical user interface (GUI) provided by the browser. These user interface devices enable users of the customer systems to navigate the GUI, interact with pages, forms, and applications, and access data and applications hosted by the distribution platform.

[0064] The customer system and its components may be operator-configurable using applications, including a web browser, running on a central processing unit, such as an Intel Pentium processor or similar processor. Similarly, the distribution platform (system 110) and its components may be operator-configurable using applications running on a central processing unit, such as an Intel Pentium processor or similar processor, and / or a processor system that may include multiple processing units.

[0065] An embodiment of a computer program product includes a machine-readable storage medium containing instructions for programming a computer to perform the processes described herein. The computer code for operating and configuring the distribution platform and customer systems, vendor systems, reseller systems, and systems of other entities to intercommunicate and process web pages, applications, and other data can be downloaded and stored on a hard disk or any other volatile or non-volatile storage medium or device, such as a ROM, RAM, floppy disk, optical disk, DVD, CD, microdrive, magneto-optical disk, magnetic or optical card, nanosystem, or any suitable medium for storing instructions and data.

[0066] Furthermore, computer code for implementing the present embodiments can be transmitted and downloaded from a software source via the Internet or any other conventional network connection using communications media and protocols such as TCP / IP, HTTP, HTTPS, Ethernet, etc. The code can also be transmitted over an extranet, VPN, LAN, or other network and executed on a client system, server, or server system using programming languages ​​such as C, C++, HTML, Java, JavaScript, ActiveX, VBScript, or others.

[0067] It will be appreciated that the present embodiment can be implemented in a variety of programming languages ​​executed on the client system, server, or server system, and the choice of language may depend on the particular requirements and environment of the distribution platform.

[0068] This allows the operating environment 200 to couple the distribution platform with one or more integration points 210 and data flows to enable efficient collaboration and a streamlined distribution process.

[0069] Figure 3 illustrates a system 300 for supply chain and distribution management. System 300 (Figure 3) is a supply chain and distribution management solution configured to address the challenges faced by a fragmented distribution ecosystem in the global distribution industry. System 300 may include several interconnected components and modules that work in harmony to optimize supply chain and distribution operations, improve collaboration, and drive business efficiency.

[0070] The Single Pane of Glass (SPoG) UI 305 serves as a centralized user interface, providing users with a unified view of the entire supply chain. It aggregates information from various sources and presents real-time data, analytics, and functionality tailored to users' specific roles and responsibilities. By offering a customizable and intuitive dashboard-style layout, the SPoG UI enables users to access relevant information and tools to make data-driven decisions and efficiently manage supply chain and distribution activities.

[0071] For example, logistics managers can use the SPoG UI to monitor shipment status, track delivery routes, and view real-time inventory levels across multiple warehouses. Data can be visualized through interactive charts, such as a map showing the current location of each shipment, or a bar graph showing inventory levels by product category. Having a unified view of the supply chain allows logistics managers to identify bottlenecks, optimize routes, and ensure timely delivery of goods.

[0072] SPoG UI 305 integrates with other modules of the system 300 to facilitate real-time data exchange, synchronized operations, and streamlined workflow. Through API integration, data synchronization mechanisms, and an event-driven architecture, SPoG UI 305 ensures smooth information flow and enables collaborative decision-making across the distribution ecosystem.

[0073] For example, when a purchase order is generated in the SPoG UI, the system automatically updates inventory levels, triggers notifications to the warehouse management system, and initiates the shipping process. This integration enables efficient order fulfillment, reduces manual errors, and improves overall supply chain visibility.

[0074] The Real-Time Data Mesh (RTDM) module 310 is another component of the system 300 that is responsible for ensuring the flow of data within the distribution ecosystem, collecting and harmonizing data from multiple sources and ensuring its availability in real time.

[0075] In a distribution network, the RTDM module collects data from various systems, including inventory management systems, point-of-sale terminals, and customer relationship management systems. It harmonizes this data by harmonizing formats, standardizing units of measure, and reconciling inconsistencies. The harmonized data can then be made available in real time, allowing users access to accurate, current information across the supply chain.

[0076] The RTDM module 310 can be configured to capture data changes across multiple transaction systems in real time. It employs an advanced change data capture (CDC) mechanism that constantly monitors transaction systems to detect updates or modifications. The CDC component can be specifically configured to work with a variety of transaction systems, including legacy ERP systems, customer relationship management (CRM) systems, and other enterprise-wide systems, ensuring compatibility and flexibility for business operations in diverse environments.

[0077] Having access to real-time data allows users to make timely decisions and respond quickly to changing market conditions. For example, if the RTDM module detects a sudden increase in demand for a particular product, it can trigger an alert to the production team so that they can adjust production schedules to prevent stockouts.

[0078] The RTDM module 310 facilitates data management within supply chain operations. It enables real-time reconciliation of data from multiple sources, freeing vendors, resellers, customers, and end customers from the constraints imposed by legacy ERP systems. This increased flexibility supports improved efficiency, customer service, and innovation.

[0079] Another component of the system 300 is the Advanced Analytics and Machine Learning (AAML) module 315. Leveraging powerful analytics tools and algorithms such as Apache Spark, TensorFlow, or scikit-learn, the AAML module extracts valuable insights from the collected data, enabling advanced analytics, predictive modeling, anomaly detection, and other machine learning capabilities.

[0080] For example, the AAML module can analyze historical sales data to identify seasonal patterns and predict future demand. It can generate forecasts that help optimize inventory levels, ensure stock availability during busy periods, and minimize excess inventory costs. By leveraging machine learning algorithms, the AAML module automates repetitive tasks, predicts customer preferences, and optimizes supply chain processes.

[0081] In addition to forecasting demand, the AAML module can provide insight into customer behavior, enabling targeted marketing campaigns and personalized customer experiences. For example, by analyzing customer data, the module can identify cross-selling or up-selling opportunities and recommend relevant products to individual customers.

[0082] Additionally, the AAML module can analyze data from various sources, such as social media feeds, customer reviews, and market trends, to gain a deeper understanding of customer intentions and preferences. This information can be used to inform product development decisions, identify emerging market trends, and adapt business strategies to meet evolving consumer expectations.

[0083] System 300 emphasizes integration and interoperability to connect with existing enterprise systems, such as ERP systems, warehouse management systems, and customer relationship management systems. By establishing connections and data flows between these systems, system 300 enables smooth data exchange, process automation, and end-to-end visibility across the supply chain. Integration protocols, APIs, and data connectors facilitate communication and interoperability between different modules and components, creating a comprehensive and connected distribution ecosystem.

[0084] The implementation and deployment of system 300 can be tailored to meet specific business needs. It can be deployed as a cloud-native solution using containerization technologies such as Docker and orchestration frameworks such as Kubernetes. This approach ensures scalability, easy management, and efficient updates across different environments. The implementation process involves configuring the system to meet specific supply chain requirements, integrating with existing systems, and customizing modules and components based on business needs and preferences.

[0085] The system 300 for supply chain and distribution management is a comprehensive and innovative solution that addresses the challenges faced by a fragmented distribution ecosystem. It combines the power of the SPoG UI 305, RTDM module 310, and AAML module 315, along with integration with existing systems. By leveraging a diverse technology stack, scalable architecture, and robust integration capabilities, the system 300 provides end-to-end visibility, data-driven decision-making, and optimized supply chain operations. The examples and options provided herein are non-limiting and can be customized to meet specific industry requirements, driving efficiency and success in supply chain and distribution management.

[0086] 4 illustrates one embodiment of a system 400 for automating configure-to-order (CTO) and quote-to-order (QTO) processes.

[0095] FIGURE 4 illustrates one embodiment of a system 400 for automating configure-to-order (CTO) and quote-to-order (QTO) processes. System 400 consists of various components, including a data mesh 410, a single pane of glass user interface (SPoG UI) 405, and an AI module 460.

[0087] The SPoG UI 405 serves as the primary user interface. Users interact with this interface to perform various tasks related to the CTO and QTO process. It displays real-time data from the data mesh 410 and provides controls to initiate actions in the system 400. For example, a user can create a bill of materials or initiate a quote directly from the SPoG UI 405. The SPoG UI is developed using web-based technology, enabling it to be accessed from various types of devices, such as desktop computers, laptops, tablets, and smartphones.

[0088] The data mesh 410 is provided for data aggregation, transformation, and distribution. It pulls data from various Ingram Micro and vendor platforms, as well as third-party databases. Data types can range from pricing information to material specifications. The data mesh 410 serves as a central repository for standardized data, allowing various operational components to access consistent, up-to-date information. The data mesh 410 can synchronize with the system of record 280, which integrates with various enterprise systems. Data feeds provided by the data mesh 410 and / or the system of record 280 are established to retrieve relevant information from the system of record, such as sales orders, purchase orders, inventory data, and customer information. These feeds enable real-time data updates, ensuring the RTDM module operates with the most current and accurate data. The data mesh 410 and / or the system of record 280 can use APIs and other data connectors for this purpose. This allows data to be standardized into a unified format, which is then made available to other components in the system 400, including the SPoG UI 405 and the AI ​​module 460.

[0089] The AI ​​module 460 is the software layer responsible for automating the CTO and QTO process. It contains intelligent rules and algorithms to automate actions such as deal registration, pricing application, and quote creation. The AI ​​module 460 interacts with both the SPoG UI 405 and the data mesh 410. When a user initiates an action from the SPoG UI 405, the AI ​​module 460 receives the input, processes it based on predefined rules, and then interacts with the data mesh 410 to fetch or update the required data.

[0090] Included within the AI ​​module 460 are specialized sub-modules for CTO and QTO: CTO module 420 and QTO module 425. Alternatively, the CTO module 420 and / or QTO module 425 can be implemented external to and operatively connected to the AI ​​module 460. When a user requests the creation of a new quote, the QTO module 425 triggers a series of automated steps. These steps may include checking existing customer data, pulling real-time pricing information from the data mesh 410, applying applicable discounts, and generating a quote. Once the quote is generated, it is displayed in the SPoG UI 405 for user review and further action. Similarly, the CTO module 420 handles tasks specific to the order configuration process.

[0091] The AI ​​module 460 also includes error-checking algorithms. These algorithms validate the configuration, pricing, and authorization of each CTO or QTO process. If errors or inconsistencies are discovered, the AI ​​module 460 flags them for user review or automatic remediation. The data mesh 410 may be implemented or supplemented for greater scalability and fault tolerance. Similarly, the AI ​​module 460 may incorporate machine learning models to improve the efficiency and accuracy of the automated rules over time. The AI ​​module may be connected to a reporting or notification module and configured to provide insights related to the CTO and / or QTO process, including insights related to the efficiency and effectiveness of the CTO and / or QTO process. This module can generate various types of reports and dashboards using data from the data mesh 410 and the processing history from the AI ​​module 460.

[0092] In one embodiment, system 400 can be extended to include a vendor management layer to facilitate real-time negotiation of pricing and terms directly within SPoG UI 405. System 400 can be configured to run on multiple operating systems, is compatible with cloud-based architectures, and can be deployed on-premise or as a software-as-a-service (SaaS) model.

[0093] The system 400 can be configured to integrate multiple third-party systems and disparate data into a single interface via the SPoG UI 405, automate and orchestrate various activities through the AI ​​module 460, and maintain a standardized real-time data repository through the data mesh 410. Doing so enables an efficient and error-minimized CTO and QTO process.

[0094] FIG. 5 illustrates an embodiment of an advanced distribution platform including a system 500 for managing a complex distribution network, which may be an embodiment of system 300, providing a technology distribution platform for optimizing the management and operation of a distribution network. System 500 includes several interconnected modules, each performing a specific function and contributing to the overall efficiency of supply chain operations. In some embodiments, these modules may include a SPoG UI 505, a CIM 510, an RTDM module 515, an AI module 520, an interface display module 525, a personalized interaction module 530, a document hub 535, a catalog management module 540, a performance and prospect marker display 545, a predictive analytics module 550, a recommendation system module 555, a notification module 560, a self-onboarding module 565, and a communications module 570.

[0095] System 500, as an embodiment of system 300, can enable supply chain and distribution management using a wide range of technologies and algorithms that facilitate efficient data processing, personalized interactions, real-time analytics, secure communications, and effective management of documents, catalogs, and performance standards.

[0096] The SPoG UI 505, in some embodiments, serves as a central interface within the system 500, providing users with a unified view of the entire distribution network. Front-end technologies such as ReactJS, TypeScript, and Node.js are utilized to create an interactive and responsive user interface. These technologies enable the SPoG UI 505 to deliver a user-friendly experience, allowing users to access relevant information, navigate through different modules, and perform tasks efficiently.

[0097] CIM510, the Customer Interaction Module, employs algorithms and technologies from Oracle Eloqua, Adobe Target, and Okta to manage customer relationships within the distribution network. These technologies enable the module to handle customer data securely, personalize the customer experience, and provide access control to users.

[0098] The RTDM module 515, or real-time data mesh module, is a component of the system 500 that ensures smooth data flow across the distribution ecosystem. It utilizes technologies such as Apache Kafka, Apache Flink, or Apache Pulsar for data ingestion, processing, and stream management. These technologies enable the RTDM module 515 to handle real-time data streams, process large volumes of data, and ensure low-latency data processing. Additionally, the module employs a change data capture (CDC) mechanism to capture real-time data updates from various transactional systems, such as legacy ERP and CRM systems. This capability allows users to access current, accurate information and make informed decisions.

[0099] The AI ​​module 520 in the system 500 can extract valuable insights from data using advanced analytics and machine learning algorithms, including Apache Spark, TensorFlow, and scikit-learn. These algorithms enable the module to automate repetitive tasks, forecast demand patterns, optimize inventory levels, and improve overall supply chain efficiency. For example, the AI ​​module 520 can utilize predictive models to forecast demand, allowing users to optimize inventory management and minimize out-of-stock or overstock situations.

[0100] The Interface Display module 525 focuses on presenting data and information in a clear, user-friendly manner. It utilizes technologies such as HTML, CSS, and JavaScript frameworks like ReactJS to create interactive and responsive user interfaces. These technologies allow users to visualize data using various data visualization techniques, such as graphs, charts, and tables, enabling efficient data understanding, comparison, and trend analysis.

[0101] The personalized interaction module 530 utilizes customer data, historical behavior, and machine learning algorithms to generate personalized recommendations for products or services. It employs technologies such as Adobe Target, Apache Spark, and TensorFlow for data analysis, modeling, and providing targeted recommendations. For example, the module can analyze customer preferences and purchase history to provide personalized product recommendations, improve customer satisfaction, and drive sales.

[0102] The document hub 535 serves as a central repository for storing and managing documents within the system 500. It utilizes technologies such as SeeBurger and Elastic Cloud for efficient document management, storage, and retrieval. For example, the document hub 535 can employ SeeBurger's document management capabilities to categorize and organize documents based on type, such as contracts, invoices, product specifications, or compliance documents, allowing users to easily access and find relevant documents when needed.

[0103] The catalog management module 540 enables the creation, management, and distribution of current product catalogs. This ensures that users have access to current product information, including specifications, pricing, availability, and promotions. Technologies such as Kentico and Akamai can be employed to facilitate catalog updates, content distribution, and caching. For example, the module can use Akamai's content delivery network (CDN) to quickly and efficiently deliver catalog information to users regardless of geographic location.

[0104] The Performance and Outlook Marker Display 545 collects, analyzes, and visualizes real-time performance metrics and insights related to supply chain operations. Utilizing tools such as Splunk and Datadog, it enables effective performance monitoring and provides actionable insights. For example, the module can utilize Splunk's log analysis capabilities to identify performance bottlenecks in the supply chain, allowing users to take proactive measures to optimize operations.

[0105] The predictive analytics module 550 employs machine learning algorithms and forecasting models to forecast demand patterns, optimize inventory levels, and improve overall supply chain efficiency. It utilizes technologies such as Apache Spark and TensorFlow for data analysis, modeling, and forecasting. For example, the module can use TensorFlow's deep learning capabilities to analyze historical sales data and predict future demand, allowing users to optimize inventory levels and minimize costs.

[0106] The recommender system module 555 focuses on providing intelligent recommendations to users within the distribution network. It generates personalized recommendations for products or services based on customer data, historical trends, and machine learning algorithms. Technologies such as Adobe Target and Apache Spark can be employed for data analysis, modeling, and providing targeted recommendations. For example, the module can use Adobe Target's recommendation engine to analyze customer preferences and behaviors and deliver personalized product recommendations across various channels, improving customer engagement and driving sales.

[0107] The notification module 560 enables the delivery of real-time notifications to users regarding important events, updates, or alerts within the supply chain. It utilizes message queues, event-driven architectures, and technologies such as Apigee X and TIBCO for notification delivery. For example, the module can utilize TIBCO's messaging infrastructure to send notifications to users' devices in real time, ensuring timely distribution of relevant information.

[0108] The self-onboarding module 565 facilitates the onboarding process for new users entering the distribution network. It provides guided steps, tutorials, or documentation to help users become familiar with the system and its functionality. Technologies such as Okta and Kentico can be employed to ensure secure user authentication, access control, and self-learning resources. For example, the module can leverage Okta's identity and access management capabilities to securely onboard new users, provide them with appropriate permissions, and guide them through the system's functionality.

[0109] The communications module 570 enables communication and collaboration within the system 500. It provides users with channels for interaction, messaging, document sharing, and project collaboration. Technologies such as Apigee Edge and Adobe Launch can be employed to facilitate secure and efficient communication, document sharing, and version control. For example, the module can leverage the API management capabilities of Apigee Edge to ensure secure and reliable communication between users, enabling effective collaboration.

[0110] This allows the system 500 to incorporate various modules that utilize a diverse range of technologies and algorithms to optimize supply chain and distribution management. These modules, including the SPoG UI 505, CIM 510, RTDM module 515, AI module 520, interface display module 525, personalized interaction module 530, document hub 535, catalog management module 540, performance and prospect marker display 545, predictive analytics module 550, recommendation system module 555, notification module 560, self-onboarding module 565, and communication module 570, work together to provide end-to-end visibility, data-driven decision-making, personalized interactions, real-time analytics, and streamlined communication within the distribution network. The incorporation of specific technologies and algorithms enables efficient data management, secure communication, personalized experiences, and effective performance monitoring, contributing to improved operational efficiency and success in supply chain and distribution management.

[0111] Real-time Data Mesh 6 illustrates an RTDM module 600 according to one embodiment. The RTDM module 600 may be an embodiment of the RTDM module 310 and may include interconnected components, processes, and subsystems configured to enable real-time data management and analysis.

[0112] The RTDM module 600 represents an effective data mesh and change capture component within the overall system architecture, as shown in Figure 5. The module can be configured to provide real-time data management and standardization capabilities, enabling efficient operations within the supply chain and distribution management domain.

[0113] The RTDM module 600 can include an integration layer 610 (also called a "system of record") that integrates with various enterprise systems. These enterprise systems can include, for example, ERPs such as SAP, Impulse, META, and I-SCALA, as well as other data sources. The integration layer 610 can handle data exchange and synchronization between the RTDM module 600 and these systems. Data feeds can be established to retrieve relevant information from the systems of record, such as sales orders, purchase orders, inventory data, and customer information. These feeds enable real-time data updates, ensuring the RTDM module operates with the most current and accurate data.

[0114] The RTDM module 600 can include a data layer 620 configured to process and translate data for search and analysis. The data layer 620 includes a data mesh, a cloud-based infrastructure configured to provide scalable, fault-tolerant data storage capabilities. Within the data mesh, multiple purpose-built data stores (PDSs) can be deployed to store specific types of data, such as customer data, product data, or inventory data. Each PDS can be optimized for efficient data retrieval based on specific use cases and requirements. PDSs can be configured to store specific types of data, such as customer data, product data, financial data, etc. These PDSs act as repositories of recognized and / or standardized data, ensuring data consistency and integrity across systems.

[0115] In some embodiments, the RTDM module 600 implements a data replication mechanism to capture real-time changes from multiple data sources, including transactional systems like ERP (e.g., SAP, Impulse, META, I-SCALA). The captured data can then be processed and normalized on the fly and transformed into a standardized format suitable for analysis and integration. This process ensures that data is readily available and current within the data mesh, facilitating real-time insights and decision-making.

[0116] More specifically, the data layer 620 within the RTDM module 600 can be configured as a powerful, flexible foundation for managing and processing data within a distribution ecosystem. In some embodiments, the data layer 620 can encompass a highly scalable and robust data lake, which can be referred to as a data lake 622, along with a set of purpose-built data stores (PDSs), which can be denoted as PDSs 624.1 through 624.N. These components are integrated to ensure efficient data management, standardization, and real-time availability.

[0117] The data layer 620 includes a data lake 622, a state-of-the-art storage and processing infrastructure configured to handle the ever-increasing volume, variety, and velocity of data generated within the supply chain. Built on a scalable distributed file system, such as the Apache Hadoop Distributed File System (HDFS) or Amazon S3, the data lake provides a unified, scalable platform for storing both structured and unstructured data. Using the elasticity and fault-tolerance of cloud-based storage, the data lake 622 can accommodate an influx of data from diverse sources.

[0118] Accompanying the data lake 622 may be a population of purpose-built data stores PDS624.1-624.N. Each PDS624 may serve as a dedicated repository optimized for storing and retrieving a particular type of data related to a supply chain domain. In some non-limiting examples, PDS624.1 may be dedicated to customer data, storing information such as customer profiles, preferences, and transaction history. PDS624.2 may focus on product data, encompassing details regarding SKU codes, descriptions, pricing, and inventory levels. These purpose-built data stores enable efficient data retrieval, analysis, and processing to meet the diverse needs of supply chain users.

[0119] To ensure real-time data synchronization, data layer 620 can be configured to employ one or more change data capture (CDC) mechanisms. These CDC mechanisms can integrate with transactional systems, such as legacy ERPs like SAP, Impulse, META, and I-SCALA, as well as other enterprise-wide systems. CDC constantly monitors these systems for updates, modifications, or new transactions and captures them in real time. By capturing these changes, data layer 620 ensures that the data in data lake 622 and PDS 624 remains current, providing users with a real-time view into the distribution ecosystem.

[0120] In some embodiments, data layer 620 can be implemented using one or more frameworks, such as .NET or Java, to facilitate integration with existing enterprise systems, ensuring compatibility with a wide range of existing systems and providing flexibility for customization and extensibility. For example, data layer 620 can utilize a Java technology stack, including frameworks such as Spring and Hibernate, to facilitate integration with systems of record with a diverse population of ERP systems and other enterprise-wide solutions. This can facilitate smooth data exchange, process automation, and end-to-end visibility across the supply chain.

[0121] In terms of data processing and analytics, the data layer 620 can use the power of distributed computing frameworks, such as Apache Spark or Apache Flink, in some non-limiting examples. These frameworks can enable parallel processing and distributed computing across large datasets stored in the data lake and PDS. By using these frameworks, supply chain users can perform complex analytical tasks, apply machine learning algorithms, and derive valuable insights from the data. For example, the data layer 620 can use Apache Spark's machine learning libraries to develop predictive models for demand forecasting, optimizing inventory levels, and identifying potential supply chain risks.

[0122] In some embodiments, data layer 620 can incorporate robust data governance and security measures. Elaborate access control mechanisms and authentication protocols ensure that only authorized users can access and modify data in the data lake and PDS. Data encryption techniques protect sensitive supply chain information from unauthorized access, both at rest and in transit. Additionally, data layer 620 can implement data lineage and audit trail mechanisms to enable users to track data provenance and history, ensuring data integrity and compliance with regulatory requirements.

[0123] In some embodiments, data layer 620 can be deployed in a cloud-native environment using containerization technologies such as Docker and orchestration frameworks like Kubernetes. This approach ensures scalability, resilience, and efficient resource allocation. For example, data layer 620 can be deployed on cloud infrastructure offered by AWS, Azure, or Google Cloud to take advantage of their managed services and scalable storage options. This enables scaling of resources based on demand, minimizes operational overhead, and provides an elastic infrastructure for managing supply chain data.

[0124] The data layer 620 of the RTDM module 600 can incorporate a highly scalable data lake, Data Lake 622, along with purpose-built PDSs, PDSs 624.1-624.N. By employing a CDC mechanism, the data layer 620 ensures efficient data management, standardization, and real-time availability. In a non-limiting example, the data layer 620 can be implemented using appropriate technologies, such as .NET or Java, and / or distributed computing frameworks like Apache Spark, enabling powerful data processing, advanced analytics, and machine learning capabilities. With robust data governance and security measures, the data layer 620 ensures data integrity, confidentiality, and compliance. Through its scalable infrastructure and integration with existing systems, the data layer 620 enables supply chain users to make data-driven decisions, optimizing operations and driving business success in dynamic, complex distribution environments.

[0125] The RTDM module 600 may include an AI module 630 configured to implement one or more algorithms and machine learning models to analyze the data stored in the data layer 620 and derive meaningful insights. In some non-limiting examples, the AI ​​module 630 may apply predictive analytics, anomaly detection, and optimization algorithms to identify patterns, trends, and potential risks within the supply chain. The AI ​​module 630 may continuously learn from new data inputs and adapt its models to provide accurate, current insights. The AI ​​module 630 may generate predictions, recommendations, and alerts and publish such insights to a dedicated data feed.

[0126] The data engine layer 640 comprises a set of interconnected systems responsible for data ingestion, processing, transformation, and integration. The data engine layer 640 of the RTDM module 600 can include a collection of autonomously operating headless engines 640.1-640.N. These engines represent distinct functionality within the system and can include, for example, one or more recommendation engines, forecasting engines, and subscription management engines. The engines 640.1-640.N can provide specific business logic and services using standardized data stored in the data mesh. Each engine can be configured to be pluggable, allowing for flexibility and future expansion of the module's capabilities. Exemplary engines are shown in FIG. 5 and are not intended to be limiting. Any additional headless engines can be included in the data engine layer 640 or other exemplary layers of the disclosed system.

[0127] These systems can be configured to receive data from multiple sources, such as transactional systems, IoT devices, and external data providers. The data ingestion process involves extracting data from these sources and converting it into a standardized format. Data processing algorithms are applied to cleanse, aggregate, and enrich the data, making it ready for further analysis and integration.

[0128] Additionally, a data distribution mechanism can be employed to facilitate integration and access to the RTDM module 600. The data distribution mechanism 645 can include one or more APIs and be configured to facilitate data distribution from the data mesh and engine to various endpoints, including user interfaces, micro-frontends, and external systems.

[0129] The experience layer 650 focuses on providing an intuitive, user-friendly interface for interacting with supply chain data. It can include data visualization tools, interactive dashboards, and user-centric functionality. Through this layer, users can search and analyze real-time data related to various supply chain metrics, such as inventory levels, sales performance, and customer demand. The user experience layer supports personalized data feeds, allowing users to customize views and receive relevant updates based on their roles and responsibilities. Users can subscribe to specific data updates, such as inventory changes, pricing updates, or new SKU notifications, tailored to their preferences and roles.

[0130] Thus, in some embodiments, the RTDM module 600 for supply chain and distribution management may include integration with systems of record and may include one or more data layers with a data mesh and purpose-built data stores, an AI component, a data engine layer, and a user experience layer. These components work together to provide users with intuitive access to real-time supply chain data, efficient data processing and analysis, and integration with existing enterprise systems. Technical feeds and searches within the module ensure users can find relevant, current information and insights, make informed decisions, and optimize supply chain operations. Thus, the RTDM module 600 facilitates supply chain and distribution management by providing a scalable, real-time data management solution. Its innovative architecture enables rich integration of disparate data sources, efficient data standardization, and advanced analytical capabilities. The module's ability to replicate and standardize data from diverse ERPs while maintaining auditable, repeatable transactions offers a distinct advantage: enabling a unified view for vendors, resellers, customers, end customers, and other entities within the distribution system, including IT distribution systems.

[0131] Configure to Order (CTO) and Quote to Order (QTO) 7 illustrates a system 700 for automating configure-to-order (CTO) and quote-to-order (QTO) processes. The system 700 includes a real-time data mesh 710, a single pane of glass user interface (SPoG UI) 705, an advanced analytics and machine learning (AAML) module 715, a configure-to-order (CTO) module 720, and a quote-to-order (QTO) module 725.

[0132] In one embodiment, the configure-to-order (CTO) module 720 and the quote-to-order (QTO) module 725 include various functional subcomponents designed for specific processes. These modules interact closely with other system components: the single pane of glass user interface (SPoG UI) 705, the real-time data mesh (RTDM) 710, and the advanced analytics and machine learning (AAML) module 715.

[0133] The CTO module 720 is specifically focused on the configure-to-order process. It contains subroutines and algorithms dedicated to tasks such as bill of materials generation, deal registration, and pricing application. When a user initiates a CTO action from the SPoG UI 705, the request can be routed to the CTO module 720 in the AAML 715. The module processes the request, interacts with the real-time data mesh 710 for the requested data, and performs the configuration. After execution, the results can be displayed in the SPoG UI 705. The CTO module 720 also incorporates a logging mechanism to track all configuration changes for auditing purposes.

[0134] The CTO Module 720 integrates several critical subcomponents. The Configuration Builder 720.1 acts as an algorithmic guide, guiding the user through product customization. This algorithm kicks in when the user selects a product for customization in the SPoG UI 705. It fetches real-time data from the RTDM 710 and ensures that all customization options are present in the current inventory. The Configuration Builder 720.1 can use a decision tree algorithm to provide compatible selections based on previous selections. The decision tree algorithm employed by the Configuration Builder 720.1 can use entropy minimization to choose the optimal selection.

[0135] Another subcomponent, the Bill of Materials (BOM) Generator 720.2, assembles a detailed list of parts, assemblies, and other required components. It cross-checks this information against real-time inventory data from RTDM 710 using a RESTful API or similar data connector. The BOM Generator 720.2 employs a recursive algorithm to list all parts and components in a multi-level hierarchical structure. The BOM Generator 720.2 can fetch real-time inventory data from RTDM 710 using a GET request via the RESTful API.

[0136] The transaction registration handler 720.3 connects to a customer relationship management (CRM) system via an API to log transactions and checks for account-based customizations or restrictions through SQL queries or similar database calls. The transaction registration handler 720.3 can use OAuth 2.0 for secure API calls to the CRM system.

[0137] Another subcomponent, the Pricing Engine 720.4, dynamically calculates the overall cost estimate. It fetches the latest part prices from the RTDM 710 and adds any available discounts or special pricing terms. The cost estimation algorithm takes into account volume discounts and contractual obligations.

[0138] The Error Check Integrator 720.5 validates all user input and configuration selections using algorithms stored in AAML 715. If errors are identified, they are flagged for review.

[0139] Similarly, the QTO module 725 specializes in the quote-to-order process. It can be configured to automate actions such as real-time pricing lookup, discount application, and quote generation. When a user initiates a QTO action from the SPoG UI 705, this module in the AAML 715 takes over. It processes the user request, retrieves the necessary data from the real-time data mesh 710, and then generates a quote. The completed quote can then be sent back to the SPoG UI 705 for display. The QTO module 725 supports JSON and XML data formats for quote generation and lookup.

[0140] Similarly, the QTO module 725 contains a pricing aggregator 725.1 that collects current pricing information from multiple sources through the RTDM 710.

[0141] Another subcomponent, Discount Calculator 725.2, applies account-specific or promotional discounts to pricing data. A weighted scoring algorithm can be used to optimize the combination of applicable discounts.

[0142] The Quote Template Filler 725.3 populates a predefined template with relevant data such as product description, terms and conditions, final pricing, etc. A string replacement algorithm can be used for this task. The string replacement algorithm used by the Quote Template Filler 725.3 can be based on the KMP (Knuth-Morris-Pratt) algorithm for efficient text searching.

[0143] Another subcomponent, Authorization Checker 725.4, verifies user privileges based on role or account history, checking them against role-based access control policies stored in AAML 715.

[0144] Additionally, the error checking integrator 725.5 of the QTO module 725 uses predefined rules stored in the AAML 715 to validate quote details and flag discrepancies.

[0145] Both modules can also be integrated with the error-checking algorithms present in AAML715. These algorithms validate the output of each module for accuracy and consistency. If errors are detected, they can be flagged for automatic correction or manual review.

[0146] In an alternative embodiment, machine learning models can be incorporated separately into the CTO module 720 and the QTO module 725. These models allow each module to refine and optimize its processes over time based on past transactions and evolving data patterns.

[0147] More specifically, in an alternative embodiment, machine learning models are integrated with configure-to-order (CTO) module 720 and quote-to-order (QTO) module 725. These models focus on refining and optimizing various processes within each module.

[0148] Within the CTO module 720, a machine learning model designed for predictive analytics is integrated with the Configuration Builder 720.1. The model utilizes historical data about customer configurations to recommend optimal customizations. The model is trained with a neural network algorithm, evaluates patterns of past transactions stored in the real-time data mesh 710, and provides recommendations when a user selects a product for customization in the SPoG UI 705. The model continually updates itself by learning from newly collected data, ensuring that the recommendations remain current. The neural network algorithm for predictive analytics can be implemented in the CTO module 720 using a multi-layer perceptron model.

[0149] Another machine learning model is paired with the Bill of Materials (BOM) Generator 720.2. This model utilizes a clustering algorithm to identify common patterns of components that typically appear together in a BOM. This information is used to suggest an initial set of components when a new BOM is created, accelerating the configuration process and reducing user workload.

[0150] Within the Deal Registration Handler 720.3, machine learning models employ natural language processing (NLP) algorithms to parse customer interactions from the CRM system, identify keywords or patterns that indicate high-value deals, and flag them for immediate action, enabling more effective deal prioritization and resource allocation.

[0151] The machine learning model within the pricing engine 720.4 can use a regression algorithm to predict future price changes for components. The model trains on a dataset that includes historical price data from the real-time data mesh 710 and external economic indicators. It provides alerts or suggestions when prices are expected to change, aiding in better budget planning.

[0152] Finally, the Error Check Integrator 720.5 incorporates an anomaly detection machine learning model to identify and flag unusual configurations that may cause errors. This model can use unsupervised learning algorithms to detect outliers in the configurations, thus improving error prevention.

[0153] Moving to the QTO module 725, the machine learning model is integrated with the pricing aggregator 725.1. This model employs a reinforcement learning algorithm to determine the optimal time for price data retrieval. By analyzing historical pricing volatility, it schedules data fetching operations during periods of low volatility to capture the most stable prices. During stable market conditions, the reinforcement learning model in the pricing aggregator 725.1 fetches pricing data.

[0154] Discount Calculator 725.2 integrates machine learning models employing decision tree algorithms to optimize discount application, taking into account variables such as customer history, product type, and seasonality to calculate the most profitable discount combinations.

[0155] Quote Template Filler 725.3 can use machine learning models that utilize text mining algorithms to autofill templates, combing through large sets of quote history to identify the most effective phrases and include these in new quotes.

[0156] Authorization Checker 725.4's role-based machine learning model predicts likely changes in authorization levels for a given user based on historical data. This proactive measure speeds up the quote approval process by alerting administrators to upcoming changes.

[0157] Finally, the error checking integrator 725.5 can use supervised learning algorithms to identify errors in estimate generation based on previously flagged errors, improving over time by learning from new errors and actions taken to correct them.

[0158] In another alternative embodiment, machine learning models can be integrated into the Advanced Analytics and Machine Learning (AAML) module 715 to improve rule-based algorithms. Unlike the models in the CTO and QTO modules, which focus on specific functionality, these models are intended to improve overall automation.

[0159] One such model may be an ensemble learning model that combines inputs from multiple machine learning models in the CTO and QTO modules, employing a weighted voting mechanism to make a final decision based on the outputs of the individual models, thereby improving the reliability of the automated process.

[0160] AAML715's reinforcement learning model is configured to optimize the overall flow of actions in the CTO and QTO processes. It identifies the most efficient sequence of actions by simulating the various paths that actions can take through the modules. AAML715's reinforcement learning model can simulate the various paths of action using Monte Carlo Tree Search (MCTS).

[0161] NLP-based models within AAML715 extract actionable insights from unstructured data, such as customer reviews or service logs. These insights serve to refine rules-based algorithms, highlighting areas where automation can be more effective or more responsive to customer needs.

[0162] Trend analysis models can use time series analysis algorithms to predict future requirements for data storage, computing power, or other resources, thereby enabling proactive scaling of resources and improving overall efficiency.

[0163] Fraud detection machine learning models add an additional layer of security to both the CTO and QTO processes. The models are trained on datasets of known fraudulent and non-fraudulent activity and can use classification algorithms to flag suspicious transactions for review, thereby improving the integrity of the entire system.

[0164] These machine learning models are continuously updated based on newly ingested data, enabling continuous optimization of the CTO, QTO, and overall automation process, resulting in a system that not only automates tasks but also self-improves over time.

[0165] As defined above, the CTO module 720 and QTO module 725 may operate within the AAML 715 or may be external modules operatively connected thereto. They interact with the real-time data mesh 710 and the SPoG UI 705 to perform their dedicated functions and contribute to the overall efficiency and accuracy of the system 700.

[0166] The SPoG UI 705 serves as a central hub for user interaction. It is developed using web-based technologies, e.g., HTML, CSS, and JavaScript, and can be accessible from a wide range of devices, including desktops, laptops, tablets, and smartphones. It displays real-time data and operational status extracted from the real-time data mesh 710. Users can initiate various CTO and QTO processes, such as generating a bill of materials or initiating a quote, directly from the SPoG UI 705.

[0167] Data aggregation, transformation, and distribution occurs in the Real-Time Data Mesh 710. This incorporates data from third-party databases in addition to Ingram Micro and vendor platforms. Data types may include, but are not limited to, pricing, material specifications, and inventory levels. APIs and data connectors can be used to pull this data, which can then be normalized into a unified format suitable for consumption by the SPoG UI 705 and AAML 715. The Real-Time Data Mesh 710 employs AES-256 encryption for secure data storage and transmission.

[0168] In an alternative embodiment, the real-time data mesh 710 can be augmented or replaced by a distributed database system to achieve greater scalability and fault tolerance. This alternative system still serves the same functional requirements of data aggregation and normalization, but offers improved resilience and performance.

[0169] AAML 715 serves as a critical automation layer. It contains intelligent rules and algorithms designed for specific CTO and QTO actions such as deal registration, pricing application, quote generation, etc. AAML 715 takes user-initiated input from SPoG UI 705, processes this input based on existing rules, and interacts with the real-time data mesh 710 to fetch or update data.

[0170] For example, if the user decides to create a quote, the AAML 715 automatically performs tasks such as pulling customer data, fetching real-time pricing information from the real-time data mesh 710, applying relevant discounts, creating the quote, etc. Once the quote is prepared, it may be redisplayed in the SPoG UI 705 for the user's review and subsequent action.

[0171] Error checking algorithms can be implemented by AAML715. These verify the configuration, pricing, and authorization for each CTO and QTO process. If discrepancies are found, they can be flagged for either automatic correction or manual review by the user.

[0172] In another alternative embodiment, machine learning models can be integrated into AAML 715 to improve the rule-based algorithms. These models can learn from previous transactions to refine and optimize the automated process over time.

[0173] Additional features may include a reporting and analytics module integrated into the system 700. This module leverages data from both the real-time data mesh 710 and the AAML 715 to generate insights into the efficiency and effectiveness of the CTO and QTO processes. Reports and dashboards may be created and viewed via the SPoG UI 705.

[0174] Another alternative option may involve voice-activated control being integrated into the SPoG UI 705. This feature allows the user to execute commands or search for data using voice input, improving ease of use.

[0175] The system 700 can be compatible with multiple operating systems and can be designed for a cloud-based architecture. Deployment options include on-premise installation or a software-as-a-service (SaaS) model.

[0176] In summary, system 700 functions to merge data from multiple sources into a unified interface via SPoG UI 705, automate various CTO and QTO tasks via AAML 715, and maintain a real-time standardized data repository via real-time data mesh 710. This architecture enables a CTO and QTO process that is efficient, accurate, and minimizes errors.

[0177] 8 is a flow diagram of a method 800 for automating the configure-to-order (CTO) and quote-to-order (QTO) process according to some embodiments of the present disclosure. The flow focuses on both the CTO module 720 and the QTO module 725 and details the sequence of operations from initiation to completion.

[0178] In operation 801, a user begins the process by accessing a Single Pane of Glass User Interface (SPoG UI) 705. User input can be collected here, which can range from selecting a product to selecting a configuration. The SPoG UI 705 communicates with an Advanced Analytics and Machine Learning (AAML) module 715 to determine which module is required, either a CTO module 720 or a QTO module 725, based on the input.

[0179] At operation 802, the AAML module 715 performs a preliminary analysis to identify the user's needs. Algorithms within the AAML 715 can be employed to route the user's request to the appropriate module, such as the CTO 720 or QTO 725. At this stage, user permissions can be verified against role-based access control policies stored in the AAML 715.

[0180] In operation 803, the request can be forwarded to a real-time data mesh (RTDM) 710, which fetches real-time inventory data or pricing information as needed. Typically, a RESTful API can be used to perform these data retrieval operations.

[0181] In operation 804, the relevant module, either the CTO module 720 or the QTO module 725, processes the request. In the case of the CTO module 720, subcomponents such as the Configuration Builder 720.1 and the Bill of Materials (BOM) Generator 720.2 are activated. For example, the Configuration Builder 720.1 uses a decision tree algorithm to guide the user through product customization, while the BOM Generator 720.2 employs a recursive algorithm to list the components. Meanwhile, the QTO module 725 uses subcomponents such as the Pricing Aggregator 725.1 and the Discount Calculator 725.2. The Pricing Aggregator 725.1 fetches current pricing data from multiple sources through the RTDM 710, and the Discount Calculator 725.2 applies discounts based on account-specific or promotional factors.

[0182] In operation 805, a validation step occurs. An error check integrator 720.5 in the CTO module 720 or an error check integrator 725.5 in the QTO module 725 validates the user input and other variables. Algorithms stored in the AAML 715 may be used for this validation process.

[0183] In operation 806, the processed data can be sent back to the SPoG UI 705 for user review. If the process involves configuration, BOM and pricing details can be displayed. If a quote is involved, a final quote can be presented.

[0184] In operation 807, the machine learning models of either the CTO module 720 or the QTO module 725 perform a post-processing review. These models may utilize reinforcement learning, predictive analytics, or other algorithms to refine and optimize the process for future transactions.

[0185] In operation 808, a logging mechanism within the CTO module 720 or QTO module 725 records transaction details for audit or future analysis, which may include user selections, the generated BOM, or the generated quote.

[0186] In operation 809, the user verifies or modifies the data presented in the SPoG UI 705. After verification, the CTO or QTO process can be considered complete.

[0187] This detailed operational flow integrates the SPoG UI 705, RTDM 710, AAML 715, CTO module 720, and QTO module 725, each performing specific functions to automate and optimize the order process from order configuration and quote. Alternative embodiments can include variations in machine learning algorithms or data mining methods, providing flexibility and scalability to the system 700.

[0188] 9 is a flow diagram of a method 900 for automating configure-to-order (CTO) according to some embodiments of the present disclosure. Method 900 describes a detailed process for performing CTO tasks in CTO module 720. The method can be configured to closely interact with RTDM 710 and SPoG UI 705. It involves automating various CTO-related tasks, such as bill of materials (BOM) generation, deal registration, and pricing.

[0189] In operation 901, the CTO module 720 receives a user-generated request to configure a product from the SPoG UI 705. The request can be initiated by a user interacting with the SPoG UI 705. The SPoG UI 705 communicates with the CTO module 720 via an internal API or direct module calls. In some instances, the request can be routed through the Advanced Analytics and Machine Learning (AAML) module 715 before reaching the CTO module 720 for additional data analysis or security checks.

[0190] Operation 902 activates the Configuration Builder 720.1 in the CTO module 720. The Configuration Builder 720.1 fetches real-time inventory data and available customization options from the RTDM 710. This real-time data ensures that the inventory information presented to the user is current and accurate. A decision tree algorithm can be used to provide the user with a set of compatible choices based on the user's initial request and available inventory. The algorithm employs entropy minimization techniques to optimize the presented choices. In an alternative embodiment, a machine learning model, such as a neural network, refines the choices based on the user's behavioral history to provide more tailored choices.

[0191] In operation 903, the BOM generator 720.2 uses a recursive algorithm to list all components in a hierarchical structure. Each parent component may have zero or more child components, and the algorithm traverses this tree structure to create the complete BOM. The algorithm can be a depth-first search (DFS), starting at the root component and exploring as far as possible before backtracking.

[0192] In operation 905, the pricing engine 720.4 employs a cost estimation algorithm that considers multiple variables, such as base price, quantity discounts, and special pricing terms. The algorithm can be a multivariate linear regression model that predicts total cost based on these variables. In an alternative embodiment, a more advanced machine learning model, such as a random forest, can be used for this purpose, which considers complex relationships between variables and provides a more dynamic pricing model. In addition to multiple variables, the pricing engine 720.4 can incorporate real-time market data to dynamically adjust pricing. The real-time market data can be fetched via the RTDM 710 and can affect factors such as base price and quantity discounts.

[0193] At operation 906, the error checking integrator 720.5 employs a set of validation algorithms. These algorithms can include basic checks, such as boundary conditions for numeric inputs, or more advanced checks, such as anomaly detection algorithms to identify abnormal behavior or configurations. The advanced algorithm can be a support vector machine (SVM) trained to classify configurations as valid or invalid based on historical data. Regarding interactions with the CRM system at operations 904 and 907, CRM data can be fetched and integrated via a real-time data mesh (RTDM) 710. Alternatively, or additionally, OAuth 2.0 secure API calls to the CRM can be made through RTDM to ensure all CRM data is current. SQL queries pull account-specific data, such as customization limits or previously negotiated pricing terms, directly from the CRM database via RTDM. This ensures that all information used in the CTO process is synchronized and current. Also in this operation, the Error Checking Integrator 720.5 applies pre-configured validation rules to ensure that the generated automated quote meets pre-defined criteria, including but not limited to pricing limits and inventory availability.

[0194] In some embodiments, method 900 can include one or more operations (not shown) for generating real-time reports related to the automated CTO process. These reports can cover aspects such as pricing variations, vendor selection, and validation results and can be accessed via the SPoG UI 705. Method 900 can also include vendor system selection based on pre-defined criteria such as delivery time, cost, and quality metrics, performed within existing operational constraints. In some embodiments, upon successful execution, a notification can be sent to one or more users through the SPoG UI 705 to confirm the CTO process is complete. Notifications can be sent to indicate errors that occur during method 900, or alternatively.

[0195] The operations described herein are described sequentially, but may occur simultaneously or be reordered based on implementation needs. These operations may be further customized to meet the requirements of a particular user or organization. Additional modules or sub-modules may be integrated into the existing structure to expand the capabilities of method 900.

[0196] 10 is a flow diagram of a method 1000 for automating quote-to-order (QTO) operations within a QTO module 725, which is part of a system 700 designed to automate configure-to-order (CTO) and quote-to-order (QTO) processes. The method 1000 is integrated with various components, including a real-time data mesh 710, a single-pane-of-glass user interface 705, and an advanced analytics and machine learning module 715, to automate and optimize QTO tasks such as real-time pricing lookup, discount application, and quote generation.

[0197] At operation 1001, a user initiates a QTO request via a single pane of glass user interface (SPoG UI) 705. This user interface can be developed using HTML, CSS, and JavaScript and communicates with a QTO module 725 within an advanced analytics and machine learning (AAML) module 715 to begin the QTO process. The request can be triggered by a user action, such as clicking a "Generate Quote" button on the interface.

[0198] In operation 1002, the QTO module 725 verifies the user's permissions through an authorization checker 725.4, which cross-references the user's role or account history against the role-based access control policies stored in AAML 715. If the user has the correct permissions, the process proceeds to the next operation; if not, an error message may be displayed and the process stops.

[0199] In operation 1003, the real-time data mesh (RTDM) 710 may be queried to fetch current pricing data. This involves the pricing aggregator 725.1 of the QTO module 725 making a data retrieval request to the RTDM 710. The data may include component prices, inventory levels, or vendor-specific pricing. In operation 1003, the real-time data mesh (RTDM) 710 may be queried to fetch current pricing data based on real-time market data. In a non-limiting example, the real-time data mesh (RTDM) 710 may be queried to fetch current pricing data from selected vendors based on pre-set criteria.

[0200] In operation 1004, the discount calculator 725.2 applies applicable discounts to the pricing data. A weighted scoring algorithm is used to determine the most effective combination of account-specific or promotional discounts. The calculated prices can be temporarily stored for subsequent operations.

[0201] In operation 1005, the Quote Template Filler 725.3 takes the calculated price and other necessary data and inputs it into a predefined quote template. The template includes the product description, terms and conditions, and final pricing. A string replacement algorithm based on the KMP algorithm can be employed for this task.

[0202] In operation 1006, the error check integrator 725.5 reviews the input quote for discrepancies or errors. Predefined rules stored in the AAML module 715 are applied to validate each element of the quote. Discrepancies can be flagged for manual review or automatic correction. In operation 1006, the error check integrator 725.5 reviews the input quote for discrepancies or errors. Predefined rules, specifically pre-configured validation rules stored in the AAML module 715, are applied to validate each element of the quote.

[0203] In operation 1007, the completed quote can be sent back to the SPoG UI 705 for display to the user. The quote can be presented in a variety of formats, such as those supported by the QTO module 725, including JSON and XML. The user has the option to approve, edit, or reject the quote. In operation 1007, the completed quote can be sent back to the SPoG UI 705 for display to the user, and a notification can be sent to the user once the quote has been successfully executed.

[0204] At operation 1008, quote details can be logged for audit and future analysis. This logging mechanism resides in the QTO module 725 and stores metadata such as quote creation time, discounts applied, and user identification information. At operation 1008, quote details and real-time reports related to the automated QTO process can be logged for audit and future analysis.

[0205] In operation 1009, a feedback loop occurs to an Advanced Analytics and Machine Learning (AAML) module 715. The AAML module can use ensemble learning or reinforcement learning algorithms to analyze the quote data and the steps taken to produce it. This analysis aids in the continuous optimization of the QTO process. In operation 1009, a feedback loop to the AAML module 715 can occur within a time frame based on user-defined parameters for order execution.

[0206] In another embodiment, machine learning models can be integrated within the QTO module 725 to refine and optimize its specific operations. For example, a machine learning model can be paired with the pricing aggregator 725.1 to determine the optimal time for pricing data lookup based on historical pricing volatility.

[0207] The method 1000 provides an automated and efficient way to handle the quote-to-order process. It integrates various modules and components such as the QTO module 725, real-time data mesh 710, and single pane of glass UI 705 to ensure a smooth, error-free, and optimized QTO operation.

[0208] The operations described herein are described sequentially, but may occur simultaneously or be reordered based on implementation needs. These operations may be further customized to meet the requirements of a particular user or organization. Additional modules or sub-modules may be integrated into the existing structure to expand the capabilities of method 1000.

[0209] 11 is a block diagram of example components of a device 1100. One or more computer systems 1100 may be used, for example, to implement any of the embodiments described herein, as well as combinations and subcombinations thereof. The computer system 1100 may include one or more processors (also referred to as central processing units or CPUs), such as processor 1104. The processor 1104 may be connected to a communications infrastructure, or bus 1106.

[0210] The computer system 1100 may also include user input / output devices 1103 , such as a monitor, keyboard, pointing device, etc., which may communicate with a communications infrastructure 1106 through a user input / output interface 1102 .

[0211] One or more of the processors 1104 may be a graphics processing unit (GPU). In one embodiment, a GPU may be a processor that can be a specialized electronic circuit configured to process mathematically intensive applications. A GPU may have a parallel structure that can be efficient for parallel processing of large blocks of data, such as mathematically intensive data common in computer graphics applications, images, videos, etc.

[0212] The computer system 1100 may also include a main or primary memory 1108, such as random access memory (RAM). The main memory 1108 may include one or more levels of cache. The main memory 1108 may have control logic (i.e., computer software) and / or data stored therein.

[0213] The computer system 1100 may also include one or more secondary storage devices or memories 1110. The secondary memory 1110 may include, for example, a hard disk drive 1112 and / or a removable storage device or drive 1114.

[0214] The removable storage drive 1114 may interact with a removable storage unit 1118. The removable storage unit 1118 may include a computer-usable or readable storage device having computer software (control logic) and / or data stored thereon. The removable storage unit 1118 may be a program cartridge and cartridge interface (such as found in video game devices), a removable memory chip (such as an EPROM or PROM) and associated socket, a memory stick and USB port, a memory card and associated memory card slot, and / or other removable storage unit and associated interface. The removable storage drive 1114 may read from and / or write to the removable storage unit 1118.

[0215] Secondary memory 1110 may include other means, devices, components, intermediaries, or other approaches for allowing computer programs and / or other instructions and / or data to be accessed by computer system 1100. Such means, devices, components, intermediaries, or other approaches may include, for example, removable storage unit 1122 and interface 1120. Examples of removable storage unit 1122 and interface 1120 may include a program cartridge and cartridge interface (such as found in a video game device), a removable memory chip (such as an EPROM or PROM) and associated socket, a memory stick and USB port, a memory card and associated memory card slot, and / or other removable storage unit and associated interface.

[0216] Computer system 1100 may further include a communications or network interface 1124. Communications interface 1124 may enable computer system 1100 to communicate and interact with a combination of external devices, external networks, external entities, etc. (individually and collectively referred to by reference numeral 1128). For example, communications interface 1124 may enable computer system 1100 to communicate with external or remote devices 1128 via communications path 1126, which may be wired and / or wireless (or a combination thereof) and may include a combination of a LAN, a WAN, the Internet, etc. Control logic and / or data may be transmitted to and from computer system 1100 via communications path 1126.

[0217] Additionally, computer system 1100 may be a personal digital assistant (PDA), a desktop workstation, a laptop or notebook computer, a netbook, a tablet, a smartphone, a smartwatch or other wearable, an appliance, part of the Internet of Things, and / or an embedded system, or any combination thereof, to name a few non-limiting examples.

[0218] The computer system 1100 may be a client or server that accesses or hosts applications and / or data through a delivery paradigm, including, but not limited to, remote or distributed cloud computing solutions, local or on-premise software ("on-premise" cloud-based solutions), "as a service" models (e.g., Content as a Service (CaaS), Digital Content as a Service (DCaaS), Software as a Service (SaaS), Managed Software as a Service (MSaaS), Platform as a Service (PaaS), Desktop as a Service (DaaS), Framework as a Service (FaaS), Backend as a Service (BaaS), Mobile Backend as a Service (MBaaS), Infrastructure as a Service (IaaS)), and / or hybrid models including combinations of the foregoing examples or other service or delivery paradigms.

[0219] Applicable data structures, file formats, and schemas in computer system 1100 may be derived from standards including, but not limited to, JavaScript Object Notation (JSON), Extensible Markup Language (XML), Yet Another Markup Language (YAML), Extensible Hypertext Markup Language (XHTML), Wireless Markup Language (WML), MessagePack, XML User Interface Language (XUL), or other functionally similar representations, alone or in combination. Alternatively, proprietary data structures, formats, or schemas may be used, either exclusively or in combination with known or open standards.

[0220] In some embodiments, a tangible, non-transitory apparatus or article of manufacture comprising a tangible, non-transitory computer-usable or readable medium having control logic (software) stored thereon may also be referred to herein as a computer program product or program storage device. This includes, but is not limited to, computer system 1100, main memory 1108, secondary memory 1110, and removable storage units 1118 and 1122, as well as tangible articles of manufacture embodying combinations of the foregoing. Such control logic, when executed by one or more data processing devices (such as computer system 1100), may cause such data processing devices to operate as described herein.

[0221] Figures 12A-12Q show various screens and functionality of the SPoG UI related to vendor onboarding, partner dashboard, customer cart, order summary, SKU generation, order tracking, shipment tracking, subscription history, and subscription changes. A detailed description of each figure is provided below.

[0222] Figure 12A shows the Vendor Onboarding Start Screen, which represents the first step in the vendor onboarding process. It provides a form or interface where vendors can express their interest in joining the distribution ecosystem. Vendors can enter basic information such as company details, contact information, and product catalog.

[0223] Figure 12B shows a vendor onboarding guide that displays a step-by-step guide or checklist for vendors to follow during the onboarding process, outlining the necessary tasks and requirements to ensure the vendor has a clear understanding of the onboarding process and can proceed smoothly.

[0224] 12C shows a vendor onboarding call scheduler that facilitates scheduling a call or meeting between a vendor and a platform partner or representative responsible for guiding the vendor through the onboarding process. The vendor can select a preferred time slot or request a call, ensuring effective communication and assistance throughout the onboarding journey.

[0225] FIG. 12D shows a vendor onboarding task list that presents a comprehensive task list or dashboard outlining the specific steps and actions required for successful vendor onboarding. An overview of pending tasks, completed tasks, and upcoming deadlines is provided to help vendors track progress and ensure timely completion of each onboarding task.

[0226] 12E shows a vendor onboarding completion screen confirming successful completion of the vendor onboarding process, which may display a congratulatory message or a summary of the tasks completed indicating that the vendor is now officially onboarded into the distribution ecosystem.

[0227] Figure 12F shows a partner dashboard that provides partners or users with an aggregated view of information and metrics related to their partnership with the distribution ecosystem, providing an overview of performance indicators, key data points, and actionable insights to facilitate effective collaboration and decision-making.

[0228] FIG. 12G shows a customer product cart, representing a customer's product cart, to which the customer can add items they wish to purchase. It displays a list of selected products, quantities, prices, and other relevant details. The customer can review and modify the contents of their cart before proceeding to the checkout process.

[0229] Figure 12H shows a customer subscription cart that allows customers to manage their subscription-based purchases. It displays the selected subscription plan, pricing, and term. Customers can review and modify subscription details before finalizing their selection.

[0230] FIG. 12I shows a customer order summary that provides a summary of the customer's order, including details such as the products or subscriptions purchased, quantities, pricing, and any discounts or promotions applied, allowing the customer to review the order before confirming the purchase.

[0231] 12J shows a vendor SKU generation screen for generating unique stock-keeping unit (SKU) codes for vendor products, which may include fields or options that allow vendors to specify product details, attributes, and pricing, and the system will auto-generate the corresponding SKU code.

[0232] Figures 12K and 12L show a dashboard order summary for displaying summary information about orders placed within a distribution ecosystem. These present key order details, such as order number, customer name, product or subscription information, quantity, and order status. The dashboard provides an overview of order activity and allows users to efficiently track and manage orders.

[0233] Figure 12M shows the customer subscription cart, which allows customers to add, modify, or delete subscription plans. A list of selected subscriptions, pricing, and renewal dates can be displayed. Customers can manage their subscriptions and make changes according to their preferences and requirements.

[0234] Figure 12N shows the Customer Order Tracking screen, which allows customers to track the status and progress of their orders within the supply chain. It displays real-time updates on order fulfillment, including processing, packing, and shipping. Customers can monitor the movement of their orders and estimate delivery times.

[0235] FIG. 12O shows customer shipment tracking, which provides customers with real-time tracking information about their shipments. Details such as the carrier, tracking number, current location, and estimated delivery date may be included. Customers can stay informed about the whereabouts of their shipments.

[0236] Figure 12P shows Customer Subscription History, which provides a historical record of a customer's subscription activity. It displays a list of past subscriptions, including subscription plan, duration, and status. Customers can review their subscription history, track previous payments, and view details of past subscriptions.

[0237] Figure 12Q shows the Customer Subscription Modification dialog, which allows customers to modify their existing subscriptions. It provides options to upgrade or downgrade subscription plans, change billing details, or adjust other subscription-related preferences. Customers can manage their subscriptions according to their evolving needs or preferences.

[0238] The UI screens shown are not limiting. In some embodiments, the UI screens of Figures 12A-12Q collectively represent the diverse functionality and features provided by the SPoG UI, providing users with a comprehensive, user-friendly interface for vendor onboarding, partnership management, customer interaction, order management, subscription management, and tracking within the distribution ecosystem.

[0239] It is understood that the Detailed Description section, and not the Summary and Abstract, is intended to be used to interpret the claims. The Summary and Abstract may describe one or more, but not all, example embodiments of the invention as contemplated by the inventors, and thus are not intended to limit the invention and the appended claims in any way.

[0240] The present invention has been described above with the aid of functional building blocks illustrating the implementation of certain functions and relationships thereof. The boundaries of these functional building blocks have been arbitrarily defined herein for convenience of description. Alternative boundaries may be defined so long as the certain functions and relationships thereof are properly performed.

[0241] The foregoing description of specific embodiments fully discloses the general nature of the present invention, and by applying the knowledge of those skilled in the art, such specific embodiments can be readily modified and / or adapted for various uses without undue experimentation and without departing from the general concept of the present invention. Such adaptations and modifications are therefore intended to be within the meaning and range of equivalents of the disclosed embodiments, based on the teaching and guidance presented herein. It should be understood that the phraseology or terminology used herein is for the purpose of description and not of limitation, and should therefore be interpreted in light of the teaching and guidance provided by those skilled in the art.

[0242] The breadth and scope of the present invention should not be limited by any of the above-described exemplary embodiments, but should be defined only in accordance with the following claims and their equivalents.

Claims

1. 1. A computerized method for performing an automated configure-to-order (CTO) process, comprising: receiving user input specifying a desired product configuration; accessing a bill of materials (BOM) database to retrieve component data corresponding to the desired product configuration; automatically registering the transaction based on the desired product configuration; applying intelligent rules to calculate optimized pricing and incentives; generating an automated quote based on the optimized pricing and the BOM; transferring said automated quote to an order in a vendor system; and executing the order by transmitting data to multiple layers of an interconnected vendor platform; The method, wherein the method is performed by a computer system having a unified platform experience that integrates data from multiple third-party systems.

2. The method of claim 1 , further comprising validating the generated automated quote using pre-configured validation rules to reduce potential errors.

3. The method of claim 1 , wherein the intelligent rules for calculating optimized pricing are based on real-time market data.

4. The method of claim 1 , wherein the BOM database is dynamically updated based on real-time inventory and product lifecycle information.

5. The method of claim 1 , further comprising generating a real-time report related to the automated CTO process.

6. The method of claim 1 , wherein the vendor system is selected based on preset criteria including vendor reputation and delivery time.

7. The method of claim 1 , further comprising sending a notification to a user when the order is successfully executed.

8. 1. A computerized method for performing an automated quote-to-order (QTO) process, comprising: Initiating a QTO request via a user interface that communicates with a dedicated QTO module; verifying user authorization, said verification including cross-referencing a role-based access control policy; querying a real-time data mesh to fetch current pricing data from one or more selected vendors; applying discounts to the fetched pricing data to determine effective combinations of one or more account-specific or promotional discounts; Enter data into predefined quote templates and validating each element of said quote using pre-defined rules; transmitting the completed quote back to the user interface for display in multiple formats; Logging details of estimates for audit and future analysis; and initiating a feedback loop for continuous optimization of the QTO process.

9. 10. The method of claim 8, further comprising applying a learning algorithm to the feedback loop to analyze data and steps taken to generate the estimate for continuous optimization.

10. The method of claim 8 , wherein the real-time data mesh is queried to fetch current pricing data based on market conditions.

11. 10. The method of claim 8, further comprising generating real-time reports related to the automated QTO process, including key metrics such as quote generation time and applied discounts.

12. The method of claim 8 , wherein vendors are selected in the real-time data mesh based on predefined criteria.

13. The method of claim 8 , further comprising sending a notification to the user upon completion of the quote.

14. The method of claim 8 , wherein the feedback loop occurs within a time frame defined by a user parameter.

15. 1. A system for automating configure-to-order (CTO) and quote-to-order (QTO) processes, comprising: a real-time data mesh configured to aggregate and distribute various types of data, including pricing and inventory levels; and a Single Pane of Glass User Interface (SPoG UI) that allows user interaction and displays real-time data; an Advanced Analytics and Machine Learning (AAML) module responsible for maintaining the rule-based and machine learning algorithms; a configure-to-order (CTO) module that interacts with the SPoG UI and the real-time data mesh to perform configure-to-order tasks, including bill-of-material generation, deal registration, and pricing application; a quote-to-order (QTO) module that interacts with the SPoG UI and the real-time data mesh to automate quote-to-order tasks, including real-time pricing lookup and quote generation.

16. The system of claim 15 , wherein the CTO module further comprises a logging mechanism for tracking all configuration changes for auditing purposes.

17. 16. The system of claim 15, wherein the CTO module and the QTO module are integrated with the AAML module for validation and error checking purposes and flag inconsistencies and errors using predefined rules stored in the AAML module.

18. The system of claim 15 , wherein the SPoG UI is accessible from a wide range of devices, including desktops, laptops, tablets, and smartphones.

19. The system of claim 15 , wherein the real-time data mesh is configured to normalize data into a unified format suitable for consumption by the SPoG UI and other system modules.

20. 16. The system of claim 15, further comprising a machine learning model integrated into the CTO module and the QTO module, the model configured to optimize and refine processes within each module over time based on past transactions and evolving data patterns.

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