System and method for end user view in B2B / B2C mode

Abstract

Systems and methods are described that enable dealers to manage their end user traffic in an own traffic environment on a distribution platform. The system includes a server configured to provide a single window user interface (SPoG UI) and a real-time data grid (RTDM) module for ingestion and standardization of data from multiple sources. An advanced analysis and machine learning (AAML) model analyzes the data to provide predictive analysis, anomaly detection, and personalized recommendations. The SPoG UI presents real-time data and insights through interactive visualization, enabling dealers to perform operations such as creating offers, issuing orders, and managing customer accounts. The system supports real-time pricing negotiation, compliance management, and integration with external systems via an API. The method and system generates an end-to-end real-time view of the provider's interaction with the end-customer.

Description

[0001] Related applications This application is a continuation-in-the-U.S. (CIP) of the following applications: U.S. Patent Application No. US18 / 341,714, filed June 26, 2023; U.S. Patent Application No. US18 / 349,836, filed July 10, 2023; U.S. Patent Application No. US18 / 424,193, filed January 26, 2024; U.S. Patent Application No. US18 / 583,256, filed February 21, 2024; U.S. Patent Application No. US18 / 583,337, filed February 21, 2024; U.S. Patent Application No. US18 / 599,388, filed March 8, 2024; U.S. Patent Application No. US18 / 614,517, filed March 22, 2024; and U.S. Patent Application No. US18 / 614,517, filed June 3, 2024. The following U.S. patent applications are incorporated herein by reference: US Patent Application No. 18 / 732,227; US Patent Application No. 18 / 768,998, filed July 10, 2024; US Patent Application No. 18 / 768,971, filed July 10, 2024; US Patent Application No. 18 / 789,602, filed July 30, 2024; and US Patent Application No. 18 / 793,346, filed August 2, 2024. The entire contents of these applications are incorporated herein by reference. Technical Field

[0002] This invention relates to a system and method that enables distributors to manage their end-user businesses within their own business environment on a distribution platform. Background Technology

[0003] Traditional IT distribution models have long been characterized by their fragmented nature, requiring various systems and touchpoints to manage different aspects of business operations. Resellers, suppliers, and customers frequently face challenges regarding data consistency, real-time visualization, and operational efficiency. As businesses strive to enhance their decision-making capabilities and deliver efficient and intuitive user experiences, the need for a unified platform that integrates business-to-business (B2B) and business-to-customer (B2C) transactions is increasingly evident.

[0004] To address these challenges, there is a growing demand for platforms that can provide end users with a comprehensive, real-time view of the entire business environment. Such platforms must integrate data from multiple sources, including customer relationship management (CRM) systems, enterprise resource planning (ERP) systems, supplier databases, and third-party applications. By fully leveraging advanced artificial intelligence (AI) and machine learning (ML) technologies, these platforms coordinate and process data, ensuring the accuracy and consistency of all interactions.

[0005] The traditional IT distribution industry has long been plagued by fragmentation, forcing resellers and suppliers to navigate between complex, disconnected systems to manage business operations. These systems are typically designed for either business-to-business (B2B) or business-to-consumer (B2C) transactions, rarely accommodating both. Consequently, resellers and distributors often need to develop their own solutions to bridge the gap between B2B and B2C transactions, resulting in inefficiencies, increased operating costs, and a lack of real-time connectivity. Current market solutions fail to provide a comprehensive platform that integrates B2B and B2C transactions within a unified system.

[0006] One of the major challenges in the distribution industry is the lack of end-to-end interconnectivity in the supply chain. Suppliers, distributors, and customers often operate independently, each using different systems to manage inventory, pricing, customer interactions, and order fulfillment. This fragmentation results in distributors lacking visibility and control, and failing to provide a consistent experience for end customers. Furthermore, distributors are forced to invest heavily in developing custom systems to attempt to provide real-time insights, data synchronization, and interactive engagement across B2B and B2C operations. However, such custom systems are often difficult to maintain, lack scalability, and fail to provide robust integration across different transaction models, such as subscription models, consumer models, and traditional models.

[0007] The growing demand for personalized financial solutions and bundled sales is further complicating the distribution landscape. Modern customers and businesses expect to purchase hardware, software, SaaS, and subscription services in a single transaction, seeking the personalized recommendations and insights typically offered by B2C e-commerce platforms. However, current B2B systems rarely offer this flexibility, creating a gap between end-customer expectations and the capabilities of the platforms used by distributors. This disconnect often leads to operational inefficiencies, missed sales opportunities, poor customer experience, and increased operational costs associated with addressing these issues. Summary of the Invention

[0008] To address the aforementioned challenges, this invention introduces a platform that provides combined B2B and B2C solutions within a single integrated environment. Unlike traditional distribution platforms, this system offers end-to-end interconnectivity, enabling interaction between suppliers, distributors, and end customers. The platform is designed to aggregate and coordinate data from multiple sources, providing a unified view to improve decision-making and operational efficiency. Furthermore, the system integrates advanced machine learning algorithms to provide personalized product recommendations, pricing strategies, and financial solutions based on real-time data analytics for both B2B and B2C markets. This ensures that distributors no longer need to build custom systems, reducing complexity and costs while significantly improving the overall user experience.

[0009] By enabling resellers to bundle hardware, software, SaaS, and subscriptions in a single deal, the platform addresses the growing demand for flexible, comprehensive purchasing options. Furthermore, the system provides personalized insights and financial solutions, allowing end customers to make more informed decisions and streamlining the procurement process. This solution not only bridges the gap between B2B and B2C businesses but also empowers resellers to meet evolving customer expectations in an increasingly complex and dynamic market.

[0010] The embodiments described herein provide a unified platform designed to address long-standing challenges in the IT distribution industry by integrating business-to-business (B2B) and business-to-customer (B2C) transactions. The platform offers end-to-end interconnectivity across the supply chain, allowing resellers, suppliers, and end customers to interact within a single system. By eliminating the need for resellers to build custom solutions themselves, the platform significantly reduces operational complexity and improves efficiency.

[0011] This disclosed system and method provide a flexible and scalable solution that aggregates data from various sources, such as Customer Relationship Management (CRM) systems, Enterprise Resource Planning (ERP) systems, supplier databases, and third-party applications. Through this aggregation, the platform provides a unified, real-time view of all transactions, including inventory levels, pricing, customer data, and order status. The platform's integration capabilities allow for data exchange between suppliers, distributors, and end customers, thereby creating a coherent user experience for managing both B2B and B2C transactions.

[0012] In some embodiments, the system and method provide the ability to offer personalized recommendations and insights based on real-time data analytics. By using advanced machine learning models, the platform analyzes historical data, customer preferences, and market trends to generate tailored product recommendations and dynamic pricing strategies. This allows resellers to offer a personalized purchasing experience, similar to that typically offered in B2C e-commerce environments, but with the added complexity of B2B transactions, such as bulk ordering, negotiated pricing, and contract management. The platform also includes targeted financial options, enabling resellers to offer customized payment options and bundled combinations of hardware, software, SaaS, and subscriptions within a single transaction.

[0013] By providing end-to-end billing capabilities, the platform simplifies the management of complex transactions involving a variety of products and services. This includes real-time inventory and pricing updates, automated invoicing, and fulfillment analytics, all within a single user interface. The platform's ability to integrate financial solutions and provide personalized recommendations ensures that both distributors and end customers benefit from a streamlined and more efficient procurement process.

[0014] This invention also addresses the industry's growing demand for flexible storefronts that mimic the functionality of B2C e-commerce platforms. The system allows distributors to create customized storefronts that offer the same ease of use and real-time connectivity as customer-facing platforms, but are tailored to the specific needs of the IT distribution industry. This flexibility ensures that distributors can adapt to market demands, thereby providing a consistent, user-friendly experience for end customers in both B2B and B2C transactions.

[0015] The platform offers a comprehensive solution for managing the complexities of modern IT distribution. By providing a unified, real-time view of both B2B and B2C business, the system enables resellers to streamline their business processes, reduce operating costs, and deliver personalized customer experiences. The platform reshapes the competitive landscape of the IT distribution industry by integrating targeted financial solutions, personalized insights, and the ability to bundle hardware, software, SaaS, and subscriptions into a single deal. This platform not only improves reseller operational efficiency but also enhances the end-customer experience, creating a more coherent and efficient distribution ecosystem.

[0016] An AI-based end-user view for managing B2B and B2C transactions aims to address the shortcomings of the distribution industry by providing a unified platform. This platform integrates various activities and systems into a single interface, enabling users to streamline the entire process. It reduces the time required for activities such as inventory management, customer interaction, order placement, and data reconciliation. Therefore, there is a pressing need for a solution that can effectively integrate, streamline, and accelerate these complex processes while ensuring data security and compliance.

[0017] The global distribution industry faces challenges such as inefficient distribution management, difficulties in SKU management, and the need to shift towards a direct-to-consumer model, necessitating innovative solutions. In particular, with changing customer expectations and regulations, traditional distribution methods are increasingly unable to meet demands. To address these challenges, this invention integrates a comprehensive suite of functionalities, including distribution management, supply chain management, and customer visualization, onto a single platform.

[0018] According to some embodiments, the system can be integrated with Real-Time Data Grid (RTDM) and Single-Window User Interface (SPoG UI). The system uses AI algorithms to optimize user interaction based on real-time inventory and customer data, and also employs generative AI technology to aggregate data into a standardized, common format, thereby ensuring consistency and accuracy.

[0019] In one non-limiting example, the system employs machine learning models such as neural networks to tailor recommendation and dynamic pricing strategies. The platform can provide personalized product recommendations and dynamic pricing strategies based on real-time data analysis.

[0020] In some embodiments, the system and method dynamically manage SKUs and synchronize inventory levels across multiple channels in real time, thereby providing a comprehensive view of both B2B and B2C transactions. The method may include user authentication, multi-source data ingestion, real-time inventory updates, automatic inventory alerts, supplier integration, customer-specific pricing, and a unified product catalog. This comprehensive data management approach provides distributors with an integrated view of their operations.

[0021] In some embodiments, the system and method process and fulfill orders from B2B and B2C customers, providing an integrated view and streamlined operations. The method may include order placement, real-time inventory checks, dynamic pricing, order verification, order confirmation, real-time tracking, automated invoicing, returns management, and fulfillment analysis. Generative AI technology is used within the AAML module to optimize the entire process, providing distributors with a unified platform to manage their orders.

[0022] In some embodiments, the system and method leverage data analytics and machine learning to enable personalized customer interaction and engagement for both B2B and B2C clients. The method may include customer data integration, segmentation, personalized marketing, customized portals, real-time support, loyalty programs, feedback loops, and real-time analytics. Integrating generative AI technologies ensures that personalization is dynamic and data-driven.

[0023] This disclosure integrates multiple systems, automates multiple processes, and verifies data configuration based on intelligent rules. This allows for efficient execution of complex tasks without requiring specialized knowledge, thereby reducing time and minimizing errors. Furthermore, the invention is adaptable and configurable, capable of meeting evolving market and customer needs, thus maintaining the relevance and sustainability of the model. Therefore, this invention provides an efficient, integrated, and highly adaptable end-user view management solution for B2B and B2C transactions in the distribution industry.

[0024] Single Pane of Glass (SPoG) Single Viewpoint (SPoG) offers a comprehensive solution configurable to address these multifaceted challenges. SPoG can be configured to provide an intuitive and efficient platform for the distribution process.

[0025] According to some 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 cargo status, thereby ensuring efficient handling of supply chain and distribution management processes.

[0026] According to some implementations, SPoG can integrate multiple touchpoints into a single platform to mimic direct-to-consumer channels within a distribution platform. This integration provides a unified, direct channel for consumer and distributor interaction, significantly reducing supply chain complexity and enhancing the overall customer experience.

[0027] SPoG offers innovative solutions for improving inventory management through advanced forecasting capabilities. These forecasting analytics can highlight demand trends, guiding businesses to manage their inventory more effectively and mitigate the risks of stockouts or overstocking.

[0028] In some embodiments, SPoG may include a global compliance database. This database, updated in real time, enables distributors to stay abreast of the latest international laws and regulations. This feature significantly reduces the burden of manual tracking, thereby ensuring smooth and compliant cross-border transactions.

[0029] According to some implementations, to facilitate dealers in generating end-user views in customized dashboards, SPoG integrates data from various OEMs into a single platform. This not only ensures data consistency but also significantly reduces the likelihood of errors. Furthermore, SPoG provides the ability to efficiently generate B2B and B2C views, thereby meeting the needs and requirements of specific markets.

[0030] According to some embodiments, SPoG is a highly configurable and user-friendly platform whose intuitive interface allows users to easily access and purchase technology, thus meeting the expectations of a new generation of technology buyers.

[0031] In addition, SPoG’s advanced analytics capabilities provide valuable insights that drive strategy and decision-making, enabling businesses to track and analyze trends in real time, thus allowing them to stay ahead of the curve and adapt to ever-changing market conditions.

[0032] SPoG’s flexibility and scalability make it a forward-looking solution that can adapt to changing business needs, allowing enterprises to scale up or down their operations as needed without major changes to their infrastructure.

[0033] SPoG employs an innovative approach to address the challenges facing the distribution industry, becoming a highly useful tool. By enhancing supply chain visibility, streamlining inventory management, ensuring compliance, simplifying the generation of distributor dashboards, and delivering a superior customer experience, SPoG provides a comprehensive solution to the complex problems that have long plagued the distribution industry. By implementing this system, distributors can envision increased efficiency, reduced errors, and improved customer satisfaction, thereby achieving sustainable growth in an ever-evolving global market.

[0034] Real-Time Data Mesh (RTDM) According to some embodiments, the platform may include an implementation of Real-Time Data Grid (RTDM). RTDM offers an innovative solution to the challenges described above. RTDM is a distributed data architecture that enables real-time data availability across multiple sources and touchpoints. This feature enhances supply chain visibility, allowing for efficient management and enabling distributors to handle disruptions more effectively.

[0035] RTDM's predictive analytics capabilities provide an efficient inventory control solution. By gaining insights into demand trends, it helps businesses manage inventory, thereby reducing the risk of overstocking or stockouts.

[0036] A real-time updated RTDM global compliance database ensures distributors are familiar with international regulations. This significantly reduces the burden of manual tracking, thereby enabling cross-border transactions.

[0037] RTDM also simplifies SKU management and localization by integrating data from various OEMs, thereby ensuring data consistency and reducing the possibility of errors. Its ability to generate real-time views for end users effectively meets the specific needs of the B2B and B2C markets.

[0038] RTDM enhances the customer experience with its intuitive interface, allowing customers to easily access and purchase technology, meeting the expectations of the next generation of technology buyers.

[0039] Advantages of SPoG integration with RTDM The integration of the SPoG platform with RTDM offers multiple advantages, providing a unified solution to the complexities of the distribution industry. Leveraging the capabilities of RTDM, SPoG enhances supply chain visibility, streamlines inventory management, ensures compliance, simplifies SKU management, and delivers a superior customer experience.

[0040] RTDM's real-time tracking and analytics enhance SPoG's ability to efficiently manage its supply chain and inventory, providing accurate current information that enables distributors to make informed decisions quickly.

[0041] SPoG integration with RTDM also ensures data consistency and reduces errors in SKU management. By providing a centralized platform to manage data from various OEMs, it simplifies product localization and helps meet market demands.

[0042] RTDM’s global compliance database is integrated with SPoG, which promotes compliance in cross-border transactions and reduces the burden of manual tracking, saving a significant amount of time and resources.

[0043] In some embodiments, the distribution platform incorporates SPoG and RTDM, providing an improved integrated distribution system. This platform can fully leverage the advantages of distribution models, address existing challenges, and achieve sustainable growth in an evolving global market. Attached Figure Description

[0044] Figure 1 An embodiment of the operating environment of a distribution platform is shown, referred to as the system in this embodiment.

[0045] Figure 2 It shows that Figure 1 One embodiment of a distribution platform operating environment based on elements.

[0046] Figure 3 An embodiment of a distribution management system is shown.

[0047] Figure 4 A system for generating an end-user view for a distributor according to an embodiment of this disclosure is described.

[0048] Figure 5 An RTDM module according to an embodiment of this disclosure is shown.

[0049] Figure 6 An SPoG UI according to an embodiment of this disclosure is shown.

[0050] Figure 7 A system for generating an automated process for a distributor to generate an end-user application, according to an embodiment of this disclosure, is shown.

[0051] Figure 8This is a flowchart of a method for dynamically managing SKUs and synchronizing inventory levels across multiple channels in real time, based on some embodiments of this disclosure, to provide a comprehensive B2B and B2C transaction view.

[0052] Figure 9 This is a flowchart of an automated process for processing and fulfilling B2B and B2C customer orders, according to some embodiments of this disclosure.

[0053] Figure 10 This is a flowchart illustrating some embodiments of the present disclosure for automating customer interactions and personalization for B2B and B2C customers.

[0054] Figure 11 This is a block diagram of example device components according to some embodiments of the present disclosure.

[0055] Figures 12A to 12Q Various screens and functions of the SPoG UI according to some embodiments of this disclosure are depicted. Detailed Implementation

[0056] Embodiments of this disclosure can be implemented through hardware, firmware, software, or any combination thereof. Embodiments of this disclosure can also be implemented as instructions stored on a machine-readable medium, readable and executable by one or more processors. A machine-readable medium can include any mechanism that stores or transmits information in a machine-readable form (e.g., a computing device). For example, a machine-readable medium can include read-only memory (ROM); random access memory (RAM); disk storage media; optical storage media; flash memory devices, etc. Furthermore, firmware, software, routines, and instructions herein can be described as performing certain actions. However, it should be understood that such descriptions are for ease of description only, and such actions actually arise from the execution of firmware, software, routines, instructions, etc., by a computing device, processor, controller, or other device.

[0057] It should be understood that the operations shown in the exemplary methods are not exhaustive, and other operations may be performed before, after, or in between any of the shown operations. In some embodiments of this disclosure, operations may be performed in different orders and / or in different ways.

[0058] Figure 1 A distribution platform operating environment 100 is illustrated, referred to as system 110 in this embodiment. System 110 operates within the context of an information technology (IT) distribution model, targeting various users such as customers 120, end customers 130, suppliers 140, distributors 150, and other entities involved in the distribution process. This operating environment includes a range of features and dynamics that contribute to the success and efficiency of the distribution platform.

[0059] Customer 120 within the System 110 operating environment represents an enterprise or individual seeking IT solutions to meet their specific needs. These customers may require a wide variety of IT products, such as hardware components, software applications, network equipment, or cloud-based services. System 110 provides customers with a user-friendly interface, allowing them to browse, search, and select the most suitable IT solutions based on their needs. Customers can also access real-time data and analytics through System 110, empowering them to make informed decisions and optimize their IT infrastructure.

[0060] End customer 130 can be the ultimate beneficiary of the IT solutions provided by system 110. End customer 130 may include businesses or individuals utilizing IT products and services to improve their operations, productivity, or daily activities. End customer 130 relies on system 110 to access a wide range of IT solutions, ensuring access to the latest technologies and innovations in the market. System 110 enables end customer 130 to track their orders, receive updates on delivery status, and access customer support services, thereby enhancing the overall customer experience.

[0061] Suppliers 140 play a critical role within the operating environment of System 110. These suppliers 140 include manufacturers, distributors, and suppliers offering a wide range of IT products and services. System 110 acts as a centralized platform for suppliers to showcase their sales offerings, manage inventory, and facilitate transactions with customers and resellers. Suppliers can leverage System 110 to streamline their supply chain and distribution operations, manage pricing and promotions, and gain insights into customer preferences and market trends. By integrating with System 110, suppliers can expand their reach, explore emerging markets, and enhance their overall visibility and competitiveness.

[0062] Reseller 150 acts as an intermediary in the distribution model, bridging suppliers and customers. Reseller 150 plays a vital role in the IT distribution ecosystem by connecting customers with the right IT solutions from a variety of vendors. Resellers can include retailers, value-added resellers (VARs), systems integrators, or managed service providers. System 110 enables resellers to access a comprehensive catalog of IT solutions, manage their sales channels, and provide value-added services to customers. By fully leveraging System 110, resellers can enhance their customer relationships, optimize their product sales strategies, and increase their revenue streams.

[0063] Within the operating environment of System 110, various dynamics and characteristics may contribute to its effectiveness. These dynamics include real-time data exchange, integration with existing enterprise systems, scalability, and flexibility. System 110 ensures that users can exchange relevant data 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, allows for communication and interoperability, thereby eliminating data silos and enabling end-to-end visibility.

[0064] System 110 offers scalability and flexibility, meeting the evolving needs of IT distribution models, whether it's an expanding customer base, a growing number of suppliers, or a wider range of IT products and services. System 110 can be configured to handle large-scale data processing, storage, and analysis, ensuring it can support the evolving needs of the distribution platform. Furthermore, System 110 leverages a technology stack including .NET, Java, and other suitable technologies, providing a robust foundation for its operation.

[0065] In summary, the operating environment of System 110 within the IT distribution model includes customers 120, end customers 130, suppliers 140, distributors 150, and other entities involved in the distribution process. System 110 acts as a centralized platform that facilitates efficient collaboration, communication, and transaction processing among these users. By fully leveraging real-time data exchange, integration, scalability, and flexibility, System 110 enables users to optimize user operations, enhance customer experience, and drive business success within the IT distribution ecosystem.

[0066] Figure 2 It shows that Figure 1 This is one embodiment of a distribution platform operating environment 200 based on key elements. This operating environment is arranged to realize an end-user view of managing B2B and B2C transactions within a unified platform. Within this operating environment, integration point 210 facilitates data flow and interconnectivity between various customer systems 220, supplier systems 240, distributor systems 260, and other entities involved in the distribution process. This diagram illustrates the interconnectivity and mechanisms for achieving efficient collaboration and data-driven decision-making. The operating environment is configured to integrate, process, and analyze data by leveraging advanced artificial intelligence (AI) and machine learning (ML) technologies to enable an end-user view of B2B and B2C transactions. In this configuration, AI algorithms can be applied to real-time inventory management, customer interaction optimization, and data reconciliation to ensure a consistent user experience. Machine learning models such as neural networks and decision trees can be used to provide users with refined personalized options. The system can also use generative AI technologies to aggregate data into a standardized, common format, thereby ensuring data consistency and accuracy.

[0067] Operating environment 200 may include system 110, which serves as a distribution platform acting as a central hub for managing and facilitating the distribution process. System 110 provides end users with a unified interface to manage and view all aspects of B2B and B2C transactions, ensuring consistency in interactions and data. System 110 can be configured to perform functions and operations as a bridge between customer system 220, supplier system 240, distributor system 260, and other entities within the ecosystem. System 110 can integrate communication, data exchange, and transaction processing to provide users with a unified and streamlined experience. Furthermore, operating environment 200 may include one or more integration points 210 to ensure smooth data flow and interconnectivity. These integration points include: Customer System Integration: Integration point 210 enables system 110 to connect with customer system 220, thereby achieving efficient data exchange and synchronization. Customer system 220 may include various entities such as customer system 221, customer system 222, and customer system 223. These systems represent internal systems utilized by the customer, such as Enterprise Resource Planning (ERP) or Customer Relationship Management (CRM) systems. Integration with customer system 220 empowers customers to access real-time inventory information, pricing details, order tracking, and other relevant data, thereby enhancing their visibility and decision-making capabilities. Integration with system 110 ensures that customer system 220 can access real-time inventory information, pricing details, order tracking, and other relevant data from B2B and B2C transactions, thereby enhancing its visibility and decision-making capabilities. Applying end-user view processes ensures that customer system 220 can participate in real-time product interaction and data access processes through integration with system 110. Specifically, the system can receive data requests from customer system 220, retrieve real-time inventory and customization options from its own database, and return an optimized data view. This integration ensures that end users have a comprehensive, real-time view of their supply chain and customer interactions, enabling them to make data-driven decisions and enhance operational efficiency in both B2B and B2C contexts.

[0068] Associate System Integration: Integration point 210 enables system 110 to connect with associated system 230, thereby achieving efficient data exchange and synchronization. Associate system 230 may include various entities such as associated system 231, associated system 232, and associated system 233. Integration with associated system 230 authorizes customers to access real-time inventory information, pricing details, order tracking, and other relevant data, thereby enhancing their visualization and decision-making capabilities.

[0069] Vendor System Integration: Integration point 210 facilitates the connection between system 110 and vendor system 240. Vendor system 240 may include entities such as vendor system 241, vendor system 242, and vendor system 243, representing the inventory management system, pricing system, and product catalog adopted by the vendor. Integration with vendor system 240 ensures that the vendor can efficiently update its product sales plans, manage pricing and promotions, and receive real-time order notifications and fulfillment details. Integration with system 110 enables vendor system 240 to efficiently update its product sales plans, manage pricing and promotions, and receive real-time order notifications and fulfillment details, thereby supporting B2B and B2C transactions. End-user view processes and components in system 110 enable vendor system 240 to automate and optimize various aspects of product interaction and data management. For example, the system can use AI algorithms to request specific inventory or customization options from the vendor system. This helps the vendor align its inventory or manufacturing processes with real-time market demands.

[0070] Reseller System Integration: Integration point 210 provides reseller system 260 with the ability to connect to system 110. Reseller system 260 may include entities such as reseller system 261, reseller system 262, and reseller system 263, representing the sales system, customer management system, and service delivery platform adopted by the reseller. Integration with reseller system 260 enables the reseller to access current product information, manage customer accounts, track sales performance, and provide value-added services to its customers.

[0071] Other Entity System Integration: Integration point 210 also enables interconnectivity with other entities involved in the distribution process. These entities may include entities such as entity system 271, entity system 272, and entity system 273. Integration with these systems ensures communication and data exchange, thereby facilitating a collaborative and efficient distribution process. In some embodiments, integration of the end-user view process with other entity systems can ensure that entity system 271 participates in real-time product interaction and data access processes via system 110.

[0072] Integration point 210 also enables interconnectivity with record system 280 for additional data management and integration. Record system 280 can represent an Enterprise Resource Planning (ERP) system or a Customer Relationship Management (CRM) system, including both future and traditional ERP systems such as SAP, Impulse, META, and I-SCALA. Record system 280 may include one or more repositories of critical and legacy business data. It facilitates data exchange and synchronization between the distribution platform, system 110, and ERP, enabling real-time updates and ensuring the availability of accurate and up-to-date information. Integration point 210 establishes interconnectivity between record system 280 and the distribution platform, allowing stakeholders to fully 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, suppliers, etc.

[0073] 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 transmission between the distribution platform and the connected systems. System 110 can establish communication channels and exchange data using industry-standard protocols such as RESTful APIs, SOAP, or GraphQL.

[0074] In some embodiments, system 110 may combine authentication and authorization mechanisms to ensure secure access and data protection. Technologies such as OAuth or JSON Web Tokens (JWT) may be used to authenticate users, authorize data access, and maintain the integrity and confidentiality of exchanged information.

[0075] In some embodiments, integration points 210 and data flows within the operating environment 200 enable users to operate within a connected ecosystem. Data generated at various stages of the distribution process (including customer orders, inventory updates, shipping details, and sales analytics) flows between customer systems 220, supplier systems 240, distributor systems 260, and other entities. This data exchange facilitates real-time visualization, enables data-driven decision-making, and improves the operational efficiency of the entire distribution platform.

[0076] In some embodiments, system 110 may leverage advanced technologies such as Typescript, NodeJS, ReactJS, .NET Core, C#, and other suitable technologies that support integration point 210 and enable integrated communication within operating environment 200. Such technologies provide a robust foundation for system 110, ensuring scalability, flexibility, and efficient data processing capabilities. Furthermore, integration point 210 can employ algorithms, data analytics, and machine learning techniques to derive valuable insights, optimize distribution processes, and personalize customer experiences. Integration point 210 and data flows within operating environment 200 enable users to operate within a connected ecosystem. Data generated at various touchpoints (including customer orders, inventory updates, pricing changes, or delivery status) flows between different entities, systems, and components. Integrated data can be processed, coordinated, and made available in real-time to relevant users through system 110. This real-time access to accurate current information enables users to make informed decisions, optimize supply chain operations, and enhance customer experience.

[0077] Figure 2 Several elements of the operating environment depicted may include commonly known elements that are only briefly outlined herein. For example, each client system (such as client system 220) may include a desktop personal computer, workstation, laptop computer, PDA, cellular phone, or any device that supports Wireless Access Protocol (WAP) or any other computing device capable of directly or indirectly interfacing with the Internet or other network connections. Each client system may typically run an HTTP client, such as Microsoft Edge, Google Chrome, Opera, or a WAP-enabled mobile device browser, allowing the client system to access, process, and view information, pages, and applications available from the distribution platform over the network.

[0078] In addition, each client system is typically equipped with a user interface device for interacting with a browser-based graphical user interface (GUI), such as a keyboard, mouse, trackball, touchpad, touchscreen, pen, or similar device. These user interface devices enable users of the client system to navigate the GUI, interact with pages, forms, and applications, and access data and applications hosted on the distribution platform.

[0079] The client system and its components can be operator-configurable, using applications (including web browsers) running on a central processing unit such as an Intel Pentium processor or similar processor. Similarly, the distribution platform (system 110) and its components can be operator-configurable, using applications running on a central processing unit such as a processor system, which may include an Intel Pentium processor or similar processor and / or multiple processor units.

[0080] Computer program product embodiments may include machine-readable storage media containing instructions for programming a computer to perform the processes described herein. Computer code for operating and configuring distribution platforms and customer systems, supplier systems, distributor systems, and other entity systems to communicate with each other, 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 disk, optical disk, DVD, CD, microdrive, magneto-optical disk, magnetic card or optical card, nanosystem, or any suitable medium for storing instructions and data.

[0081] Furthermore, the computer code used to implement the embodiments can be transferred 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 transferred via extranets, VPNs, local area networks, or other networks, and executed on client systems, servers, or server systems using programming languages ​​such as C, C++, HTML, Java, JavaScript, ActiveX, VBScript, etc.

[0082] It should be understood that the embodiments of this disclosure can be implemented using various programming languages ​​executed on a client system, a server, or a server system, and the choice of language may depend on the specific requirements and environment of the distribution platform.

[0083] Therefore, the operating environment 200 can couple the distribution platform with one or more integration points 210 and data flows to achieve efficient collaboration and a streamlined distribution process.

[0084] Figure 3 The Supply Chain and Distribution Management System 300 is shown. System 300 ( Figure 3 System 300 is a supply chain and distribution management solution configured to address the challenges faced by the fragmented distribution ecosystem in the global distribution industry. System 300 may include several interconnected components and modules that work in concert to optimize supply chain and distribution operations, enhance collaboration, and drive business efficiency.

[0085] The Single Viewpoint (SPoG) UI 305 serves as a centralized user interface, providing users with a unified view of the entire supply chain. The SPoG UI integrates information from various sources and presents real-time data, analytics, and functions tailored to the specific user role and responsibilities. By offering a customizable and intuitive dashboard layout, the SPoG UI enables users to access relevant information and tools, allowing them to make data-driven decisions and efficiently manage their supply chain and distribution activities.

[0086] For example, logistics managers can use the SPoG UI to monitor cargo status, track delivery routes, and view real-time inventory levels across multiple warehouses. They can visualize data through interactive charts and graphs, such as maps showing the current location of each type of transported goods or bar charts displaying inventory levels by product category. With a unified view of the supply chain, logistics managers can identify bottlenecks, optimize routes, and ensure timely delivery of goods.

[0087] SPoG UI 305 integrates with other modules of System 300, facilitating real-time data exchange, synchronized operations, and streamlined workflows. Through API integration, data synchronization mechanisms, and an event-driven architecture, SPoG UI 305 ensures smooth information flow and enables collaborative decision-making across the distribution ecosystem. With a user-centric design, SPoG UI 305 features a clean, intuitive, and responsive layout, utilizing front-end technologies to present dynamic and interactive data visualizations. Customizable dashboards allow users to tailor their views based on specific roles and needs. The UI supports drag-and-drop functionality for easy use, and its adaptive design ensures compatibility across various devices and platforms. Advanced filtering and search capabilities enable users to efficiently browse and access relevant supply chain data and insights.

[0088] For example, when a purchase order is generated in the SPoG UI, the system automatically updates the inventory level, triggers a notification to the warehouse management system, and initiates the shipping process. This integration enables efficient order fulfillment, reduces human error, and improves overall supply chain visibility.

[0089] Another component of System 300 is the Real-Time Data Mesh (RTDM) module 310, which is responsible for ensuring data flow within the distribution system. This module aggregates data from multiple sources, coordinates the data, and ensures data availability in real time.

[0090] Within the distribution network, the RTDM module 310 collects data from various systems, including different inventory management systems, point-of-sale terminals, and customer relationship management systems, harmonizing this data by aligning formats, standardizing units of measurement, and reconciling any discrepancies. The harmonized data is then available in real time, allowing users to access accurate, current information across the supply chain.

[0091] The RTDM module 310 can be configured to capture data changes across multiple transaction systems in real time. This module employs an advanced Change Data Capture (CDC) mechanism that continuously monitors transaction systems to detect any updates or modifications. The CDC component can be specifically configured to work with a variety of transaction systems, including traditional ERP systems, customer relationship management (CRM) systems, and other enterprise-level systems, ensuring compatibility and flexibility for business operations across various environments.

[0092] By accessing real-time data, users can make timely decisions and respond quickly to changing market conditions. For example, if the RTDM module detects a sudden surge in demand for a specific product, it can trigger an alert to the production team, enabling them to adjust manufacturing schedules and prevent stockouts.

[0093] The RTDM Module 310 facilitates data management operations within the supply chain. This module enables real-time coordination of multi-source data, freeing suppliers, distributors, customers, and end customers from the limitations imposed by traditional ERP systems. This enhanced flexibility supports improved efficiency, enhanced customer service, and innovation.

[0094] Another component of System 300 is the Advanced Analytics and Machine Learning (AAML) module 315. By fully leveraging powerful analytics tools and algorithms such as Apache Spark, TensorFlow, or scikit-learn, the AAML module extracts valuable insights from collected data. This module enables advanced analytics, predictive modeling, anomaly detection, and other machine learning capabilities.

[0095] For example, the AAML module can analyze historical sales data to identify seasonal patterns and predict future demand. This module can generate forecasts that help optimize inventory levels, ensure available inventory during peak seasons, and minimize the costs of excess inventory. By fully leveraging machine learning algorithms, the AAML module automates repetitive tasks, predicts customer preferences, and optimizes supply chain processes.

[0096] Beyond demand forecasting, the AAML module provides 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 upsell opportunities and recommend relevant products to individual customers.

[0097] Furthermore, the AAML module can analyze data from various sources, such as social media feedback, customer testimonials, and market trends, to gain a deeper understanding of customer sentiment and preferences. This information can be used for informed product development decisions, identifying emerging market trends, and adjusting business strategies to meet evolving customer expectations.

[0098] System 300 emphasizes integration and interoperability to connect existing enterprise systems such as ERP systems, warehouse management systems, and customer relationship management systems. By establishing connections and data flows between these systems, System 300 enables smooth data exchange across the supply chain, process automation, and end-to-end visibility. Integration protocols, APIs, and data connectors facilitate communication and interoperability between different modules and components, thereby creating a unified, interconnected distribution ecosystem.

[0099] 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, manageability, and efficient updates across diverse environments. The implementation process involves configuring the system to meet specific supply chain and distribution requirements, integrating it with existing systems, and customizing modules and components based on business needs and preferences.

[0100] The Supply Chain and Distribution Management System 300 is a comprehensive and innovative solution that addresses the challenges of a fragmented distribution ecosystem. This system combines the functionality of SPoG UI 305, RTDM Module 310, and AAML Module 315 and integrates with existing systems. By leveraging diverse technology stacks, a scalable architecture, and robust integration capabilities, System 300 provides end-to-end visibility, data-driven decision-making, and optimized supply chain operations. Non-limiting examples and options are provided in this specification and can be customized to meet specific industry requirements and drive efficiency and success in supply chain and distribution management.

[0101] Figure 4 System 400 is shown, configured to enable end users to view and manage B2B and B2C transactions. System 400 provides resellers with a comprehensive, real-time integrated platform that allows them to manage their operations within a single environment. This platform is similar to the Shopify model, allowing resellers to view their supplier and end-customer interactions in an agile, end-to-end view.

[0102] System 400 may include SPoG UI 405, RTDM 410, and AAML module 415. SPoG UI 405 can serve as the user's main interface, providing access to various functions through a unified and intuitive interface. This interface can be developed using web-based technologies, making it accessible to a variety of devices, including desktop computers, laptops, tablets, and smartphones.

[0103] RTDM 410 can be used as a data integration layer, aggregating data from multiple sources, such as vendor platforms, customer relationship management (CRM) systems, enterprise resource planning (ERP) systems, and third-party databases. RTDM 410 can standardize this data into a uniform format, thereby ensuring consistency and accuracy throughout the platform. This data can include real-time inventory levels, pricing information, customer data, and transaction history.

[0104] AAML 415 can be used as a processing layer, employing advanced algorithms and machine learning models to analyze and interpret data aggregated by RTDM410. AAML 415 can provide capabilities such as predictive analytics, anomaly detection, and personalized recommendations. These capabilities can help dealers optimize operations, predict demand, and improve customer satisfaction.

[0105] In some embodiments, the SPoG UI 405 may include an interactive dashboard 406 that provides a comprehensive view of all business activities. The interactive dashboard 406 can integrate supply chain data and customer interactions, enabling distributors to monitor and manage orders, inventory, and customer engagement in real time, thus providing an agile, unified platform for end-to-end business management. The SPoG UI 405 may also include an order management subsystem 407. The order management subsystem 407 facilitates the creation, tracking, and management of orders from both B2B and B2C customers. The SPoG UI 405 enables distributors to configure and manage bundled sales packages that combine hardware, software, SaaS, and subscriptions in a single transaction. This provides the flexibility to address diverse customer needs, offering a personalized user experience typical of B2C platforms, but relying on the backend infrastructure required for B2B operations. The platform also allows distributors to offer targeted financial solutions based on customer-specific data, thereby optimizing purchasing and acquisition decisions through dynamic pricing and financial modeling. By consolidating order processing within the SPoG UI 405, distributors can efficiently process transactions and reduce the complexity of managing multiple sales channels.

[0106] In one non-restricted example, when a user accesses the SPoG UI 405, they can view a comprehensive dashboard displaying real-time data and insights from interactions between their suppliers and end customers. For instance, a distributor can see current inventory levels, pending orders, customer interactions, and sales analytics all within the same interface. This interface provides interactive charts and graphs, allowing users to drill down to specific data points for detailed analysis. The SPoG UI 405 also allows users to initiate actions directly from the interface, such as creating quotes, placing orders, and managing customer accounts.

[0107] The RTDM 410 can continuously ingest data from various sources to ensure that the information displayed on the SPoG UI 405 is current. In some embodiments, the RTDM 410 can use a data replication mechanism to capture real-time changes from transactional systems such as ERP and CRM systems. This data can be processed and coordinated to make it available for analysis and reporting.

[0108] The RTDM 410 enables real-time aggregation and coordination of data from B2B and B2C transactions, achieving end-to-end connectivity between distributors, suppliers, and customers. The integrated system provides real-time insights into inventory levels, customer purchasing trends, and financial performance, ensuring distributors can optimize procurement processes and customer interactions. This end-to-end connectivity allows distributors to efficiently manage their suppliers and customers without needing to build custom platforms.

[0109] RTDM 410 may include an integration layer 411 that collects data from various sources such as supplier platforms, CRM systems, ERP systems, and third-party databases. Integration layer 411 ensures that all relevant data is integrated into a single platform, providing a comprehensive view of supplier and end-user activities.

[0110] The RTDM 410 may also include a data layer 413 that processes and updates data in real time. The data layer 413 ensures that the information displayed on the SPoG UI 405 is always current, enabling dealers to make informed decisions based on the latest data.

[0111] AAML 415 can analyze data in real time, generating insights and recommendations displayed on the SPoG UI 405. For example, AAML 415 can use machine learning models to predict future demand for specific products, allowing dealers to adjust their inventory levels accordingly. AAML 415 can also detect anomalies in transaction data, such as unusually high order volumes, and alert users to potential problems.

[0112] AAML Module 415 can provide personalized recommendations to customers by analyzing historical purchasing data and real-time transaction activity. These recommendations can include bundled hardware / software / SaaS sales packages or targeted financial plans that reflect the specific needs and preferences of end users. The predictive analytics engine within AAML 415 can dynamically adjust pricing strategies and product recommendations based on evolving market trends and customer needs, further enhancing the platform's ability to streamline complex procurement workflows in real time.

[0113] In some embodiments, AAML module 415 may include a predictive analytics engine 416. Predictive analytics engine 416 can analyze historical and current data to predict demand and optimize inventory, thereby helping distributors adjust inventory levels accordingly. AAML module 415 may also include a recommendation engine 418. Recommendation engine 418 can generate personalized recommendations for inventory management, pricing strategies, and customer engagement, thereby helping distributors make data-driven decisions.

[0114] In some embodiments, system 400 may include additional modules to enhance its functionality. For example, a supplier management module can facilitate real-time negotiation of prices and terms directly within the SPoG UI 405. This module can integrate with supplier systems to provide real-time updates on product availability and pricing. Another module (i.e., a compliance management module) can ensure that all transactions comply with applicable regulations and standards, providing audit trails and automated compliance checks.

[0115] System 400 also supports integration with external systems and applications through APIs and data connectors, allowing users to extend the platform's capabilities and integrate it with their existing IT infrastructure. For example, a reseller can integrate System 400 with its warehouse management system to streamline order fulfillment processes.

[0116] System 400's flexibility and scalability make it suitable for a wide range of use cases in the IT distribution industry. By providing a unified platform for managing B2B and B2C transactions, System 400 helps resellers improve efficiency, reduce errors, and enhance customer satisfaction. Its real-time data integration and advanced analytics ensure users have accurate and actionable information, enabling informed decision-making and rapid response to changing market conditions.

[0117] In one non-restricted example, System 400 is deployed by a reseller managing both business-to-business (B2B) and business-to-consumer (B2C) sales channels. The reseller needs to offer bundled sales packages combining hardware, software, and SaaS, while also providing personalized pricing and financial options for its end customers.

[0118] By using SPoG UI 405, resellers create bundled solutions for B2B customers, integrating hardware and a 12-month SaaS subscription while customizing software packages. The platform, powered by RTDM 410, continuously updates the reseller's real-time inventory levels, ensuring inventory availability across multiple vendors. Resellers use the AAML module 415 to analyze customer historical purchasing behavior, apply personalized pricing, and provide dynamic financial solutions with flexible payment options tailored to customer purchasing policies. Customers can view bundled options and pricing recommendations directly within the system using a single interface and place orders instantly through the order management subsystem 407. This integration of B2B and B2C processes allows resellers to optimize their sales channels, reduce sourcing complexity, and provide a personalized user experience for their end customers—all within a single platform.

[0119] Therefore, System 400 fully leverages SPoG UI 405, RTDM 410, and AAML 415 to provide a comprehensive solution for managing B2B and B2C transactions. This solution integrates multi-source data, processes data in real time, and presents data through an intuitive interface, enabling distributors to manage their end-user business within their own environment. This innovative approach addresses the challenges of traditional IT distribution models, supporting the industry's transformation towards a more agile and efficient business environment, bringing self-operated businesses together on a single platform, and providing an end-to-end, real-time view of supplier-customer interactions.

[0120] Figure 5 An embodiment of an advanced distribution platform, including a complex distribution network management system 500, is described, which can be used as an embodiment of system 300 to provide a technical distribution platform for optimizing distribution network management and operation. System 500 includes several interconnected modules, each with a specific function and contributing to the overall efficiency of supply chain operations. In some embodiments, these modules may include an 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 insight marker display 545, a predictive analytics module 550, a recommendation system module 555, a notification module 560, a self-service network entry module 565, and a communication module 570.

[0121] System 500, as an embodiment of System 300, can utilize a range of technologies and algorithms to achieve supply chain and distribution management. These technologies and algorithms facilitate efficient data processing of documents, catalogs, and performance metrics, personalized interaction, real-time analytics, secure communication, and effective management.

[0122] In some embodiments, SPoG UI 505 serves as a central interface within system 500, providing users with a unified view of the entire distribution network. This module leverages front-end technologies such as ReactJS, TypeScript, and Node.js to create an interactive and responsive user interface. These technologies enable SPoG UI 505 to deliver a user-friendly experience, allowing users to access relevant information, navigate through different modules, and perform tasks efficiently.

[0123] The CIM 510, or Customer Interaction Module, employs algorithms and technologies such as Oracle Eloqua, Adobe Target, and Okta to manage customer relationships within the distribution network. These technologies enable the module to securely process customer data, personalize customer experiences, and provide user access control.

[0124] The RTDM Module 515, or Real-Time Data Grid Module, is a component of System 500 that ensures smooth data flow across the distribution ecosystem. This module utilizes technologies such as Apache Kafka, Apache Flink, or Apache Pulsar for data ingestion, processing, and stream management. These technologies enable the RTDM Module 515 to handle real-time data streams, process large volumes of data, and ensure low-latency data processing. Additionally, the module employs a Change Data Capture (CDC) mechanism to capture real-time data updates from various transactional systems, such as traditional ERP and CRM systems. This capability allows users to access accurate, current information for informed decision-making.

[0125] The AI ​​module 520 within System 500 can use advanced analytics and machine learning algorithms, including Apache Spark, TensorFlow, and scikit-learn, to extract valuable insights from data. 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 leverage predictive models to forecast demand, allowing users to optimize inventory management and minimize stockouts or overstocking.

[0126] The interface display module 525 focuses on presenting data and information in a clear and user-friendly manner. This module utilizes technologies such as HTML, CSS, and JavaScript frameworks (like ReactJS) to create interactive and responsive user interfaces. These technologies allow users to visualize data using various data visualization techniques, such as graphs, charts, and tables, enabling efficient data understanding, comparison, and trend analysis.

[0127] The Personalized Interaction Module 530 utilizes customer data, historical trends, and machine learning algorithms to generate personalized recommendations for products or services. This module employs technologies such as Adobe Target, Apache Spark, and TensorFlow for data analysis, modeling, and delivering targeted recommendations. For example, it can analyze customer preferences and purchasing history to provide personalized product recommendations, improving customer satisfaction and boosting sales.

[0128] Document Hub 535 serves as a centralized repository for storing and managing documents within the system 500. This module leverages technologies such as SeeBurger and Elastic Cloud to achieve efficient document management, storage, and retrieval. For example, Document Hub 535 can utilize SeeBurger's document management capabilities to categorize and organize documents based on document type (such as contracts, invoices, product specifications, or compliance documents), allowing users to easily access and retrieve relevant documents as needed.

[0129] The catalog management module 540 enables the creation, management, and distribution of the current product catalog. This module ensures users have access to the latest product information, including specifications, pricing, availability, and promotions. Technologies such as Kentico and Akamai can be used to facilitate catalog updates, content delivery, and caching. For example, this module can leverage Akamai's Content Delivery Network (CDN) to quickly and efficiently deliver catalog information to users, regardless of their geographical location.

[0130] The Performance Insights Markup Display 545 collects, analyzes, and visualizes real-time performance metrics and insights related to supply chain operations. This module leverages tools such as Splunk and Datadog to enable effective performance monitoring and deliver actionable insights. For example, it can utilize Splunk's log analytics capabilities to identify performance bottlenecks in the supply chain, enabling users to take proactive steps to optimize operations.

[0131] 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. This module utilizes technologies such as Apache Spark and TensorFlow for data analysis, modeling, and forecasting. For example, it can leverage TensorFlow's deep learning capabilities to analyze historical sales data and predict future demand, allowing users to optimize inventory levels and minimize costs.

[0132] The Recommendation System Module 555 focuses on providing intelligent recommendations to users within the distribution network. This module generates personalized recommendations for products or services based on customer data, historical trends, and machine learning algorithms. Technologies such as Adobe Target and Apache Spark can be used for data analysis, modeling, and delivering targeted recommendations. For example, this module can leverage Adobe Target's recommendation engine to analyze customer preferences and behaviors and deliver personalized product recommendations across various channels, thereby increasing customer engagement and driving sales.

[0133] The notification module 560 enables the distribution of real-time notifications to users regarding important events, updates, or alerts within the supply chain. This module utilizes technologies such as Apigee X and TIBCO to implement message queuing, event-driven architecture, and integrated notification delivery. For example, the module can leverage TIBCO's messaging infrastructure to send real-time notifications to user devices, ensuring timely dissemination of relevant information.

[0134] The Self-Service Onboarding Module 565 facilitates the onboarding process for new users joining the distribution network. This module provides guided steps, tutorials, or documentation to help users familiarize themselves with the system and its functions. Technologies such as Okta and Kentico can be used to ensure secure user authentication, access control, and self-learning resources. For example, this module can leverage Okta's identity and access management capabilities to securely onboard new users, provide them with appropriate access permissions, and guide them through the system's functionalities.

[0135] The communication module 570 enables communication and collaboration within system 500. This module provides users with channels for interaction, message exchange, document sharing, and project collaboration. Technologies such as Apigee Edge and Adobe Launch can be used to facilitate secure and efficient communication, document sharing, and version control. For example, this module can leverage Apigee Edge's API management capabilities to ensure secure and reliable communication between users, enabling effective collaboration.

[0136] Therefore, System 500 can include various modules that utilize diverse technologies and algorithms to optimize supply chain and distribution management. These modules (including 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 insight marker display 545, predictive analytics module 550, recommendation system module 555, notification module 560, self-service network entry module 565, and communication module 570) work together to provide end-to-end visualization, data-driven decision-making, personalized interaction, real-time analytics, and simplified communication within the distribution network. Combining specific technologies and algorithms to achieve efficient data management, secure communication, personalized experiences, and effective performance monitoring contributes to improved operational efficiency and the success of supply chain and distribution management.

[0137] Real-time data grid Figure 6 An RTDM module 600 according to an embodiment of the present disclosure is shown. The RTDM module 600 may be an embodiment of the RTDM module 310 and may include interconnect components, processes, and subsystems configured to implement real-time data management and analysis.

[0138] like Figure 5 As shown, RTDM module 600 represents the effective data grid and change capture component within the overall system architecture. This module can be configured to provide real-time data management and standardization capabilities, thereby enabling efficient operation within the supply chain and distribution management domains.

[0139] The RTDM module 600 may include an integration layer 610 (also known as a "record system") for integration with various enterprise systems. These enterprise systems may include ERP systems (such as SAP, Impulse, META, and I-SCALA) and other data sources. The integration layer 610 handles data exchange and synchronization between the RTDM module 600 and these systems. Data feedback can be established to retrieve relevant information from the record system, such as sales orders, purchase orders, inventory data, and customer information. This feedback enables real-time data updates and ensures that the RTDM module operates with accurate and up-to-date data.

[0140] 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 grid, a cloud-based infrastructure configured to provide scalable and fault-tolerant data storage capabilities. Within the data grid, multiple Purposive Datastores (PDS) can be deployed to store specific types of data, such as customer data, product data, or inventory data. Each PDS can be optimized based on specific use cases and requirements for efficient data retrieval. PDSs can be configured to store specific types of data, such as customer data, product data, financial data, etc. These PDSs serve as repositories for classical and / or standardized data, ensuring data consistency and integrity across systems.

[0141] In some embodiments, the RTDM module 600 implements a data replication mechanism to capture real-time changes from multiple data sources, including transactional systems such as ERPs (e.g., Impulse, META, I-SCALA). The captured data can then be processed and standardized in-process, transforming it into a standardized format suitable for analysis and integration. This process ensures that data is readily available and up-to-date within the data grid, thereby facilitating real-time insights and decision-making.

[0142] 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 may include a highly scalable and robust data lake (referred to as data lake 622) and a set of deliberate data storage areas (PDS), denoted as PDS 624.1 to 624.N. The integration of these components ensures efficient data management, standardization, and real-time availability.

[0143] Data Layer 620 includes Data Lake 622, an advanced storage and processing infrastructure configured to handle the ever-increasing volume, diversity, and speed of data within the supply chain. Relying on scalable distributed file systems such as Apache Hadoop Distributed File System (HDFS) or Amazon S3, Data Lake provides a scalable, unified platform for storing both structured and unstructured data. Leveraging the resilience and fault tolerance of cloud storage, Data Lake 622 can adapt to data inflows from diverse sources.

[0144] Associated with Data Lake 622, multiple targeted data storage areas (PDS 624.1 to 624.N) can be employed. Each PDS 624 can act as a dedicated repository, optimized for storing and retrieving specific types of data relevant to the supply chain domain. In some non-limiting examples, PDS 624.1 can be dedicated to customer data, storing information such as customer profiles, preferences, and transaction history. PDS 624.2 can focus on product data, including details about SKU codes, descriptions, pricing, and inventory levels. These targeted data storage areas allow for efficient data retrieval, analysis, and processing to meet the diverse needs of supply chain users.

[0145] 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 transactional systems such as legacy ERP systems like SAP, Impulse, META, and I-SCALA, as well as other enterprise-level systems. CDC continuously monitors and captures any updates, modifications, or new transactions in these systems in real time. By capturing these changes, Data Layer 620 ensures that the data within Data Lake 622 and PDS 624 remains up-to-date, thereby providing users with real-time insights into the distribution ecosystem.

[0146] In some embodiments, data layer 620 may be implemented using one or more frameworks such as .NET or Java to facilitate integration with existing enterprise systems, thereby ensuring compatibility with a variety of existing systems and providing flexibility for customization and extension capabilities. For example, data layer 620 may leverage the Java technology stack, including frameworks such as Spring and Hibernate, to integrate with a range of record systems with various ERP systems and other enterprise-level solutions. This enables smooth data exchange across the supply chain, process automation, and end-to-end visibility.

[0147] In terms of data processing and analysis, in some non-restricted examples, Data Layer 620 can utilize the capabilities of distributed computing frameworks such as Apache Spark or Apache Flink. These frameworks enable parallel processing and distributed computing across large-scale datasets stored in data lakes and PDSs. By leveraging 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 utilize Apache Spark's machine learning libraries to develop predictive models for demand forecasting, optimize inventory levels, and identify potential supply chain risks.

[0148] In some embodiments, data layer 620 can combine robust data governance and security measures. Fine-grained access control mechanisms and authentication protocols ensure that only authorized users can access and modify data within the data lake and PDS. During rest and transportation, data encryption protects sensitive supply chain information from unauthorized access. Additionally, data layer 620 can implement data traceability and audit trail mechanisms, allowing users to track the origin and history of data, ensuring data integrity and regulatory compliance.

[0149] In some embodiments, the 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, the 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 allows resources to be scaled on demand, minimizing operational overhead and providing a resilient infrastructure for managing supply chain data.

[0150] The data layer 620 of the RTDM module 600 can be combined with a highly scalable data lake (data lake 622) and a dedicated PDS (PDS 624.1 to 624.N), employing a CDC mechanism. Data layer 620 ensures efficient data management, standardization, and real-time availability. In a non-limiting example, data layer 620 can be implemented using any appropriate technology (such as .NET or Java) and / or distributed computing framework (such as Apache Spark) to achieve powerful data processing, advanced analytics, and machine learning capabilities. Leveraging 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 complex and dynamic distribution environments.

[0151] RTDM module 600 may include AI module 630, configured to implement one or more algorithms and machine learning models to analyze data stored in data layer 620 and derive meaningful insights. In some non-limiting examples, AI module 630 may apply predictive analytics, anomaly detection, and optimization algorithms to identify patterns, trends, and potential risks within the supply chain. AI module 630 can continuously learn from new data inputs and adapt its models to provide accurate current insights. AI module 630 can generate predictions, recommendations, and alerts, and publish such insights to dedicated data feedback.

[0152] The data engine layer 640 may include 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 series of autonomous, headless engines 640.1 to 640.N. These engines represent different functions within the system, and may include one or more recommendation engines, insight engines, and subscription management engines. Engines 640.1 to 640.N can leverage standardized data stored in a data grid to deliver specific business logic and services. Each engine can be configured as a pluggable engine, allowing for flexibility and future expansion of module capabilities. Figure 5 An exemplary engine is shown, but it is not intended to be limiting. Any additional headless engine may be included in the data engine layer 640 or other example layers of the system disclosed herein.

[0153] 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 transforming it into a standardized format. Data processing algorithms can be applied to clean, aggregate, and enrich the data for further analysis and integration.

[0154] Furthermore, to facilitate integration and access to the RTDM module 600, a data distribution mechanism can be employed. The data distribution mechanism 645 can be configured to include one or more APIs to facilitate the distribution of data from the data grid and engine to various endpoints, including user interfaces, micro-frontends, and external systems.

[0155] Experience Layer 650 focuses on delivering 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 retrieve and analyze real-time data on various supply chain metrics, such as inventory levels, sales performance, and customer demand. The user experience layer supports personalized data feedback, allowing users to customize their views and receive relevant updates based on their roles and responsibilities. Users can subscribe to specific data updates tailored to their preferences and roles, such as inventory changes, pricing updates, or new SKU notifications.

[0156] Therefore, in some embodiments, the RTDM module 600 for supply chain and distribution management can be integrated with a record system and includes one or more data layers, AI components, a data engine layer, and a user experience layer with data grids and deliberate data storage areas. 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 feedback and retrieval within the module ensure that users can retrieve relevant current information and insights to make informed decisions and optimize supply chain operations. Accordingly, the RTDM module 600 facilitates supply chain and distribution management by providing a scalable real-time data management solution, with its innovative architecture allowing for rich integration of different data sources, efficient data standardization, and advanced analytics capabilities. The module is capable of replicating and standardizing data from different ERPs while maintaining the auditability and repeatability of transactions, offering significant advantages in achieving a unified view of suppliers, distributors, customers, end customers, and other entities in the distribution system (including IT distribution systems).

[0157] End user view for B2B / B2C models In one embodiment, Figure 7 System 700 is described, configured to enable end users to view and manage B2B and B2C transactions within a unified platform. System 700 provides resellers with a real-time, end-to-end solution to integrate their supplier operations with end-customer interactions. This system addresses the fragmented nature of traditional IT distribution models by providing a comprehensive, unified experience.

[0158] System 700 may include several key components, such as a Single Window User Interface (SPoG UI) 705, a Real-Time Data Grid (RTDM) 710, and an Advanced Analytics and Machine Learning (AAML) module 715. SPoG UI 705 serves as the central interface for users, providing access to various functions through a unified and intuitive dashboard. This interface can be developed using web-based technologies, making it accessible to a wide range of devices, including desktop computers, laptops, tablets, and smartphones.

[0159] In some embodiments, the SPoG UI 705 may include an interactive dashboard 706 that provides a comprehensive view of all business activities. The interactive dashboard 706 can integrate supply chain data and customer interactions, enabling distributors to monitor and manage orders, inventory, and customer engagement in real time, thus providing an agile, unified platform for end-to-end business management. The SPoG UI 705 may also include an order management subsystem 707. The order management subsystem 707 facilitates the creation, tracking, and management of orders from both B2B and B2C customers. By consolidating order processing within the SPoG UI 705, distributors can efficiently process transactions and reduce the complexity of managing multiple sales channels.

[0160] SPoG UI 705 provides resellers with an integrated platform for managing B2B and B2C transactions, offering end users access to tailored, personalized recommendations and the ability to bundle hardware, software, and SaaS product portfolios. The platform supports real-time dynamic pricing and targeted financial plans to reflect customer needs, making procurement similar to a B2C e-commerce experience, but supporting the complex infrastructure required for B2B operations. Through this interface, resellers can create customized storefronts and offer product bundles tailored to individual customers or market segments.

[0161] RTDM 710 can be used as a data integration layer, aggregating data from multiple sources, such as vendor platforms, customer relationship management (CRM) systems, enterprise resource planning (ERP) systems, and third-party databases. RTDM 710 can standardize this data into a unified format, ensuring consistency and accuracy across the entire platform. This data can include real-time inventory levels, pricing information, customer data, and transaction history.

[0162] By aggregating real-time data from supplier systems, CRM platforms, ERP systems, and third-party databases, the RTDM710 facilitates end-to-end connectivity across B2B and B2C transactions. This allows distributors to fulfill orders and manage inventory in real time while maintaining consistent and accurate insights into customer interactions. By integrating financial and operational data, the system empowers distributors with the flexibility to offer complex bundled deals and dynamically adjust pricing based on market trends and customer data.

[0163] In some embodiments, RTDM 710 may include an ingestion layer 711 dedicated to collecting data from various sources such as supplier platforms, CRM systems, ERP systems, and third-party databases. The data aggregation subsystem 711 ensures all relevant data is integrated into a single platform, providing a comprehensive view of supplier and end-user activities. RTDM 710 may also include a data layer 713 that processes and updates data in real time. The data layer 713 ensures that the information displayed on the SPoG UI 705 remains current, enabling distributors to make informed decisions based on the latest data.

[0164] AAML 715 can be used as a processing layer, employing advanced algorithms and machine learning models to analyze and interpret data aggregated by RTDM710. AAML 715 can provide capabilities such as predictive analytics, anomaly detection, and personalized recommendations. These capabilities can help dealers optimize operations, predict demand, and improve customer satisfaction.

[0165] The AAML module 715 analyzes data collected from the RTDM 710 to provide personalized recommendations for product bundling, targeted pricing models, and financial plans tailored to individual customer needs. For example, the system can dynamically bundle hardware, software, and SaaS subscriptions based on customer purchase history and real-time demand forecasts. This module can also recommend targeted financial plans, including installment payments or pay-as-you-go discounts, specifically tailored for end customers in both B2B and B2C environments.

[0166] AAML module 715 may include a predictive analytics engine 716. The predictive analytics engine 716 can analyze historical and current data to forecast demand and optimize inventory, thereby helping distributors adjust inventory levels accordingly. AAML module 715 may also include a recommendation engine 718. The recommendation engine 718 can generate personalized recommendations for inventory management, pricing strategies, and customer engagement, thereby helping distributors make data-driven decisions.

[0167] When users access SPoG UI 705, they can view a comprehensive dashboard displaying real-time data and insights from interactions with their suppliers and end customers. For example, distributors can see current inventory levels, pending orders, customer interactions, and sales analytics all in one interface. The interface offers interactive charts and graphs, allowing users to drill down to specific data points for detailed analysis. SPoG UI 705 also allows users to initiate actions directly from the interface, such as creating quotes, placing orders, and managing customer accounts.

[0168] The RTDM 710 can continuously ingest data from various sources to ensure that the information displayed on the SPoG UI 705 is current. In some embodiments, the RTDM 710 can use a data replication mechanism to capture real-time changes from transactional systems such as ERP and CRM systems. This data can be processed and coordinated to make it available for analysis and reporting.

[0169] AAML 715 can analyze data in real time, generating insights and recommendations displayed on the SPoG UI 705. For example, AAML 715 can use machine learning models to predict future demand for specific products, allowing dealers to adjust their inventory levels accordingly. AAML 715 can also detect anomalies in transaction data, such as unusually high order volumes, and alert users to potential problems.

[0170] In some embodiments, system 700 may include additional modules to enhance its functionality. For example, a supplier management module can facilitate real-time negotiation of prices and terms directly within the SPoG UI 705. This module can be integrated with supplier systems to provide real-time updates on product availability and pricing. Another module (i.e., the compliance management module) can ensure that all transactions comply with applicable regulations and standards, providing audit trails and automated compliance checks.

[0171] System 700 also supports integration with external systems and applications through APIs and data connectors, allowing users to extend the platform's capabilities and integrate it with their existing IT infrastructure. For example, resellers can integrate System 700 with their warehouse management system to streamline order fulfillment processes.

[0172] System 700's flexibility and scalability make it suitable for a wide range of use cases in the IT distribution industry. By providing a unified platform for managing B2B and B2C transactions, System 700 helps resellers improve efficiency, reduce errors, and enhance customer satisfaction. Its real-time data integration and advanced analytics ensure users have accurate and actionable information, enabling informed decision-making and rapid response to changing market conditions.

[0173] In one non-restricted example, system 700 can be used by a technology distributor managing a mix of B2B and B2C customers. Distributors can interact with the SPoG UI 705 to create customized storefronts where customers can browse various hardware, software, and SaaS sales packages. The interactive dashboard 706 allows distributors to view real-time inventory, pricing information, and customer purchase history, providing a unified view of their business activities.

[0174] For specific B2B customers, distributors use the RTDM 710 to collect real-time data from supplier systems and ERP platforms, ensuring that inventory levels and pricing information are always up-to-date. Customers place large orders bundled with hardware and SaaS services. The AAML module 715 analyzes the customer's historical purchases and recommends customized financial plans, offering tiered pricing based on order volume and flexible payment terms. The system also generates personalized product recommendations, suggesting supplementary value-added services to complement the purchased hardware.

[0175] When customers settle payments via the order management subsystem 707, RTDM 710 verifies inventory availability and updates real-time pricing before confirming the transaction. The platform allows distributors to offer both B2B-style bulk transactions and B2C-style personalized services throughout the end-to-end process, optimizing the customer purchasing experience. This integration of B2B and B2C capabilities enables distributors to provide highly customized and flexible sales solutions while reducing operational complexity and maintaining real-time visibility into all transaction data.

[0176] Therefore, System 700 fully leverages SPoG UI 705, RTDM 710, and AAML 715 to provide a comprehensive solution for managing B2B and B2C transactions. This solution integrates multi-source data, processes data in real time, and presents it through an intuitive interface, enabling resellers to manage their end-user business within their own environment. This innovative approach addresses the challenges of traditional IT distribution models, supporting the industry's transformation towards a more agile and efficient business environment, bringing self-operated businesses together on a single platform, and providing an end-to-end, real-time view of supplier-customer interactions.

[0177] Figure 8 This paper presents a method 800 for dynamically managing SKUs and synchronizing inventory levels in real time across multiple channels, providing a comprehensive view of B2B and B2C transactions. This method leverages generative AI techniques within the Advanced Analytics and Machine Learning (AAML) module to aggregate data into a standardized, common format.

[0178] Method 800 can begin with operation 805 and may include user authentication and role-based access control. Users can be authenticated through a secure login process that can leverage multi-factor authentication (MFA) to enhance security. Role-based access control mechanisms can then be applied to grant appropriate access levels based on user roles, such as resellers, suppliers, or end customers. This ensures that users can only access data and perform actions relevant to their specific roles.

[0179] Operation 810 can include ingesting data from multiple sources. Real-time Data Grid (RTDM) can ingest data from vendor platforms, customer relationship management (CRM) systems, enterprise resource planning (ERP) systems, and third-party databases. RTDM can continuously aggregate this data, ensuring it is current. Data ingestion can utilize APIs and secure data connectors to facilitate integration with these external systems.

[0180] Operation 815 allows the use of generative AI technologies within the AAML module to process ingested data. These AI technologies can aggregate data and transform it into a standardized, common format. This can include harmonizing data by applying predefined rules and patterns, thereby ensuring consistency and accuracy across the platform. Generative AI can analyze incoming data streams, identify patterns, and adjust data patterns in real time as needed.

[0181] Operation 820 can include updating real-time inventory levels. The system can continuously synchronize inventory levels across all sales channels, including online stores, wholesale portals, and retail locations. RTDM provides a real-time data replication mechanism to capture changes as inventory levels change. This ensures that the inventory data reflected in the Single Window User Interface (SPoG UI) is always the current data.

[0182] Operation 825 enables automated inventory alerts. The system can generate automatic alerts based on predefined thresholds regarding low inventory levels, excess inventory, and stockouts. These alerts can be communicated to users via the SPoG UI, allowing them to take immediate action. For example, when inventory levels fall below a critical threshold, a distributor can receive an alert prompting them to reorder from the supplier.

[0183] Operation 830 can include supplier integration. The system can integrate with supplier systems to automatically update SKU details and inventory levels based on real-time data provided by the supplier. This integration can use APIs to retrieve data from the supplier's database and update the RTDM accordingly. This ensures that SKU information is always accurate and up-to-date, reducing the need for manual updates and minimizing the risk of errors.

[0184] Operation 835 allows for the configuration of customer-specific pricing. The system can apply dynamic pricing rules within the AAML module to provide personalized pricing for different customer segments. For example, bulk discounts can be applied to B2B customers, while promotional pricing can be offered to B2C customers. These pricing rules can be managed through the SPoG UI, allowing users to adjust their pricing strategies in real time based on market conditions and inventory levels.

[0185] Operation 840 can include presenting a unified product catalog. The SPoG UI can display a unified product catalog that dynamically adjusts based on user roles. For example, B2B users can see products and prices tailored for wholesale transactions, while B2C users can see retail products and pricing. The catalog can be interactive, allowing users to filter and search for products based on various criteria such as category, price range, and availability.

[0186] Throughout the methodology, the SPoG UI serves as the central interface for users, providing access to all functions through a unified and intuitive dashboard. This interface can be developed using web-based technologies, making it accessible across a variety of devices, including desktop computers, laptops, tablets, and smartphones. The SPoG UI offers interactive charts, graphs, and tables, allowing users to drill down to specific data points for detailed analysis.

[0187] Method 800's flexibility and scalability make it suitable for a wide range of use cases in the IT distribution industry. By providing a unified platform for managing B2B and B2C transactions, Method 800 helps resellers improve efficiency, reduce errors, and enhance customer satisfaction. Its real-time data integration and advanced analytics capabilities ensure users have accurate and actionable information, enabling informed decision-making and rapid response to changing market conditions.

[0188] Therefore, Method 800 can fully leverage SPoG UI 405, RTDM 410, and AAML 415 to provide a comprehensive solution for managing B2B and B2C transactions. This method can integrate data from multiple sources, process data in real time using generative AI technology, and present the data through an intuitive interface. This enables distributors to manage their end-user business within their own environment, bringing their own operations to a single platform and providing an end-to-end, real-time view of supplier-customer interactions.

[0189] Figure 9 This document demonstrates a method 900 for processing and fulfilling orders from both B2B and B2C customers, ensuring an integrated view and streamlined operations. This method can leverage generative AI technologies within an Advanced Analytics and Machine Learning (AAML) module to optimize the entire order processing workflow.

[0190] Method 900 can begin with operation 905 and may include placing an order via a Single Window User Interface (SPoG UI). Users (whether B2B or B2C customers) can place an order by selecting a product, specifying a quantity, and choosing a delivery option. The SPoG UI provides a user-friendly interface that dynamically adjusts based on user roles and transaction types.

[0191] Operation 910 can include real-time inventory checks. Once an order is placed, the system can use the Real-Time Data Grid (RTDM) to check real-time inventory levels across multiple warehouses and supplier systems. This ensures that the system only allows orders for currently available products, thus reducing the risk of overselling and stockouts.

[0192] Operation 915 allows the system to verify order details. The AAML module can apply predefined business rules and verification algorithms to ensure orders meet all necessary criteria. This can include verifying customer information, checking payment details, and confirming the shipping address. Any discrepancies or issues can be flagged via the SPoG UI for user review.

[0193] Operation 920 includes dynamic pricing and discount applications. The system can apply dynamic pricing rules and discounts based on real-time data from RTDM. For B2B customers, this can include bulk discounts and negotiated pricing terms, while B2C customers can enjoy promotional offers and discounts. The AAML module can optimize these pricing strategies based on current market conditions and inventory levels.

[0194] Operation 925 can include order confirmation and fulfillment initiation. Once an order is verified and priced, the system can confirm the order and trigger the fulfillment process. RTDM can update inventory levels in real time to reflect new orders, and the system can notify relevant warehouses or suppliers to prepare for shipment. This ensures that orders are processed quickly and efficiently.

[0195] Operation 930 allows the system to provide real-time order tracking. Customers and distributors can track their order status through the SPoG UI, which displays updates from logistics and transportation partners integrated with the system. This can include information such as shipping status, estimated delivery time, and any exceptions or delays.

[0196] Operation 935 can include automated invoicing and billing. The system can generate invoices and bills tailored to the specific terms and conditions of B2B and B2C transactions. These documents can be automatically sent to customers and made accessible within their accounts on the SPoG UI. The invoicing process can also be integrated with the user's accounting system to ensure accurate financial records.

[0197] Operation 940 can include integrated returns management. The system allows B2B and B2C customers to initiate returns directly through the SPoG UI and track their status. The AAML module can analyze return patterns to identify potential problems with products or processes, helping to reduce future returns and improve customer satisfaction.

[0198] Operation 945 can include fulfillment analytics. The AAML module can analyze fulfillment data to identify bottlenecks, optimize transportation routes, and predict future fulfillment demand. These insights can be presented to users through the SPoG UI, enabling them to make data-driven decisions to improve efficiency and reduce costs.

[0199] Throughout Method 900, the SPoG UI serves as the central interface for users, providing a unified view of all order processing and fulfillment activities. This interface can be developed using web-based technologies, making it accessible across a variety of devices, including desktop computers, laptops, tablets, and smartphones. The SPoG UI offers interactive visualizations and real-time updates, allowing users to effectively monitor and manage their orders.

[0200] Integrating generative AI technology into the AAML module enhances the system's ability to efficiently process and fulfill orders. By analyzing large amounts of data and identifying patterns, the AAML module can optimize pricing strategies, validate orders, and improve fulfillment processes. This ensures that Method 900 provides an integrated solution for managing both B2B and B2C transactions.

[0201] Therefore, Method 900 can fully leverage SPoG UI 405, RTDM 410, and AAML 415 to provide a comprehensive solution for order processing and fulfillment integration. This method can include order placement, real-time inventory checks, dynamic pricing, order verification, order confirmation, real-time tracking, automated invoicing, returns management, and fulfillment analytics. This enables distributors to manage their end-user business within their own environment and provides an end-to-end, real-time view of supplier-customer interactions.

[0202] Figure 10 A method 1000 for achieving personalized customer interaction and engagement for B2B and B2C customers by fully leveraging data analytics and machine learning is presented. This method can utilize generative AI technologies within an Advanced Analytics and Machine Learning (AAML) module to enhance the personalization and effectiveness of customer engagement strategies.

[0203] Method 1000 can begin with operation 1005 and may include customer data integration. The Real-Time Data Grid (RTDM) can ingest data from multiple sources, such as customer relationship management (CRM) systems, enterprise resource planning (ERP) systems, sales records, and third-party databases. This data can be aggregated and standardized into a standardized, common format by the AAML module, thereby ensuring consistency and accuracy.

[0204] Operation 1010 can include customer segmentation. The AAML module can apply machine learning algorithms to analyze integrated customer data and segment customers into different groups based on various criteria, such as purchase history, engagement level, and demographics. These segments can be dynamic and updated in real time as new data is ingested.

[0205] Operation 1015 allows the system to create personalized marketing campaigns. Leveraging insights gained from customer segments, the system can generate targeted marketing campaigns tailored to the specific needs and preferences of each customer segment. These campaigns can include personalized product recommendations, targeted promotions, and customized content. Campaigns can be managed and executed through a single-window user interface (SPoG UI).

[0206] Operation 1020 can include a customized customer portal. The SPoG UI can provide a personalized experience for each customer, displaying customized dashboards, order history, and product recommendations based on their segmentation and interaction history. This ensures that customers receive a relevant and engaging experience every time they interact with the platform.

[0207] Operation 1025 can include real-time customer support. The system can integrate real-time customer support features such as live chat, chatbots, and help desk systems into the SPoG UI. These support tools can be powered by an AAML module that analyzes customer queries and provides immediate, context-aware responses. This ensures customers receive timely and accurate assistance, thereby improving the overall customer experience.

[0208] Operation 1030 may include implementing loyalty programs. The system can design and manage loyalty programs that reward B2B and B2C customers for purchases and participation. These programs can provide personalized rewards and incentives based on customer behavior and preferences, and are tracked and managed through the SPoG UI. The AAML module can analyze loyalty program data to optimize rewards and identify opportunities to improve customer loyalty.

[0209] Operation 1035 allows the system to establish a customer feedback loop. The SPoG UI provides mechanisms for collecting customer feedback through surveys, reviews, and support interactions. The AAML module analyzes this feedback to identify trends and areas for improvement. The SPoG UI can then be used to present users with actionable insights gained from this analysis, enabling them to make informed decisions to enhance products, services, and the customer experience.

[0210] Operation 1040 can include real-time analytics and insights. The AAML module continuously analyzes customer interaction data to generate real-time insights into customer behavior, preferences, and trends. These insights can be displayed on the SPoG UI, helping users better understand their customers and make data-driven decisions to improve engagement and satisfaction.

[0211] Throughout Method 1000, the SPoG UI serves as the central interface for users, providing a unified and personalized view of all customer interactions and engagement activities. This interface can be developed using web-based technologies, making it accessible across a variety of devices, including desktops, laptops, tablets, and smartphones. The SPoG UI offers interactive visualizations, real-time updates, and actionable insights, allowing users to effectively manage and enhance customer engagement.

[0212] Integrating generative AI technology within the AAML module significantly enhances the personalization and effectiveness of customer engagement strategies. By analyzing vast amounts of data and generating real-time insights, the AAML module helps users better understand their customers, create more effective marketing campaigns, and deliver personalized experiences. This ensures that Method1000 provides an integrated and comprehensive solution for customer interaction and engagement management in both B2B and B2C contexts.

[0213] Therefore, Method 1000 can fully leverage SPoG UI 405, RTDM 410, and AAML 415 to provide a comprehensive solution for personalized customer interaction and engagement. This method can include customer data integration, segmentation, personalized marketing, customized portals, real-time support, loyalty programs, feedback loops, and real-time analytics. This enables resellers to manage their end-user business within their own environment, providing an end-to-end, real-time view of supplier-customer interactions and improving overall customer satisfaction and loyalty.

[0214] Figure 11 A block diagram depicting example components of device 1100 is provided. One or more computer systems 1100 may be used, for example, to implement any of the embodiments described herein and combinations and sub-combinations thereof. Computer system 1100 may include one or more processors (also known as central processing units or CPUs), such as processor 1104. Processor 1104 may be connected to communication infrastructure or bus 1106.

[0215] The computer system 1100 may also include user input / output devices 1103, such as monitors, keyboards, pointing devices, etc., which can communicate with the communication infrastructure 1106 through user input / output interfaces 1102.

[0216] One or more processors 1104 may be graphics processing units (GPUs). In one embodiment, a GPU may be a processor, i.e., a dedicated electronic circuit configured to process mathematically intensive applications. GPUs may be in a parallel architecture, capable of efficiently processing large blocks of data in parallel, such as common mathematically intensive data in computer graphics applications, images, and videos.

[0217] Computer system 1100 may also include main memory or primary memory 1108, such as random access memory (RAM). Main memory 1108 may include one or more levels of cache. Control logic (i.e., computer software) and / or data may be stored in main memory 1108.

[0218] The computer system 1100 may also include one or more secondary storage devices or memories 1110. The secondary memory 1110 may include, for example, a hard disk drive 1112 and / or a removable storage device or drive 1114.

[0219] The removable storage drive 1114 can interact with the removable storage unit 1118. The removable storage unit 1118 may include a computer-usable or readable storage device storing computer software (control logic) and / or data. The removable storage unit 1118 may be a program box and box interface (such as a program box and box interface in a video game device), a removable memory chip (such as an EPROM or PROM) and associated slot, a memory stick and USB port, a memory card and associated memory card slot, and / or any other removable storage unit and associated interface. The removable storage drive 1114 can read from and / or write to the removable storage unit 1118.

[0220] Secondary memory 1110 may include other means, devices, components, tools, or other means that allow computer system 1100 to access computer programs and / or other instructions and / or data. Such means, devices, components, tools, or other means may include, for example, removable storage unit 1122 and interface 1120. Examples of removable storage unit 1122 and interface 1120 may include a program box and box interface (such as a program box and box interface in a video game device), a removable memory chip (such as an EPROM or PROM) and associated slot, a memory stick and USB port, a memory card and associated memory card slot, and / or any other removable storage unit and associated interface.

[0221] Computer system 1100 may also include a communication or network interface 1124. Communication interface 1124 enables computer system 1100 to communicate and interact with any combination of external devices, external networks, external entities, etc. (each individually and collectively labeled with reference numeral 1128). For example, communication interface 1124 may allow computer system 1100 to communicate with external or remote devices 1128 via communication path 1126, which may be wired and / or wireless (or a combination thereof), and may include any combination of LAN, WAN, Internet, etc. Control logic and / or data may be transmitted to / from computer system 1100 via communication path 1126.

[0222] Computer system 1100 may also be any personal digital assistant (PDA), desktop workstation, laptop or notebook computer, netbook, tablet computer, smartphone, smartwatch or other wearable device, appliance, local Internet of Things and / or embedded system (to name just a few non-limiting examples) or any combination thereof.

[0223] Computer system 1100 can be a client or server that accesses or hosts any application and / or data through any delivery paradigm, including but not limited to remote or distributed cloud computing solutions; on-premises or internal software (“internal” cloud 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), etc.); and / or hybrid models, including any combination of the foregoing examples or other service or delivery paradigms.

[0224] Any available data structures, file formats, and schemas in computer system 1100 may be derived from standards including, but not limited to, JavaScript Object Notation (JSON), Extensible Markup Language (XML), YAML, Extensible Hypertext Markup Language (XHTML), Wireless Markup Language (WML), message packets, XML User Interface Language (XUL), or any other functionally similar representation (alone or in combination). Alternatively, proprietary data structures, formats, or schemas may be used alone or in combination with known or open standards.

[0225] In some embodiments, a tangible non-transitory device or article of art, including a tangible non-transitory computer-usable or readable medium storing control logic (software), may also be referred to herein as a computer program product or program storage device. This includes, but is not limited to, computer system 1100, main memory 1108, secondary memory 1110, and mobile storage units 1118 and 1122, as well as tangible articles of art implementing any combination thereof. When such control logic is executed by one or more data processing devices (such as computer system 1100), it enables such data processing devices to operate as described herein.

[0226] Figures 12A to 12Q This diagram depicts various screens and functions within the SPoG UI related to supplier loading, partner dashboard, customer cart, order summary, SKU generation, order tracking, shipping tracking, subscription history, and subscription modification. Detailed descriptions of each diagram are provided below: Figure 12A This diagram depicts the Vendor Onboarding Initiation screen, representing the initial steps of the vendor onboarding process. It provides a form or interface through which a vendor can express their interest in joining the distribution ecosystem. Vendors can enter their basic information, such as company details, contact information, and product catalog.

[0227] Figure 12B This diagram depicts a Vendor Onboarding Guide, showing the step-by-step guidelines or checklist that vendors should follow during the onboarding process. It outlines the necessary tasks and requirements to ensure vendors have a clear understanding of the onboarding process and can proceed smoothly.

[0228] Figure 12C This describes a Vendor Onboarding Call Scheduler, which facilitates the scheduling of calls or meetings between vendors and platform affiliates or representatives responsible for guiding them through the onboarding process. Vendors can select appropriate time slots or request calls, thereby ensuring effective communication and collaboration throughout the onboarding process.

[0229] Figure 12D This section describes the Vendor Onboarding Task List, presenting a comprehensive task list or dashboard that outlines the specific steps and actions required for successful vendor onboarding. This chart provides an overview of pending tasks, completed tasks, and approaching deadlines, helping vendors track their progress and ensure onboarding tasks are completed on time.

[0230] Figure 12EThe Vendor Onboarding Completion Screen is depicted to confirm the successful completion of the vendor onboarding process. This screen can display a congratulatory message or a brief explanation indicating that the vendor is now officially onboarded into the distribution ecosystem.

[0231] Figure 12F This diagram depicts the Partner Dashboard, providing partners or users with a centralized view of relevant information and metrics regarding their distribution ecosystem partner relationships. The diagram offers an overview of performance indicators, key data points, and actionable insights to facilitate effective collaboration and decision-making.

[0232] Figure 12G This image depicts a customer's product cart, showing how customers can add items they wish to purchase. It displays a list of selected products, quantities, prices, and other relevant details. Customers can view and modify their cart contents before proceeding to checkout.

[0233] Figure 12H This diagram depicts a Customer Subscription Cart, allowing customers to manage their subscription-based shopping. The diagram shows the selected subscription plan, pricing, and duration. Customers can view and modify their subscription details before finalizing their selections.

[0234] Figure 12I This diagram depicts a Customer Order Summary, providing a concise description of each customer's order, including details such as the purchased product or subscription, quantity, pricing, and any applicable discounts or promotions. This diagram allows customers to review their orders before confirming their purchase.

[0235] Figure 12J This describes the Vendor SKU Generation screen, which generates unique stock unit (SKU) codes for vendor products. This screen may include fields or options where vendors can specify product details, attributes, and pricing, and the system automatically generates the corresponding SKU code.

[0236] Figure 12K and Figure 12L The dashboard depicts an order summary, displaying aggregated information about orders within the distribution ecosystem. This chart presents 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.

[0237] Figure 12M This diagram depicts a Customer Subscription Cart, allowing customers to add, modify, or cancel subscription plans. It displays a list of selected subscriptions, pricing, and renewal dates. Customers can manage their subscriptions and make changes based on their preferences and requirements.

[0238] Figure 12N This diagram depicts a Customer Order Tracking screen, enabling customers to track the status and progress of their orders within the supply chain. The diagram shows real-time updates on order fulfillment, including processing, packaging, and shipping. Customers can monitor their order flow and estimate delivery times.

[0239] Figure 12O This graph depicts customer shipment tracking, providing customers with real-time tracking information about their shipments. The graph can include details such as the means of transport, tracking number, current location, and estimated delivery date. Customers can stay informed about the whereabouts of their shipments.

[0240] Figure 12P This chart depicts the customer's subscription history, presenting a historical record of their subscription activity. It displays a list of previous subscriptions, including the subscription plan, duration, and status. Customers can view their subscription history, track past payments, and refer to previous subscription details.

[0241] Figure 12Q This diagram illustrates the Customer Subscription Modification dialog, allowing 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 based on their evolving needs or preferences.

[0242] The depicted UI screen is not limiting. In some embodiments, Figures 12A to 12Q The UI screens collectively represent the various functions and features provided by SPoG UI, thus providing users with a comprehensive, user-friendly interface for supplier loading, partner relationship management, customer interaction, order management, and subscription management tracking within the distribution ecosystem.

[0243] It should be understood that the detailed description section, rather than the summary and abstract section, is intended to interpret the claims. The summary and abstract section may set forth one or more, but not all, exemplary embodiments of the invention conceived by the inventors, and is therefore not intended to limit the invention or the appended claims in any way.

[0244] The invention has been described above by way of illustrating the functional building blocks that implement specified functions and their relationships. For ease of description, the boundaries of these functional building blocks are arbitrarily defined herein. Alternative boundaries can be defined as long as the specified functions and their relationships are properly performed.

[0245] The specific embodiments described above will fully reveal the overall nature of the invention. Without departing from the overall concept of the invention, others can easily modify and / or adapt them for various applications (such as the specific embodiments) by applying the common knowledge of those skilled in the art, without excessive experimentation. Therefore, based on the teachings and guidance presented herein, such modifications and adaptations are intended to fall within the meaning and scope of equivalents of the embodiments disclosed herein. It should be understood that the wording or terminology herein is for descriptive purposes and not restrictive, and thus the terminology or terminology in this specification should be interpreted by those skilled in the art based on the teachings and guidance.

[0246] The scope and extent of this invention should not be limited to any of the exemplary embodiments described above, but should be defined solely by the appended claims and their equivalents.

Claims

1. A system enabling distributors to manage their end-user business within their own business environment on a distribution platform, comprising a server coupled to a processor and configured to execute the following instructions: Provide a single-window user interface, defined by a unified dashboard accessible from multiple devices; The real-time data grid module ingests data from multiple sources, including supplier platforms, CRM systems, ERP systems, and third-party databases; The real-time data grid module standardizes the acquired data into a uniform format; Standardized data is analyzed by advanced analytics and machine learning models to provide predictive analytics, anomaly detection, and personalized recommendations; The single-window user interface presents real-time data and insights through interactive charts and graphs; as well as The single-window user interface generates one or more interactive visualizations that support drill-down to specific data points for detailed analysis, enabling distributors to perform operations such as creating quotes, placing orders, and managing customer accounts directly through the single-window user interface, wherein the single-window user interface provides an end-to-end real-time view of the interaction between the supplier and the end customer.

2. The system according to claim 1, wherein, The real-time data grid module continuously ingests and standardizes data from multiple sources, ensuring consistency and accuracy across the platform by capturing real-time changes from the self-transactional system through a data replication mechanism.

3. The system according to claim 1, wherein, The advanced analytics and machine learning models predict future product demand, allowing distributors to adjust inventory levels accordingly by using machine learning algorithms to analyze historical data and predict trends.

4. The system according to claim 1, wherein, The advanced analytics and machine learning model detects transaction data anomalies and warns users of potential problems by analyzing transaction patterns and identifying deviations from expected behavior.

5. The system according to claim 1, further comprising: The supplier management module facilitates real-time negotiation of pricing and terms directly within the single-window user interface, integrates with the supplier system to provide real-time updates on product availability and pricing, and enables automatic updates based on negotiated terms.

6. The system according to claim 1, further comprising: The compliance management module ensures that all transactions comply with relevant laws and standards, provides audit trails and automated compliance checks, and generates compliance reports accessible through the single-window user interface.

7. The system according to claim 1, wherein, The server is also configured to integrate with external systems and applications via APIs and data connectors, thereby allowing resellers to extend platform functionality and integrate the platform with their existing IT infrastructure, and perform data exchange and synchronization.

8. A computer-implemented method enabling distributors to manage their end-user business within their own business environment on a distribution platform, comprising: The real-time data grid module ingests data from multiple sources, including supplier platforms, CRM systems, ERP systems, and third-party databases; The real-time data grid module standardizes the acquired data into a uniform format; Standardized data is analyzed by advanced analytics and machine learning models to provide predictive analytics, anomaly detection, and personalized recommendations; The single-window user interface presents real-time data and insights through interactive charts and graphs; as well as The single-window user interface generates one or more interactive visualizations that support drill-down to specific data points for detailed analysis, enabling distributors to perform operations such as creating quotes, placing orders, and managing customer accounts directly through the single-window user interface, wherein the single-window user interface provides an end-to-end real-time view of the interaction between the supplier and the end customer.

9. The method according to claim 8, further comprising: The advanced analytics and machine learning model is used to predict future product demand by analyzing historical data and forecasting trends using machine learning algorithms.

10. The method of claim 8, further comprising: The advanced analytics and machine learning models described above are used to detect transaction data anomalies and warn users of potential problems by analyzing transaction patterns and identifying deviations from expected behavior.

11. The method of claim 8, further comprising: The supplier management module facilitates real-time negotiation of pricing and terms directly within the single-window user interface, integrates with the supplier system to provide real-time updates on product availability and pricing, and enables automatic updates based on negotiated terms.

12. The method according to claim 8, further comprising: The compliance management module ensures that transactions comply with relevant laws and standards, provides audit trails and automated compliance checks, and generates compliance reports accessible through the single-window user interface.

13. The method of claim 8, further comprising: By integrating the system with external systems through APIs and data connectors, resellers can extend the platform's functionality and integrate it with their existing IT infrastructure, and perform data exchange and synchronization.

14. The method of claim 8, further comprising: Interactive visualizations are presented on the single-window user interface for detailed data analysis, allowing users to drill down to specific data points.

15. A non-transient tangible computer-readable device, wherein instructions stored thereon, when executed by a computing device, cause the computing device to perform the following operations: The real-time data grid module ingests data from multiple sources, including supplier platforms, CRM systems, ERP systems, and third-party databases; The real-time data grid module standardizes the acquired data into a uniform format; Standardized data is analyzed by advanced analytics and machine learning models to provide predictive analytics, anomaly detection, and personalized recommendations; The single-window user interface presents real-time data and insights through interactive charts and graphs; as well as The single-window user interface generates one or more interactive visualizations that support drill-down to specific data points for detailed analysis, enabling distributors to perform operations such as creating quotes, placing orders, and managing customer accounts directly through the single-window user interface, wherein the single-window user interface provides an end-to-end real-time view of the interaction between the supplier and the end customer.

16. The computer-readable device according to claim 15, wherein, The instructions also enable the computer-readable device to use the advanced analytics and machine learning model to predict future product demand by employing machine learning algorithms to analyze historical data and predict trends.

17. The computer-readable device according to claim 15, wherein, The instructions also enable the computer-readable device to use the advanced analytics and machine learning model to detect transaction data anomalies and warn the user of potential problems by analyzing transaction patterns and identifying deviations from expected behavior.

18. The computer-readable device according to claim 15, wherein, The instructions also enable the computer-readable device to facilitate real-time negotiation of pricing and terms directly within the single-window user interface using a supplier management module, integrate with the supplier system to provide real-time updates on product availability and pricing, and implement automatic updates based on negotiated terms.

19. The computer-readable device according to claim 15, wherein, The instructions also enable the computer-readable device to use the compliance management module to ensure that transactions comply with relevant laws and standards, provide audit trails and automated compliance checks, and generate compliance reports accessible through the single-window user interface.

20. The computer-readable device of claim 15, wherein, The instructions also enable the computer-readable device to integrate the system with external systems via APIs and data connectors, thereby allowing resellers to extend platform functionality and integrate the platform with their existing IT infrastructure, and to perform data exchange and synchronization.

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