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

Abstract

We provide a system that improves decision-making capabilities. [Solution] System 300 includes a server that provides a Single Pane of Glass User Interface (SPoG UI) and a Real-Time Data Mesh (RTDM) module for ingesting and standardizing data from multiple sources. An Advanced Analytics and Machine Learning (AAML) module analyzes the data to provide predictive analytics, anomaly detection, and personalized recommendations. The SPoG UI presents real-time data and insights through interactive visualizations, enabling resellers to take actions such as creating quotes, placing orders, and managing customer accounts. The system supports real-time pricing negotiation, compliance management, and integration with external systems via APIs, generating a real-time, end-to-end view of interactions between both suppliers and end-user customers.

Description

Technical Field

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

[0002] (Background) Previous IT distribution models have long been characterized by their fragmented nature, requiring various systems and touchpoints to manage different aspects of business operations. Resellers, vendors, and customers often face challenges related to data consistency, real - time visibility, and business efficiency. As businesses strive to improve decision - making capabilities and provide an efficient and intuitive user experience, the need for a unified platform that integrates business - to - business (B2B) and business - to - consumer (B2C) transactions is becoming increasingly apparent.

[0003] To address these challenges, there is a growing demand for platforms that 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, vendor databases, and third-party applications. By leveraging advanced artificial intelligence (AI) and machine learning (ML) technologies, these platforms can harmonize and process data, ensuring accuracy and consistency across all interactions.

[0004] The IT distribution industry has long been plagued by fragmentation, forcing resellers and vendors to navigate complex, fragmented systems to manage their business operations. These systems typically cater to either business-to-business (B2B) or business-to-consumer (B2C) transactions, rarely both. As a result, resellers and distributors often need to develop their own solutions to bridge the gap between B2B and B2C transactions, leading to inefficiencies, increased operational costs, and a lack of real-time connectivity. Current solutions on the market fail to provide a comprehensive platform that integrates both B2B and B2C transactions into a single, unified system.

[0005] One of the key challenges in the distribution industry is the lack of end-to-end connectivity across the supply chain. Vendors, resellers, and customers often operate in silos, managing inventory, pricing, customer interaction, and order processing through heterogeneous systems. This fragmentation creates a lack of visibility and control for resellers, making it impossible to deliver a consistent experience to end users. In addition, resellers are forced to invest heavily in developing custom-made systems that attempt to provide real-time insights, data synchronization, and engagement across B2B and B2C operations. However, such custom systems are often difficult to maintain, lack scalability, and fail to provide robust integration between different transaction models such as subscription, consumption, and traditional models.

[0006] Further complicating the distribution landscape is the growing need for personalized financial solutions and bundled offerings. Modern consumers and businesses alike demand the ability to purchase hardware, software, SaaS, and subscriptions in the same transaction, and expect the kind of personalized recommendations and insights typically found on B2C e-commerce platforms. However, current B2B systems offer little of this flexibility, resulting in a gap between end-customer expectations and the capabilities of the platforms used by resellers. This discrepancy often leads to operational inefficiencies, lost sales opportunities, subpar customer experience, and subpar operational efficiency, resulting in increased operational costs to address these issues. [Overview of the Initiative]

[0007] The invention described herein addresses these challenges by introducing a platform that provides a combined B2B and B2C solution within a single, integrated environment. Unlike conventional distribution platforms, this system provides end-to-end connectivity, enabling engagement among vendors, resellers, and end customers. The platform is designed to aggregate and harmonize data from multiple sources, providing a unified view that improves 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 analysis, catering to both B2B and B2C markets. This ensures that resellers no longer need to build custom systems, reducing complexity and costs while significantly improving the overall user experience.

[0008] By enabling resellers to bundle hardware, software, SaaS, and subscriptions in the same transaction, the platform addresses the growing demand for flexible and comprehensive purchasing options. Furthermore, the system streamlines the procurement process by providing personalized insights and financial solutions that enable end customers to make informed decisions. This solution not only bridges the gap between B2B and B2C but also positions resellers to meet evolving customer expectations in a dynamic and increasingly complex market.

[0009] The embodiments provided herein present a unified platform designed to address long-standing challenges in the IT distribution industry by integrating business-to-business (B2B) and business-to-consumer (B2C) transactions. The platform provides end-to-end connectivity across the entire supply chain, enabling resellers, vendors, and end-users to interact within a single system. By eliminating the need for resellers to build their own custom solutions, the platform significantly reduces operational complexity and improves efficiency.

[0010] The disclosed systems and methods provide a flexible and scalable solution for aggregating data from various sources, including customer relationship management (CRM) systems, enterprise resource planning (ERP) systems, vendor 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 enable data exchange between vendors, resellers, and end customers, creating a unified user experience for managing both B2B and B2C transactions.

[0011] In some embodiments, the system and method provide the capability to deliver personalized recommendations and insights based on real-time data analysis. Using advanced machine learning models, the platform analyzes historical data, customer preferences, and market trends to generate customized product recommendations and dynamic pricing strategies. This enables resellers to deliver a personalized purchasing experience similar to what is typically offered in a B2C e-commerce environment, but with the added complexity of B2B transactions, such as bulk ordering, price negotiation, and contract management. The platform also includes targeted financial solutions, enabling resellers to offer customized payment options and bundles that combine hardware, software, SaaS, and subscriptions within a single transaction.

[0012] By providing end-to-end billing capabilities, the platform simplifies the management of complex transactions involving multiple products and services. This includes real-time updates of inventory and pricing, automated billing, and order fulfillment analysis, all within a single user interface. The platform's ability to integrate financial solutions and provide personalized recommendations ensures that both resellers and end customers benefit from a more streamlined and efficient procurement process.

[0013] Furthermore, this invention addresses the growing industry demand for flexible storefronts that emulate the functionality of B2C e-commerce platforms. The system enables resellers to create customized storefronts that offer the same ease of use and real-time connectivity as consumer-facing platforms, while also meeting the specific needs of the IT distribution industry. This flexibility ensures that resellers can adapt to market demands and provide end customers with a consistent, user-friendly experience in both B2B and B2C transactions.

[0014] The platform provides a comprehensive solution for managing the complexities of modern IT distribution. By offering a unified, real-time view of both B2B and B2C operations, the system enables resellers to streamline business processes, reduce operational costs, and deliver personalized customer experiences. With targeted financial solutions, personalized insights, and the ability to bundle hardware, software, SaaS, and subscriptions into a single transaction, this platform is a game-changer for the IT distribution industry. It not only improves the operational efficiency of resellers but also enhances the end-customer experience and creates a more integrated and efficient distribution ecosystem.

[0015] An AI-based end-user view for managing B2B and B2C transactions aims to address shortcomings in the distribution industry by providing a unified platform experience. 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, ordering, and data harmonization. Therefore, there is a critical need for a technological solution that can effectively integrate, streamline, and accelerate these complex processes while simultaneously ensuring data security and compliance.

[0016] The global distribution industry needs innovative solutions to address challenges such as inefficient distribution management, SKU management, and the shift to direct-to-consumer (DTT) models. Traditional distribution methods are becoming increasingly inadequate, particularly in light of changing consumer expectations and regulations. This invention addresses these challenges by integrating a comprehensive set of functionalities focused on distribution management, supply chain management, and customer visibility into a single platform.

[0017] According to several embodiments, the system can be integrated with a real-time data mesh (RTDM) and a single-pane-of-glass user interface (SPoG UI). The system uses AI algorithms to optimize user interactions based on real-time inventory and customer data. It also employs generative AI techniques to aggregate data into a standard, independent format, ensuring consistency and accuracy.

[0018] In non-specific examples, the system uses machine learning models such as neural networks to provide personalized recommendations and dynamic pricing strategies for customers. This platform can provide personalized product recommendations and dynamic pricing strategies based on real-time data analysis.

[0019] In some embodiments, the system and method dynamically manages SKUs, synchronizes inventory levels in real time across multiple channels, and provides a comprehensive view of both B2B and B2C transactions. The method includes user authentication, data ingestion from multiple sources, real-time inventory updates, automated inventory alerts, vendor integration, customer-specific pricing, and a unified product catalog. This comprehensive data management approach provides resellers with a unified view of their operations.

[0020] In some embodiments, the system and method process and fulfill orders from B2B and B2C customers, providing an integrated view and streamlined operations. This method can include order placement, real-time inventory checks, dynamic pricing, order validation, order confirmation, real-time tracking, automated billing, return management, and fulfillment analysis. The use of generative AI technology within the AAML module optimizes the entire process and provides resellers with a unified platform for managing orders.

[0021] In some embodiments, the system and method enable the personalization of customer interactions and engagement for both B2B and B2C customers, leveraging data analytics and machine learning. The method may include customer data integration, segmentation, personalized marketing, customized portals, real-time support, loyalty programs, feedback loops, and real-time analytics. Integration of generative AI technology ensures that personalization is dynamic and data-driven.

[0022] Embodiments disclosed herein integrate multiple systems, automate processes, and validate data configurations based on intelligent rules. This enables the efficient execution of complex tasks without requiring specialized knowledge, reducing time and minimizing errors. Furthermore, the present invention is adaptable and configurable to evolving market and customer demands, thereby maintaining the validity and sustainability of the distribution model. Thus, the present invention provides an efficient, integrated, and highly adaptable solution for managing the end-user view of B2B and B2C transactions in the distribution industry.

[0023] (Single pane of glass) A single pane of glass (SPoG) can provide a comprehensive solution configured to address such multifaceted challenges. It can be configured to provide an intuitive and efficient platform for realizing distribution processes.

[0024] According to several embodiments, SPoG can be configured to address supply chain and distribution management by enhancing visibility and control over supply chain processes. Through real-time tracking and analysis, SPoG can provide valuable insights into inventory levels and product status, ensuring that supply chain and distribution management processes are handled efficiently.

[0025] In some embodiments, SPoG can integrate multiple touchpoints into a single platform and emulate direct consumer channels within the distribution platform. This integration provides a unified direct channel for consumers to interact with distributors, significantly reducing supply chain complexity and improving the overall customer experience.

[0026] SPoG provides an innovative solution for improved inventory management through advanced forecasting capabilities. These predictive analytics highlight demand trends and guide companies to manage inventory more efficiently, reducing the risk of stockouts or overstock.

[0027] In some embodiments, SPoG can include a global compliance database. This database is updated in real-time, enabling distributors to stay up-to-date with the latest international regulations. This feature significantly reduces the burden of manual tracking and ensures smooth and compliant cross-border transactions.

[0028] In some embodiments, to facilitate the generation of end-user views in a dashboard customized for resellers, SPoG integrates data from various OEMs onto a single platform. This not only ensures data consistency but also significantly reduces the possibility of errors. Additionally, it efficiently generates B2B and B2C views, thereby providing the ability to align with specific market needs and requirements.

[0029] In some embodiments, SPoG is a highly configurable and user-friendly platform. Its intuitive interface allows users to easily access and purchase the technology, thereby meeting the expectations of a new generation of technology buyers.

[0030] Furthermore, the advanced analytical capabilities of SPoG provide valuable insights that can drive strategies and decision-making. Trends can be tracked and analyzed in real-time, enabling companies to always stay ahead and adapt to changing market conditions.

[0031] SPoG offers a future-proof solution thanks to its flexibility and scalability. It can adapt to changing business needs, allowing companies to scale operations up or down as needed without significantly altering their infrastructure.

[0032] SPoG's innovative approach to solving challenges in the distribution industry is a valuable tool. By improving supply chain visibility, streamlining inventory management, ensuring compliance, simplifying the creation of reseller dashboards, and delivering superior customer experiences, it provides comprehensive solutions to the complex problems that have plagued the distribution industry for years. Through its implementation, distributors can expect improved efficiency, reduced errors, and enhanced customer satisfaction, leading to sustainable growth in an ever-evolving global market.

[0033] (Real-time data mesh (RTDM)) In some embodiments, the platform can include a real-time data mesh (RTDM) implementation. RTDM offers an innovative solution to address these challenges. RTDM is a distributed data architecture that enables real-time data availability across multiple sources and touchpoints. This feature improves supply chain visibility, enables efficient management, and allows distributors to handle disruptions more effectively.

[0034] RTDM's predictive analytics capabilities provide a solution for efficient inventory control. By providing insights into demand trends, it helps companies manage their inventory and reduce the risk of excess inventory or stockouts.

[0035] RTDM's global compliance database is updated in real time, ensuring that distributors are prepared to comply with international regulations. This significantly reduces the burden of manual tracking and enables cross-border transactions.

[0036] RTDM also simplifies SKU management and localization by integrating data from various OEMs, ensuring data consistency and reducing the potential for errors. Its ability efficiently generates a real-time view of end-users with specific needs in both B2B and B2C markets.

[0037] RTDM enhances the customer experience with its intuitive interface, enabling easy access to and purchase of technology, and meeting the expectations of a new generation of technology buyers.

[0038] (Advantages of SPoG and RTDM integration) Integrating the SPoG platform with RTDM offers countless benefits. It provides a unified solution to complex issues in the distribution industry. With RTDM's capabilities, SPoG can improve supply chain visibility, streamline inventory management, ensure compliance, simplify SKU management, and deliver a superior customer experience.

[0039] Real-time tracking and analytics provided by RTDM improve SPoG's ability to effectively manage supply chains and inventory. It provides accurate, current information, enabling distributors to make informed decisions quickly.

[0040] Furthermore, integrating SPoG with RTDM ensures data consistency and reduces errors in SKU management. By providing a centralized platform for managing data from various OEMs, it simplifies product localization and helps align with market needs.

[0041] RTDM's global compliance database integrates with SPoG to facilitate compliant cross-border transactions. It also reduces the burden of manual tracking, saving significant time and resources.

[0042] In some embodiments, the distribution platform incorporates SPoG and RTDM to provide an improved, comprehensive distribution system. This platform can leverage the advantages of the distribution model, address its existing challenges, and position itself for sustainable growth in the ever-evolving global market. [Brief explanation of the drawing]

[0043] [Figure 1] This embodiment illustrates one example of the operating environment of a distribution platform, referred to as the system in this embodiment. [Figure 2] Figure 1 illustrates one embodiment of the operating environment of a distribution platform constructed with the elements shown. [Figure 3] An embodiment of a distribution management system is illustrated. [Figure 4] This document describes a system for generating an end-user view for resellers, according to one embodiment. [Figure 5] An RTDM module according to one embodiment is shown. [Figure 6] An illustration shows a SPoG UI according to one embodiment. [Figure 7] A system for an automated process to generate an end-user view for resellers, according to one embodiment, is illustrated. [Figure 8] This is a flowchart illustrating some embodiments of the present disclosure of methods for dynamically managing SKUs, synchronizing inventory levels in real time across multiple channels, and providing a comprehensive view of B2B and B2C transactions. [Figure 9] This is a flowchart of an automated process for processing and fulfilling orders from B2B and B2C customers, according to some embodiments of the present disclosure. [Figure 10] This is a flowchart illustrating the automated personalization of customer interactions and engagement with B2B and B2C customers, according to several embodiments of the present disclosure. [Figure 11]This is a block diagram of exemplary components of a device according to some embodiments of the present disclosure. [Figures 12A-12Q] This document illustrates various screens and functionalities of the SPoG UI in several embodiments. [Modes for carrying out the invention]

[0044] This embodiment may be implemented in hardware, firmware, software, or any combination thereof. Alternatively, this embodiment may be implemented as instructions stored in a machine-readable medium, which can be read and executed by one or more processors. The machine-readable medium may include any mechanism for storing or transmitting information in a format readable by a machine (e.g., a computing device). For example, the machine-readable medium may include read-only memory (ROM), random-access memory (RAM), magnetic disk storage media, optical storage media, flash memory devices, and others. Furthermore, firmware, software, routines, and instructions may be described herein as performing specific actions. However, such descriptions are merely for convenience, and it should be understood that such actions are actually the results obtained by a computing device, processor, controller, or other device executing the firmware, software, routines, instructions, etc.

[0045] The actions shown in the illustrative methods are not exhaustive, and it should be understood that other actions may similarly be performed before, after, or between any of the illustrated actions. In some embodiments of this disclosure, the actions may be performed in a different order and / or different order.

[0046] Figure 1 illustrates the operating environment 100 of a distribution platform called System 110 in this embodiment. System 110 operates within the context of an information technology (IT) distribution model and responds to the demands of various users, including customers 120, end customers 130, vendors 140, resellers 150, and other entities involved in the distribution process. This operating environment includes a wide range of characteristics and dynamics that contribute to the success and efficiency of the distribution platform.

[0047] Customers 120 within the operating environment of System 110 represent businesses or individuals seeking IT solutions to meet specific needs. These customers may require a diverse range of IT products, such as hardware components, software applications, network equipment, or cloud-based services. System 110 provides customers with a user-friendly interface, enabling them to browse, search, and select the most suitable IT solutions based on their requirements. Furthermore, customers can access real-time data and analytics through System 110 to make informed decisions and optimize their IT infrastructure.

[0048] The end customer 130 can be the ultimate beneficiary of the IT solutions provided by System 110. The end customer may include businesses or individuals who use IT products and services to improve their operations, productivity, or daily activities. The end customer relies on System 110 to access a wide range of IT solutions and is ensured to have access to the latest technologies and innovations in the market. System 110 enables the end customer to track orders, receive delivery status updates, and access customer support services, thereby improving the overall experience.

[0049] Vendor 140 plays a crucial role within the operating environment of System 110. These vendors include manufacturers, distributors, and suppliers providing a diverse range of IT products and services. System 110 acts as a centralized platform for vendors to showcase their offerings, manage inventory, and facilitate transactions with customers and resellers. Vendors can leverage System 110 to simplify supply chain operations, manage pricing and promotions, and gain insights into customer preferences and market trends. By integrating with System 110, vendors can expand their reach, access new markets, and improve overall visibility and competitiveness.

[0050] Resellers 150 can act as intermediaries within a distribution model, bridging the gap between vendors and customers. Resellers play a crucial role in the IT distribution ecosystem by connecting customers with the right IT solutions from various vendors. Resellers may include retailers, value-added resellers (VARs), system integrators, or managed service providers. System 110 enables resellers to access a comprehensive catalog of IT solutions, manage their sales pipelines, and provide value-added services to customers. By leveraging System 110, resellers can improve customer relationships, optimize product offerings, and increase revenue streams.

[0051] Within the operating environment of System 110, various dynamics and characteristics may exist that contribute to its effectiveness. These dynamics include real-time data exchange, integration with existing enterprise systems, scalability, and flexibility. System 110 ensures that relevant data can be exchanged between users in real time, enabling accurate decision-making and timely action. Integration with existing enterprise systems such as Enterprise Resource Planning (ERP) systems, Customer Relationship Management (CRM) systems, and warehouse management systems enables communication and interoperability, eliminates data silos, and provides end-to-end visibility.

[0052] System 110 can provide scalability and flexibility. It can accommodate the growing demands of IT distribution models, whether it involves an expanding customer base, an increase in the number of vendors, or a wide range of IT products and services. System 110 can be configured to handle large-scale data processing, storage, and analytics, ensuring it can support the evolving needs of distribution platforms. In addition, System 110 leverages a technology stack including .NET, Java, and other preferred technologies, providing a robust foundation for its operation.

[0053] In summary, the operating environment for System 110 within the IT distribution model includes customers 120, end customers 130, vendors 140, resellers 150, and other entities involved in the distribution process. System 110 functions as a centralized platform that facilitates efficient collaboration, communication, and transaction processes among these users. By leveraging real-time data exchange, integration, scalability, and flexibility, System 110 enables users to optimize their operations within the IT distribution ecosystem, improve customer experience, and drive business success.

[0054] Figure 2 illustrates the operating environment 200 of a distribution platform constructed with the elements shown in Figure 1. This operating environment is configured to enable an end-user view for managing both B2B and B2C transactions within a unified platform. Within this operating environment, integration points 210 facilitate data flow and connectivity between various customer systems 220, vendor systems 240, reseller systems 260, and other entities involved in the distribution process. The figure illustrates the interconnectivity and mechanisms that enable efficient collaboration and data-driven decision-making. This operating environment is configured to enable an end-user view for both B2B and B2C transactions for integrating, processing, and analyzing data using advanced artificial intelligence (AI) and machine learning (ML) technologies. In this configuration, AI algorithms can be applied for real-time inventory management, optimization of customer interactions, and data harmonization to ensure a unified user experience. Machine learning models such as neural networks and decision trees can be employed to provide users with sophisticated and personalized options. Furthermore, the system can use generative AI technology to aggregate data into a standardized, independent format, ensuring data consistency and accuracy.

[0055] The operating environment 200 may include system 110 as a distribution platform that functions as a central hub for managing and facilitating the distribution process. System 110 can provide a unified interface for end users to manage and view all aspects of B2B and B2C transactions, ensuring the consistency of interactions and data. System 110 can be configured to function and operate as a bridge between customer systems 220, vendor systems 240, reseller systems 260, and other entities within the ecosystem. It can integrate communication, data exchange, and transaction processes to provide users with a unified and streamlined experience. Furthermore, the operating environment 200 may include one or more integration points 210 to ensure smooth data flow and connectivity. These integration points may include:

[0056] Customer System Integration: Integration point 210 can enable system 110 to connect with customer system 220, allowing for 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 used 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, improving customer 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 for both B2B and B2C transactions, improving visibility and decision-making capabilities. The application of the end-user view process 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, fetch 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 the supply chain and customer interactions, enabling data-driven decision-making and improved operational efficiency in both B2B and B2C contexts.

[0057] Partner System Integration: Integration point 210 allows system 110 to connect to partner system 230, enabling efficient data exchange and synchronization. Partner system 230 may include various entities such as partner system 231, partner system 232, and partner system 233. Integration with partner system 230 empowers customers to access real-time inventory information, pricing details, order tracking, and other relevant data, improving visibility and decision-making capabilities.

[0058] Vendor System Integration: Integration point 210 facilitates the connection between system 110 and vendor system 240. Vendor system 240 may include entities representing the inventory management system, pricing system, and product catalog adopted by the vendor, such as vendor system 241, vendor system 242, and vendor system 243. Integration with vendor system 240 ensures that the vendor can efficiently update product offerings, manage pricing and promotions, and receive real-time order notifications and fulfillment details. Integration with system 110 enables vendor system 240 to efficiently update product offerings, manage pricing and promotions, and receive real-time order notifications and fulfillment details, supporting both B2B and B2C transactions. End-user view processes and components within 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 for requests from vendor systems for specific inventory or customization options. This helps vendors align their stock and manufacturing processes with real-time market demand.

[0059] Reseller System Integration: Integration point 210 provides the capability for the reseller system 260 to connect with system 110. The reseller system 260 may encompass entities representing sales systems, customer management systems, and service delivery platforms adopted by the reseller, such as reseller system 261, reseller system 262, and reseller system 263. Integration with the reseller system 260 empowers the reseller to access current product information, manage customer accounts, track sales performance, and provide value-added services to customers.

[0060] Integration with other entity systems: Integration point 210 further enables connectivity with other entities involved in the distribution process. These entities may include entities such as entity system 271, entity system 272, and entity system 273. Integration with these systems ensures communication and data exchange, facilitating collaboration and efficient distribution processes. In some embodiments, integration of the end-user view with other entity systems can ensure that entity system 271 is involved in real-time product interaction and data access processes via system 110.

[0061] Furthermore, integration point 210 enables connection 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 legacy ERP systems, such as SAP, Impulse, META, and I-SCALA, as well as future systems. Record system 280 can contain one or more storage repositories of critical legacy business data. This facilitates data exchange and synchronization integration 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 a connection between record system 280 and the distribution platform, enabling stakeholders to leverage the rich data stored in the ERP for efficient collaboration, data-driven decision-making, and streamlined distribution processes. These systems represent internal systems used by customers, vendors, and others.

[0062] Integration points 210 within the operating environment 200 can be facilitated through standardized protocols, APIs, and data connectors. These mechanisms ensure compatibility, interoperability, and secure data transfer between the distribution platform and connected systems. System 110 adopts industry-standard protocols, such as RESTful API, SOAP, or GraphQL, to establish communication channels and enable data exchange.

[0063] In some embodiments, system 110 can incorporate an authentication and authorization mechanism to ensure secure access and data integrity. Technologies such as OAuth or JSON Web Token (JWT) can be employed to authenticate users, authorize data access, and maintain the integrity and confidentiality of exchanged information.

[0064] In some embodiments, the integration point 210 and the data flow within the operating environment 200 enable user interaction within a connected ecosystem. Data generated at various stages of the distribution process, including customer orders, inventory updates, shipping details, and sales analytics, flows between customer systems 220, vendor systems 240, reseller systems 260, and other entities. This data exchange facilitates real-time visibility, enables data-driven decision-making, and improves operational efficiency across the entire distribution platform.

[0065] In some embodiments, system 110 can support integration points 210 and enable communication within the operating environment 200 using advanced technologies such as Typescript, NodeJS, ReactJS, .NET Core, C#, and other suitable technologies. These technologies provide a robust foundation for system 110, ensuring scalability, flexibility, and efficient data processing capabilities. Furthermore, integration points 210 can also employ algorithms, data analysis, and machine learning techniques to derive valuable insights, optimize distribution processes, and personalize customer experiences. The integration points 210 and the data flow within the operating environment 200 enable users to operate within a connected ecosystem. Data generated at various touchpoints, including customer orders, inventory updates, pricing changes, or delivery status, flows between different entities, systems, and components. The integrated data can be processed, harmonized, and made available 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 improve customer experiences.

[0066] Some elements of the operating environment shown in Figure 2 may include conventional, well-known elements that are only briefly described herein. For example, each of the customer systems, such as customer system 220, may include a desktop personal computer, workstation, laptop, PDA, mobile phone, or any Wireless Access Protocol (WAP) enabled device, or any other computing device that can interface directly or indirectly with the Internet or other network connectivity. Each of the customer systems may typically run an HTTP client such as Microsoft Edge, Google Chrome, Opera, or a WAP-enabled browser for mobile devices, and the customer systems may access, process, and display information, pages, and applications available from the distribution platform over the network.

[0067] Furthermore, each customer system may typically be equipped with user interface devices, such as a keyboard, mouse, trackball, touchpad, touchscreen, pen, or similar devices for interacting with a graphical user interface (GUI) provided by a browser. These user interface devices enable users of the customer system to navigate the GUI, interact with pages, forms, and applications, and access data and applications hosted by the distribution platform.

[0068] The customer system and its components can be configured by an operator using an application that includes a web browser running on a central processing unit such as an Intel Pentium processor or a similar processor. Similarly, the distribution platform (system 110) and its components can be configured by an operator using an application that runs on a central processing unit such as an Intel Pentium processor or a similar processor, and / or a processor system that may include multiple processor units.

[0069] Embodiments of a computer program product may include a machine-readable storage medium containing instructions for programming a computer to perform the processes described herein. Computer code for operating and configuring distribution platforms and customer systems, vendor systems, reseller systems, and systems of other entities to communicate with each other and to process web pages, applications, and other data may be downloaded and stored on a hard disk or any other volatile or non-volatile storage medium or device, such as ROM, RAM, floppy disks, optical disks, DVDs, CDs, microdrives, magneto-optical disks, magnetic or optical cards, nanosystems, or any suitable medium for storing instructions and data.

[0070] Furthermore, computer code for implementing this embodiment can be transmitted and downloaded from the software source via the Internet or any other conventional network connection using communication media and protocols such as TCP / IP, HTTP, HTTPS, Ethernet, etc. The code can also be transmitted over an extranet, VPN, LAN, or other network and executed on a client system, server, or server system using a programming language such as C, C++, HTML, Java, JavaScript, ActiveX, VBScript, or others.

[0071] This embodiment can be implemented in various programming languages ​​running on a client system, server, or server system, and it will be understood that the choice of language may depend on the specific requirements and environment of the distribution platform.

[0072] Therefore, the operating environment 200 can connect the distribution platform with one or more integration points 210 and data flows, enabling efficient collaboration and a streamlined distribution process.

[0073] Figure 3 illustrates System 300 for supply chain and distribution management. System 300 (Figure 3) is a supply chain and distribution management solution configured to address the challenges faced by fragmented distribution ecosystems in the global distribution industry. System 300 can include several interconnected components and modules that work in harmony to optimize supply chain and distribution operations, improve collaboration, and drive business efficiency.

[0074] The Single Pane of Glass (SPoG) UI305 functions as a centralized user interface, providing users with a unified view of the entire supply chain. It aggregates information from various sources and presents real-time data, analytics, and functionality tailored to the user's specific role and responsibilities. By providing a customizable and intuitive dashboard-style layout, the SPoG UI enables users to access relevant information and tools to manage data-driven decision-making and efficient supply chain and distribution activities.

[0075] For example, logistics managers can use the SPoG UI to monitor shipment status, track delivery routes, and view real-time inventory levels across multiple warehouses. This data can be visualized through interactive charts, such as maps showing the current location of each shipment, or bar graphs showing inventory levels by product category. Having a unified view of the supply chain allows logistics managers to identify bottlenecks, optimize routes, and ensure timely product delivery.

[0076] SPoG UI305 integrates with other modules of System 300, facilitating real-time data exchange, synchronized operations, and workflows. Through API integration, data synchronization mechanisms, and an event-driven architecture, SPoG UI305 ensures a smooth information flow and enables collaborative decision-making across the distribution ecosystem. Designed with a user-centric approach, SPoG UI305 features an intuitive and responsive layout. Leveraging front-end technologies, it provides dynamic and interactive data visualizations. Customizable dashboards allow users to tailor views based on specific roles and requirements. The UI supports drag-and-drop functionality for ease of use, and its adaptive design ensures compatibility across various devices and platforms. Advanced filtering and search capabilities enable users to efficiently navigate and access relevant supply chain data and insights.

[0077] For example, when a purchase order is generated in the SPoG UI, the system automatically updates inventory levels, triggers notifications to the warehouse management system, and initiates the shipping process. This integration enables efficient order fulfillment, reduces manual errors, and improves overall supply chain visibility.

[0078] The Real-Time Data Mesh (RTDM) module 310 is another component of system 300 and is responsible for ensuring data flow within the distribution ecosystem. It collects and harmonizes data from multiple sources and ensures its real-time availability.

[0079] In a distribution network, the RTDM module collects data from various systems, including inventory management systems, point-of-sale terminals, and customer relationship management systems. This data is harmonized by aligning formats, standardizing units of measurement, and reconciling inconsistencies. The harmonized data can then be made available in real time, allowing users to access accurate and current information across the supply chain.

[0080] The RTDM module 310 can be configured to capture data changes across multiple transaction systems in real time. It employs an advanced Change Data Capture (CDC) mechanism that continuously monitors transaction systems to detect updates or modifications. The CDC component can be specifically configured to work with a variety of transaction systems, including legacy ERP systems, customer relationship management (CRM) systems, and other enterprise-scale systems, ensuring compatibility and flexibility for business operations in diverse environments.

[0081] Having access to real-time data allows users to make timely decisions and respond quickly to changing market conditions. For example, if the RTDM module detects a sudden surge in demand for a particular product, it can trigger an alert to the production team, allowing them to adjust the manufacturing schedule and prevent stockouts.

[0082] The RTDM module 310 simplifies data management within supply chain operations. It enables real-time harmonization of data from multiple sources, freeing vendors, resellers, customers, and end customers from the constraints imposed by legacy ERP systems. This increased flexibility supports improved efficiency, customer service, and innovation.

[0083] Another component of System 300 is the Advanced Analytics and Machine Learning (AAML) module 315. Leveraging powerful analytics tools and algorithms such as Apache Spark, TensorFlow, or scikit-learn, the AAML module extracts valuable insights from collected data, enabling advanced analytics, predictive modeling, anomaly detection, and other machine learning capabilities.

[0084] For example, the AAML module can analyze sales data history to identify seasonal patterns and predict future demand. It can generate forecasts that help optimize inventory levels, ensure inventory availability during peak seasons, and minimize the costs of excess inventory. By leveraging machine learning algorithms, the AAML module automates repetitive tasks, predicts customer preferences, and optimizes supply chain processes.

[0085] In addition to forecasting demand, the AAML module can provide insights into customer behavior, enabling targeted marketing campaigns and personalized customer experiences. For example, by analyzing customer data, the module can identify cross-selling or upselling opportunities and recommend products relevant to individual customers.

[0086] Furthermore, the AAML module can analyze data from various sources, such as social media feeds, customer reviews, and market trends, to gain a deeper understanding of customer intentions and preferences. This information can be used to inform product development decisions, identify emerging market trends, and adapt business strategies to meet evolving consumer expectations.

[0087] System 300 emphasizes integration and interoperability for connecting with existing enterprise systems, such as ERP systems, warehouse management systems, and customer relationship management systems. By establishing connectivity and data flow between these systems, System 300 enables seamless data exchange, process automation, and end-to-end visibility across the supply chain. Integrated protocols, APIs, and data connectors facilitate communication and interoperability between different modules and components, creating a unified, interconnected distribution ecosystem.

[0088] The implementation and deployment of System 300 can be tailored to meet specific business needs. It can be deployed as a cloud-native solution using containerization technologies such as Docker and orchestration frameworks such as Kubernetes. This approach ensures scalability, ease of management, and efficient updates across different environments. The implementation process involves configuring the system to align with specific supply chain requirements, integrating it with existing systems, and customizing modules and components based on business needs and preferences.

[0089] System 300 for supply chain and distribution management is a comprehensive and innovative solution that addresses the challenges faced by fragmented distribution ecosystems. It combines the power of SPoG UI305, RTDM module 310, and AAML module 315 with integration with existing systems. By leveraging a diverse technology stack, scalable architecture, and robust integration capabilities, System 300 delivers end-to-end visibility, data-driven decision-making, and optimized supply chain operations. The examples and options provided herein are non-exclusive and can be customized to meet specific industry requirements, driving efficiency and success in supply chain and distribution management.

[0090] Figure 4 illustrates System 400, configured to enable end users to view and manage B2B and B2C transactions. System 400 provides a comprehensive, real-time, integrated platform for resellers to manage their operations within a single environment similar to the Shopify model, allowing them to view both supply and end-user customer interactions in one agile end-to-end view.

[0091] System 400 may include SPoG UI 405, RTDM 410, and AAML module 415. SPoG UI 405 can serve as the primary interface for users, providing access to various functionalities through a unified, intuitive interface. This interface can be developed using web-based technology and allows access from a variety of devices, including desktop computers, laptops, tablets, and smartphones.

[0092] RTDM410 can function 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. RTDM410 can standardize this data into a unified format, ensuring consistency and accuracy across platforms. This data can include real-time inventory levels, pricing information, customer data, and transaction history.

[0093] AAML415 functions as a processing layer, employing advanced algorithms and machine learning models to analyze and interpret data aggregated by RTDM410. AAML415 can provide functionalities such as predictive analytics, anomaly detection, and personalized recommendations. These capabilities can help resellers optimize operations, forecast demand, and improve customer satisfaction.

[0094] 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 both supply chain data and customer interactions, enabling resellers 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 can facilitate the creation, tracking, and management of orders from both B2B and B2C customers. The SPoG UI 405 enables resellers to configure and manage bundled products that combine hardware, software, SaaS, and subscriptions in a single transaction. This provides flexibility to address diverse customer needs and offers the backend infrastructure required for B2B operations while providing the personalized user experience typical of B2C platforms. Furthermore, the platform enables resellers to deliver targeted financial solutions based on customer-specific data, enabling optimized sourcing and purchasing decisions through dynamic pricing and financial modeling. By centralizing order processing within SPoG UI405, resellers can efficiently handle transactions and reduce the complexity of managing multiple sales channels.

[0095] In a non-limiting example, when a user accesses SPoG UI405, they can view a comprehensive dashboard displaying real-time data and insights derived from both supply-side and end-user customer interactions. For example, a reseller can see current inventory levels, pending orders, customer interactions, and sales analytics all in one place. The interface can provide interactive charts and graphs, allowing users to drill down into specific data points for detailed analysis. SPoG UI405 can also allow users to initiate actions such as creating quotes, confirming orders, and managing customer accounts directly from the interface.

[0096] The RTDM410 can continuously ingest data from various sources, ensuring that the information displayed in the SPoG UI405 is current. In some embodiments, the RTDM410 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 harmonized and made available for analysis and reporting.

[0097] The RTDM410 enables real-time aggregation and harmonization of data from both B2B and B2C transactions, providing end-to-end connectivity across resellers, vendors, and customers. The integrated system provides real-time insights into inventory levels, customer purchasing trends, and financial performance, ensuring resellers can optimize both their procurement processes and customer interactions. This end-to-end connectivity allows resellers to efficiently manage both supply-side and customer-side operations without the need for custom-built platforms.

[0098] RTDM410 may include an integration layer 411 that collects data from various sources such as vendor platforms, CRM systems, ERP systems, and third-party databases. The integration layer 411 ensures that all relevant data is integrated into a single platform, providing a holistic view of both supply-side and end-user activity.

[0099] Furthermore, the RTDM410 may include a data layer 413 that processes and updates data in real time. The data layer 413 ensures that the information presented to the SPoG UI405 is always current, enabling resellers to make informed decisions based on the latest data.

[0100] AAML415 can analyze data in real time and generate insights and recommendations displayed in SPoG UI405. For example, AAML415 can use machine learning models to predict future demand for specific products, enabling resellers to adjust inventory levels accordingly. AAML415 can also detect anomalies in transaction data, such as unusual surges in order volume, and alert users to potential problems.

[0101] The AAML module 415 can provide personalized recommendations to customers by analyzing historical purchase data and real-time transaction activity. These recommendations can include hardware / software / SaaS bundles or targeted financial solutions that reflect the specific needs and preferences of end users. The predictive analytics engine within AAML415 can dynamically adjust pricing strategies and product recommendations based on evolving market trends and customer demand, further enhancing the platform's ability to streamline complex procurement workflows in real time.

[0102] In some embodiments, the AAML module 415 may include a predictive analytics engine 416. The predictive analytics engine 416 can analyze historical and current data to forecast demand and optimize inventory, helping resellers adjust inventory levels accordingly. The AAML module 415 may also include a recommendation engine 418. The recommendation engine 418 can generate personalized recommendations regarding inventory management, pricing strategies, and customer engagement, helping resellers make data-driven decisions.

[0103] In some embodiments, the system 400 may include additional modules to enhance functionality. For example, a vendor management module may facilitate real-time negotiation of pricing and terms within the SPoG UI 405. This module can integrate with the vendor system to provide real-time updates on product availability and pricing. Another module, namely a compliance management module, may ensure that all transactions comply with relevant regulations and standards and provide audit trails and automated compliance checks.

[0104] Furthermore, System 400 can support integration with external systems and applications through APIs and data connectors, enabling users to extend the platform's capabilities and integrate with existing IT infrastructure. For example, a reseller can integrate System 400 with their warehouse management system to streamline their order processing process.

[0105] The flexibility and scalability of System 400 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 can help resellers improve efficiency, reduce errors, and enhance customer satisfaction. Real-time data integration and advanced analytics capabilities enable users to have access to accurate and actionable information, make informed decisions, and respond quickly to changing market conditions.

[0106] In a non-limiting example, System 400 is deployed by resellers who manage both business-to-business (B2B) and business-to-consumer (B2C) sales channels. These resellers need to provide end customers with further personalized pricing and financial solutions, along with bundled products combining hardware, software, and SaaS.

[0107] Using SPoG UI405, resellers create bundled solutions for B2B customers, integrating hardware and a 12-month SaaS subscription, alongside custom software packages. The platform, equipped with RTDM410, ensures resellers receive real-time, continuous updates on inventory levels and guarantees stock availability across multiple vendors. Resellers use AAML module 415 to analyze customers' past purchasing behavior and apply personalized pricing to provide dynamic financial solutions with flexible payment options tailored to customer procurement policies. Customers can use the same interface to directly view bundled options with pricing recommendations within the system and place orders immediately from the order management subsystem 407. This integration of B2B and B2C processes enables resellers to optimize sales channels, reduce procurement complexity, and deliver personalized user experiences to end customers, all within the same platform.

[0108] As a result, System 400 leverages SPoG UI405, RTDM410, and AAML415 to provide a comprehensive solution for managing B2B and B2C transactions. It integrates data from multiple sources, processes it in real time, and displays it through an intuitive interface, enabling resellers to manage their end-user businesses within their own environment. This innovative approach addresses the challenges of the traditional IT distribution model, supports the industry's transition towards a more agile and efficient business environment, brings businesses together, and provides a real-time, end-to-end view of both supply and customer interactions.

[0109] Figure 5 shows an embodiment of an advanced distribution platform including a system 500 for managing a complex distribution network, which can be an embodiment of system 300, providing a technology distribution platform for optimizing the management and operation of the distribution network. System 500 includes several interconnected modules, each performing a specific function and contributing to the overall efficiency of supply chain operations. In some embodiments, these modules may include a SPoG UI 505, a CIM 510, an RTDM module 515, an AI module 520, an interface display module 525, a personalized interaction module 530, a document hub 535, a catalog management module 540, a performance and insight marker display 545, a predictive analytics module 550, a recommendation system module 555, a notification module 560, a self-onboarding module 565, and a communication module 570.

[0110] System 500, as an embodiment of System 300, can enable supply chain and distribution management using a wide range of technologies and algorithms. These technologies and algorithms facilitate efficient data processing, personalized interactions, real-time analysis, secure communication, and effective management of documents, catalogs, and performance standards.

[0111] In some embodiments, SPoG UI505 functions as the central interface within System 500, providing users with a unified view of the entire distribution network. Frontend technologies such as ReactJS, TypeScript, and Node.js are used to create an interactive and responsive user interface. These technologies enable SPoG UI505 to deliver a user-friendly experience, allowing users to access relevant information, navigate through different modules, and perform tasks efficiently.

[0112] CIM510, or Customer Interaction Module, employs algorithms and technologies from Oracle Eloqua, Adobe Target, and Okta to manage customer relationships within the distribution network. These technologies enable the module to securely handle customer data, personalize the customer experience, and provide access control to users.

[0113] The RTDM module 515, or Real-Time Data Mesh module, is a component of System 500 and ensures a smooth data flow across the distribution ecosystem. It utilizes technologies such as Apache Kafka, Apache Flink, or Apache Pulsar for data ingestion, processing, and stream management. These technologies enable the RTDM module 515 to handle real-time data streams, process large volumes of data, and ensure low-latency data processing. In addition, the module employs a Change Data Capture (CDC) mechanism to capture real-time data updates from various transaction systems, such as legacy ERP and CRM systems. This capability allows users to access accurate, current information and make informed decisions.

[0114] The AI ​​module 520 within system 500 can extract valuable insights from data using advanced analytical and machine learning algorithms, including Apache Spark, TensorFlow, and scikit-learn. These algorithms enable the module to automate repetitive tasks, predict demand patterns, optimize inventory levels, and improve overall supply chain efficiency. For example, AI module 520 can use predictive models to forecast demand, allowing users to optimize inventory management and minimize situations of stockouts or excess inventory.

[0115] The Interface Display Module 525 focuses on presenting data and information in a clear and user-friendly manner. It utilizes technologies such as HTML, CSS, and JavaScript frameworks like ReactJS to create an interactive and responsive user interface. These technologies enable users to visualize data using various data visualization techniques, such as graphs, charts, and tables, facilitating efficient data understanding, comparison, and trend analysis.

[0116] The Personalized Interaction Module 530 utilizes customer data, behavioral history, and machine learning algorithms to generate personalized recommendations for products or services. It employs technologies such as Adobe Target, Apache Spark, and TensorFlow for data analysis, modeling, and the delivery of targeted recommendations. For example, the module can analyze customer preferences and purchase history to provide personalized product recommendations, improve customer satisfaction, and drive sales.

[0117] Document Hub 535 functions as a centralized repository for storing and managing documents within System 500. It utilizes technologies such as SeeBurger and Elastic Cloud for efficient document management, storage, and retrieval. For example, Document Hub 535 employs SeeBurger's document management capabilities to classify and organize documents based on type, such as contracts, invoices, product specifications, or compliance documents, allowing users to easily access and search for relevant documents when needed.

[0118] The catalog management module 540 enables the creation, management, and distribution of current product catalogs. This ensures that users have access to current product information, including specifications, pricing, availability, and promotions. Technologies such as Kentico and Akamai can be employed to facilitate catalog updates, content distribution, and caching. For example, the module can use Akamai's Content Delivery Network (CDN) to deliver catalog information to users quickly and efficiently, regardless of their geographical location.

[0119] The Performance and Insights Marker Display 545 collects, analyzes, and visualizes real-time performance metrics and insights related to supply chain operations. Leveraging tools like Splunk and Datadog, it enables effective performance monitoring and provides actionable insights. For example, the module can use Splunk's log analysis capabilities to identify performance bottlenecks in the supply chain, enabling users to take proactive steps to optimize their operations.

[0120] The Predictive Analytics Module 550 employs machine learning algorithms and predictive models to forecast demand patterns, optimize inventory levels, and improve overall supply chain efficiency. It utilizes technologies such as Apache Spark and TensorFlow for data analysis, modeling, and forecasting. For example, the module leverages TensorFlow's deep learning capabilities to analyze sales history data and predict future demand, enabling users to optimize inventory levels and minimize costs.

[0121] The recommendation system module 555 focuses on providing intelligent recommendations to users within a distribution network. It generates personalized product or service recommendations based on customer data, behavioral history, and machine learning algorithms. Technologies such as Adobe Target and Apache Spark can be employed for data analysis, modeling, and the delivery of targeted recommendations. For example, the module can use Adobe Target's recommendation engine to analyze customer preferences and behavior, deliver personalized product recommendations across various channels, improve customer engagement, and drive sales.

[0122] The notification module 560 enables the delivery of real-time notifications to users regarding critical events, updates, or alerts within the supply chain. It utilizes technologies such as Apigee X and TIBCO for message queuing, an event-driven architecture, and notification delivery. For example, the module can leverage TIBCO's messaging infrastructure to send real-time notifications to users' devices, ensuring the timely distribution of relevant information.

[0123] The Self-Onboarding Module 565 simplifies the onboarding process for new users entering the distribution network. It provides guided steps, tutorials, or documentation to help users become familiar with the system and its functionality. By employing technologies such as Okta and Kentico, it can ensure secure user authentication, access control, and self-learning resources. For example, the module can leverage Okta's identification and access management capabilities to securely onboard new users, provide appropriate access permissions, and guide them through the system's functionality.

[0124] The communication module 570 enables communication and collaboration within the system 500. It provides users with channels for interaction, message exchange, document sharing, and project collaboration. By employing technologies such as Apigee Edge and Adobe Launch, it facilitates secure and efficient communication, document sharing, and version control. For example, the module can leverage the API management capabilities of Apigee Edge to ensure secure and reliable communication between users, enabling effective collaboration.

[0125] This allows System 500 to incorporate a variety of modules that utilize a diverse range of technologies and algorithms to optimize supply chain and distribution management. These modules include SPoG UI 505, CIM 510, RTDM module 515, AI module 520, interface display module 525, personalized interaction module 530, document hub 535, catalog management module 540, performance and insight marker display 545, predictive analytics module 550, recommendation system module 555, notification module 560, self-onboarding module 565, and communication module 570, which work together to provide end-to-end visibility, data-driven decision-making, personalized interaction, real-time analytics, and streamlined communication within the distribution network. By incorporating specific technologies and algorithms, efficient data management, secure communication, personalized experiences, and effective performance monitoring become possible, contributing to improved operational efficiency and success in supply chain and distribution management.

[0126] (Real-time data mesh) Figure 6 illustrates an RTDM module 600 according to one embodiment. The RTDM module 600 can be an embodiment of the RTDM module 310 and may include interconnected components, processes, and subsystems configured to enable real-time data management and analysis.

[0127] The RTDM module 600, as shown in Figure 5, represents an effective data mesh and change capture component within the overall system architecture. This module can be configured to provide real-time data management and standardization capabilities, enabling efficient operation within the supply chain and distribution management domain.

[0128] The RTDM module 600 may include an integration layer 610 (also called the “record system”) that integrates with various enterprise systems. These enterprise systems may include ERPs such as SAP, Impulse, META, and I-SCALA, as well as other data sources. The integration layer 610 can handle data exchange and synchronization between the RTDM module 600 and these systems. Data feeds can be established to retrieve relevant information from the record system, such as sales orders, purchase orders, inventory data, and customer information. These feeds enable real-time data updates, ensuring that the RTDM module operates with the most up-to-date and accurate data.

[0129] The RTDM module 600 may include a data layer 620 configured to process and translate data for retrieval and analysis. The data layer 620 includes a data mesh, i.e., a cloud-based infrastructure configured to provide scalable and fault-tolerant data storage capabilities. Within the data mesh, multiple purpose-specific data stores (PDSs) are deployed, which can store specific types of data, such as customer data, product data, or inventory data. Each PDS can be optimized for efficient data retrieval based on specific use cases and requirements. PDSs can be configured to store specific types of data, such as customer data, product data, financial data, etc. These PDSs function as repositories of normalized and / or standardized data, ensuring data consistency and integrity across the system.

[0130] In some embodiments, the RTDM module 600 implements a data replication mechanism for capturing real-time changes from multiple data sources, including transactional systems such as ERP (e.g., SAP, Impulse, META, I-SCALA). The captured data can then be processed and standardized on the fly and converted into a standardized format suitable for analysis and integration. This process ensures that the data is readily available and current within the data mesh, facilitating real-time insights and decision-making.

[0131] More specifically, the data layer 620 within the RTDM module 600 can be configured as a robust and flexible foundation for managing and processing data within the distribution ecosystem. In some embodiments, the data layer 620 may include a highly scalable and robust data lake, which may be referred to as the data lake 622, along with a set of purpose-specific data stores (PDSs) which may be denoted as PDS 624.1–624.N. These components are integrated to ensure efficient data management, standardization, and real-time availability.

[0132] Data Layer 620 includes Data Lake 622, a modern storage and processing infrastructure configured to handle the ever-increasing volume, diversity, and speed of data generated within the supply chain. Built on a scalable distributed file system, such as Apache Hadoop Distributed File System (HDFS) or Amazon S3, the data lake provides a unified, scalable platform for storing both structured and unstructured data. By leveraging the adaptability and fault tolerance of cloud-based storage, Data Lake 622 can accept data inflows from diverse sources.

[0133] In conjunction with Data Lake 622, a population of purpose-specific data stores PDS624.1–624.N can be employed. Each PDS624 can function as a dedicated repository optimized for storing and retrieving specific types of data related to the supply chain domain. In some non-limiting examples, PDS624.1 might be dedicated to customer data, storing information such as customer profiles, preferences, and transaction history. PDS624.2 might focus on product data, including details such as SKU codes, descriptions, pricing, and inventory levels. These purpose-specific data stores enable efficient data retrieval, analysis, and processing, meeting the diverse needs of supply chain users.

[0134] To ensure real-time data synchronization, data layer 620 can be configured to employ one or more change data capture (CDC) mechanisms. These CDC mechanisms can be integrated with other enterprise-scale systems, in addition to transaction systems such as SAP, Impulse, META, and legacy ERPs like I-SCALA. CDC constantly monitors updates, modifications, or new transactions in these systems and captures them in real time. By capturing these changes, data layer 620 ensures that the data in data lake 622 and PDS 624 remains current, providing users with real-time insights into the distribution ecosystem.

[0135] In some embodiments, the data layer 620 can be implemented using one or more frameworks, such as .NET or Java, to facilitate integration with existing enterprise systems, ensuring broad compatibility with existing systems and providing flexibility for customization and extensibility. For example, the data layer 620 can leverage a Java technology stack, including frameworks such as Spring and Hibernate, to facilitate integration with record systems that have a diverse population of ERP systems and other enterprise-scale solutions. This can facilitate smooth data exchange, process automation, and end-to-end visibility across the supply chain.

[0136] In terms of data processing and analysis, Data Layer 620 can, in some non-limiting examples, utilize the capabilities of distributed computing frameworks, such as Apache Spark or Apache Flink. These frameworks can enable parallel processing and distributed computing across large datasets stored in data lakes and PDSs. By using these frameworks, supply chain users can perform complex analytical tasks, apply machine learning algorithms, and derive valuable insights from data. For example, Data Layer 620 can use Apache Spark's machine learning libraries to develop forecasting models for demand forecasting, optimize inventory levels, and identify potential supply chain risks.

[0137] In some embodiments, the data layer 620 can incorporate robust data governance and security measures. Strict access control mechanisms and authentication protocols ensure that only authorized users can access and modify data in the data lake and PDS. Data encryption techniques protect sensitive supply chain information from unauthorized access, both at rest and in transit. In addition, the data layer 620 can implement data lineage and audit trail mechanisms to enable users to track the origin and history of data, ensuring data integrity and compliance with regulatory requirements.

[0138] In some embodiments, Data Layer 620 can be deployed in a cloud-native environment using containerization technologies such as Docker and orchestration frameworks such as Kubernetes. This approach ensures scalability, resilience, and efficient resource allocation. For example, Data Layer 620 can be deployed on cloud infrastructure provided by AWS, Azure, or Google Cloud, leveraging their managed services and scalable storage options. This enables demand-based resource scaling, minimizes operational overhead, and provides an adaptable infrastructure for managing supply chain data.

[0139] The RTDM module 600's data layer 620 can integrate the highly scalable data lake, data lake 622, along with application-specific PDSs, PDS624.1–624.N. By employing a CDC mechanism, data layer 620 ensures efficient data management, standardization, and real-time availability. In non-limiting examples, data layer 620 can be implemented using appropriate technologies, such as .NET or Java, and / or distributed computing frameworks like Apache Spark, enabling powerful data processing, advanced analytics, and machine learning capabilities. With robust data governance and security measures, data layer 620 ensures data integrity, confidentiality, and compliance. Through its scalable infrastructure and integration with existing systems, data layer 620 enables supply chain users to make data-driven decisions, optimize operations, and drive business success in dynamic and complex distribution environments.

[0140] The RTDM module 600 may include an AI module 630 configured to implement one or more algorithms and machine learning models and analyze data stored in the data layer 620 to derive meaningful insights. In some non-limiting examples, the AI ​​module 630 may apply predictive analytics, anomaly detection, and optimization algorithms to identify patterns, trends, and potential risks within the supply chain. The AI ​​module 630 can continuously learn from new data inputs and adapt its model to provide accurate, current insights. The AI ​​module 630 can generate predictions, recommendations, and alerts and publish such insights to a dedicated data feed.

[0141] The data engine layer 640 comprises a set of interconnected systems responsible for data ingestion, processing, transformation, and integration. The data engine layer 640 of the RTDM module 600 may include a set of autonomously operating headless engines 640.1–640.N. These engines represent distinct functionalities within the system and may include, for example, one or more recommendation engines, insight engines, and subscription management engines. Engines 640.1–640.N can deliver specific business logic and services using standardized data stored in the data mesh. Each engine can be configured to be pluggable, allowing for flexibility and future expansion of the module's capabilities. Exemplary engines are shown in Figure 5, and these are not intended to be limiting. Any additional headless engines may be included in the data engine layer 640 or other exemplary layers of the disclosed system.

[0142] These systems can be configured to receive data from multiple sources, such as transaction systems, IoT devices, and external data providers. The data ingestion process involves extracting data from these sources and converting it into a standardized format. Data processing algorithms can be applied to cleanse, aggregate, and enhance the data, preparing it for further analysis and integration.

[0143] Furthermore, a data distribution mechanism can be employed to facilitate integration with and access to the RTDM module 600. The data distribution mechanism 645 can include one or more APIs and be configured to facilitate data distribution from the data mesh and engine to various endpoints, including user interfaces, micro-frontends, and external systems.

[0144] The Experience Layer 650 focuses on providing an intuitive, user-friendly interface for interacting with supply chain data. Experience Layer 650 can include data visualization tools, interactive dashboards, and user-centric functionality. Through this layer, users can search and analyze real-time data related to various supply chain metrics, such as inventory levels, sales performance, and customer demand. The User Experience Layer supports personalized data feeds, allowing users to customize views based on their roles and responsibilities and receive relevant updates. Users can subscribe to specific data updates, such as inventory changes, pricing updates, or new SKU notifications, based on their preferences and roles.

[0145] In some embodiments, the RTDM module 600 for supply chain and distribution management can include integration with record systems and may include one or more data layers with a data mesh and purpose-specific data stores, AI components, a data engine layer, and a user experience layer. These components work together to provide users with intuitive access to real-time supply chain data, efficient data processing and analysis, and integration with existing enterprise systems. Technical feeds and search within the module ensure that users can find relevant current information and insights, make informed decisions, and optimize supply chain operations. Thus, the RTDM module 600 facilitates supply chain and distribution management by providing a scalable, real-time data management solution. Its innovative architecture enables rich integration of heterogeneous data sources, efficient data standardization, and advanced analytical capabilities. The module's ability to replicate and standardize data from diverse ERPs while maintaining auditable and repeatable transactions provides a clear advantage, enabling a unified view for vendors, resellers, customers, end customers, and other entities within the distribution system, including IT distribution systems.

[0146] (End-user view for B2B / B2C mode) In one embodiment, Figure 7 shows a system 700 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 for integrating supply-side operations with end-user customer interactions. The system addresses the fragmented nature of previous IT distribution models by providing a comprehensive, unified experience.

[0147] System 700 can include key components such as the Single Pane-of-Glass User Interface (SPoG UI) 705, Real-Time Data Mesh (RTDM) 710, and Advanced Analytics and Machine Learning (AAML) module 715. The SPoG UI 705 can function as a central interface for users, providing access to various functionalities through a unified, intuitive dashboard. This interface can be developed using web-based technologies and allows access from a variety of devices, including desktop computers, laptops, tablets, and smartphones.

[0148] 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 both supply chain data and customer interactions, enabling resellers 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 can facilitate the creation, tracking, and management of orders from both B2B and B2C customers. By centralizing order processing within the SPoG UI 705, resellers can efficiently handle transactions and reduce the complexity of managing multiple sales channels.

[0149] SPoG UI705 can provide resellers with an integrated platform for managing both B2B and B2C transactions, offering the capability to access end-user-tailored and personalized recommendations, and bundled purchases combining hardware, software, and SaaS products. The platform supports real-time dynamic pricing and targeted financial solutions that reflect customer needs, ensuring that procurement is backed by the complex infrastructure required for B2B operations, while maintaining a B2C e-commerce experience. Through this interface, resellers can create custom storefronts and offer product bundles tailored to individual customers or market segments.

[0150] RTDM710 can function 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. RTDM710 can standardize this data into a unified format, ensuring consistency and accuracy across platforms. This data can include real-time inventory levels, pricing information, customer data, and transaction history.

[0151] The RTDM710 facilitates end-to-end connectivity across B2B and B2C transactions by aggregating real-time data from vendor systems, CRM platforms, ERP systems, and third-party databases. This enables resellers to provide real-time order processing and inventory management, all while maintaining consistent and accurate insights into customer interactions. By integrating financial and operational data, the system provides resellers with complex, bundled transactions and the flexibility to dynamically adjust pricing based on market trends and customer data.

[0152] In some embodiments, the RTDM 710 may include an ingestion layer 711 dedicated to collecting data from various sources such as vendor platforms, CRM systems, ERP systems, and third-party databases. The data aggregation subsystem 711 ensures that all relevant data is integrated into a single platform, providing a holistic view of both supply-side and end-user activity. Furthermore, the RTDM 710 may include a data layer 713 that processes and updates data in real time. The data layer 713 ensures that the information presented to the SPoG UI 705 is always current, enabling resellers to make informed decisions based on the most up-to-date data.

[0153] The AAML715 acts as a processing layer, employing advanced algorithms and machine learning models to analyze and interpret data aggregated by the RTDM710. The AAML715 can provide functionalities such as predictive analytics, anomaly detection, and personalized recommendations. These capabilities can help resellers optimize operations, forecast demand, and improve customer satisfaction.

[0154] The AAML module 715 can analyze data collected from RTDM 710 to provide personalized recommendations for product bundles, targeted pricing models, and financial solutions 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. The module can also recommend targeted financial solutions, including installment payments or quantity-based discounts, specifically tailored to end users in B2B and B2C environments.

[0155] The AAML module 715 can include a predictive analytics engine 716. The predictive analytics engine 716 can analyze historical and current data to forecast demand and optimize inventory, helping resellers adjust inventory levels accordingly. The AAML module 715 can also include a recommendation engine 718. The recommendation engine 718 can generate personalized recommendations regarding inventory management, pricing strategies, and customer engagement, helping resellers make data-driven decisions.

[0156] When users access SPoG UI705, they can view a comprehensive dashboard that displays real-time data and insights derived from both supply-side and end-user customer interactions. For example, resellers can see current inventory levels, pending orders, customer interactions, and sales analytics all in one place. The interface can provide interactive charts and graphs, allowing users to drill down into specific data points for detailed analysis. SPoG UI705 can also enable users to initiate actions such as creating quotes, confirming orders, and managing customer accounts directly from the interface.

[0157] The RTDM710 can continuously ingest data from various sources, ensuring that the information displayed in the SPoG UI705 is current. In some embodiments, the RTDM710 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 harmonized, making it available for analysis and reporting.

[0158] AAML715 can analyze data in real time and generate insights and recommendations displayed in SPoG UI705. For example, AAML715 can use machine learning models to predict future demand for specific products, enabling resellers to adjust inventory levels accordingly. AAML715 can also detect anomalies in transaction data, such as unusual surges in order volume, and alert users to potential problems.

[0159] In some embodiments, the system 700 may include additional modules to enhance functionality. For example, a vendor management module may facilitate real-time negotiation of pricing and terms within the SPoG UI 705. This module can integrate with the vendor system to provide real-time updates on product availability and pricing. Another module, namely a compliance management module, may ensure that all transactions comply with relevant regulations and standards and provide audit trails and automated compliance checks.

[0160] Furthermore, System 700 can support integration with external systems and applications through APIs and data connectors, enabling users to extend the platform's capabilities and integrate with existing IT infrastructure. For example, a reseller can integrate System 700 with their warehouse management system to streamline their order processing process.

[0161] The flexibility and scalability of System 700 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 can help resellers improve efficiency, reduce errors, and enhance customer satisfaction. Real-time data integration and advanced analytics capabilities enable users to have access to accurate and actionable information, make informed decisions, and respond quickly to changing market conditions.

[0162] In a non-limiting example, System 700 can be used by a technology vendor managing a mix of B2B and B2C customers. The vendor can interact via SPoG UI 705 to create a custom storefront where customers can browse a wide range of offerings, including hardware, software, and SaaS. The interactive dashboard 706 allows the vendor to view real-time inventory, pricing information, and customer purchase history, providing an aggregated view of business activity.

[0163] For specific B2B customers, sellers use RTDM710 to collect real-time data from vendor systems and ERP platforms to ensure inventory levels and pricing information are up-to-date. Customers place large orders bundling hardware and SaaS services. The AAML module 715 analyzes the customer's purchase history to recommend customized financial solutions, provides tiered pricing based on order volume, and offers flexible payment terms. The system also generates personalized product recommendations and suggests add-on services to complement purchased hardware.

[0164] As a customer proceeds to checkout via the order management subsystem 707, RTDM710 checks inventory status and updates real-time pricing before confirming the transaction. This platform enables sellers to deliver both B2B-style bulk transactions and B2C-style personalization in an end-to-end process that optimizes the customer's sourcing experience. This integration of both B2B and B2C capabilities allows sellers to achieve highly customized and flexible offerings while reducing operational complexity and maintaining real-time visibility into all transaction data.

[0165] As a result, System 700 leverages SPoG UI705, RTDM710, and AAML715 to provide a comprehensive solution for managing B2B and B2C transactions. It integrates data from multiple sources, processes it in real time, and displays it through an intuitive interface, enabling resellers to manage their end-user businesses within their own environment. This innovative approach addresses the challenges of the traditional IT distribution model, supports the industry's transition towards a more agile and efficient business environment, brings businesses together, and provides a real-time, end-to-end view of both supply and customer interactions.

[0166] Figure 8 illustrates Method 800 for dynamically managing SKUs, synchronizing inventory levels in real time across multiple channels, and providing a comprehensive view of both B2B and B2C transactions. This method can aggregate data into a standardized, independent format by utilizing generative AI techniques within an Advanced Analytics and Machine Learning (AAML) module.

[0167] Method 800 can be initiated in Operation 805 and may include user authentication and role-based access control. Users can be authenticated through a secure login process that may utilize multi-factor authentication (MFA) for enhanced security. A role-based access control mechanism can then be applied to grant appropriate access levels based on the user's role, such as reseller, vendor, or end customer. This ensures that users can access data and perform actions only those relevant to their specific role.

[0168] Operation 810 can include data ingestion from multiple sources. Real-time data mesh (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 and ensure it is current. Data ingestion can be easily integrated with these external systems using APIs and secure data connectors.

[0169] In operation 815, the ingested data can be processed using generative AI techniques within the AAML module. These AI techniques can aggregate the data and transform it into a standardized, independent format. This can include harmonizing the data by applying predefined rules and schemas, ensuring consistency and accuracy across platforms. The generative AI can analyze the incoming data stream, identify patterns, and make real-time adjustments to the data schema as needed.

[0170] Operation 820 can include real-time inventory level updates. The system can continuously synchronize inventory levels across all sales channels, including online stores, wholesale portals, and retail stores. RTDM can provide a real-time data replication mechanism to capture changes in inventory levels as they occur. This ensures that the inventory data reflected in the Single Pane of Glass User Interface (SPoG UI) is always current.

[0171] Operation 825 allows for the implementation of automated stock alerts. The system can generate automated alerts regarding declining stock levels, excess inventory, and stockouts based on predefined thresholds. These alerts can be communicated to users through the SPoG UI, enabling immediate action. For example, a reseller could receive an alert if their inventory level falls below a dangerous threshold, prompting them to reorder stock from their vendor.

[0172] Operation 830 can include vendor integration. The system can integrate with vendor systems and automatically update SKU details and inventory levels based on real-time data from supplier feeds. This integration can use APIs to pull data from vendor databases and update 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.

[0173] Operation 835 allows for the configuration of customer-specific pricing. The system can apply dynamic pricing rules within the AAML module to provide individualized pricing for different customer segments. For example, bulk discounts can be applied to B2B clients, while promotional pricing can be offered to B2C customers. These pricing rules can be managed through the SPoG UI, allowing users to adjust pricing strategies in real time based on market conditions and inventory levels.

[0174] Action 840 may include presenting a unified product catalog. The SPoG UI can display a unified product catalog that dynamically adjusts based on the user's role. For example, B2B users can see products and prices tailored to 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.

[0175] Throughout this methodology, the SPoG UI can function as a central interface for users, providing access to various functions through a unified, intuitive dashboard. This interface can be developed using web-based technologies and is accessible from a variety of devices, including desktop computers, laptops, tablets, and smartphones. The SPoG UI can provide interactive charts, graphs, and tables, allowing users to drill down into specific data points for detailed analysis.

[0176] The flexibility and scalability of Method 800 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 can help resellers improve efficiency, reduce errors, and enhance customer satisfaction. Real-time data integration and advanced analytical capabilities enable users to have access to accurate and actionable information, make informed decisions, and respond quickly to changing market conditions.

[0177] This enables Method 800 to leverage SPoG UI405, RTDM410, and AAML415 to provide a comprehensive solution for managing B2B and B2C transactions. It can integrate data from multiple sources, process it in real time using generative AI technology, and present it through an intuitive interface. This allows resellers to manage their end-user businesses within their own environment, bringing the business together and providing a real-time, end-to-end view of both supply and customer interactions.

[0178] Figure 9 illustrates Method 900 for processing and fulfilling orders from both B2B and B2C customers, ensuring an integrated view and streamlined operations. This method can optimize the entire order processing workflow by utilizing generative AI techniques within an Advanced Analytics and Machine Learning (AAML) module.

[0179] Method 900 can begin from Operation 905 and may include order processing via a Single Pane of Glass User Interface (SPoG UI). Users, whether B2B clients or B2C customers, can place an order by selecting products, specifying quantities, and selecting delivery options. The SPoG UI can provide a user-friendly interface that dynamically adjusts based on the user's role and the type of transaction.

[0180] Operation 910 can include real-time inventory checks. Once an order is placed, the system can use a real-time data mesh (RTDM) to check real-time inventory levels across multiple warehouses and vendor systems. This ensures that the system only allows orders for products that are currently available, reducing the risk of overselling and stockouts.

[0181] In operation 915, the system can verify order details. The AAML module can apply predefined business rules and validation algorithms to ensure that the order meets all required criteria. This may include verifying customer information, checking payment information, and confirming the shipping address. Any discrepancies or issues can be flagged for user review through the SPoG UI.

[0182] Operation 920 can include dynamic pricing and discount application. The system can apply dynamic pricing rules and discounts based on real-time data from RTDM. For B2B clients, this can include quantity discounts and negotiated pricing, while B2C clients can benefit from promotional offers and discounts. The AAML module can optimize these pricing strategies based on current market conditions and inventory levels.

[0183] Operation 925 can include order confirmation and execution initiation. Once an order is validated and priced, the system can confirm the order and trigger the execution process. RTDM can update inventory levels in real time to reflect new orders, and the system can notify the relevant warehouse or vendor that the shipment is ready. This ensures that orders are processed quickly and efficiently.

[0184] In Operation 930, the system can provide real-time order tracking. Customers and resellers can track orders through the SPoG UI, which displays updates from logistics and delivery partners integrated into the system. This can include information such as shipping status, estimated delivery time, and any exceptions or delays.

[0185] Operation 935 can include automated invoice generation and billing processing. The system can generate customized invoices and bills tailored to the respective terms and conditions of B2B and B2C transactions. These documents can be automatically sent to customers and made available in the customer account within the SPoG UI. Furthermore, the invoice generation process can be integrated with the user's accounting and billing system to ensure accurate financial records.

[0186] Operation 940 can include integrated returns management. The system can allow both B2B and B2C customers to initiate returns directly through the SPoG UI and track the return status. The AAML module can analyze return patterns to identify potential problems in the product or process, helping to reduce future returns and improve customer satisfaction.

[0187] Operation 945 can include performance analysis. The AAML module can analyze performance data to identify bottlenecks, optimize delivery routes, and anticipate future performance needs. These insights can be presented to the user through the SPoG UI, enabling data-driven decision-making to improve efficiency and reduce costs.

[0188] Throughout Method 900, the SPoG UI can function as a central interface for users, providing a unified view of all order processing and fulfillment activities. This interface can be developed using web-based technologies and allows access from a variety of devices, including desktop computers, laptops, tablets, and smartphones. The SPoG UI can provide interactive visualizations and real-time updates, enabling users to effectively monitor and manage orders.

[0189] By integrating generative AI technology into the AAML module, the performance of the system for processing and fulfilling orders can be efficiently improved. By analyzing large amounts of data and identifying patterns, the AAML module can optimize pricing strategies, validate orders, and improve the fulfillment process. This ensures that Method 900 provides an integrated solution for managing B2B and B2C transactions.

[0190] This enables Method 900 to leverage SPoG UI405, RTDM410, and AAML415 to provide a comprehensive solution for order processing and fulfillment integration. This can include order placement, real-time inventory checks, dynamic pricing, order validation, order confirmation, real-time tracking, automated billing, returns management, and fulfillment analytics. This allows resellers to manage their end-user businesses within their own environment, providing a real-time, end-to-end view of both supply and customer interactions.

[0191] Figure 10 illustrates Method 1000 for leveraging data analytics and machine learning to personalize customer interactions and engagement for both B2B and B2C customers. This Method can improve the personalization and effectiveness of customer engagement strategies by utilizing generative AI techniques within an Advanced Analytics and Machine Learning (AAML) module.

[0192] Method 1000 can be initiated with Operation 1005, which may include customer data integration. Real-time data mesh (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 by the AAML module and standardized into a standardized, independent format to ensure consistency and accuracy.

[0193] Operation 1010 can include customer segmentation. The AAML module can apply machine learning algorithms to analyze integrated customer data and segment customers into distinct groups based on various criteria such as purchase history, engagement level, and demographics. These segments can be created dynamically and updated in real time as new data is ingested.

[0194] In operation 1015, the system can create personalized marketing campaigns. Using insights gained from customer segmentation, 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-pane-of-glass user interface (SPoG UI).

[0195] Action 1020 can include customization of the customer portal. SPoG UI can provide a personalized experience for each customer, displaying customized dashboards, order history, and product recommendations based on customer segments and interaction history. This ensures that customers receive a relevant and engaging experience every time they interact with the platform.

[0196] Operation 1025 can include real-time customer support. The system can integrate real-time customer support functions such as live chat, chatbots, and help desk systems into the SPoG UI. These support tools can operate via AAML modules, which can analyze customer inquiries and provide context-aware, rapid responses. This ensures that customers receive timely and accurate support, improving the overall experience.

[0197] Operation 1030 may include the implementation of a loyalty program. The system can design and manage loyalty programs that reward both B2B and B2C customers for purchases and engagement. These programs can provide personalized rewards and incentives based on customer behavior and preferences managed and tracked through the SPoG UI. The AAML module can analyze loyalty program data to optimize rewards and identify opportunities to improve customer loyalty.

[0198] In operation 1035, the system can establish a customer feedback loop. The SPoG UI can provide a mechanism for collecting customer feedback through surveys, reviews, and support interactions. The AAML module can analyze this feedback to identify trends and areas for improvement. Actionable insights derived from this analysis can be presented to the user through the SPoG UI, enabling informed decision-making to improve products, services, and customer experience.

[0199] Operation 1040 can include real-time analytics and insights. The AAML module can continuously analyze customer interaction data to generate real-time insights into customer behavior, preferences, and trends. These insights can be displayed in the SPoG UI, helping users to better understand their customers and make data-driven decisions to improve engagement and satisfaction.

[0200] Throughout Method 1000, the SPoG UI can function as a 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 and is accessible from a variety of devices, including desktop computers, laptops, tablets, and smartphones. The SPoG UI can provide interactive visualizations, real-time updates, and actionable insights, enabling users to effectively manage and improve customer engagement.

[0201] By integrating generative AI technology into the AAML module, the personalization and efficiency of customer engagement strategies can be significantly improved. By analyzing large amounts of data and generating real-time insights, the AAML module can help users better understand their customers, create more effective marketing campaigns, and deliver personalized experiences. This ensures that Method 1000 provides a comprehensive and integrated solution for managing customer interactions and engagement in both B2B and B2C contexts.

[0202] This enables Method 1000 to leverage SPoG UI405, RTDM410, and AAML415 to provide a comprehensive solution for personalized customer interaction and engagement. This can include data integration, segmentation, personalized marketing, customized portals, real-time support, loyalty programs, feedback loops, and real-time analytics. This allows resellers to manage their end-user businesses within their own environment, providing a real-time, end-to-end view of both supply and customer interactions, and improving overall customer satisfaction and loyalty.

[0203] Figure 11 is a block diagram of exemplary components of device 1100. One or more computer systems 1100 may be used, for example, to implement any of the embodiments described herein, as well as combinations and subcombinations thereof. Computer system 1100 may include one or more processors (also called central processing units or CPUs), for example, processor 1104. Processor 1104 may be connected to a communication infrastructure or bus 1106.

[0204] Furthermore, the computer system 1100 may include user input / output devices 1103 such as a monitor, keyboard, and pointing device, which can communicate with the communication infrastructure 1106 through a user input / output interface 1102.

[0205] One or more processors 1104 may be graphics processing units (GPUs). In one embodiment, the GPU may be a processor that is a special electronic circuit configured to process mathematically intensive applications. The GPU may have a parallel structure that can efficiently process large data blocks, such as mathematically intensive data common to computer graphics applications, images, videos, etc.

[0206] Furthermore, the computer system 1100 may include main or primary memory 1108, such as random access memory (RAM). The main memory 1108 may include one or more levels of cache. The main memory 1108 may have control logic (i.e., computer software) and / or data stored internally.

[0207] Furthermore, the computer system 1100 may include one or more secondary storage devices or memories 1110. The secondary memory 1110 may include, for example, a hard disk drive 1112 and / or a removable storage device or drive 1114.

[0208] The removable storage drive 1114 may interact with the removable storage unit 1118. The removable storage unit 1118 may include a computer-accessible or readable storage device having computer software (control logic) and / or data stored thereon. The removable storage unit 1118 may also include a program cartridge and cartridge interface (such as those found in video game devices), a removable memory chip (such as an EPROM or PROM) and associated socket, a memory stick and USB port, a memory card and associated memory card slot, and / or other removable storage units and associated interfaces. The removable storage drive 1114 may read from and / or write to the removable storage unit 1118.

[0209] The secondary memory 1110 may include other means, devices, components, mediators, or other approaches to enable computer programs and / or other instructions and / or data to be accessed by the computer system 1100. Such means, devices, components, mediators, or other approaches may include, for example, a removable storage unit 1122 and an interface 1120. Examples of the removable storage unit 1122 and interface 1120 may include a program cartridge and cartridge interface (such as those found in video game devices), a removable memory chip (such as an EPROM or PROM) and associated socket, a memory stick and USB port, a memory card and associated memory card slot, and / or other removable storage units and associated interfaces.

[0210] The computer system 1100 may further include a communication or network interface 1124. The communication interface 1124 may enable the computer system 1100 to communicate with and interact with a combination of external devices, external networks, external entities, etc. (referenced individually and collectively in reference number 1128). For example, the communication interface 1124 may enable the computer system 1100 to communicate with an external or remote device 1128 via a communication path 1126, which may be wired and / or wireless (or a combination thereof) and may include a combination of LAN, WAN, Internet, etc. Control logic and / or data may be transmitted to and from the computer system 1100 via the communication path 1126.

[0211] Furthermore, the computer system 1100 may be, to give some non-limiting examples, a personal digital assistant (PDA), a desktop workstation, a laptop or notebook computer, a netbook, a tablet, a smartphone, a smartwatch or other wearable, an appliance, part of the Internet of Things, and / or an embedded system, or a combination thereof.

[0212] The computer system 1100 may be a client or server that accesses or hosts applications and / or data through a delivery model, and may include, but is not limited to, remote or distributed cloud computing solutions, local or on-premises software ("on-premises" cloud-based solutions), "as-a-service" models (e.g., Content as-a-service (CaaS), Digital Content as-a-service (DCaaS), Software as-a-service (SaaS), Managed Software as-a-service (MSaaS), Platform as-a-service (PaaS), Desktop as-a-service (DaaS), Framework as-a-service (FaaS), Backend as-a-service (BaaS), Mobile Backend as-a-service (MBaaS), Infrastructure as-a-service (IaaS)), and / or hybrid models including combinations of the aforementioned examples or other services or delivery models.

[0213] The applicable data structures, file formats, and schemas in computer system 1100 may be derived from standards including, but not limited to, JavaScript Object Notation (JSON), Extended Markup Language (XML), Yet Another Markup Language (YAML), Extended Hypertext Markup Language (XHTML), Wireless Markup Language (WML), MessagePack, XML User Interface Language (XUL), or other functionally similar expressions, either alone or in combination. Alternatively, proprietary data structures, formats, or schemas may be used either exclusively or in combination with known or open standards.

[0214] In some embodiments, a tangible, non-temporary device or product comprising a tangible, non-temporary computer-readable or computer-compatible medium on which control logic (software) is stored may also be referred to herein as a computer program product or program storage device. This includes, but is not limited to, the computer system 1100, the main memory 1108, the secondary memory 1110, and the removable storage units 1118 and 1122, as well as tangible products embodying the aforementioned combination. When such control logic is executed by one or more data processing devices (such as the computer system 1100), such data processing devices can be made to operate as described herein.

[0215] Figures 12A–12Q illustrate various screens and functionalities of the SPoG UI related to vendor onboarding, partner dashboard, customer cart, order summary, SKU generation, order tracking, shipping tracking, subscription history, and subscription changes. Detailed descriptions of each figure are provided below.

[0216] Figure 12A shows the vendor onboarding start screen, representing the first step in the vendor onboarding process. It provides a form or interface where vendors can express their interest in participating in the distribution ecosystem. Vendors can enter basic information such as company details, contact information, and product catalogs.

[0217] Figure 12B shows a vendor onboarding guide that displays a step-by-step guide or checklist for the vendor to follow during the onboarding process. It outlines the necessary tasks and requirements, ensuring that the vendor has a clear understanding of the onboarding process and can proceed smoothly.

[0218] Figure 12C shows a vendor onboarding call scheduler that facilitates scheduling calls or meetings between vendors and platform partners or representatives responsible for guiding them through the onboarding process. Vendors can select a suitable time slot or request a call, ensuring effective communication and assistance throughout the onboarding process.

[0219] Figure 12D shows a vendor onboarding task list, which presents a comprehensive task list or dashboard outlining the specific steps and actions required for successful vendor onboarding. It provides an overview of pending tasks, completed tasks, and upcoming deadlines, helping vendors track progress and ensuring that each onboarding task is completed in a timely manner.

[0220] Figure 12E shows the vendor onboarding completion screen, confirming the successful completion of the vendor onboarding process. It may display a congratulatory message indicating that the vendor has been officially onboarded into the distribution ecosystem at this point, or a summary of completed tasks.

[0221] Figure 12F shows a partner dashboard that provides partners or users with an aggregated view of relevant information and metrics regarding their partnerships with the distribution ecosystem. It provides an overview of performance indicators, key data points, and actionable insights to facilitate effective collaboration and decision-making.

[0222] Figure 12G shows a customer product cart, where the customer can add items they wish to purchase. It displays a list of selected products, quantities, prices, and other relevant details. The customer can review and modify the contents of their cart before proceeding to the checkout process.

[0223] Figure 12H shows a customer subscription cart that allows customers to manage their subscription-based purchases. It displays the selected subscription plan, pricing, and duration. Customers can review and modify their subscription details before finalizing them.

[0224] Figure 12I shows a customer order summary, which provides a summary of the customer's order including details such as the purchased product or subscription, quantity, pricing, and any applied discounts or promotions. This allows the customer to review their order before confirming the purchase.

[0225] Figure 12J shows the vendor SKU generation screen for generating unique stock unit (SKU) codes for vendor products. It may include fields or options where the vendor can specify product details, attributes, and pricing, and the system automatically generates the corresponding SKU codes.

[0226] Figures 12K and 12L show dashboard order summaries for displaying summary information about orders placed within the distribution ecosystem. These present key order details, such as order number, customer name, product or subscription information, quantity, and order status. The dashboard provides an overview of order activity, enabling users to efficiently track and manage orders.

[0227] Figure 12M shows a customer subscription cart that allows customers to add, modify, or delete subscription plans. It can display a list of selected subscriptions, pricing, and renewal dates. Customers can manage their subscriptions and make changes according to their preferences and requirements.

[0228] Figure 12N shows a customer order tracking screen that allows customers to track the status and progress of their orders within the supply chain. It displays real-time updates on order fulfillment, including processing, packaging, and shipping. Customers can monitor the movement of their orders and predict delivery times.

[0229] Figure 12O shows customer shipment tracking, which provides customers with real-time tracking information about their shipments. This may include details such as carrier, tracking number, current location, and estimated delivery date. Customers can always get information about the whereabouts of their shipments.

[0230] Figure 12P shows the customer subscription history, which presents a historical record of the customer's subscription activity. It displays a list of previous subscriptions, including the subscription plan, duration, and status. Customers can review their subscription history, track past payments, and view details of previous subscriptions.

[0231] Figure 12Q shows the customer subscription modification dialog, which allows customers to modify their existing subscriptions. It provides options for upgrading or downgrading subscription plans, changing billing details, or adjusting other subscription-related preferences. Customers can manage their subscriptions according to their evolving needs or preferences.

[0232] The UI screens shown are not limited. In some embodiments, the UI screens in Figures 12A–12Q collectively represent the diverse functionality and features provided by the SPoG UI, offering users a comprehensive and user-friendly interface for vendor onboarding, partnership management, customer interaction, order management, subscription management, and tracking within the distribution ecosystem.

[0233] It should be understood that the detailed description section, rather than the abstract section, is intended to be used to interpret the claims. The abstract section may describe one or more, but not all, exemplary embodiments of the invention as intended by the inventors, and is therefore not intended to limit the invention and the appended claims in any way.

[0234] The present invention has been described above with the assistance of function-building blocks illustrating the implementation of specific functions and their relationships. The boundaries of these function-building blocks are arbitrarily defined herein for the sake of explanation. Alternative boundaries can be defined, as long as the specific functions and their relationships are adequately implemented.

[0235] The prior description relating to specific embodiments fully illustrates the general nature of the invention, and such specific embodiments can be readily modified and / or adapted to various uses without departing from the general concept of the invention, without requiring any unnecessary experimentation, by applying the knowledge of those skilled in the art. Such adaptations and modifications are therefore intended to be within the meaning and scope of equivalents of the disclosed embodiments, based on the teachings and guidance presented herein. The expressions or terms herein are for illustrative purposes only and not intended to limit, and therefore should be understood to those skilled in the art to be interpreted in light of the teachings and guidance.

[0236] The scope and breadth of the present invention should not be limited by any of the exemplary embodiments described above, but should be defined solely by the following claims and their equivalents.

Claims

1. A system that enables resellers to manage their end-user businesses within their own business environment on a distribution platform, which is coupled to a processor, To provide a single-pane-of-glass user interface (SPoG UI), wherein the SPoG UI is defined by a unified dashboard accessible from multiple devices. The Real-Time Data Mesh (RTDM) module allows for the ingestion of data from multiple sources, including vendor platforms, CRM systems, ERP systems, and third-party databases. The RTDM module standardizes the acquired data into a unified format, Advanced analytical and machine learning (AAML) models are used to analyze the standardized data and provide predictive analytics, anomaly detection, and personalized recommendations. The aforementioned SPoG UI presents real-time data and insights through interactive charts and graphs, A system comprising a server configured to generate and execute commands to generate and cause the SpoG UI to generate one or more interactive visualizations that allow drilling down to specific data points for detailed analysis, enabling resellers to directly perform actions such as creating quotes, placing orders, and managing customer accounts through the SpoG UI, the SpoG UI providing a real-time end-to-end view of interactions with both suppliers and end-user customers.

2. The system according to claim 1, wherein the RTDM module ensures consistency and accuracy across the platform by continuously ingesting and standardizing data from multiple sources and by using a data replication mechanism to capture real-time changes from the transaction system.

3. The system according to claim 1, wherein the AAML model predicts future demand for the product, and enables resellers to adjust inventory levels accordingly by employing machine learning algorithms to analyze historical data and predict trends.

4. The system according to claim 1, wherein the AAML model analyzes transaction patterns and identifies deviations from expected behavior to detect anomalies in transaction data and alert the user to potential problems.

5. The system according to claim 1, further comprising a vendor management module for facilitating real-time negotiation of direct pricing and terms within the SPoG UI, integrating with a vendor system to provide real-time updates on product availability and pricing, and enabling automatic updates based on negotiated terms.

6. The system according to claim 1, further comprising a compliance management module for ensuring that all transactions comply with relevant regulations and standards, providing audit trails and automated compliance checks, and generating compliance reports accessible through the SPoG UI.

7. The system according to claim 1, wherein the server is further configured to integrate with external systems and applications through APIs and data connectors, enabling resellers to extend the functionality of the platform and integrate it with their existing IT infrastructure, and to perform data exchange and synchronization.

8. A computer implementation method for enabling resellers to manage their end-user businesses within their own business environment on a distribution platform, comprising a real-time data mesh (RTDM) module that retrieves data from multiple sources, including vendor platforms, CRM systems, ERP systems, and third-party databases. The RTDM module standardizes the acquired data into a unified format, Advanced analytical and machine learning (AAML) models are used to analyze the standardized data and provide predictive analytics, anomaly detection, and personalized recommendations. The Single Pane of Glass User Interface (SPoG UI) presents real-time data and insights through interactive charts and graphs, A method comprising: generating one or more interactive visualizations through the SPOG UI that allow drilling down to specific data points for detailed analysis, enabling resellers to directly perform actions such as creating quotes, placing orders, and managing customer accounts through the SPOG UI, wherein the SPOG UI provides a real-time end-to-end view of interactions with both suppliers and end-user customers.

9. The method according to claim 8, wherein future demand for a product is predicted by using the AAML model, employing a machine learning algorithm to analyze historical data, and predicting trends.

10. The method according to claim 8, further comprising detecting anomalies in transaction data and alerting the user to potential problems by analyzing transaction patterns using the AAML model and identifying deviations from expected behavior.

11. The method according to claim 8, further comprising using a vendor management module to facilitate real-time negotiation of direct pricing and terms within the SPoG UI, integrating with a vendor system to provide real-time updates on product availability and pricing, and enabling automatic updates based on negotiated terms.

12. The method according to claim 8, further comprising using a compliance management module to ensure that a transaction complies with relevant regulations and standards, providing an audit trail and automated compliance checks, and generating compliance reports accessible through the SPoG UI.

13. The method of claim 8, further comprising integrating the system with an external system through APIs and data connectors, enabling a reseller to extend the functionality of the platform and integrate it with an existing IT infrastructure, and performing data exchange and synchronization.

14. The method according to claim 8, further comprising presenting an interactive visualization on the SPoG UI for detailed data analysis, enabling the user to drill down to a specific data point.

15. A non-temporary, tangible, computer-readable device having stored instructions, which, when executed by a computing device, on the computing device, The Real-Time Data Mesh (RTDM) module allows for the ingestion of data from multiple sources, including vendor platforms, CRM systems, ERP systems, and third-party databases. The RTDM module standardizes the acquired data into a unified format, Advanced analytical and machine learning (AAML) models are used to analyze the standardized data and provide predictive analytics, anomaly detection, and personalized recommendations. The Single Pane of Glass User Interface (SPoG UI) presents real-time data and insights through interactive charts and graphs, A non-transient, tangible, computer-readable device that performs the operation of generating one or more interactive visualizations that enable drilling down to specific data points for detailed analysis, thereby allowing resellers to directly perform actions such as creating quotes, placing orders, and managing customer accounts through the SpoG UI, wherein the SpoG UI provides a real-time, end-to-end view of interactions between suppliers and end-user customers.

16. The computer-readable device according to claim 15, wherein the instruction further causes the computing device to predict future demand for the product by using the AAML model, employing a machine learning algorithm to analyze historical data, and predicting trends.

17. The computer-readable device according to claim 15, wherein the instruction further causes the computing device to detect anomalies in transaction data and alert the user to potential problems by analyzing transaction patterns using the AAML model and identifying deviations from expected behavior.

18. The computer-readable device according to claim 15, wherein the instruction further causes the computing device to use a vendor management module to facilitate real-time negotiation of direct pricing and terms within the SPoG UI, integrate with a vendor system to provide real-time updates on product availability and pricing, and enable automatic updates based on negotiated terms.

19. The computer-readable device according to claim 15, wherein the instruction further causes the computing device to use a compliance management module to ensure that transactions comply with relevant regulations and standards, provide audit trails and automated compliance checks, and generate compliance reports accessible through the SPoG UI.

20. The computer-readable device according to claim 15, wherein the instructions cause the computing device to further perform data exchange and synchronization, enabling a reseller to extend the functionality of the platform and integrate it with existing IT infrastructure through APIs and data connectors.

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