A system and method for vendor-independent CTO / QTO (order configuration / quote-to-order).

Abstract

We provide a system for managing vendor-independent order configuration (CTO) and quote-to-order (QTO) processes. [Solution] A method for streamlining vendor processes, improving scalability, and optimizing pricing strategies within a scalable and adaptable framework, wherein a single-pane-of-glass user interface (SPoG UI) 705 facilitates dynamic interaction and visibility into vendor performance. An advanced analytics and machine learning (AAML) module 715 analyzes product compatibility, optimizes pricing strategies, and predicts market trends. A vendor-independent CTO / QTO integration module (VACIM) 720 includes a process standardization engine 725 and a vendor data conversion gateway 730 to ensure uniformity across vendors.

Description

Technical Field

[0001] (Cross - reference to Related Applications) This application is a Continued - in - Part (CIP) of U.S. Patent Application No. 18 / 341,714 filed on June 26, 2023; U.S. Patent Application No. 18 / 349,836 filed on July 10, 2023; U.S. Patent Application No. 18 / 424,193 filed on January 26, 2024; U.S. Patent Application No. 18 / 583,256 filed on February 21, 2024; U.S. Patent Application No. 18 / 583,337 filed on February 21, 2024; U.S. Patent Application No. 18 / 599,388 filed on March 8, 2024; U.S. Patent Application No. 18 / 614,517 filed on March 22, 2024; U.S. Patent Application No. 18 / 732,227 filed on June 3, 2024; U.S. Patent Application No. 18 / 768,998 filed on July 10, 2024; U.S. Patent Application No. 18 / 768,971 filed on July 10, 2024; U.S. Patent Application No. 18 / 789,602 filed on July 30, 2024; and U.S. Patent Application No. 18 / 793,346 filed on August 2, 2024. Each of these applications is hereby incorporated by reference in its entirety.

[0002] (Background) Previous order processes in distribution and supply chain platforms have been plagued by inefficiencies, delays, and inaccuracies. In a traditional environment, it is common for multiple systems and vendors to perform each activity independently, from creating a parts list to registering a transaction, applying pricing, generating a quote, and issuing an order. This approach leads to increased business inefficiencies and the potential for errors.

[0003] Enterprise Resource Planning (ERP) systems have served as the cornerstone of managing business processes, including distribution and supply chain. These systems function as a central repository where different departments, such as finance, human resources, and inventory management, can access and share real-time data. While ERP is comprehensive, it presents several challenges in today's complex distribution and supply chain environment. 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 understanding of key distribution and supply chain metrics, which negatively impacts decision-making processes.

[0004] Furthermore, ERP systems often fail to provide effective data integration capabilities. Existing ERP systems are not designed for efficient integration with external systems or between different modules within the same ERP suite. This design results in reliance on cumbersome and error-prone manual processes for transferring data between systems, negatively impacting the flow of information across the entire supply chain. When information exists in different formats across systems, data inconsistencies arise, hindering 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 ERP systems, standardizing this data for meaningful analysis becomes a laborious process. Businesses often rely on time-consuming manual processes for data transformation and validation, which further delays decision-making. In addition, existing ERP systems often lack the ability to effectively process 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, particularly given 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. Existing ERP systems often lack robust security features that are agile enough to adapt to the ever-evolving landscape of cybersecurity threats and compliance requirements. [Overview of the project]

[0007] The vendor-independent CTO / QTO management system and methodology provides a comprehensive platform for simplifying and automating vendor relationship dynamics, standardizing processes, optimizing pricing strategies, and improving customer experience across diverse markets and regions. The system and methodology integrate various modules, including a real-time data mesh (RTDM), a single-pane-of-glass user interface (SPoG UI), advanced analytics and machine learning (AAML) modules, and a vendor-independent CTO / QTO module.

[0008] In the global distribution industry, challenges such as inefficient distribution management, SKU management, and the shift to direct-to-consumer (DTO) models necessitate innovative solutions. Traditional distribution methods are becoming increasingly inadequate, particularly due to shifts in consumer expectations and regulations. By integrating functionality for distribution management, supply chain management, and customer visibility, the platform supports the simplification and automation of vendor-independent CTO / QTO operations.

[0009] According to several embodiments, the conversion module can be configured to incorporate algorithms for optimizing product and service selection based on real-time market data and customer preferences. The system includes a conversion module, which integrates with a real-time data mesh (RTDM) and a single-pane-of-glass user interface (SPoG UI) to optimize the delivery of subscription-based services. Advanced algorithms are used to adapt offerings based on real-time market data and customer usage patterns, improving the flexibility and scalability of service options.

[0010] In a non-limiting example, RTDM functions as a centralized data hub that aggregates and standardizes real-time data from multiple sources, such as ERP, CRM systems, and market intelligence. Techniques such as ETL processes and data normalization are employed to ensure data uniformity and accessibility. This standardized data is essential for the functionality of vendor-independent CTO / QTO integration modules and is responsible for standardizing processes across vendors, regions, and markets.

[0011] In some embodiments, the vendor-independent CTO / QTO integration module standardizes processes across vendors by implementing a process standardization engine for uniformity and a vendor data conversion gateway for real-time data synchronization. In addition, modules such as dynamic pricing and configuration optimization modules optimize pricing strategies, and a vendor compatibility analyzer evaluates product compatibility for service conversion. In some embodiments, these components ensure consistency and uniformity across diverse vendor offerings and can convert vendor raw data into a standardized format compatible with RTDM. The vendor-independent CTO / QTO integration module standardizes processes, pricing structures, and configuration options across vendors, ensuring consistency and scalability.

[0012] In some embodiments, a market intelligence integration module collects and analyzes market trends, competitor strategies, and customer preferences to provide actionable insights for informed decision-making. In some embodiments, an adaptive rebate management system automates rebate calculations based on real-time sales data, and a geographic market mapper provides insights into regional trends.

[0013] Furthermore, embodiments may include a cross-vendor configuration analyzer configured to evaluate product configurations across vendors, improving configurability and scalability. A global pricing harmonizer can take into account currency fluctuations and regional purchasing power to ensure pricing parity across diverse markets.

[0014] Integration with SPoG UI provides a vendor relationship management dashboard, offering real-time visibility into vendor performance and partnerships. It aggregates vendor-related data for informed decision-making, enhancing transparency and reliability.

[0015] The system also includes features such as a vendor relationship management dashboard integrated with the SPoG UI, providing stakeholders with real-time visibility into vendor performance and strategic partnerships. This dashboard aggregates vendor-related data into actionable insights, enhancing transparency and trust in vendor relationships.

[0016] Some embodiments may include methodologies for process standardization and scalability to ensure efficiency and scalability across vendors, as well as machine learning and data processing operations to address missing data, automate decision-making, and ensure continuous improvement.

[0017] This system and methodology enables efficient processes for managing vendor relationships across diverse markets and regions, standardizing processes, optimizing pricing strategies, and improving customer experience. It integrates data from multiple sources, automates various tasks in vendor relationship management processes, and maintains a real-time, standardized data repository to ensure scalability and adaptability in today's dynamic business environment.

[0018] (Single pane of glass) Single Pane of Glass (SPoG) can provide a comprehensive solution aimed at addressing these multifaceted challenges. It can be configured to provide a holistic, user-friendly, and efficient platform that facilitates the distribution process.

[0019] According to several embodiments, SPoG can be configured to address supply chain and distribution management by enhancing visibility and control over supply chain processes. Through real-time tracking and analysis, SPoG can provide valuable insights into inventory levels and product status, ensuring that supply chain and distribution management processes are handled efficiently.

[0020] In some embodiments, SPoG can integrate multiple touchpoints into a single platform, effectively creating a direct consumer channel on the distribution platform. This integration provides a unified direct channel for consumers to interact with distributors, significantly reducing supply chain complexity and improving the overall customer experience.

[0021] SPoG provides innovative solutions for improved inventory management through advanced predictive capabilities. These predictive analytics highlight demand trends and guide companies to manage inventory more efficiently, mitigating the risk of stockouts or excess inventory.

[0022] In some embodiments, SPoG can include a global compliance database. This database is updated in real time, enabling distributors to stay up-to-date with the latest international regulations. This feature significantly reduces the burden of manual tracking and ensures smooth, compliant cross-border transactions.

[0023] According to several embodiments, SPoG integrates data from various OEMs into a single platform to facilitate vendor relationship management. This not only ensures data consistency but also significantly reduces the possibility of errors. Furthermore, it provides the ability to efficiently manage vendor relationships, thereby aligning them with the specific needs and requirements of the market.

[0024] In some embodiments, SPoG is a highly configurable and user-friendly platform. Its intuitive interface allows users to easily access and purchase technology, thereby meeting the expectations of a new generation of technology buyers.

[0025] Furthermore, SPoG's advanced analytical capabilities provide valuable insights that can drive strategy and decision-making. It enables real-time tracking and analysis of trends, allowing companies to stay ahead and adapt to changing market conditions.

[0026] SPoG offers a future-proof solution thanks to its flexibility and scalability. It can adapt to changing business needs, allowing companies to scale operations up or down as needed without significantly altering their infrastructure.

[0027] The innovative approach of SPoG to solve the problems in the distribution industry becomes a very valuable tool. By enhancing supply chain visibility, facilitating inventory management, ensuring compliance, simplifying vendor relationship management, and bringing about an excellent customer experience, it provides a comprehensive solution to the complex problems that have long troubled the distribution industry. Through its implementation, distributors can expect improved efficiency, reduced errors, and enhanced customer satisfaction, leading to sustainable growth in the ever-evolving global market.

[0028] (Real-Time Data Mesh (RTDM)) According to some embodiments, the platform can include the implementation of a Real-Time Data Mesh (RTDM). RTDS provides an innovative solution to address these issues. 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.

[0029] The predictive analytics performance of RTDM provides a solution for efficient inventory control. By providing insights into demand trends, it supports the company's inventory management and reduces the risk of overstock or stockouts.

[0030] The global compliance database of RTDM is updated in real-time, ensuring that distributors are in a state to comply with international regulations. This significantly reduces the burden of manual tracking and enables cross-border transactions.

[0031] RTDM also simplifies vendor relationship management and ensures data consistency and reduces the possibility of errors by integrating data from various OEMs. Its ability to manage product and market data is efficiently aligned with specific market needs.

[0032] RTDM enhances the customer experience with its intuitive interface, enabling easy access to and purchase of technology, and meeting the expectations of a new generation of technology buyers.

[0033] (Advantages of SPoG and RTDM integration) Integrating the SPoG platform with RTDM offers countless benefits. Firstly, it provides a comprehensive solution to long-standing problems in the distribution industry. RTDM's capabilities enable SPoG to improve supply chain visibility, facilitate the management of vendor-independent order configuration (CTO) and quote-to-order (QTO) processes, and deliver a superior customer experience.

[0034] Real-time tracking and analytics provided by RTDM improve SPoG's ability to effectively manage supply chains and inventory. It provides accurate, current information, enabling distributors to make informed decisions quickly.

[0035] Furthermore, integrating SPoG with RTDM ensures data consistency and reduces errors in managing vendor-independent processes. By providing a centralized platform for managing data from various OEMs, it simplifies product localization and helps align with market needs.

[0036] RTDM's global compliance database integrates with SPoG to facilitate compliant cross-border transactions. It also reduces the burden of manual tracking, saving significant time and resources.

[0037] In some embodiments, the distribution platform incorporates SPoG and RTDM to provide an improved, comprehensive distribution system. This platform can leverage the advantages of the distribution model, address its existing challenges, and position itself for sustainable growth in the ever-evolving global market. [Brief explanation of the drawing]

[0038] [Figure 1] This embodiment illustrates one example of the operating environment of a distribution platform, referred to as the system in this embodiment. [Figure 2] Figure 1 illustrates one embodiment of the operating environment of a distribution platform constructed with the elements shown. [Figure 3] An embodiment of a distribution management system is illustrated. [Figure 4] A system for vendor relationship management according to one embodiment is illustrated. [Figure 5] An RTDM module according to one embodiment is shown. [Figure 6] An illustration shows a SPoG UI according to one embodiment. [Figure 7] A system for vendor relationship management according to one embodiment is illustrated. [Figure 8] This is a flowchart of a method for a vendor relationship management process according to some embodiments of the present disclosure. [Figure 9] This is a flowchart for automated service configuration in a vendor relationship management system, according to some embodiments of the present disclosure. [Figure 10] This is a flowchart for automated data management and analysis processes in a vendor relationship management system, according to some embodiments of the present disclosure. [Figure 11] This is a block diagram of exemplary components of a device according to some embodiments of the present disclosure. [Figures 12A-12Q] This document illustrates various screens and functionalities of the SPoG UI in several embodiments. [Modes for carrying out the invention]

[0039] This embodiment may be implemented in hardware, firmware, software, or any combination thereof. Alternatively, this embodiment may be implemented as instructions stored in a machine-readable medium, which can be read and executed by one or more processors. The machine-readable medium may include any mechanism for storing or transmitting information in a format readable by a machine (e.g., a computing device). For example, the 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 specific actions. However, such descriptions are merely for convenience, and it should be understood that such actions are actually the results obtained by a computing device, processor, controller, or other device executing the firmware, software, routines, instructions, etc.

[0040] The actions shown in the illustrative methods are not exhaustive, and it should be understood that other actions may similarly be performed before, after, or between any of the illustrated actions. In some embodiments of this disclosure, the actions may be performed in a different order and / or different order.

[0041] Figure 1 illustrates the operating environment 100 of a distribution platform called System 110 in this embodiment. System 110 operates within the context of an information technology (IT) distribution model and responds to the demands of various users, including 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.

[0042] Customers 120 within the operating environment of System 110 represent businesses or individuals seeking IT solutions to meet 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, enabling them to browse, search, and select the most suitable IT solutions based on their requirements. Furthermore, customers can access real-time data and analytics through System 110 to make informed decisions and optimize their IT infrastructure.

[0043] The end customer 130 can be the ultimate beneficiary of the IT solutions provided by System 110. The end customer may include businesses or individuals who use IT products and services to improve their operations, productivity, or daily activities. The end customer relies on System 110 to access a wide range of IT solutions and is ensured to have access to the latest technologies and innovations in the market. System 110 enables the end customer to track orders, receive delivery status updates, and access customer support services, thereby improving the overall experience.

[0044] Vendor 140 plays a crucial role within the operating environment of System 110. These vendors encompass manufacturers, distributors, and suppliers providing 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 can leverage System 110 to simplify supply chain operations, manage pricing and promotions, and gain insights into customer preferences and market trends. By integrating with System 110, vendors can expand their reach, access new markets, and improve overall visibility and competitiveness.

[0045] Resellers 150 can act as intermediaries within a distribution model, bridging the gap between vendors and customers. Resellers play a crucial role in the IT distribution ecosystem by connecting customers with appropriate 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 pipelines, and provide value-added services to customers. By leveraging System 110, resellers can improve customer relationships, optimize product offerings, and increase revenue streams.

[0046] Within the operating environment of System 110, various dynamics and characteristics may exist that contribute to its effectiveness. These dynamics include real-time data exchange, integration with existing enterprise systems, scalability, and flexibility. System 110 ensures that relevant data can be exchanged 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, eliminates data silos, and provides end-to-end visibility.

[0047] System 110 can deliver scalability and flexibility. It can meet the growing demands of IT distribution models, whether through expanding customer bases, increasing vendor numbers, or a wide range of IT products and services. System 110 can be configured to handle large-scale data processing, storage, and analytics, ensuring it can support the evolving needs of distribution platforms. In addition, System 110 leverages a technology stack including .NET, Java, and other preferred technologies, providing a robust foundation for its operation.

[0048] 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 functions 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 their operations within the IT distribution ecosystem, improve customer experience, and drive business success.

[0049] Figure 2 shows the operating environment 200 of the distribution platform, providing further details on the elements shown in Figure 1. This environment has an integration point 210, enabling data flow and connectivity between various systems such as customer systems 220, vendor systems 240, reseller systems 260, and other entities within the vendor-independent CTO / QTO integration process. Figure 2 illustrates the network interconnectivity and mechanisms that facilitate collaborative, data-driven decision-making for vendor-independent CTO / QTO integration. The operating environment 200 is configured to automate the vendor-independent CTO / QTO integration process using AI and ML technologies to process and analyze data for integration.

[0050] Several embodiments of the vendor-independent CTO / QTO integration process involve a systematic approach to integrating diverse vendor systems and processes with minimal manual intervention. This process encompasses several technical components: Collection of diverse data, including vendor specifications, trading relationships, and market trends. This data is aggregated from sources such as vendor inventory systems and pricing databases and fed into a real-time data mesh (RTDM). The RTDM processes and standardizes this data, acting as a centralized repository for real-time data updates and retrieval. The AAML module analyzes this aggregated data to identify the optimal strategy for integration. Vendor processes are segmented based on data-driven insights and predicted market preferences. The vendor-independent CTO / QTO integration module, informed by the AAML module's insights, standardizes processes for each vendor or market segment. Predictive models and heuristic algorithms are applied to determine an integration strategy aligned with specific vendor requirements. Users interact with these integration processes through the SPoG UI, customizing and confirming their integration choices. The system includes a feedback loop where responses to the integration process are collected and analyzed, continuously refining the delivery of the integration.

[0051] AI algorithms in the vendor-independent CTO / QTO integration process address inventory management, process standardization, and vendor selection optimization. Machine learning models such as neural networks and decision trees refine the integration strategy. The integration uses ML-based algorithms for real-time configuration. Advanced analytics, such as ensemble learning or reinforcement learning, continuously optimize the integration process. AI and ML technologies within the operating environment 200 employ supervised and unsupervised learning algorithms, including convolutional neural networks for pattern recognition and logistic regression for decision-making. These components dynamically adapt to changing data inputs, such as vendor preferences and market conditions, and optimize the decision-making path through reinforcement learning. The ML component leverages predictive analytics and continuously refines its output by learning new data, improving the accuracy and relevance of the integration.

[0052] The operating environment 200 includes system 110 as a central hub for managing vendor-independent CTO / QTO integration processes. System 110 acts as a bridge between customer systems 220, vendor systems 240, reseller systems 260, and other entities. It integrates communication, data exchange, and transaction processes to provide a cohesive experience. Furthermore, environment 200 features an integration point 210 using a hybrid architecture combining RESTful APIs and WebSockets for real-time data exchange and synchronization. This architecture is secured by the SSL / TLS protocol to protect data in transit.

[0053] Customer System Integration: Integration point 210 enables system 110 to connect with customer system 220, facilitating efficient data exchange and synchronization. Customer system 220 may include entities such as customer system 221, customer system 222, and customer system 223. These systems represent internal systems used by the customer, such as ERP or CRM systems. Integration with customer system 220 allows the customer to access real-time information about the vendor-independent CTO / QTO integration, including personalized bundles, pricing details, order tracking, and other relevant data, improving their decision-making capabilities. This integration provides an automated, real-time solution for creating and managing the vendor-independent CTO / QTO integration process, improving the customer's operational efficiency.

[0054] Data exchange between customer system 220, vendor system 240, and reseller system 260 is enabled by robust ETL (Extract, Transform, Load) operations, as described below, referencing a real-time data mesh architecture to ensure data consistency and reliability. This interaction can be governed by predefined business rules and logic that define data flow and processing methods. Advanced mapping and transformation tools are employed to harmonize heterogeneous data formats, enabling data integration and utilization across these systems. Orchestrated data exchange supports synchronized operations and enables efficient and informed decision-making across the distribution network.

[0055] Partner System Integration: Integration point 210 enables system 110 to connect to partner system 230, facilitating efficient data exchange and synchronization. These systems contribute to the overall efficiency of vendor-independent CTO / QTO integration processing by providing relevant market and product data.

[0056] Vendor System Integration: Integration point 210 facilitates the connection between system 110 and vendor system 240. Vendor system 240 may include entities representing inventory management, pricing systems, and product catalogs, 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, receive real-time notifications, and facilitate a vendor-independent CTO / QTO integration process.

[0057] Reseller System Integration: Integration point 210 enables the reseller system 260 to connect with system 110. The reseller system 260 encompasses entities such as reseller system 261, reseller system 262, and reseller system 263, which handle sales, customer management, and service delivery. The integration allows resellers to access the latest product information and effectively manage customer relationships.

[0058] Other Entity System Integration: Integration Point 210 further connects with other entities involved in the distribution process, facilitating collaboration and efficient distribution. This integration ensures real-time data exchange for vendor-independent CTO / QTO integrated processing and decision-making within the distribution ecosystem.

[0059] The configuration of System 110 includes advanced AI and ML capabilities to automate vendor-independent CTO / QTO integration processing according to individual preferences, ensuring relevance and optimization of the distribution process.

[0060] Furthermore, integration point 210 enables connection with the record system 280 for additional data management and integration. Representing the record system 280 can represent an Enterprise Resource Planning (ERP) system or a Customer Relationship Management (CRM) system, including both future systems and legacy ERP systems such as SAP, Impulse, META, and I-SCALA. The record system can contain one or more storage repositories of critical business and legacy data. This facilitates data exchange and synchronization integration between the distribution platform, system 110, and the ERP, enabling real-time updates and ensuring the availability of accurate and up-to-date information. Integration point 210 establishes a connection between the record system 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 used by customers, vendors, and others.

[0061] Integration points 210 within the 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 adopts industry-standard protocols, such as RESTful API, SOAP, or GraphQL, to establish communication channels and enable data exchange.

[0062] In some embodiments, the system 110 can incorporate an authentication and authorization mechanism to ensure secure access and data protection. Technologies such as OAuth or JSON Web Token (JWT) can be employed to authenticate users, authorize data access, and maintain the integrity and confidentiality of exchanged information.

[0063] In some embodiments, the integration point 210 and the data flow within the operating environment 200 enable user interaction within a coordinated 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 entire distribution platform.

[0064] In some embodiments, System 110 leverages advanced technologies such as Typescript, NodeJS, ReactJS, .NET Core, C#, and other preferred technologies to support the integration point 210 and enable communication within the operating environment 200. These technologies provide a robust foundation for System 110, ensuring scalability, flexibility, and efficient data processing capabilities. Furthermore, the integration point 210 can also employ algorithms, data analysis, and machine learning techniques to derive valuable insights, optimize distribution processes, and personalize customer experiences. The integration point 210 and the data flow within the operating environment 200 enable users to operate within a coordinated 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 in real time to relevant users 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.

[0065] Some elements of the operating environment shown in Figure 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, workstation, laptop, PDA, mobile phone, or any Wireless Access Protocol (WAP) enabled device, or any other computing device that can interface directly or indirectly with the Internet or other network connectivity. Each of the customer systems may typically run an HTTP client such as Microsoft Edge, Google Chrome, Opera, or a WAP-enabled browser for mobile devices, and the customer systems may access, process, and display information, pages, and applications available from the distribution platform over the network.

[0066] Furthermore, each customer system may typically include user interface devices, such as a keyboard, mouse, trackball, touchpad, touchscreen, pen, or similar devices for interacting with a graphical user interface (GUI) provided by a browser. These user interface devices enable users of the customer system to navigate the GUI, interact with pages, forms, and applications, and access data and applications hosted by the distribution platform.

[0067] The customer system and its components can be configured by an operator using an application that includes a web browser running on a central processing unit such as an Intel Pentium processor or a similar processor. Similarly, the distribution platform (system 110) and its components can be configured by an operator using an application that runs on a central processing unit such as an Intel Pentium processor or a similar processor, and / or a processor system that may include multiple processor units.

[0068] Embodiments of a computer program product include a machine-readable storage medium containing instructions for programming a computer to perform the processes described herein. Computer code for operating and configuring distribution platforms and customer systems, vendor systems, reseller systems, and systems of other entities to communicate with each other and to process web pages, applications, and other data may be downloaded and stored on a hard disk or any other volatile or non-volatile storage medium or device, such as ROM, RAM, floppy disks, optical disks, DVDs, CDs, microdrives, magneto-optical disks, magnetic or optical cards, nanosystems, or any suitable medium for storing instructions and data.

[0069] Furthermore, computer code for implementing this embodiment can be transmitted and downloaded from the software source via the Internet or any other conventional network connection using communication 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 a programming language such as C, C++, HTML, Java, JavaScript, ActiveX, VBScript, or others.

[0070] This embodiment can be implemented in various programming languages ​​running on a client system, server, or server system, and it will be understood that the choice of language may depend on the specific requirements and environment of the distribution platform.

[0071] This allows the operating environment 200 to connect the distribution platform with one or more integration points 210 and data flows, enabling efficient collaboration and a streamlined distribution process.

[0072] Figure 3 illustrates 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 fragmented distribution ecosystems in the global distribution industry. System 300 can include several interconnected components and modules that work in harmony to optimize supply chain and distribution operations, improve collaboration, and drive business efficiency.

[0073] The Single Pane of Glass (SPoG) UI305 functions 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 the user's specific role and responsibilities. By providing a customizable and intuitive dashboard-style layout, the SPoG UI enables users to access relevant information and tools to manage data-driven decision-making and efficient supply chain and distribution activities.

[0074] 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. This data can be visualized through interactive charts, such as maps showing the current location of each shipment, or bar graphs 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 product delivery.

[0075] SPoG UI305 integrates with other modules of System 300, facilitating real-time data exchange, synchronized operations, and workflows. Through API integration, data synchronization mechanisms, and an event-driven architecture, SPoG UI305 ensures a smooth information flow and enables collaborative decision-making across the distribution ecosystem. Designed with a user-centric approach, SPoG UI305 features an intuitive and responsive layout. Leveraging front-end technologies, it provides dynamic and interactive data visualizations. Customizable dashboards allow users to tailor views based on specific roles and requirements. The UI supports drag-and-drop functionality for ease of use, and its adaptive design ensures compatibility across various devices and platforms. Advanced filtering and search capabilities enable users to efficiently navigate and access relevant supply chain data and insights.

[0076] 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.

[0077] The Real-Time Data Mesh (RTDM) module 310 is another component of system 300 and is responsible for ensuring data flow within the distribution ecosystem. It collects and harmonizes data from multiple sources and ensures its real-time availability.

[0078] In a distribution network, the RTDM module collects data from various systems, including inventory management systems, point-of-sale (POS) terminals, and customer relationship management systems. This data is harmonized by aligning formats, standardizing units of measurement, and reconciling inconsistencies. The harmonized data can then be made available in real time, allowing users to access accurate and current information across the supply chain.

[0079] 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 continuously 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-scale systems, ensuring compatibility and flexibility for business operations in diverse environments.

[0080] 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 surge in demand for a particular product, it can trigger an alert to the production team, allowing them to adjust the manufacturing schedule and prevent stockouts.

[0081] The RTDM module 310 simplifies data management within supply chain operations. It enables real-time harmonization 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.

[0082] Another component of 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 collected data, enabling advanced analytics, predictive modeling, anomaly detection, and other machine learning capabilities.

[0083] For example, the AAML module can analyze sales data history to identify seasonal patterns and predict future demand. It can generate demand forecasts, optimize inventory levels based on those forecasts, ensure inventory availability during peak seasons, and minimize the costs of excess inventory. By leveraging machine learning algorithms, the AAML module can automate repetitive tasks, predict customer preferences, and optimize supply chain processes.

[0084] In addition to forecasting demand, the AAML module can provide insights into customer behavior, enabling targeted marketing campaigns and personalized customer experiences. For example, by analyzing customer data, the module can identify cross-selling or upselling opportunities and recommend products relevant to individual customers.

[0085] Furthermore, 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 consumer psychology and preferences. This information can be used to identify emerging market trends that can be used to inform product development decisions and to adapt business strategies to meet evolving consumer expectations.

[0086] System 300 emphasizes integration and interoperability for connecting with existing enterprise systems, such as ERP systems, warehouse management systems, and customer relationship management systems. By establishing connectivity and data flow between these systems, System 300 enables seamless data exchange, process automation, and end-to-end visibility across the supply chain. Integrated protocols, APIs, and data connectors facilitate communication and interoperability between different modules and components, creating a comprehensive and interconnected distribution ecosystem.

[0087] 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, ease of management, and efficient updates across different environments. The implementation process involves configuring the system to align with specific supply chain requirements, integrating it with existing systems, and customizing modules and components based on business needs and preferences.

[0088] System 300 for supply chain and distribution management is a comprehensive and innovative solution that addresses the challenges faced by fragmented distribution ecosystems. It combines the capabilities of SPoG UI305, 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, System 300 delivers end-to-end visibility, data-driven decision-making, and optimized supply chain operations. The examples and options provided herein are non-exclusive and can be customized to meet specific industry requirements, driving efficiency and success in supply chain and distribution management.

[0089] Figure 4 shows one embodiment of System 400 for automated, vendor-independent CTO / QTO, integrating SPoG UI, RTDM, and AI / ML technologies that interact to realize a comprehensive service transformation system. System 400 is configured for integration with existing reseller systems, ensuring efficient data exchange and system synchronization.

[0090] SPoG UI405 functions as the primary user interface. Users interact with this interface to perform various tasks, and it offers simple, clear operation and customization. It displays information and options related to the reseller's unique business model and customer attributes. It displays real-time data from data mesh 410 and provides control for initiating actions within system 400. For example, users can directly interact with dynamic displays of service options, interactive elements for subscription customization, and tools for real-time feedback on user selections from SPoG UI405. It integrates with other system components to reflect accurate service information and user customization options. SPoG UI is developed using web-based technology, enabling access from various devices such as desktop computers, laptops, tablets, and smartphones. SPoG UI405 provides a comprehensive view of the entire distribution ecosystem, aggregating data and functionality from various modules into a centralized, easily navigable platform. SPoG UI405 simplifies the management of complex distribution tasks and provides resellers with a streamlined experience. In some embodiments, SPoG405 includes a dynamic pricing tool that displays fluctuating costs based on individual user consumption patterns. The dynamic pricing tool enables the system to manage its own pricing without requiring vendor-specific customizations, promoting pricing transparency and adaptability.

[0091] The Data Mesh 410 is an advanced data management layer. It aggregates and harmonizes data from various sources, including ERP, vendor platforms, and third-party databases. This component ensures that all operational modules within System 400 have access to consistent and up-to-date information. System 400 can synchronize with existing reseller systems, ensuring efficient data exchange and system functionality.

[0092] Data Mesh 410 aggregates and reconciles data from various systems, including inventory management, point-of-sale (POS), and CRM, to ensure real-time availability. It employs Change Data Capture (CDC) to track real-time changes in transactional systems. This module standardizes data formats and units, ensuring data consistency and accuracy for decision-making processes related to service delivery.

[0093] AI Module 460 automates the conversion of products and services to subscription models using machine learning algorithms and predictive modeling. AI Module 460 dynamically adjusts service offerings by analyzing market trends, user preferences, and consumption data. It is configured to dynamically adjust pricing and service options based on real-time usage data. This enables a flexible subscription model that adapts to changing user needs and consumption habits.

[0094] AI Module 460 includes a decision support system for adapting subscriptions based on advanced data analysis. In some embodiments, AI Module 460 employs deep learning neural networks, specifically convolutional neural networks (CNNs) and recurrent neural networks (RNNs), for pattern recognition and time series analysis. For example, CNNs can be used to identify trends and patterns in market data, while RNNs, particularly LSTM (Long Short-Term Memory) networks, can analyze sequential data such as time-based user interaction patterns. In some embodiments, AI Module 460 can use decision trees for classification and regression tasks. These decision trees analyze user data and market conditions and segment users into different categories based on their service preferences. Random forests and gradient boosting algorithms, i.e., ensemble methods of decision trees, provide improved prediction accuracy and stability. In some embodiments, clustering, particularly K-means clustering and hierarchical clustering, is employed to segment the market and user base into heterogeneous groups. Market / user segmentation helps the AI ​​module 460 understand diverse user preferences and customize vendor-independent CTO / QTO for different market segments.

[0095] In some embodiments, the AI ​​module 460 can use reinforcement learning (RL) to adapt service delivery based on user feedback. RL algorithms, particularly Q-learning and policy gradient methods, can be used to tune the model to maximize user satisfaction and learn from each interaction to improve recommendation accuracy. This module integrates reinforcement learning algorithms to continuously adapt service delivery based on user feedback, improving the accuracy and relevance of customized subscriptions over time. Furthermore, NLP techniques can be employed to analyze user feedback and queries. Utilizing tokenization, sentiment analysis, and named entity recognition, the AI ​​module 460 interprets user feedback and improves the service customization process.

[0096] Real-time processing based on the data mesh 410 enables the AI ​​module 460 to dynamically adjust service delivery based on current usage patterns and immediate feedback from the market. The data mesh 410 also allows for precise tracking of real-time usage data and the implementation of usage-based pricing strategies. The data mesh 410 includes collaborative filtering and content-based recommendation systems that can analyze user behavior and preferences and suggest appropriate service adjustments by comparing them to similar user profiles or content characteristics.

[0097] In some embodiments, the AI ​​module 460 can integrate predictive analytics tools and employ time series forecasting methods (e.g., autoregressive integrated moving average, exponential smoothing, etc.) to predict future service demand. Optimization algorithms such as linear programming and genetic algorithms can consider various factors such as cost, user preferences, and resource availability to recommend the most effective service bundle and facilitate the optimal subscription configuration. The AI ​​module 460 can employ Monte Carlo simulation and scenario analysis for risk assessment and strategic planning, simulating various market scenarios and evaluating the potential impact of different subscription models under different conditions.

[0098] The Vendor Integration Module (VIM) 415 integrates with various vendor systems and APIs via RTDM 410 to collect data on processes, pricing, configurations, rebates, and discounts. RTDM 410 is incorporated as a bridge between System 400 and various vendor platforms, ensuring comprehensive and timely data exchange. In some embodiments, VIM 415 can be configured to run a vendor process mapping algorithm, mapping each vendor's unique processes to standardized processes within System 400. VIM 415 can be configured to identify similarities and differences in vendor workflows and configure the system according to vendor-specific requirements.

[0099] The Configuration and Pricing Engine (CPE) 420 standardizes and manages configuration and pricing structures across different vendors. The CPE 420 can be configured to implement algorithms for performing additional data normalization processes on data received from various vendors, ensuring consistency and compatibility of heterogeneous data ingested into the system 400. In a non-limiting example, the CPE 420 may run normalization algorithms to standardize data received from different vendors, ensuring uniformity of format, units, and terminology. The CPE 420 may harmonize heterogeneous data structures to generate vendor-independent data structures, improving processing and analysis. In another non-limiting example, the CPE 420 may be configured to run dynamic pricing algorithms that dynamically adjust product prices based on market demand, competition, and other factors determined by insights gathered through the data mesh 410 and the AI ​​module 460. The CPE 420 may incorporate machine learning models via the AI ​​module 460 to optimize pricing strategies and improve revenue and / or conversions.

[0100] The Rebate and Discount Management Module (RDM) 425 manages rebates and discounts offered by different vendors. RDM425 employs algorithms to determine the applicability of rebates and discounts based on various factors such as order volume, product mix, and contractual agreements. In a non-limiting example, RDM425 can be configured to run a rebate optimization algorithm to optimize the rebate structure by analyzing historical data and vendor contracts. RDM425 can identify opportunities derived from the AI ​​Module 460 to maximize rebate benefits while minimizing costs and ensuring compliance with contractual obligations. RDM425 can also be configured, via the AI ​​Module 460, to implement a discount allocation algorithm to strategically distribute discounts across different products and customers, maximizing sales and profitability. The algorithm can optimize discount distribution by considering factors such as customer segmentation, product margins, and promotional objectives.

[0101] The AI ​​module 460 can function as a key component for improving the accuracy and adaptability of algorithms within the system 400. In some embodiments, integration with the data mesh 410 aggregates and combines real-time data from various sources, ensuring that the AI ​​module 460 always has current information for model training, thereby facilitating continuous model training. The system 400 may include a feedback mechanism configured to allow the system 400 to capture user interactions and results when establishing a connection between the SPoG UI 405 and the AI ​​module 460. Such a feedback mechanism can facilitate learning and adaptation based on user feedback. Integration of the AI ​​module 460 with the CPE 420 enables dynamic model tuning, leveraging real-time insights from the data mesh 410 to dynamically adjust model parameters and hyperparameters for optimal performance. The AI ​​module 460 can be configured to implement an ensemble learning technique that combines predictions from multiple machine learning models trained on vendor-specific data, utilizing integration with the vendor integration module (VIM) 415, thereby improving prediction accuracy and robustness. SPoG UI405 can integrate Model Explainability methods, enabling users to interpret model predictions and understand the rationale behind decision-making processes, thereby improving transparency. By leveraging these strategies and the capabilities of System 400's modules and models, the accuracy and adaptability of algorithms can be significantly improved within a vendor-independent CTO / QTO framework.

[0102] System 400 provides a scalable and adaptable system for standardizing processes across various vendors, regions, and markets, while simultaneously optimizing configuration, pricing, rebates, and discounts. Leveraging real-time data processing, AI-driven analytics, and user customization capabilities, System 400 delivers an efficient, vendor-independent platform.

[0103] Figure 5 shows an embodiment of an advanced distribution platform including a system 500 for managing a complex distribution network, which can be an embodiment of system 300, providing a technology distribution platform for optimizing the management and operation of the 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 insights indicator display 545, a predictive analytics module 550, a recommendation system module 555, a notification module 560, a self-onboarding module 565, and a communication module 570.

[0104] System 500, as an embodiment of System 300, can enable supply chain and distribution management using a wide range of technologies and algorithms. These technologies and algorithms facilitate efficient data processing, personalized interactions, real-time analysis, secure communication, and effective management of documents, catalogs, and performance metrics.

[0105] In some embodiments, SPoG UI505 functions as the central interface within System 500, providing users with a unified view of the entire distribution network. Frontend technologies such as ReactJS, TypeScript, and Node.js are used to create an interactive and responsive user interface. These technologies enable SPoG UI505 to deliver a user-friendly experience, allowing users to access relevant information, navigate between different modules, and perform tasks efficiently.

[0106] CIM510, or 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 securely handle customer data, personalize the customer experience, and provide control over user access.

[0107] The RTDM module 515, or Real-Time Data Mesh module, is a component of System 500 and ensures a 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. In addition, the module employs a Change Data Capture (CDC) mechanism to capture real-time data updates from various transaction systems, such as legacy ERP and CRM systems. This capability allows users to access accurate, current information and make informed decisions.

[0108] The AI ​​module 520 within system 500 can extract valuable insights from data using advanced analytical and machine learning algorithms, including Apache Spark, TensorFlow, and scikit-learn. These algorithms enable the module to automate repetitive tasks, predict demand patterns, optimize inventory levels, and improve overall supply chain efficiency. For example, AI module 520 can use predictive models to forecast demand, allowing users to optimize inventory management and minimize situations of stockouts or excess inventory.

[0109] The Interface Display Module 525 focuses on presenting data and information in a clear and user-friendly manner. It utilizes technologies such as HTML, CSS, and JavaScript frameworks like ReactJS to create an interactive and responsive user interface. These technologies enable users to visualize data using various data visualization techniques, such as graphs, charts, and tables, facilitating efficient data understanding, comparison, and trend analysis.

[0110] The Personalized Interaction Module 530 utilizes customer data, behavioral history, 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 the delivery of targeted recommendations. For example, the module can analyze customer preferences and purchase history to provide personalized product recommendations, improve customer satisfaction, and drive sales.

[0111] Document Hub 535 functions as a centralized repository for storing and managing documents within System 500. It utilizes technologies such as SeeBurger and Elastic Cloud for efficient document management, storage, and retrieval. For example, Document Hub 535 employs SeeBurger's document management capabilities to classify and organize documents based on type, such as contracts, invoices, product specifications, or compliance documents, allowing users to easily access and search for relevant documents when needed.

[0112] 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 deliver catalog information to users quickly and efficiently, regardless of their geographical location.

[0113] The Performance and Insights Metrics Display 545 collects, analyzes, and visualizes real-time performance metrics and insights related to supply chain operations. Leveraging tools like Splunk and Datadog, it enables effective performance monitoring and provides actionable insights. For example, the module can use Splunk's log analysis capabilities to identify performance bottlenecks in the supply chain, enabling users to take proactive steps to optimize their operations.

[0114] The Predictive Analytics Module 550 employs machine learning algorithms and predictive 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 leverages TensorFlow's deep learning capabilities to analyze sales history data and predict future demand, enabling users to optimize inventory levels and minimize costs.

[0115] The recommendation system module 555 focuses on providing intelligent recommendations to users within a distribution network. It generates personalized product or service recommendations based on customer data, behavioral history, and machine learning algorithms. Technologies such as Adobe Target and Apache Spark can be employed for data analysis, modeling, and the delivery of targeted recommendations. For example, the module can use Adobe Target's recommendation engine to analyze customer preferences and behavior, deliver personalized product recommendations across various channels, improve customer engagement, and drive sales.

[0116] The notification module 560 enables the delivery of real-time notifications to users regarding critical events, updates, or alerts within the supply chain. It utilizes technologies such as Apigee X and TIBCO for message queuing, an event-driven architecture, and notification delivery. For example, the module can leverage TIBCO's messaging infrastructure to send real-time notifications to users' devices, ensuring the timely distribution of relevant information.

[0117] The Self-Onboarding Module 565 simplifies 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. By employing technologies such as Okta and Kentico, it can ensure secure user authentication, access control, and self-learning resources. For example, the module can leverage Okta's identification and access management capabilities to securely onboard new users, provide them with appropriate access permissions, and guide them through the system's functionality.

[0118] The communication module 570 enables communication and collaboration within the system 500. It provides users with channels for interaction, message exchange, document sharing, and project collaboration. By employing technologies such as Apigee Edge and Adobe Launch, it facilitates 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.

[0119] This allows System 500 to incorporate a variety of modules that utilize a diverse range of technologies and algorithms to optimize supply chain and distribution management. These modules include 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 insights indicator display 545, predictive analytics module 550, recommendation system module 555, notification module 560, self-onboarding module 565, and communication module 570, which work together to provide end-to-end visibility, data-driven decision-making, personalized interaction, real-time analytics, and streamlined communication within the distribution network. By incorporating specific technologies and algorithms, efficient data management, secure communication, personalized experiences, and effective performance monitoring become possible, contributing to improved operational efficiency and success in supply chain and distribution management.

[0120] (Real-time data mesh) Figure 6 illustrates an RTDM module 600 according to one embodiment. The RTDM module 600 can 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.

[0121] The RTDM module 600, as shown in Figure 5, represents an effective data mesh and change capture component within the overall system architecture. This module can be configured to provide real-time data management and standardization capabilities, enabling efficient operation within the supply chain and distribution management domain.

[0122] The RTDM module 600 may include an integration layer 610 (also called the “record system”) that integrates with various enterprise systems. These enterprise systems may include 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 record system, such as sales orders, purchase orders, inventory data, and customer information. These feeds enable real-time data updates, ensuring that the RTDM module operates with the most up-to-date and accurate data.

[0123] The RTDM module 600 may include a data layer 620 configured to process and transform data for retrieval and analysis. The data layer 620 includes a data mesh, i.e., a cloud-based infrastructure configured to provide scalable and fault-tolerant data storage capabilities. Within the data mesh, multiple purpose-specific data stores (PDSs) are deployed, which can 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 function as repositories of normalized and / or standardized data, ensuring data consistency and integrity across the system.

[0124] In some embodiments, the RTDM module 600 implements a data replication mechanism for capturing real-time changes from multiple data sources, including transactional systems such as ERP (e.g., SAP, Impulse, META, I-SCALA). The captured data can then be processed and standardized on the fly and converted into a standardized format suitable for analysis and integration. This process ensures that the data is readily available and current within the data mesh, facilitating real-time insights and decision-making.

[0125] More specifically, the data layer 620 within the RTDM module 600 can be configured as a robust and flexible foundation for managing and processing data within the distribution ecosystem. In some embodiments, the data layer 620 can encompass a highly scalable and robust data lake, which may be called the data lake 622, along with a set of purpose-specific data stores (PDSs) which may be denoted as PDS 624.1–624.N. These components are integrated to ensure efficient data management, standardization, and real-time availability.

[0126] Data Layer 620 includes Data Lake 622, a modern storage and processing infrastructure configured to handle the ever-increasing volume, diversity, and speed of data generated within the supply chain. Built on a scalable distributed file system, such as Apache Hadoop Distributed File System (HDFS) or Amazon S3, the data lake provides a unified, scalable platform for storing both structured and unstructured data. Leveraging the flexibility and fault tolerance of cloud-based storage, Data Lake 622 can accept data inflows from diverse sources.

[0127] In conjunction with Data Lake 622, a population of purpose-specific data stores PDS624.1–624.N can be employed. Each PDS624 can function as a dedicated repository optimized for storing and retrieving specific types of data related to the supply chain domain. In some non-limiting examples, PDS624.1 might be dedicated to customer data, storing information such as customer profiles, preferences, and transaction history. PDS624.2 might focus on product data, encompassing details such as SKU codes, descriptions, pricing, and inventory levels. These purpose-specific data stores enable efficient data retrieval, analysis, and processing, meeting the diverse needs of supply chain users.

[0128] 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 be integrated with other enterprise-scale systems, in addition to transaction systems such as SAP, Impulse, META, and legacy ERPs like I-SCALA. CDC constantly monitors updates, modifications, or new transactions in these systems 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 real-time insights into the distribution ecosystem.

[0129] In some embodiments, the data layer 620 can be implemented using one or more frameworks, such as .NET or Java, to facilitate integration with existing enterprise systems, ensuring broad compatibility with existing systems and providing flexibility for customization and extensibility. For example, the data layer 620 can leverage a Java technology stack, including frameworks such as Spring and Hibernate, to facilitate integration with record systems that have a diverse population of ERP systems and other enterprise-scale solutions. This can facilitate smooth data exchange, process automation, and end-to-end visibility across the supply chain.

[0130] In terms of data processing and analysis, Data Layer 620 can, in some non-limiting examples, utilize the capabilities of distributed computing frameworks, such as Apache Spark or Apache Flink. These frameworks can enable parallel processing and distributed computing across large datasets stored in data lakes and PDSs. By using these frameworks, supply chain users can perform complex analytical tasks, apply machine learning algorithms, and derive valuable insights from data. For example, Data Layer 620 can use Apache Spark's machine learning libraries to develop predictive models for demand forecasting, optimize inventory levels, and identify potential supply chain risks.

[0131] In some embodiments, the data layer 620 can incorporate robust data governance and security measures. Strict 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. In addition, the data layer 620 can implement data lineage and audit trail mechanisms to enable users to track the origin and history of data, ensuring data integrity and compliance with regulatory requirements.

[0132] In some embodiments, Data Layer 620 can be deployed in a cloud-native environment using containerization technologies such as Docker and orchestration frameworks such as Kubernetes. This approach ensures scalability, resilience, and efficient resource allocation. For example, Data Layer 620 can be deployed on cloud infrastructure provided by AWS, Azure, or Google Cloud, leveraging their managed services and scalable storage options. This enables demand-based resource scaling, minimizes operational overhead, and provides an adaptable infrastructure for managing supply chain data.

[0133] The RTDM module 600's data layer 620 can integrate the highly scalable data lake, data lake 622, along with application-specific PDSs, PDS624.1–624.N. By employing a CDC mechanism, data layer 620 ensures efficient data management, standardization, and real-time availability. In non-limiting examples, 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, data layer 620 ensures data integrity, confidentiality, and compliance. Through its scalable infrastructure and integration with existing systems, data layer 620 enables supply chain users to make data-driven decisions, optimize operations, and drive business success in dynamic and complex distribution environments.

[0134] The RTDM module 600 may include an AI module 630 configured to implement one or more algorithms and machine learning models and analyze data stored in the data layer 620 to 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 can continuously learn from new data inputs and adapt its model to provide accurate, current insights. The AI ​​module 630 can generate predictions, recommendations, and alerts and publish such insights to a dedicated data feed.

[0135] 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 may include a set of autonomously operating headless engines 640.1–640.N. These engines represent distinct functionalities within the system and may include, for example, one or more recommendation engines, insight engines, and subscription management engines. Engines 640.1–640.N can deliver 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 Figure 5, and these are not intended to be limiting. Any additional headless engines may be included in the data engine layer 640 or other exemplary layers of the disclosed system.

[0136] These systems can be configured to receive data from multiple sources, such as transaction 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 can be applied to cleanse, aggregate, and enhance the data, preparing it for further analysis and integration.

[0137] Furthermore, a data distribution mechanism can be employed to facilitate integration with 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.

[0138] The Experience Layer 650 focuses on providing an intuitive, user-friendly interface for interacting with supply chain data. Experience Layer 650 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 their views based on their roles and responsibilities and receive relevant updates. Users can subscribe to specific data updates, such as inventory changes, pricing updates, or new SKU notifications, based on their preferences and roles.

[0139] This means that, in some embodiments, the RTDM module 600 for supply chain and distribution management can include integration with record systems and may include one or more data layers with a data mesh and purpose-specific data stores, AI components, 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 search within the module ensure that 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 heterogeneous data sources, efficient data standardization, and advanced analytical capabilities. The module's ability to replicate and standardize data from diverse ERPs while maintaining auditable and repeatable transactions provides a clear advantage, enabling a unified view for vendors, resellers, customers, end customers, and other entities within the distribution system, including IT distribution systems.

[0140] (Vendor-independent CTO / QTO management) In one embodiment, Figure 7 shows a system 700 for managing vendor relationships, standardizing processes, optimizing pricing strategies, and improving customer experience across diverse markets and regions. 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, and a vendor-independent CTO / QTO integration module 720.

[0141] In some embodiments, SPoG UI705 can be an embodiment of the SPoG UI described above, enhanced by a one-click conversion function that allows users to instantly convert their shopping cart to a subscription-based service model, thereby simplifying the process across multiple vendors, regions, and markets, and ensuring scalability and adaptability.

[0142] RTDM710 aggregates and standardizes real-time data from various sources, which is essential for the efficient operation of the vendor-independent CTO / QTO integration module 720. This includes data on product specifications, subscription usage patterns, and market trends. RTDM710 aggregates and standardizes data from multiple sources, such as ERP, CRM systems, and market intelligence, establishing a centralized, unified data hub. It efficiently handles both structured and unstructured data by utilizing a hybrid of data warehouses and data lakes. RTDM710 employs ETL processes and data normalization techniques to ensure data uniformity and accessibility. This standardized data is essential for the vendor-independent CTO / QTO integration module 720 to function, providing the input required to precisely and effectively convert products into subscription-based services. RTDM710 maintains data integrity and relevance, which is essential for automated processes to standardize processes across multiple vendors, regions, and markets. In some embodiments, the RTDM710 is configured to interface with an asset management system to support asset management across multiple vendors, regions, and markets, ensuring scalability and adaptability.

[0143] The AAML Module 715 functions as a central processing unit for vendor-independent processes. It encompasses dedicated rules and algorithms designed for key tasks in vendor-independent CTO / QTO processes, including vendor relationship management, data and process standardization, pricing strategy optimization, and dynamic pricing strategy application. The AAML Module 715 employs analytical tools for big data processing and deep learning capabilities. It performs sentiment analysis, trend forecasting, and behavioral analysis to understand and anticipate market and user demand. The AAML Module 715 integrates and trains machine learning algorithms based on historical datasets, identifying usage patterns to generate predictive recommendations for subscription models. It adapts its algorithms based on a continuous feedback loop, refining accuracy over time. This module performs essential functions for automating processes across multiple vendors, regions, and markets, ensuring scalability and adaptability.

[0144] In one embodiment, the Vendor-Independent CTO / QTO Integration Module (VACIM) 720 is configured to standardize processes across multiple vendors, regions, and markets, ensuring scalability and adaptability. VACIM 720 implements a real-time data mesh (RTDM) as a bridge between RTDM and vendor systems, facilitating vendor relationship management, process standardization, and optimization of pricing strategies, thereby improving the customer experience across diverse markets and regions.

[0145] VACIM710 can include a process standardization engine 725 for standardizing vendor-specific processes, pricing structures, configuration options, and discount mechanisms. Leveraging advanced algorithms and rules, this engine ensures consistency and uniformity across diverse vendor offerings. It dynamically adjusts to adapt to changes in vendor processes, effectively addressing scalability challenges.

[0146] The VACIM710 can optionally include a vendor data conversion gateway 730 for converting vendor raw data into a standardized format compatible with RTDM. It employs intelligent data mapping techniques and machine learning algorithms to interpret vendor-specific data schemas and convert them into a unified format. This gateway facilitates the ingestion and synchronization of real-time data from various vendors, enabling timely updates and insights.

[0147] The Dynamic Pricing and Configuration Optimization Module 740, integrated with the AAML Module 715, can implement and extend its functionality to support dynamic pricing strategies and product configuration optimization specifically tailored to vendor-independent CTO / QTO frameworks. This module leverages historical sales data, market trends, and vendor-specific pricing structures to optimize subscription models and configuration options. It employs advanced analytics and machine learning algorithms to predict demand patterns and recommend personalized subscription plans tailored to individual users and market segments.

[0148] The Vendor Compatibility Analyzer 750, integrated with the AAML Module 715, can assess the compatibility of products from different vendors for service transformation within a vendor-independent CTO / QTO framework. The Vendor Compatibility Analyzer 750 can analyze product specifications, feature sets, and interoperability requirements to determine the feasibility of bundling products into a subscription-based service. This analyzer utilizes machine learning algorithms to identify synergies and dependencies between vendor offerings and optimize service combinations to maximize value and customer satisfaction.

[0149] The Market Intelligence Integration Module 760, via the RTDM 710 and AAML Module 715, can collect and analyze information including market trends, competitor strategies, and customer preferences related to vendor-independent CTO / QTO frameworks. Through the RTDM 710, the Market Intelligence Integration Module 760 can aggregate external data sources such as industry reports, social media feeds, and customer feedback platforms to provide actionable insights for product positioning, pricing, and market segmentation. This module integrates with the AAML Module to enhance the decision-making process and ensure alignment with market dynamics.

[0150] VACIM720 can integrate with the Adaptive Rebate Management System 770 to automate the management of vendor rebates, incentives, and discounts within a vendor-independent CTO / QTO framework. The Adaptive Rebate Management System 770 can dynamically calculate and apply rebates based on predefined rules, vendor agreements, and real-time sales data. Leveraging machine learning algorithms, the system optimizes the rebate structure and encourages desired behavior while maximizing profitability and vendor relationships.

[0151] The Geographic Market Mapper 780, when integrated with RTDM 710, can map geographical regions to specific vendor offerings, price ranges, and regulatory requirements within a vendor-independent CTO / QTO framework. The Geographic Market Mapper 780 can provide insights into regional demand patterns, competitive landscapes, and market saturation levels, enabling strategic decision-making and localized product customization. Integration of the Geographic Market Mapper 780 with VACIM 720 ensures alignment between geographical market dynamics and vendor engagement strategies.

[0152] The cross-vendor configuration analyzer 785, integrated with the AAML module 715, can be configured to evaluate the compatibility and interoperability of product configurations across multiple vendors within a vendor-independent CTO / QTO framework. It employs machine learning algorithms to analyze configuration data, identify conflicts or dependencies, and recommend harmonious configurations that meet customer requirements while maximizing vendor flexibility. This analyzer improves the configurability and scalability of subscription-based services across a diverse vendor portfolio.

[0153] The Global Pricing Harmonizer 790, integrated with VACIM720, can be configured to harmonize pricing structures and discount mechanisms across different geographical markets within a vendor-independent CTO / QTO framework. It ensures pricing parity and affordability across diverse customer segments by considering currency fluctuations, tax systems, and regional purchasing power parity. The Global Pricing Harmonizer 790 executes predictive analytics and optimization algorithms to balance global competitiveness with local market trends, fostering sustainable growth and customer loyalty.

[0154] The Vendor Relationship Management Dashboard 706, integrated with SPoG UI 705, can provide stakeholders with real-time visibility into vendor performance, engagement metrics, and strategic partnerships within a vendor-independent CTO / QTO framework. The Vendor Relationship Management Dashboard 706 can aggregate vendor-related data, including transaction history, communication logs, and contractual terms, into actionable insights for informed decision-making and collaborative vendor management. This dashboard enhances transparency, accountability, and trust in vendor relationships, fostering mutual value creation and innovation.

[0155] This configuration allows System 700 to integrate data from multiple sources into a unified interface via SPoG UI705, automate various tasks in vendor-independent process management via AAML715, and maintain a real-time, standardized data repository via RTDM710. This architecture enables efficient and precise processes for vendor relationship management, process standardization, pricing strategy optimization, and improving customer experience across various markets and regions.

[0156] Figure 8 illustrates a flowchart of Method 800 for process standardization and scalability. This flowchart describes the operation for vendor process analysis, development of a standardized framework, scalability design, and implementation of a rule engine. This method can be implemented in any embodiment described herein, such as System 700.

[0157] Operation 801 begins with a thorough analysis of existing vendor processes. The Real-Time Data Mesh (RTDM) 710 aggregates and standardizes real-time data from various sources, including vendor processes, which are essential for understanding the operational complexity of each vendor.

[0158] Operation 801 may include analyzing vendor processes using the Advanced Analytics and Machine Learning (AAML) module 715. Machine learning algorithms are employed to identify commonalities and differences across vendors and extract insights useful for standardization processes.

[0159] In operation 802, a standardized framework for order configuration and estimation is developed based on the analysis performed in operation 801. The Vendor-Independent CTO / QTO Integration Module (VACIM) 720 incorporates a transformation layer that absorbs and standardizes different vendor processes, enabling the development of this framework.

[0160] Operation 802 may include developing standardized processes using the process standardization engine 725 within VACIM 720. This engine employs advanced algorithms and rules to ensure consistency and uniformity across diverse vendor offerings and to address variations in vendor processes.

[0161] In operation 803, the framework is designed to be scalable, ensuring that it can handle multiple vendors without the need for vendor-specific customization. This scalability design is essential for the efficient operation of a vendor-independent CTO / QTO framework across diverse regions and markets.

[0162] Operation 803 may include the integration of scalability design capabilities within VACIM720. This involves designing a framework architecture to address dynamic scaling requirements and leveraging cloud-based infrastructure and distributed computing technologies.

[0163] Operation 804 implements a rules engine to carry out standardized processes and automate decision-making based on predefined rules. This rules engine plays a crucial role in ensuring consistency and efficiency within the standardized framework.

[0164] Operation 804 may include integrating a rule engine within VACIM720. The rule engine utilizes algorithms to automate decision-making by implementing standardized processes, reducing manual intervention and ensuring compliance with predefined rules.

[0165] This ensures that Method 800 promotes the standardization and scalability of vendor processes within a vendor-independent CTO / QTO framework, and efficiently handles orders across diverse vendors, regions, and markets.

[0166] Figure 9 illustrates a flowchart of Method 900 for integrating rules into the transformation layer and gateway and automating data processing tasks by applying standardized rules. This flowchart describes the actions required to efficiently process vendor data and apply standardized rules within a vendor-independent CTO / QTO framework implemented in System 700.

[0167] Operation 901 begins with the conversion gateway receiving raw vendor data from various sources, including ERP, CRM systems, and market information systems. The conversion gateway can be configured to efficiently process the vendor data and integrate it with the vendor's systems.

[0168] Operation 901 may include a transformation gateway that employs data mapping techniques to interpret vendor-specific data schemas and convert them into a unified format. This process ensures that data from different vendors is standardized and compatible with downstream systems, facilitating integration.

[0169] In operation 902, the conversion gateway passes the standardized data to the conversion layer. The conversion layer can be set up to absorb and standardize processes from different vendors and convert them into a unified format.

[0170] Operation 902 may include a transformation layer that applies predefined standardization rules to incoming data. These rules enforce consistency and uniformity across diverse vendor offerings, ensuring that all data processed by the system conforms to established standards.

[0171] In operation 903, the transformation layer integrates the rule engine and applies standardized rules to automate data processing tasks. This operation involves the integration of the rule engine within system 700, ensuring that standardized rules are consistently applied throughout the entire data processing pipeline.

[0172] Operation 903 may include the rule engine analyzing incoming data and applying predefined rules to identify and correct inconsistencies and deviations from standard formats. This automated process reduces the need for manual intervention and improves the efficiency and accuracy of data processing tasks.

[0173] In operation 904, the transformation layer outputs standardized and processed data for downstream processing or analysis. This operation completes the data processing pipeline within the vendor-independent CTO / QTO framework, ensuring that vendor data is effectively standardized and available for order configuration and quotation.

[0174] As a result, Method 900 integrates a transformation layer, a transformation gateway, and a rule engine to apply standardized rules, automate data processing tasks, and facilitate the efficient processing of vendor data across various regions and markets.

[0175] Figure 10 illustrates a flowchart of Method 1000 for machine learning and data processing. This flowchart describes operations aimed at analyzing vendor data, handling missing data, automating decision-making, and ensuring continuous improvement using machine learning algorithms. This method can be implemented in any one of the embodiments described herein, such as System 700.

[0176] In operation 1001, vendor data is collected and prepared for analysis. This operation involves extracting data from various sources, such as ERP systems, CRM systems, and external market intelligence sources. The Real-Time Data Mesh (RTDM) 710 ensures that this data is aggregated, standardized, and prepared for analysis.

[0177] Operation 1001 may include preprocessing steps such as data cleaning, normalization, and feature engineering to ensure the quality and relevance of the data for analysis. Techniques such as outlier detection and removal algorithms may be used to guarantee data quality. Normalization methods such as Min-Max scaling or Z-score normalization may be employed to standardize the data across different scales. Feature engineering techniques or feature selection algorithms such as principal component analysis (PCA) may be used to extract features relevant to the analysis.

[0178] In operation 1002, machine learning algorithms are used to analyze the prepared data and identify patterns and trends. The Advanced Analytics and Machine Learning (AAML) module 715 acts as the central processing unit for this analysis. Techniques such as regression analysis, clustering, and classification are employed to extract insights from the data.

[0179] Operation 1002 may include the application of algorithms such as K-means clustering to segment vendors based on their behavior, or decision trees to identify factors influencing pricing strategies. Clustering algorithms such as K-means clustering or hierarchical clustering may be employed to segment vendors based on their behavior or characteristics. Classification algorithms such as decision trees, support vector machines (SVMs), and random forests can be applied to classify vendors into different categories based on predefined criteria.

[0180] Operation 1003 allows the execution of an algorithm to handle missing data within a vendor dataset. The missing data handling component of AAML module 715 can be built to fill gaps in the data using techniques such as imputation or predictive modeling, ensuring completeness and accuracy.

[0181] Operation 1003 may involve using algorithms such as linear regression or random forests, or leveraging techniques such as mean imputation or interpolation, to predict missing values ​​based on available data. Imputation methods such as mean imputation, median imputation, and mode imputation can be implemented to replace missing values ​​with central tendencies. Predictive modeling techniques such as linear regression, decision trees, and K-nearest neighbors (KNN) can be used to predict missing values ​​based on other variables in the dataset.

[0182] Operation 1004 implements a machine learning algorithm to automate the decision-making process based on historical data and predefined rules. This operation leverages the automated decision-making component of AAML module 715 to make decisions such as pricing optimization, product configuration, or rebate management.

[0183] Operation 1004 may include algorithms such as decision trees or reinforcement learning to automate the decision-making process based on predefined rules and historical data. A decision tree algorithm can be integrated to create a decision-making model based on predefined rules and historical data. Reinforcement learning techniques may be used to enable the system to learn from past decisions and improve its decision-making over time.

[0184] Action 1005 establishes a process for continuously improving the machine learning model based on feedback and changing market trends. This action ensures that the model maintains relevance and effectiveness over time and adapts to the evolution of vendor behavior and market trends. Action 1005 may include techniques such as retraining the model, A / B testing, or incorporating a feedback loop to update the algorithm based on new data.

[0185] This enables method 1000 for machine learning and data processing to use machine learning algorithms within system 700 to efficiently analyze vendor data, handle missing data, make automated decisions, and continuously improve.

[0186] Operation 1005 can involve techniques such as retraining the model, A / B testing, or incorporating a feedback loop to update the algorithm based on new data. An online learning algorithm can be implemented to update the machine learning model in real time as new data becomes available. A / B testing methods can be used to compare and evaluate the performance of the updated model versus the existing model, and a feedback loop can be incorporated to refine the algorithm based on user interaction.

[0187] Figure 11 is a block diagram of exemplary components of 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 partial combinations thereof. Computer system 1100 may include one or more processors (also called central processing units or CPUs), for example, processor 1104. Processor 1104 may be connected to a communication infrastructure or bus 1106.

[0188] Furthermore, the computer system 1100 may include user input / output devices 1103 such as a monitor, keyboard, and pointing device, which can communicate with the communication infrastructure 1106 through a user input / output interface 1102.

[0189] One or more processors 1104 may be graphics processing units (GPUs). In one embodiment, the GPU may be a processor that is a special electronic circuit configured to process mathematically intensive applications. The GPU may have a parallel structure that can efficiently process large data blocks, such as mathematically intensive data common to computer graphics applications, images, videos, etc.

[0190] Furthermore, the computer system 1100 may include 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 internally.

[0191] Furthermore, the computer system 1100 may 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.

[0192] The removable storage drive 1114 may interact with the removable storage unit 1118. The removable storage unit 1118 may include a computer-accessible or readable storage device having computer software (control logic) and / or data stored thereon. The removable storage unit 1118 may also include a program cartridge and cartridge interface (such as those 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 units and associated interfaces. The removable storage drive 1114 may read from and / or write to the removable storage unit 1118.

[0193] The secondary memory 1110 may include other means, devices, components, mediators, or other approaches to enable computer programs and / or other instructions and / or data to be accessed by the computer system 1100. Such means, devices, components, mediators, or other approaches may include, for example, a removable storage unit 1122 and an interface 1120. Examples of the removable storage unit 1122 and interface 1120 may include a program cartridge and cartridge interface (such as those 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 units and associated interfaces.

[0194] The computer system 1100 may further include a communication or network interface 1124. The communication interface 1124 may enable the computer system 1100 to communicate with and interact with a combination of external devices, external networks, external entities, etc. (referenced individually and collectively in reference number 1128). For example, the communication interface 1124 may enable the computer system 1100 to communicate with an external or remote device 1128 via a communication path 1126, which may be wired and / or wireless (or a combination thereof) and may include a combination of LAN, WAN, Internet, etc. Control logic and / or data may be transmitted to and from the computer system 1100 via the communication path 1126.

[0195] Furthermore, the computer system 1100 may be, to give some non-limiting examples, 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 a combination thereof.

[0196] The computer system 1100 may be a client or server that accesses or hosts applications and / or data through a delivery model, and may include, but is not limited to, remote or distributed cloud computing solutions, local or on-premises software ("on-premises" 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 aforementioned examples or other services or delivery models.

[0197] The 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), Extended Markup Language (XML), Yet Another Markup Language (YAML), Extended Hypertext Markup Language (XHTML), Wireless Markup Language (WML), MessagePack, XML User Interface Language (XUL), or other functionally similar expressions, either alone or in combination. Alternatively, proprietary data structures, formats, or schemas may be used either exclusively or in combination with known or open standards.

[0198] In some embodiments, a tangible, non-temporary device or product comprising a tangible, non-temporary computer-readable or computer-compatible medium on which control logic (software) is stored may also be referred to herein as a computer program product or program storage device. This includes, but is not limited to, the computer system 1100, the main memory 1108, the secondary memory 1110, and the removable storage units 1118 and 1122, as well as tangible products embodying the aforementioned combination. When such control logic is executed by one or more data processing devices (such as the computer system 1100), such data processing devices can be made to operate as described herein.

[0199] Figures 12A–12Q illustrate various screens and functionalities of the SPoG UI related to vendor onboarding, partner dashboard, customer cart, order summary, SKU generation, order tracking, shipping tracking, subscription history, and subscription changes. Detailed descriptions of each figure are provided below.

[0200] Figure 12A shows the vendor onboarding start screen, representing the first step in the vendor onboarding process. It provides a form or interface where vendors can express their interest in participating in the distribution ecosystem. Vendors can enter basic information such as company details, contact information, and product catalogs.

[0201] Figure 12B shows a vendor onboarding guide that displays a step-by-step guide or checklist for the vendor to follow during the onboarding process. It outlines the necessary tasks and requirements, ensuring that the vendor has a clear understanding of the onboarding process and can proceed smoothly.

[0202] Figure 12C shows a vendor onboarding call scheduler that facilitates scheduling calls or meetings between vendors and platform partners or representatives responsible for guiding them through the onboarding process. Vendors can select a suitable time slot or request a call, ensuring effective communication and assistance throughout the onboarding process.

[0203] Figure 12D shows a vendor onboarding task list, which presents a comprehensive task list or dashboard outlining the specific steps and actions required for successful vendor onboarding. It provides an overview of pending tasks, completed tasks, and upcoming deadlines, helping vendors track progress and ensuring that each onboarding task is completed in a timely manner.

[0204] Figure 12E shows the vendor onboarding completion screen, confirming the successful completion of the vendor onboarding process. It may display a congratulatory message indicating that the vendor has been officially onboarded into the distribution ecosystem at this point, or a summary of completed tasks.

[0205] Figure 12F shows a partner dashboard that provides partners or users with an aggregated view of relevant information and metrics regarding their partnerships with the distribution ecosystem. It provides an overview of performance indicators, key data points, and actionable insights to facilitate effective collaboration and decision-making.

[0206] Figure 12G shows a customer product cart, where 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.

[0207] Figure 12H shows a customer subscription cart that allows customers to manage their subscription-based purchases. It displays the selected subscription plan, pricing, and duration. Customers can review and modify their subscription details before confirming their selections.

[0208] Figure 12I shows a customer order summary, which provides a summary of the customer's order including details such as the purchased product or subscription, quantity, pricing, and any applied discounts or promotions. This allows the customer to review their order before confirming the purchase.

[0209] Figure 12J shows the vendor SKU generation screen for generating unique stock unit (SKU) codes for vendor products. It may include fields or options where the vendor can specify product details, attributes, and pricing, and the system automatically generates the corresponding SKU codes.

[0210] Figures 12K and 12L show dashboard order summaries for displaying summary information about orders placed within the 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, enabling users to efficiently track and manage orders.

[0211] Figure 12M shows a customer subscription cart that allows customers to add, modify, or delete subscription plans. It can display a list of selected subscriptions, pricing, and renewal dates. Customers can manage their subscriptions and make changes according to their preferences and requirements.

[0212] Figure 12N shows a customer order tracking screen that allows customers to track the status and progress of their orders throughout the supply chain. It displays real-time updates on order fulfillment, including processing, packaging, and shipping. Customers can monitor the movement of their orders and predict delivery times.

[0213] Figure 12O shows customer shipment tracking, which provides customers with real-time tracking information about their shipments. This may include details such as carrier, tracking number, current location, and estimated delivery date. Customers can always get information about the whereabouts of their shipments.

[0214] Figure 12P shows the customer subscription history, which presents a historical record of the customer's subscription activity. It displays a list of previous subscriptions, including the subscription plan, duration, and status. Customers can review their subscription history, track past payments, and view details of previous subscriptions.

[0215] Figure 12Q shows the customer subscription modification dialog, which allows customers to modify their existing subscriptions. It provides options for upgrading or downgrading subscription plans, changing billing details, or adjusting other subscription-related preferences. Customers can manage their subscriptions according to their evolving needs or preferences.

[0216] The UI screens shown are not limited. In some embodiments, the UI screens in Figures 12A–12Q collectively represent the diverse functionality and features provided by the SPoG UI, offering users a comprehensive and user-friendly interface for vendor onboarding, partnership management, customer interaction, order management, subscription management, and tracking within the distribution ecosystem.

[0217] It should be understood that the detailed description section, rather than the abstract section, is intended to be used to interpret the claims. The abstract section may describe one or more, but not all, exemplary embodiments of the invention as intended by the inventors, and is therefore not intended to limit the invention and the appended claims in any way.

[0218] The present invention has been described above with the assistance of function-building blocks illustrating the implementation of specific functions and their relationships. The boundaries of these function-building blocks are arbitrarily defined herein for the sake of explanation. Alternative boundaries can be defined, as long as the specific functions and their relationships are adequately implemented.

[0219] The prior description relating to specific embodiments fully illustrates the general nature of the invention, and such specific embodiments can be readily modified and / or adapted to various uses without departing from the general concept of the invention, without requiring any unnecessary experimentation, by applying the knowledge of those skilled in the art. Such adaptations and modifications are therefore intended to be within the meaning and scope of equivalents of the disclosed embodiments, based on the teachings and guidance presented herein. The expressions or terms herein are for illustrative purposes only and not intended to limit, and therefore should be understood to those skilled in the art to be interpreted in light of the teachings and guidance.

[0220] The breadth and scope of the present invention should not be limited by any of the exemplary embodiments described above, but should be defined solely by the following claims and their equivalents.

Claims

1. A system for managing vendor-independent order configuration (CTO) and quote-to-order (QTO) processes, Real-time data mesh (RTDM) configured to aggregate, standardize, and normalize real-time data from diverse sources, including ERP, CRM systems, and market intelligence, using data warehouses and data lakes for structured and unstructured data processing. A single-pane-of-glass user interface (SPoG UI) configured to facilitate user interaction with features such as instant conversion of shopping carts to subscription-based models, dynamic pricing tools, and real-time visibility into vendor performance and strategic partnerships, Advanced analytics and machine learning (AAML) modules for product compatibility, subscription model optimization, dynamic pricing strategies, sentiment analysis, trend forecasting, and behavioral analysis based on deep learning capabilities and machine learning algorithms, A vendor-independent CTO / QTO integration module (VACIM), A process standardization engine configured to run algorithms for uniformly implementing processes across vendors, A vendor data conversion gateway configured to convert raw vendor data into a standardized format, A system comprising a vendor-independent CTO / QTO integration module (VACIM) with an adaptive rebate management system configured to perform dynamic rebate calculations.

2. The system according to claim 1, wherein the RTDM is configured to perform an ETL process for data integration and normalization in order to ensure uniformity and accessibility.

3. The system according to claim 1, further comprising a geographic market mapper for aligning product offerings with local demand patterns and compliance requirements.

4. The system according to claim 1, wherein the SPoG UI integrates a one-click conversion function to streamline the transition to a subscription-based model.

5. The system according to claim 1, wherein the AAML module is integrated with the vendor compatibility analyzer for evaluating the interoperability of products across vendors.

6. The system according to claim 1, further comprising a global pricing harmonizer for ensuring pricing parity across different markets, taking into account currency fluctuations and purchasing power parity.

7. The system according to claim 1, wherein the VACIM dynamically adjusts process and pricing strategies based on insights into market trends and vendor performance obtained through machine learning.

8. A method for standardizing a vendor-independent CTO / QTO process, wherein the method is Analyzing existing vendor processes to identify uniformity and variability using one or more machine learning algorithms, To develop a scalable, adaptable, and standardized framework for configuring and quoting orders across multiple vendors, wherein the development includes implementing a transformation layer for standardizing diverse vendor processes, and the framework is configured to adapt to dynamic scaling requirements. A method comprising: implementing a rule engine within the framework to automate decision-making and process execution based on predefined rules and insights derived from machine learning.

9. The method of claim 8, comprising employing a process standardization engine configured to verify uniformity across vendor processes.

10. The method of claim 8, further comprising a scalability design feature configured to support the expansion of the vendor network without the need for customization.

11. The method of claim 8, further comprising optimizing subscription models and configuration options using a dynamic pricing strategy, wherein the rule engine implements one or more algorithms for real-time process adjustments based on market dynamics.

12. The method according to claim 8, wherein the rule engine is configured to perform predictive analysis for demand forecasting and inventory optimization.

13. The method according to claim 8, further comprising dynamically adjusting the standardized framework to address one or more vendor-specific requirements and / or one or more market changes.

14. The method according to claim 8, wherein the framework implements a vendor data conversion gateway for standardizing vendor-specific data and converting it into a unified format.

15. A method for automating data processing and standardization in a vendor-independent CTO / QTO system, This involves receiving diverse vendor data, including specifications and pricing, and employing data mapping techniques for standardization. By applying predefined rules for data consistency across vendor datasets, and utilizing a transformation gateway to convert vendor-specific data schemas into a unified format, A method comprising: integrating a rule engine to automate decision-making using machine learning algorithms for data analysis and process automation based on historical data and predefined criteria.

16. The method according to claim 15, further comprising performing an ETL process for efficient data integration and normalization.

17. The method according to claim 15, further comprising running one or more machine models for predictive analysis and optimization of a subscription model.

18. The method according to claim 15, further comprising generating real-time updates and insights facilitated by the RTDM for dynamic process and pricing strategy adjustments.

19. The method according to claim 15, further comprising using a vendor compatibility analyzer to evaluate and recommend product configurations that maximize interoperability and customer value.

20. The method according to claim 15, further comprising incorporating an adaptive rebate management system for dynamically calculating and applying rebates based on sales data and vendor agreements.

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