System and method for dynamic SKU generation in distribution management

Abstract

The invention provides a system and method for automating SKU management. Embodiments include a user interface for receiving various directory files, a directory translation module, a real-time data grid (RTDM) module, a master data governance (MDG) module, a global data repository (GDR), and a search platform. The directory conversion module converts a directory file into a standard format through iterative learning, and predicts classification and attribute mapping. The RTDM module is configured to perform real-time data exchange. The MDG module verifies the converted directory. The GDR stores the verified directory. Embodiments may include a dynamic SKU creation module and a global pricing engine for real-time pricing. Embodiments improve data accuracy and SKU management, facilitating integrated order processing and fulfillment.

Description

[0001] Cross - Reference to Related Applications

[0002] This application claims priority to U.S. Provisional Patent Application No. 63 / 513,078, filed on July 11, 2023, U.S. Provisional Patent Application No. 63 / 515,076, filed on July 21, 2023, and U.S. Provisional Patent Application No. 18 / 768,998, filed on July 10, 2024. Each of these applications is hereby incorporated by reference in its entirety. Technical Field

[0003] The disclosed embodiments relate to aspects of user interface (UI) methods and systems. The traditional global distribution industry faces many challenges, including supply chain and distribution management, inventory control, stock - keeping unit (SKU) management, compliance, and evolving consumer expectations. Background Art

[0004] Managing SKUs has traditionally presented many challenges that impede operational efficiency, scalability, and customer satisfaction. Existing platforms and systems struggle to address these complexities, resulting in manual processes, data inconsistencies, and limited scalability. The IT distribution industry faces major challenges in handling large volumes and diversity of SKUs. The process of creating, classifying, and pricing these SKUs involves a significant amount of manual intervention, which leads to errors, inconsistencies, and delays. This inefficiency not only consumes valuable time and resources but also hinders the effective handling of a large number of SKUs. Additionally, collaborating with suppliers poses further difficulties because existing platforms lack streamlined processes and self - service capabilities, which result in delays, inaccuracies, and inefficiencies when synchronizing product data across platforms.

[0005] Scalability represents another key challenge for IT distribution platforms, especially when dealing with suppliers offering a wide range of product lines, including long - tail and mid - tail suppliers. Maintaining data integrity and ensuring quality control in SKU management is of utmost importance. However, current platforms are often lacking in guaranteeing accurate and consistent data, leading to internal operational challenges and a poor customer experience. Due to cross - platform inconsistent and inaccurate data, inconsistent classifications, attribute mapping errors, and data discrepancies hinder customers from finding and comparing products.

[0006] Considering factors such as special pricing, margin rules, and real-time market conditions, pricing SKUs adds complexity. Existing platforms lack an effective mechanism for calculating and updating prices, resulting in delays, inaccuracies, and missed revenue opportunities. The manual price management process exacerbates this problem, leading to inefficiencies, errors, and potential revenue loss. Additionally, the concept of virtual SKUs complicates SKU management in the IT distribution domain. A virtual SKU represents a product that is available for customers to view but has not been fully processed. Converting virtual SKUs to actual SKUs at the time of order placement requires integration, real-time updates, and an efficient backend process. However, current systems struggle to handle this dynamic transition smoothly, resulting in delays, data inconsistencies, and an unsatisfactory customer experience.

[0007] Furthermore, traditional systems and outdated architectures present significant operational challenges. The improper handling of different data formats, types, and traditional systems employed by different vendors further increases complexity and hinders integration. As a result, SKU management platforms struggle to efficiently process, transform, and validate vendor catalogs, leading to delays, errors, and operational inefficiencies. There is a need for a real-time and synchronous solution that can efficiently handle SKU generation, pricing, and integration with vendors. Summary of the Invention

[0008] The global distribution industry is at a critical juncture, struggling to address a series of challenges spanning multiple domains. These historical and emerging obstacles require the creation of innovative and effective solutions to steer the industry towards growth and efficiency. Among these numerous obstacles, the most significant lie in the areas of supply chain and distribution management, inventory and compliance issues, SKU management, the shift to a direct-to-consumer model, and the rapidly evolving expectations and behaviors of consumers.

[0009] In some embodiments, a real-time and synchronous solution is provided for SKU generation in a distribution platform. Due to the difficulty of handling disruption events, the management of distribution and supply chains, which is not traditionally within the core capabilities of distributors, is inefficient. Market trends are increasingly favoring the direct-to-consumer model, thus necessitating a reevaluation of existing business strategies to adapt to this dynamic shift.

[0010] Inventory management is another prominent issue in distribution. The volatility of market demand requires a flexible distribution and supply chain, and the complexity of distribution models including cloud services, XaaS (everything as a service), etc. adds additional complexity to the process.

[0011] Even more complex issues revolve around product localization, changing distribution rights, and managing global SKUs. Simplifying the introduction of data from different OEMs (each with unique systems and processes) adds complexity, while complying with the requirements of different jurisdictions increases inefficiencies and potential errors.

[0012] Moreover, the ever-changing consumer behavior and expectations necessitate the creation of a user-friendly, efficient, and configurable platform for technology purchases. Traditional customer interaction methods are being replaced by the need for real-time and synchronous interactions, driving companies to evolve and meet these new customer expectations.

[0013] Despite these daunting challenges, the distribution model still retains many advantages compared to the direct-to-consumer model. Dedicated entities handling logistics and distribution allow manufacturers to focus on their core competencies. Expanding the distribution network to reach customers in remote areas and adding value-added services enhance the overall customer experience. To realize these benefits and maintain the effectiveness of the distribution model, adopting a real-time and synchronous SKU generation solution is necessary. Addressing current pain points and streamlining processes through cutting-edge technologies ensures the sustainability and competitiveness of the distribution model.

[0014] Single-pane glass

[0015] The single-pane glass (SPoG) disclosed in this article can provide a comprehensive solution that aims to address these challenges through a real-time and synchronous SKU generation approach. It offers a comprehensive, user-friendly, and efficient platform that simplifies the distribution process, enhances supply chain visibility, and inventory management.

[0016] Combined with real-time tracking and analytics, SPoG delivers valuable insights into inventory levels and the status of goods, ensuring that supply chain management is handled efficiently in real-time. By integrating multiple communication channels into a single platform, SPoG emulates direct consumer channels into the distribution platform, enhancing the overall customer experience through synchronous interactions.

[0017] SPoG's advanced forecasting capabilities provide innovative solutions for improving inventory management through real-time predictive analytics. These insights highlight demand trends, guiding companies to mitigate the risk of stockouts or overstocking in real-time.

[0018] The platform also includes a real-time global distribution database, enabling distributors to stay up-to-date with international laws and regulations in real-time. This feature reduces the manual tracking burden and facilitates compliant cross-border transactions without delays.

[0019] Simplify SKU management and product localization by integrating data from various OEMs into a single platform, ensuring data consistency and reducing potential errors in real-time. The highly configurable and user-friendly interface of the platform aligns with the expectations of the new generation of technology buyers, providing real-time access to technology.

[0020] SPoG's flexible and scalable design ensures that it remains a future-proof solution, adapting to changing business needs without significant infrastructure changes, thus meeting the requirements for real-time and synchronous interactions in a dynamic distribution environment.

[0021] Real - Time Data Mesh (RTDM)

[0022] Implementing a Real - Time Data Mesh (RTDM) in the platform provides an innovative solution to address the need for real - time and synchronous SKU generation capabilities in the distribution domain. RTDM offers a distributed data architecture that enables real - time data availability across multiple sources and touchpoints.

[0023] RTDM supports predictive analytics, providing real - time solutions for efficient inventory control. Insights into demand trends help companies manage inventory in sync with market fluctuations, thereby reducing the risk of overstock or out - of - stock situations in real time.

[0024] Facilitating global distribution and compliance through real - time updates provided by RTDM ensures that distributors are informed in real time about compliance, as well as changes and SKU management. The platform significantly reduces the manual tracking burden, thus facilitating the integration of cross - border transactions without delays.

[0025] The integration of data from various OEMs by RTDM simplifies SKU management and localization, enhances data consistency, and reduces the likelihood of errors, further meeting the requirements of real - time synchronous interactions.

[0026] Enhancing the customer experience, RTDM integrates and synchronizes data in an intuitive interface, allowing easy access and real - time technology transactions, meeting the expectations of a consumer - driven generation of technology partners.

[0027] Advantages of the SPoG and RTDM Integration

[0028] Integrating the SPoG UI platform with RTDM enables a comprehensive holistic approach to the technical challenges encountered in distribution platforms with an emphasis on real - time and synchronous SKU generation. SPoG leverages the capabilities of RTDM to enhance supply chain visibility, streamline inventory management, ensure compliance, simplify SKU management, and provide an excellent customer experience.

[0029] The real - time tracking and analysis of RTDM significantly improve SPoG's ability to effectively manage the supply chain and inventory, thereby providing accurate and up - to - date information for informed decision - making in real time.

[0030] Integrating SPoG with RTDM ensures synchronous data consistency, reducing errors and delays in SKU management and pricing. The centralized platform for managing data from various OEMs simplifies product localization, meeting market needs in real time. This integration highlights the groundbreaking aspect of real - time / synchronous SKU generation in distribution platforms, making it a novel and innovative solution for the evolving global market. Brief Description of the Drawings

[0031] Figure 1Shows an embodiment of the operating environment of a distribution platform referred to as a system in this embodiment.

[0032] Figure 2 Shows an embodiment of the operating environment of a distribution platform built on the components introduced in Figure 1 .

[0033] Figure 3 Shows an embodiment of a system for supply chain management.

[0034] Figure 4 Depicts an embodiment of an advanced distribution platform including a system for managing a complex distribution network, which can be an embodiment of the system, and provides a technical distribution platform for optimizing the management and operation of the distribution network.

[0035] Figure 5 Shows an RTDM module according to an embodiment.

[0036] Figure 6 Shows an SPoGUI according to an embodiment.

[0037] Figure 7 Is a flowchart of a method for performing integrated supply chain management operations using the SPoGUI according to some embodiments of the present disclosure.

[0038] Figure 8 Is a flowchart of a method for vendor login using the SPoGUI according to some embodiments of the present disclosure.

[0039] Figure 9 Is a flowchart of a method for distributor login using the SPoGUI according to some embodiments of the present disclosure.

[0040] Figure 10 Is a flowchart of a method for customer and end - customer login using the SPoGUI according to some embodiments of the present disclosure.

[0041] Figure 11 Depicts an embodiment of a system for managing SKUs in a distribution network according to some embodiments.

[0042] Figure 12 Is a flowchart of a method for managing SKUs in a distribution network according to some embodiments of the present disclosure.

[0043] Figure 13 Is a block diagram of an example component of a device according to some embodiments of the present disclosure.

[0044] Figures 14A to 14Q Depicts various screens and functions of the SPoGUI according to some embodiments. Detailed Description

[0045] Embodiments may be implemented in hardware, firmware, software, or any combination thereof. Embodiments may also be implemented as instructions stored on 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 form 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, etc. Additionally, firmware, software, routines, instructions may be described herein as performing certain actions. However, it should be understood that such descriptions are merely for convenience, and such actions are actually generated by a computing device, processor, controller, or other device executing the firmware, software, routines, instructions, etc.

[0046] It should be understood that the operations shown in the exemplary methods are not exhaustive, and other operations may be performed before, after, or between any of the shown operations. In some embodiments of the present disclosure, the operations may be performed in a different order and / or varied.

[0047] Figure 1 An operating environment 100 of a distribution platform referred to as system 110 in this embodiment is shown. System 110 operates in the context of an information technology (IT) distribution model, catering to various stakeholders such as customers 120, end-customers 130, sellers 140, resellers 150, and other entities involved in the distribution process. This operating environment includes a wide range of features and dynamics that contribute to the success and efficiency of the distribution platform.

[0048] Customers 120 within the operating environment of system 110 represent enterprises or individuals seeking IT solutions to meet their specific needs. These customers may require a variety of IT products, such as hardware components, software applications, network devices, or cloud-based services. System 110 provides customers with a user-friendly interface, allowing them to browse, search, and select the most suitable IT solutions based on their needs. Customers can also access real-time data and analytics through system 110, empowering them to make informed decisions and optimize their IT infrastructure.

[0049] End-customers 130 are the ultimate beneficiaries of the IT solutions provided by system 110. They may include enterprises or individuals that utilize IT products and services to enhance their operations, productivity, or daily activities. End-customers rely on system 110 to access a vast array of IT solutions, ensuring that they have access to the latest technologies and innovations in the market. System 110 enables end-customers to track their orders, receive updates on delivery status, and access customer support services, enhancing their overall experience.

[0050] Vendors 140 play a crucial role in the operating environment of System 110. These vendors include manufacturers, distributors, and suppliers that offer a variety of IT products and services. System 110 serves as a centralized platform for resellers to showcase their products, manage inventory, and facilitate transactions with customers and distributors. Vendors can leverage System 110 to streamline their supply chain operations, manage pricing and promotions, and gain insights into customer preferences and market trends. By integrating with System 110, resellers can expand their reach, access new markets, and enhance their overall visibility and competitiveness.

[0051] Resellers 150 are intermediaries within the distribution model that bridge the gap between resellers and customers. They play an important role in the IT distribution ecosystem by connecting customers with the right IT solutions from various resellers. Resellers can include retailers, value-added resellers (VARs), system integrators, or managed service providers. System 110 enables resellers to access an integrated catalog of IT solutions, manage their sales channels, and provide value-added services to customers. By leveraging System 110, resellers can enhance their customer relationships, optimize their product offerings, and increase their revenue streams.

[0052] Within the operating environment of System 110, there are various dynamics and features 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 is exchanged in real time among stakeholders, enabling accurate decision-making and timely actions. Integration with existing enterprise systems such as enterprise resource planning (ERP) systems, customer relationship management (CRM) systems, and warehouse management systems allows for integrated communication and interoperability, eliminating data silos and enabling end-to-end visibility.

[0053] Scalability and flexibility are key features of System 110. It can adapt to the growing demands of the IT distribution model, whether it involves an expanding customer base, an increasing number of resellers, or a wider range of IT products and services. System 110 is designed to handle large-scale data processing, storage, and analysis, ensuring that it can support the evolving needs of the distribution platform. Additionally, System 110 leverages a technology stack that includes.NET, Java, and other suitable technologies to provide a robust foundation for its operations.

[0054] In summary, the operating environment of system 110 within the IT distribution model includes customers 120, end-customers 130, resellers 140, distributors 150, and other entities involved in the distribution process. System 110 serves as a centralized platform to facilitate efficient collaboration, communication, and transaction processing among these stakeholders. By leveraging real-time data exchange, integration, scalability, and flexibility, system 110 empowers stakeholders to optimize their operations, improve the customer experience, and drive business success within the IT distribution ecosystem.

[0055] Figure 2 illustrates an embodiment of the operating environment 200 of a distribution platform built on the components introduced in Figure 1 Within this operating environment, integration points 210 facilitate integrated data flows and connectivity among various customer systems 220, associated systems 230, reseller systems 240, distributor systems 250, and other entities involved in the distribution process. The figure depicts interconnectivity and mechanisms for achieving efficient collaboration and data-driven decision-making.

[0056] The operating environment 200 may include system 110, which serves as a distribution platform acting as a central hub for managing and facilitating the distribution process. System 110 can be configured to perform functions and operations that act as a bridge between customer systems 220, reseller systems 240, distributor systems 250, and other entities within the ecosystem. It can integrate communication, data exchange, and transaction processing, thus providing a unified and streamlined experience for stakeholders. Additionally, the operating environment 200 may include one or more integration points 210 to ensure smooth data flows and connectivity. These integration points include:

[0057] Customer system integration: The integration point 210 can enable system 110 to connect with customer systems 220, thereby enabling efficient data exchange and synchronization. Customer systems 220 may include various entities such as customer systems 221, customer systems 222, and customer systems 223. Integration with customer systems 220 empowers customers to access real-time inventory information, pricing details, order tracking, and other relevant data, thereby enhancing their visibility and decision-making capabilities.

[0058] Associated system integration: The integration point 210 can enable system 110 to connect with associated systems 230, thereby enabling efficient data exchange and synchronization. Associated systems 230 may include various entities such as associated systems 231, associated systems 233, and associated systems 233. Integration with associated systems 220 empowers customers to access real-time inventory information, pricing details, order tracking, and other relevant data, thereby enhancing their visibility and decision-making capabilities.

[0059] Seller System Integration: Integration point 210 facilitates an integrated connection between system 110 and seller system 240. Seller system 240 may include entities such as seller system 241, seller system 242, and seller system 243, representing an inventory management system, a pricing system, and a product catalog adopted by the seller. Integration with seller system 240 ensures that sellers can efficiently update their product offerings, manage pricing and promotions, and receive real-time order notifications and fulfillment details.

[0060] Dealer System Integration: Integration point 210 provides the ability for dealer system 250 to connect with system 110. Dealer system 250 may include entities such as dealer system 251, dealer system 252, and dealer system 253, representing a sales system, a customer management system, and a service delivery platform adopted by the dealer. Integration with dealer system 250 allows dealers to access up-to-date product information, manage customer accounts, track sales performance, and provide value-added services to their customers.

[0061] Other Entity System Integration: Integration point 210 also enables connections 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 integrated communication and data exchange, thus facilitating collaboration and an efficient distribution process.

[0062] Integration point 210 also enables a connection with record system 280 for additional data management and integration. Record system 280 may represent an enterprise resource planning (ERP) system or a customer relationship management (CRM) system, including future systems as well as traditional ERP systems such as SAP, Impulse, META, I-SCALA, etc. The record system may include one or more repositories of critical and traditional business data. It facilitates the integration of data exchange and synchronization between the distribution platform, system 110, and the ERP, enabling real-time updates and ensuring the availability of accurate and up-to-date information. Integration point 210 establishes connectivity between record system 280 and the distribution platform, allowing stakeholders to fully utilize the rich data stored in the ERP for efficient collaboration, data-driven decision-making, and a streamlined distribution process. These systems represent internal systems used by customers, sellers, etc.

[0063] Integration point 210 within operating environment 200 is facilitated through standardized protocols, APIs, and data connectors. These mechanisms ensure compatibility, interoperability, and secure data transfer between the distribution platform and the connected systems. System 110 adopts industry-standard protocols such as RESTful API, SOAP, or GraphQL to establish communication channels and enable integrated data exchange.

[0064] In some embodiments, system 110 can incorporate authentication and authorization mechanisms to ensure secure access and data protection. Technologies such as OAuth or JSON Web Tokens (JWT) can be employed to authenticate users, authorize data access, and maintain the integrity and confidentiality of the exchanged information.

[0065] In some embodiments, the integration points 210 and data flows within the operating environment 200 enable stakeholders to operate within a connected ecosystem. Data generated at various stages of the distribution process (including customer orders, inventory updates, shipping details, and sales analysis) flows efficiently among the customer system 220, the vendor system 240, the distributor system 250, and other entities. This data exchange promotes real-time visibility, enables data-driven decision-making, and improves the operational efficiency of the entire distribution platform.

[0066] In some embodiments, system 110 makes full use of advanced technologies such as TypeScript, NodeJS, ReactJS, NETcore, C#, and other suitable technologies that support the integration points 210 and enable integrated communication within the operating environment 200. These technologies provide a robust foundation for system 110, ensuring scalability, flexibility, and efficient data processing capabilities. Additionally, the integration points 210 can also employ algorithms, data analysis, and machine learning techniques to derive valuable insights, optimize the distribution process, and personalize the customer experience. The integration points 210 and data flows within the operating environment 200 enable stakeholders to operate within a connected ecosystem. Data generated at various touchpoints (including customer orders, inventory updates, pricing changes, or delivery status) flows efficiently among different entities, systems, and components. The integrated data is processed, coordinated, and made available to relevant stakeholders in real time by system 110. This real-time access to accurate and up-to-date information empowers stakeholders to make informed decisions, optimize supply chain operations, and improve the customer experience.

[0067] Figure 2 Several elements in the depicted operating environment may include conventional, well-known elements that are only briefly explained here. For example, each customer system (such as customer system 220) may include a desktop personal computer, a workstation, a laptop computer, a PDA, a cellular phone, or any device supporting a wireless access protocol (WAP)-enabled device, or any other computing device capable of directly or indirectly interfacing with the Internet or other networks. Each customer system typically runs an HTTP client, such as Microsoft's Edge browser, Google's Chrome browser, Opera's browser, or a WAP-enabled browser for mobile devices, allowing the customer system to access, process, and view information, pages, and applications available from the distribution platform via the network.

[0068] In addition, each client system can typically be equipped with user interface devices for interacting with the graphical user interface (GUI) provided by the browser, such as a keyboard, mouse, trackball, touchpad, touch screen, pen, or similar devices. These user interface devices enable the users of the client system to navigate the GUI, interact with pages, forms, and applications, and access the data and applications hosted by the distribution platform.

[0069] The client system and its components can be operator-configurable using applications (including web browsers) running on a central processing unit such as an Intel Pentium processor or similar processor. Similarly, the distribution platform (system 110) and its components can be operator-configured using applications running on a central processing unit (such as a processor system) that can include an Intel Pentium processor or similar processor and / or multiple processor units.

[0070] Embodiments of computer program products include machine-readable storage media that contain instructions for programming a computer to perform the processes described herein. The computer code for operating and configuring the distribution platform and client systems, seller systems, dealer systems, and other entity systems to communicate with each other, process web pages, applications, and other data can be downloaded and stored on a hard disk or any other volatile or non-volatile storage medium or device, such as ROM, RAM, floppy disk, optical disk, DVD, CD, microdrive, magneto-optical disk, magnetic card, optical card, nanosystems, or any suitable medium for storing instructions and data.

[0071] In addition, the computer code for implementing the embodiments can be transmitted and downloaded from a software source over 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, local area network, or other network and executed on the client system, server, or server system using programming languages such as C, C++, HTML, Java, JavaScript, ActiveX, VBScript, etc.

[0072] It should be understood that the embodiments can be implemented in various programming languages that execute on the client system, server, or server system, and the choice of language can depend on the specific requirements and environment of the distribution platform.

[0073] Thus, the operating environment 200 can couple the distribution platform with one or more integration points 210 and data flows to enable efficient collaboration and a streamlined distribution process.

[0074] Figure 3A system 300 for supply chain management is shown. The system 300 is a supply chain management solution that is designed to address the challenges faced by a fragmented supply chain ecosystem in the global distribution industry. The system 300 may include a number of interconnected components and modules that work in concert to optimize supply chain operations, enhance collaboration, and drive business efficiency.

[0075] In some embodiments, the SPoG UI 305 serves as a centralized user interface, providing a unified view of the entire supply chain to stakeholders. It consolidates information from various sources and presents real-time data, analytics, and functionality customized to the specific roles and responsibilities of the users. By providing a customizable and intuitive dashboard layout, the SPoG UI 305 enables users to access relevant information and tools, empowering them to make data-driven decisions and effectively manage their supply chain activities.

[0076] For example, a logistics manager can use the SPoG UI 305 to monitor the status of goods, track delivery routes, and view real-time inventory levels across multiple warehouses. They can visualize the data through interactive charts and graphs, such as a map showing the current location of each shipped item or a bar chart depicting inventory levels by product category. With a unified view of the supply chain, the logistics manager can identify bottlenecks, optimize routes, and ensure timely delivery of goods.

[0077] In some embodiments, the SPoG UI 305 integrates with other modules of the system 300, facilitating real-time data exchange, synchronized operations, and streamlined workflows. Through API integration, data synchronization mechanisms, and an event-driven architecture, the SPoG UI 305 ensures smooth information flow and enables collaborative decision-making across the supply chain ecosystem.

[0078] For example, when a purchase order is generated in the SPoG UI 305, the system 300 automatically updates the inventory levels, triggers a notification 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.

[0079] In some embodiments, the Real-Time Data Mesh (RTDM) module 310 may be configured to provide an integrated data stream within the supply chain ecosystem. It aggregates data from multiple sources, coordinates it, and ensures its availability in real time.

[0080] In a non - limiting example, the RTDM module 310 can collect data from the recording system 280, which can represent various systems, including different inventory management systems, point - of - sale terminals, and customer relationship management systems. It coordinates the data by aligning formats, standardizing measurement units, and reconciling any differences. Then the unified data is made available in real - time, allowing stakeholders to access accurate and up - to - date information across the supply chain.

[0081] In some embodiments, the RTDM module 310 can be configured to capture changes in data across multiple transaction systems in real - time. It employs an advanced change - data - capture (CDC) mechanism that continuously monitors the transaction systems to detect any updates or modifications. The CDC component is specifically designed to work with a variety of transaction systems, which can include future and legacy ERP systems, customer relationship management (CRM) systems, and other enterprise - wide systems, ensuring compatibility and flexibility for businesses operating in various environments.

[0082] By providing continuous access to real - time data, stakeholders can make decisions in a timely manner and quickly respond to changing market conditions. For example, if the RTDM module 310 detects a sudden spike in demand for a particular product, it can trigger an alert to the production team, enabling them to adjust the manufacturing schedule and prevent stockouts.

[0083] In some embodiments, the RTDM module 310 facilitates data management operations within the supply chain. It enables real - time coordination of data from multiple sources, freeing vendors, distributors, customers, and end - customers from the constraints imposed by traditional ERP systems. This enhanced flexibility supports improved efficiency, customer service, and innovation.

[0084] The system 300 can also include an advanced analytics and machine learning (AAML) module 315. The AAML module 315 can leverage powerful analytics tools and algorithms, such as Apache Spark, TensorFlow, or scikit - learn, to extract valuable insights from the collected data. It performs advanced analytics, predictive modeling, anomaly detection, and other machine - learning operations.

[0085] For example, the AAML module 315 can analyze historical sales data to identify seasonal patterns and predict future demand. It can generate predictions that help optimize inventory levels, ensure inventory availability during peak seasons, and minimize excess inventory costs. By leveraging machine - learning algorithms, the AAML module 315 automates repetitive tasks, predicts customer preferences, and optimizes supply - chain processes.

[0086] In addition to demand forecasting, the AAML module 315 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 relevant products to individual customers.

[0087] Furthermore, the AAML module 315 can analyze data from various sources, such as social media feedback, customer reviews, and market trends, to gain a deeper understanding of customer sentiment and preferences. This information can be used for informed product development decisions, identifying emerging market trends, and adjusting business strategies to meet evolving customer expectations.

[0088] The system 300 can perform integration and interoperability functions to connect with existing enterprise systems such as ERP systems, warehouse management systems, and customer relationship management systems. By establishing connections and data flows between these systems, the system 300 enables smooth data exchange, process automation, and end-to-end visibility across the supply chain. Integration protocols, APIs, and data connectors facilitate integrated communication and interoperability between different modules and components, creating an overall and connected supply chain ecosystem.

[0089] The implementation and deployment of the system 300 can be customized to meet specific business needs. In some non-limiting examples, it can be deployed as a cloud-native solution using containerization technologies (such as ) and orchestration frameworks (such as ). 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 with existing systems, and customizing modules and components based on business needs and preferences.

[0090] The system 300 for supply chain management is a comprehensive and innovative solution that addresses the challenges faced by fragmented supply chain ecosystems. It combines the functions of the SPoG UI 305, RTDM module 310, and AAML module 315, as well as integration with existing systems. By leveraging different technology stacks, scalable architectures, and robust integration capabilities, the system 300 provides end-to-end visibility, data-driven decision-making, and optimized supply chain operations. The examples and options provided in this specification are non-limiting and can be customized to meet specific industry requirements, drive efficiency, and achieve success in supply chain management.

[0091] Figure 4Depicts an embodiment of an advanced distribution platform including system 400 for managing a complex distribution network, which can be an embodiment of system 300, and provides a technical distribution platform for optimizing the management and operation of the distribution network. System 400 includes several interconnected modules, each with a specific function and contributing to the overall efficiency of supply chain operations. In some embodiments, these modules can include SPoG UI 405, Customer Interaction Module (CIM) 410, RTDM Module 415, AI Module 420, Interface Display Module 425, Personalized Interaction Module 430, Document Hub 435, Catalog Management Module 440, Performance and Insights Tag Display 445, Predictive Analytics Module 450, Recommendation System Module 455, Notification Module 460, Self-Login Module 465, and Communication Module 470.

[0092] As an embodiment of system 300, system 400 utilizes a series of technologies and algorithms to integrate and consolidate supply chain management. These technologies and algorithms facilitate efficient data processing, personalized interaction, real-time analysis, secure communication, and effective management of documents, catalogs, and performance metrics.

[0093] In some embodiments, SPoG UI 405 serves as the central interface within system 400 in some embodiments, providing stakeholders with a unified view of the entire distribution network. It utilizes front-end technologies such as ReactJS, TypeScript, and Node.js to create an interactive and responsive user interface. These technologies enable SPoG UI 405 to deliver a user-friendly experience, allowing stakeholders to access relevant information, navigate through different modules, and perform tasks efficiently.

[0094] In some embodiments, CIM 410 or the Customer Interaction Module employs algorithms and technologies such as Target and to manage customer relationships within the distribution network. These technologies enable the module to securely process customer data, personalize the customer experience, and provide stakeholders with integrated access control.

[0095] In some embodiments, RTDM Module 415 or the Real-Time Data Mesh Module is a key component of system 400 that ensures smooth data flow across the supply chain ecosystem. It utilizes such as or or Pulsar's technology is used for data ingestion, processing, and stream management. These technologies enable the RTDM module 415 to handle real-time data streams, process large amounts of data, and ensure low-latency data processing. Additionally, the module employs a change data capture (CDC) mechanism to capture real-time data updates from various transactional systems such as legacy ERP systems and CRM systems. This functionality allows stakeholders to access the latest and accurate information for informed decision-making.

[0096] In some embodiments, the AI module 420 within the system 400 utilizes advanced analytics and machine learning algorithms (including Spark, and ) to extract valuable insights from the data. These algorithms enable the module to automate repetitive tasks, predict demand patterns, optimize inventory levels, and improve overall supply chain efficiency. For example, the AI module 420 can utilize predictive models to forecast demand, allowing stakeholders to optimize inventory management and minimize out-of-stock or overstock situations.

[0097] In some embodiments, the interface display module 425 focuses on presenting data and information in a clear and user-friendly manner. It utilizes technologies such as HTML, CSS, and JavaScript frameworks (such as ReactJS) to create interactive and responsive user interfaces. These technologies allow stakeholders to visualize data using various data visualization techniques (such as graphs, charts, and tables), enabling efficient data understanding, comparison, and trend analysis.

[0098] In some embodiments, the personalized interaction module 430 utilizes customer data, historical trends, and machine learning algorithms to generate personalized recommendations for products or services. In some non-limiting examples, it can utilize Target, Spark, and to implement for data analysis, modeling, and delivering targeted recommendations. For example, the module can analyze customer preferences and purchase history to provide personalized product recommendations, enhancing customer satisfaction and promoting sales.

[0099] In some embodiments, the document hub 435 serves as a centralized repository for storing and managing documents within the system 400. In some non-limiting examples, it can be implemented using and elastic cloud for efficient document management, storage, and retrieval. For example, the document hub 435 can adopt SeeBurger's document management capabilities to classify and organize documents based on the type of document (such as contracts, invoices, product specifications, or compliance documents), allowing stakeholders to easily access and retrieve relevant documents when needed.

[0100] In some embodiments, the catalog management module 440 enables the creation, management, and distribution of up-to-date product catalogs. It ensures that stakeholders have access to the latest product information, including specifications, pricing, availability, and promotions. In some non-limiting examples, and can be utilized to integrate and consolidate catalog updates, content delivery, and caching. For example, the module can utilize Akamai's Content Delivery Network (CDN) to quickly and efficiently deliver catalog information to stakeholders regardless of their geographical location.

[0101] In some embodiments, the Performance and Insights Dashboard 445 collects, analyzes, and visualizes real-time performance metrics and insights related to supply chain operations. It utilizes tools such as and Datadog to enable effective performance monitoring and provide actionable insights. For example, the module can utilize Spunk's log analysis capabilities to identify performance bottlenecks in the supply chain, enabling stakeholders to take proactive measures to optimize operations.

[0102] In some embodiments, the Predictive Analytics Module 450 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 Spark and for data analysis, modeling, and prediction. For example, the module can utilize TensorFlow's deep learning capabilities to analyze historical sales data and predict future demand, allowing stakeholders to optimize inventory levels and minimize costs.

[0103] In some embodiments, the Recommendation System Module 455 focuses on providing intelligent recommendations to stakeholders within the distribution network. It generates personalized recommendations for products or services based on customer data, historical trends, and machine learning algorithms. In some non-limiting examples, it can utilize Target and Spark for data analysis, modeling, and delivering targeted recommendations. For example, the module can utilize Adobe Target's recommendation engine to analyze customer preferences and behavior and deliver personalized product recommendations across various channels, enhancing customer engagement and driving sales.

[0104] In some embodiments, the Notification Module 460 enables the distribution of real-time notifications to stakeholders regarding important events, updates, or alerts within the supply chain. In some non-limiting examples, it can utilize X and It is implemented to be used in message queues, event-driven architectures, and integrated notification delivery. For example, this module can utilize TIBCO's messaging infrastructure to send real-time notifications to stakeholders' devices, thus ensuring timely and relevant information dissemination.

[0105] In some embodiments, the self-login module 465 facilitates the login process for new stakeholders entering the distribution network. It provides guiding steps, tutorials, or documentation to help users familiarize themselves with the system and its functions. In some non-limiting examples, it can be implemented using technologies such as and to ensure secure user authentication, access control, and self-learning resources. For example, this module can utilize Okta's identity and access management capabilities to securely log in new stakeholders, provide them with appropriate access rights, and guide them through the system's functions.

[0106] In some embodiments, the communication module 470 enables integrated and combined communication and collaboration within the system 400. It provides channels for stakeholders to interactively exchange messages, share documents, and collaborate on projects. In some non-limiting examples, it can be implemented using Edge and Launch to facilitate secure and efficient communication, document sharing, and version control. For example, this module can utilize Apiege Edge's API management capabilities to ensure secure and reliable communication between stakeholders, enabling them to collaborate effectively.

[0107] Therefore, the system 400 can include various modules that utilize various technologies and algorithms to optimize supply chain management. These modules (including the SPoG UI 405, CIM 410, RTDM module 415, AI module 420, interface display module 425, personalized interaction module 430, document hub 435, directory management module 440, performance and insight marker display 445, predictive analytics module 450, recommendation system module 455, notification module 460, self-login module 465, and communication module 470) work together to provide end-to-end visibility, data-driven decision-making, personalized interaction, real-time analysis, and simplified communication within the distribution network. The combination of specific technologies and algorithms enables efficient data management, secure communication, personalized experiences, and effective performance monitoring, contributing to improved operational efficiency and the success of supply chain management.

[0108] Real-time data grid

[0109] Figure 5 Shows an RTDM module 500 according to an embodiment. The RTDM module 500 can be an embodiment of the RTDM module 310, which can include interconnected components, processes, and subsystems configured to implement real-time data management and analysis.

[0110] In some embodiments, as Figure 5 shown, the RTDM module 500 represents the effective data grid and change capture components within the overall system architecture. This module is designed to provide real-time data management and coordination capabilities, thus enabling efficient operations within the supply chain management domain.

[0111] The RTDM module 500 may include an integration layer 510 (also referred to as the "system of record") that integrates with various enterprise systems. These enterprise systems may include ERPs such as Impulse, META, and I-SCALA, among others, as well as other data sources. The integration layer 510 can handle data exchange and synchronization between the RTDM module 500 and these systems. Data feeds are established to retrieve relevant information from the systems of record, such as sales orders, purchase orders, inventory data, and customer information. These feeds enable real-time data updates and ensure that the RTDM module operates with the latest and accurate data.

[0112] The RTDM module 500 may include a data layer 520 configured to process and transform data for retrieval and analysis. The RTDM module 500 generates a data grid as a cloud-based infrastructure designed to provide scalable and fault-tolerant data storage capabilities. Within the data grid, multiple Purchase Data Stores (PDS) are deployed to store specific types of data, such as customer data, product data, or inventory data. Each PDS is optimized for efficient data retrieval based on specific use cases and requirements. The PDSs are configured to store specific types of data, such as customer data, product data, financial data, etc. These PDSs serve as repositories for coordinating and standardizing data, ensuring data consistency and integrity across systems.

[0113] In some embodiments, the RTDM module 500 implements a data replication mechanism to capture real-time changes from multiple data sources, including transaction systems like ERPs (e.g., Impulse, META, I-SCALA). Then, the captured data is processed and coordinated during runtime and transformed into a standardized format suitable for analysis and integration. This process ensures that the data is readily available and up-to-date within the data grid, thus facilitating real-time insights and decision-making.

[0114] More specifically, the data layer 520 within the RTDM module 500 can be configured as a powerful and flexible foundation for managing and processing data within the supply chain ecosystem. In some embodiments, the data layer 520 may include a highly scalable and robust data lake, which may be referred to as the data lake 522, and a set of purposeful data stores (PDS), which may be represented as PDS 524.1 to 524.N. These components work together to ensure efficient data management, coordination, and real-time availability.

[0115] In some embodiments, the data layer 520 includes a data lake 522, a novel storage and processing infrastructure designed to handle the ever-increasing volume, variety, and velocity of data generated within the supply chain. Dependent on a scalable distributed file system such as the Hadoop Distributed File System (HDFS) or S3, the data lake can provide a unified and scalable platform for storing both structured and unstructured data. Leveraging the elasticity and fault tolerance of cloud-based storage, the data lake 522 can aggregate and accommodate data inflows from different sources.

[0116] Associated with the data lake 522, multiple purpose-built data stores, PDS 524.1 to 524.N, can be employed. Each PDS 524 can serve as a dedicated repository optimized for storing and retrieving specific types of data relevant to the supply chain domain. In some non-limiting examples, PDS 524.1 can be dedicated to customer data, storing information such as customer profiles, preferences, and transaction histories. PDS 524.2 can focus on product data, including details about SKU codes, descriptions, pricing, and inventory levels. These purpose-built data stores allow for efficient data retrieval, analysis, and processing, thus meeting the diverse needs of supply chain stakeholders.

[0117] To ensure real-time data synchronization, the data layer 520 can be configured to employ one or more sophisticated change data capture (CDC) mechanisms. These CDC mechanisms are integrated with transaction systems such as traditional ERPs like Impulse, META, and I-SCALA, as well as other enterprise-wide systems. The CDC continuously monitors any updates, modifications, or new transactions in these systems and captures them in real time. By capturing these changes, the data layer 520 ensures that the data within the data lake 522 and PDS 524 remains up-to-date, thus providing stakeholders with real-time insights into the supply chain ecosystem.

[0118] In some embodiments, the data layer 520 can be implemented to use one or more frameworks such as.NET or Java to facilitate integration with existing enterprise systems, thus ensuring compatibility with a variety of existing systems and providing flexibility for customization and scalability. For example, the data layer 520 can utilize the Java technology stack, including frameworks such as Spring and to facilitate integration with the record systems of a group with multiple ERP systems and other enterprise-wide solutions. This can promote smooth data exchange, process automation, and end-to-end visibility across the supply chain.

[0119] In some embodiments, to facilitate data processing and analysis, in some non-limiting examples, the data layer 520 can include one or more distributed computing frameworks such as Spark or Flink. These frameworks enable parallel processing and distributed computing across large-scale datasets stored in the data lake and PDS. By leveraging these frameworks, supply chain stakeholders can perform complex analytical tasks, apply machine learning algorithms, and derive valuable insights from the data. For example, the data layer 520 can utilize the machine learning libraries of Apache Spark to develop predictive models for demand forecasting, optimize inventory levels, and identify potential supply chain risks.

[0120] In some embodiments, the data layer 520 can incorporate robust data governance and security measures. Fine-grained access control mechanisms and authentication protocols ensure that only authorized users can access and modify the data within the data lake and PDS. During rest and transit, data encryption techniques protect sensitive supply chain information from unauthorized access. Additionally, the data layer 520 can implement data lineage and audit trail mechanisms, allowing stakeholders to track the origin and history of the data, ensuring data integrity and compliance with regulatory requirements.

[0121] In some embodiments, the data layer 520 can utilize containerization technologies (such as ) and orchestration frameworks (such as ) to be deployed in a cloud-native environment. This approach ensures scalability, elasticity, and efficient resource allocation. For example, the data layer 520 can be deployed on cloud infrastructure provided by or with its managed services and scalable storage options. This allows for efficient resource scaling based on demand, minimizing operational overhead and providing a resilient infrastructure for managing supply chain data.

[0122] The data layer 520 of the RTDM module 500 can incorporate a highly scalable data lake (data lake 522) and dedicated PDSs (PDS 5241 to 524.N), and adopt sophisticated CDC mechanisms. The data layer 520 ensures efficient data management, coordination, and real-time availability. The integration of various technology stacks such as.NET or Java and distributed computing frameworks such as Spark enables powerful data processing, advanced analytics, and machine learning capabilities. By leveraging robust data governance and security measures, the data layer 520 ensures data integrity, confidentiality, and compliance. Through its scalable infrastructure and efficient integration with existing systems, the data layer 520 allows supply chain stakeholders to make data-driven decisions, optimize operations, and drive business success in dynamic and complex supply chain scenarios.

[0123] The RTDM module 500 may include an AI module 530, which is configured to implement one or more algorithms and machine learning models to analyze the data stored in the data layer 520 and export meaningful insights. In some non-limiting examples, the AI module 530 may apply predictive analytics, anomaly detection, and optimization algorithms to identify patterns, trends, and potential risks within the supply chain. The AI module 530 can continuously learn from new data inputs and adapt its models to provide accurate and up-to-date insights. The AI module 530 can generate predictions, recommendations, and alerts and publish such insights to a dedicated data feed.

[0124] The data engine layer 540 may include a set of interconnected systems responsible for specialized data ingestion, processing, transformation, and integration. Within the RTDM module 500, these systems include a collection of headless engines 540 that operate autonomously, representing different functions. These engines represent different functions within the system and may include, for example, one or more recommendation engines, insight engines, and subscription management engines. Non-limiting examples of these headless engines include engines for subscriptions, solutions / bundles, ITAD (IT asset disposition), renewals, marketing, special pricing, financing, returns / claims, end-users, order tracking, superchain, search, vendor management, professional services, and ESG (environmental, social, and governance). These headless engines utilize the coordinated data stored in the data grid to deliver specific business logic and services. The engines 540 can utilize the coordinated data stored in the data grid to deliver specific business logic and services. Each engine 5 is designed to be pluggable, allowing for flexibility and future expansion of module capabilities.

[0125] 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 are applied to cleanse, aggregate, and enrich the data, preparing it for further analysis and integration.

[0126] In addition, to facilitate the integration and access to the RTDM module 500, a data distribution mechanism 545 can be employed. The data distribution mechanism 545 can be configured to include one or more APIs to facilitate the distribution of data from the data grid and engines to various endpoints, including user interfaces, micro frontends, and external systems.

[0127] The Experience Layer 550 focuses on delivering an intuitive and user-friendly interface for interacting with supply chain data. The Experience Layer 550 may include data visualization tools, interactive dashboards, and user-centric features. Through this layer, users can retrieve 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 feedback, allowing users to customize their views and receive relevant updates based on their roles and responsibilities. Users can subscribe to specific data updates tailored to their preferences and roles, such as inventory changes, pricing updates, or new SKU notifications.

[0128] Accordingly, in some embodiments, the RTDM module 500 for supply chain management may include an integration with the record system and include one or more of a data layer with a data grid and purposeful data storage, an AI component, a data engine layer, and a user experience layer. These components work together to provide users with intuitive access to real-time supply chain data, efficient data processing and analysis, and efficient integration with existing enterprise systems. Technical feedback and retrieval within the module ensure that users can retrieve relevant up-to-date information and insights to make informed decisions and optimize supply chain operations. Thus, the RTDM module 500 facilitates supply chain management by providing a scalable real-time data management solution. Its innovative architecture allows for rich integration of different data sources, efficient data coordination, and advanced analytics capabilities. The module's ability to replicate and reconcile data from different ERPs while maintaining auditable and repeatable transactions provides significant advantages in achieving a unified view of vendors, distributors, customers, end-customers, and other entities in the distribution system, including IT distribution systems.

[0129] Single-pane glass UI

[0130] Figure 6 Illustrated is a SPoG UI according to an embodiment, generally denoted as SPoG UI 600. In some embodiments, the SPoG UI 600 may be an embodiment of the SPoG UI 305, which represents a comprehensive and intuitive user interface designed to provide a unified and customizable view of the entire supply chain ecosystem. It combines a series of features and functions that enable users to gain a comprehensive understanding of the supply chain and effectively manage their operations.

[0131] The SPoG 600 can incorporate high-speed data in a data-rich environment. In contemporary data-rich environments, conventional UI designs often struggle to present large amounts of information in an understandable, efficient, and visually appealing manner. The challenge intensifies when the data is dynamic, changing in real-time, and needs to be effectively displayed in a single-pane environment that emphasizes a clean, whitespace-oriented design.

[0132] The SPoG UI 600 can integrate capabilities with the RTDM modules 310 / 500 and stakeholders to provide a powerful and user-friendly interface for supply chain management. In some embodiments, the SPoG UI 600 can include a Unified View (UV) module 605 to provide a customizable and holistic view of the supply chain, and a Real-Time Data Exchange module 610 to ensure accurate and up-to-date data synchronization based on the RTDM modules 310 / 500. The Collaborative Decision-Making module 615 facilitates effective communication and collaboration among different groups. The RBAC module 620 can be configured to safeguard access control. The Customization module 625, the Data Visualization module 630, and the Mobile and Cross-Platform Accessibility module 635 can be configured to enhance the user experience, data analysis, and accessibility, respectively. In some embodiments, the above modules can enable stakeholders to make informed decisions, optimize supply chain operations, and drive business efficiency within the supply chain ecosystem.

[0133] The SPoG UI 600 can include the UV module 605, which provides a centralized and customizable dashboard-style layout for stakeholders. This module allows users to access real-time data, analytics, and functions customized for their specific roles and responsibilities within the supply chain ecosystem. The UV module 605 serves as a single entry point for users, providing an overall and comprehensive view of supply chain operations and empowering them to make data-driven decisions. The UV module 605 can be configured to efficiently manage real-time data, thus maintaining a visually clean interface without sacrificing performance. This innovative approach includes a unique configuration of the UI structure, responsive data visualization, real-time data processing methods, adaptive information architecture, and whitespace optimization.

[0134] The UV module 605 can be constructed around a grid-based layout system using CSS grid and Flexbox technologies. This structure provides the flexibility to create fluid layouts with elements that automatically adjust to the available space and content. HTML5 and CSS3 are used as the underlying technologies for creating the UI, while JavaScript, particularly React.js, manages the dynamic aspects of the UI.

[0135] The SPoG UI 600 can integrate the UV module 605 with the Real-Time Data Exchange module 610 to facilitate continuous exchange of data between the SPoG UI 600 and the RTDM module 310 to leverage one or more data sources, which can include one or more ERPs, CRMs, or other sources. Through this module, stakeholders can access the latest, accurate, and coordinated data. Real-time data synchronization ensures that the information presented in the SPoG UI 600 reflects the latest insights and developments across the supply chain. This integration enables stakeholders to make informed decisions based on accurate and synchronized data.

[0136] In some embodiments, the collaborative decision-making module 615 within the SPoG UI 600 facilitates real-time collaboration and communication among stakeholders. This module enables the exchange of information, initiation of workflows, and sharing of insights and recommendations. By integrating with the RTDM module 310 / 500, the collaborative decision-making module 615 ensures that stakeholders can effectively collaborate based on accurate and synchronized data. This promotes overall operational efficiency and collaboration within the supply chain ecosystem.

[0137] To ensure secure and controlled access to functions and data, the SPoG UI 600 incorporates a role-based access control (RBAC) module 620. Administrators can define roles, assign permissions, and control user access based on their responsibilities and organizational hierarchy. The RBAC module 620 ensures that only authorized users can access specific features and information, thereby protecting data privacy, security, and compliance within the supply chain ecosystem.

[0138] In some embodiments, the customization module 625 allows users to personalize their dashboards and customize the interface according to their preferences and needs. Users can arrange widgets, charts, and data visualizations to prioritize the information most relevant to their specific roles and tasks. This module allows stakeholders to customize their view of the supply chain operations, thereby providing a user-centric experience that enhances productivity and usability.

[0139] The SPoG UI 600 can include a data visualization module 630, which enables stakeholders to analyze and interpret supply chain data through interactive dashboards, charts, graphs, and visual representations. Using advanced visualization techniques, this module presents complex data in a clear and intuitive manner. Users can gain insights into key performance indicators (KPIs), trends, patterns, and anomalies, thereby facilitating data-driven decision-making and strategic planning.

[0140] The SPoG UI 600 can include a mobile and cross-platform accessibility module 635 to ensure accessibility across multiple devices and platforms. Stakeholders can access the interface from desktop computers, laptops, smartphones, and tablets, allowing them to stay connected and informed while on the move. This module optimizes the user experience for different screen sizes, resolutions, and operating systems, thereby ensuring integrated access to real-time data and functions across various devices.

[0141] It should be understood that the operations shown in the exemplary methods are not exhaustive, and other operations may be performed before, after, or between any of the shown operations. In some embodiments of the present disclosure, the operations may be performed in a different order and / or varied.

[0142] Figure 7It is a flowchart of a method 700 for performing integrated supply chain management operations using a SPoGUI according to some embodiments of the present disclosure. In some embodiments, method 700 provides operational steps to streamline the supply chain process, enhance decision-making, and optimize operations within the supply chain ecosystem. In some embodiments, method 700 performs real-time data retrieval, visualization, customization, collaboration, access control, and cross-platform accessibility functions through the SPoGUI. Based on the disclosure herein, the operations in method 700 may be performed in different orders and / or varied to accommodate specific implementation requirements.

[0143] At operation 705, the computing device receives user input via the SPoGUI representing various requests and commands related to supply chain management. The user input includes actions such as selecting specific data visualizations, accessing different modules or functions, initiating workflows, configuring the interface, and performing data-driven analysis. This interactive input mechanism enables stakeholders to effectively interact with the SPoGUI and gain relevant insights to support their decision-making processes.

[0144] At operation 710, the computing device processes the user input and interacts with the real-time data exchange module using its integration capabilities. This integration ensures efficient data retrieval and synchronization, allowing the computing device to access the latest and accurate information from different data sources within the supply chain ecosystem. By establishing a connection with the RTDM module and leveraging real-time data exchange, the computing device ensures that the insights presented in the SPoGUI reflect the latest developments and provide a comprehensive view of supply chain operations.

[0145] At operation 715, the computing device employs a data visualization module to generate visually appealing and interactive representations of the retrieved supply chain data. This module utilizes advanced visualization techniques to create dynamic dashboards, charts, graphs, and other visual elements that effectively communicate key performance indicators, trends, patterns, anomalies, and correlations within the supply chain ecosystem. Through these visualizations, stakeholders can gain valuable insights, identify critical areas, and assess the overall health of their supply chain operations.

[0146] At operation 720, the computing device enables the user to personalize their dashboard and customize the SPoGUI interface according to their specific preferences and needs. The customization module allows stakeholders to arrange widgets, charts, data visualizations, and other UI components to prioritize the information most relevant to their roles and responsibilities. This flexibility ensures a user-centric experience, allowing stakeholders to focus on key data points and streamline their decision-making processes within the SPoGUI.

[0147] At operation 725, the computing device facilitates real-time collaboration and communication among stakeholders through a collaborative decision-making module. This module provides features that enable stakeholders to exchange information, share insights and recommendations, initiate workflows, and participate in discussions within the SPoG UI. By integrating with the RTDM module, the collaborative decision-making module ensures that stakeholders can effectively collaborate based on accurate and synchronized data, thus promoting a cohesive and agile supply chain ecosystem.

[0148] At operation 730, the computing device implements a secure access control mechanism through a role-based access control (RBAC) module integrated into the SPoG UI. This module enables administrators to define roles, assign permissions, and control user access based on their responsibilities and organizational hierarchy. By implementing RBAC, the computing device protects data privacy, ensures confidentiality, and maintains regulatory compliance within the supply chain ecosystem. Authorized stakeholders can access specific features, functions, and information based on their assigned roles, thus minimizing the risk of unauthorized data access or misuse.

[0149] At operation 735, the computing device optimizes the SPoG UI for integrated accessibility across multiple devices and platforms through a mobile and cross-platform accessibility module. This module ensures that stakeholders can access the SPoG UI interface from desktop computers, laptops, smartphones, and tablets, enabling them to stay connected, informed, and engaged in supply chain operations while on the move. The interface is optimized to provide a consistent and intuitive user experience across different screen sizes, resolutions, and operating systems, thus promoting real-time data access and increasing stakeholder productivity.

[0150] At operation 740, the computing device utilizes high-speed data in a data-rich environment module to efficiently process real-time data and maintain a visually clean interface. This module combines unique configurations of the SPoG UI structure, responsive data visualization, real-time data processing methods, adaptive information architecture, and optimization techniques. Operation 740 can include processing large amounts of dynamic supply chain data in an understandable, efficient, and visually appealing manner. The grid and The grid-based layout system of the SPoG UI supported by the technology enables UI elements to fluidly adapt to the available space and content, while HTML5, CSS3, and JavaScript (specifically React.js) manage the dynamic aspects of the interface.

[0151] In summary, Figure 7The method 700 depicted outlines a comprehensive approach to supply chain management via the SPoG UI. By leveraging real-time data retrieval, visualization, customization, collaboration, access control, and cross-platform accessibility features, stakeholders gain valuable insights into supply chain operations and can make informed decisions. This method facilitates efficient integration with the RTDM module, ensuring accurate and up-to-date data synchronization. Through personalized dashboards, interactive data visualization, collaborative decision-making, secure access control, and cross-device accessibility, the SPoG UI empowers stakeholders to optimize their supply chain operations, enhance collaboration, and drive efficiency in dynamic and complex supply chain ecosystems.

[0152] Figure 8 is a flowchart of a method 800 for vendor onboarding using the SPoG UI according to some embodiments of the present disclosure. In some embodiments, method 800 outlines a simplified and efficient process for leveraging the capabilities of the SPoG UI to facilitate vendor entry into the supply chain ecosystem. By integrating real-time data, collaborative decision-making, and role-based access control features, the SPoGUI enables stakeholders to effectively manage and optimize the vendor onboarding process. Based on the disclosure herein, the operations in method 800 may be performed in a different order and / or varied to accommodate specific implementation requirements.

[0153] At operation 805, the process is initiated when a vendor expresses interest in joining the supply chain ecosystem. The computing device utilizes the SPoG UI to receive the vendor's information and related details. This may include company profile, contact information, product catalog, certifications, and any other relevant data required for the vendor onboarding process.

[0154] At operation 810, the computing device uses its integration capabilities with the real-time data exchange module to verify the vendor's information. By leveraging real-time data synchronization and access to external systems, the computing device ensures that the vendor's details are accurate and up-to-date. This verification step helps maintain data integrity, minimize errors, and establish a reliable foundation for the vendor onboarding process.

[0155] At operation 815, the computing device initiates the vendor onboarding workflow via the collaborative decision-making module. This module allows stakeholders involved in the onboarding process (such as procurement staff, legal teams, and vendor managers) to collaborate and make informed decisions based on the vendor's information. The SPoG UI facilitates efficient communication, file sharing, and workflow initiation, enabling stakeholders to jointly evaluate the vendor's suitability and progress efficiently through the onboarding steps.

[0156] At operation 820, the computing device employs a Role - Based Access Control (RBAC) module to manage access control and permissions throughout the vendor login process. The RBAC module ensures that stakeholders can only access specific information and functions required for their roles. This control mechanism protects sensitive data, maintains privacy, and complies with regulatory requirements. Authorized stakeholders can securely view and contribute to the vendor login process, thus promoting a transparent and compliant environment.

[0157] At operation 825, the computing device provides a comprehensive view of the vendor login process to stakeholders through the Unified View (UV) module of the SPoG UI. This module presents an intuitive and customizable dashboard - style layout, incorporating relevant information, milestones, and tasks associated with the vendor login process. Stakeholders can monitor progress, track document requirements, and access real - time updates to ensure the efficient and timely completion of the login tasks.

[0158] In operation 830, the computing device enables stakeholders to interact with the data visualization module of the SPoG UI, which provides dynamic visualizations and analytics related to the vendor login process. Through interactive charts, graphs, and reports, stakeholders can evaluate key performance indicators, identify bottlenecks, and gain insights into the overall efficiency of the vendor login process. This data - driven approach enables stakeholders to make informed decisions autonomously, allocate resources effectively, and optimize the login workflow.

[0159] In operation 835, the computing device facilitates integrated collaboration among stakeholders involved in the vendor login process through a collaborative decision - making module. This module enables real - time communication, file sharing, and workflow coordination, allowing stakeholders to streamline the login process. By providing a centralized platform for discussion, feedback, and approval, the SPoG UI promotes efficient collaboration and reduces latency in the vendor login workflow.

[0160] In operation 840, the computing device uses the workflow management module of the SPoG UI to ensure the effective management and tracking of the vendor login process. This module enables stakeholders to define and manage the sequence of tasks, approvals, and reviews required for a successful vendor login. Workflow templates can be configured, allowing for standardization and repeatability in the login process. Stakeholders can monitor the status of each task, track completion, and receive notifications to ensure timely progress.

[0161] In operation 845, the computing device captures and records the vendor login activity within the audit trail module of the SPoG UI. This module maintains a detailed history of the login process, including actions taken, documents viewed, and decisions made. The audit trail enhances transparency, accountability, and compliance, providing stakeholders with a reliable record for future reference and potential audits.

[0162] In operation 850, the computing device terminates the vendor login process within the SPoG UI. Once all necessary steps, reviews, and approvals are complete, the vendor is officially logged into the supply chain ecosystem. The SPoG UI can provide stakeholders with a summary of the login process, allowing them to verify the completion of all requirements and initiate further actions, such as contract signing, product listing, and collaboration.

[0163] In summary, Figure 8 The method 800 depicted outlines a streamlined and efficient vendor login process using the SPoG UI. By leveraging real-time data integration, collaborative decision-making, role-based access control, comprehensive visualization, and workflow management capabilities, the SPoG UI enables stakeholders to successfully onboard vendors into the supply chain ecosystem autonomously. This process ensures data accuracy, promotes transparency, enhances collaboration, and facilitates informed decision-making throughout the vendor onboarding workflow. The intuitive interface of the SPoG UI, combined with its customizable features and notifications, streamlines the login process, reduces manpower, and optimizes vendor integration within dynamic and complex supply chain scenarios.

[0164] Figure 9 is a flowchart of a method 900 for using the SPoG UI for distributor login according to some embodiments of the present disclosure. The method 900 outlines a streamlined and efficient process that leverages the capabilities of the SPoG UI to facilitate the onboarding of distributors into the supply chain ecosystem. By integrating real-time data, collaborative decision-making, and role-based access control capabilities, the SPoG UI enables stakeholders to effectively manage and optimize the distributor login process. Based on the disclosure herein, the operations in the method 900 can be performed in a different order and / or varied to accommodate specific implementation requirements.

[0165] In operation 905, the process begins when a distributor expresses interest in joining the supply chain ecosystem. The computing device uses the SPoG UI to receive the distributor's information and related details. This includes company profile, contact information, business certifications, distributor agreements, and any other relevant data required for the distributor login process.

[0166] In operation 910, the computing device uses its integration capabilities with the real-time data exchange module to verify the information of the dealer. By leveraging real-time data synchronization and access to external systems, the computing device ensures that the dealer's details are accurate and up-to-date. This verification step helps maintain data integrity, minimize errors, and establish a reliable foundation for the dealer login process.

[0167] In operation 915, the computing device initiates the dealer login workflow through the collaborative decision-making module. This module allows stakeholders involved in the login process, such as sales representatives, legal teams, and account managers, to collaborate and make informed decisions based on the dealer's information. The SPoG UI facilitates integrated communication, file sharing, and workflow initiation, enabling stakeholders to jointly assess the suitability of the dealer and efficiently carry out the login steps.

[0168] In operation 920, the computing device employs a role-based access control (RBAC) module to manage access control and permissions throughout the dealer login process. The RBAC module ensures that stakeholders can only access the specific information and functions necessary for their roles. This control mechanism protects sensitive data, maintains privacy, and complies with regulatory requirements. Authorized stakeholders can securely view and contribute to the dealer login process, thus promoting a transparent and compliant environment.

[0169] In operation 925, the computing device provides stakeholders with a comprehensive view of the dealer login process through the unified view (UV) module of the SPoG UI. This module presents an intuitive and customizable dashboard-style layout, consolidating relevant information, milestones, and tasks associated with the dealer login process. Stakeholders can monitor progress, track document requirements, and access real-time updates to ensure the efficient and timely completion of the login tasks.

[0170] In operation 930, the computing device enables stakeholders to interact with the data visualization module of the SPoG UI, which provides dynamic visualizations and analytics related to the dealer login process. Through interactive charts, graphs, and reports, stakeholders can evaluate key performance indicators, identify bottlenecks, and gain insights into the overall efficiency of the login process. This data-driven approach enables stakeholders to make informed decisions autonomously, allocate resources effectively, and optimize the dealer login workflow.

[0171] In operation 935, the computing device facilitates efficient collaboration among the stakeholders involved in the dealer login process through a collaborative decision-making module. This module enables real-time communication, file sharing, and workflow coordination, allowing the stakeholders to streamline the login process. By providing a centralized platform for discussion, feedback, and approval, the SPoG UI promotes efficient collaboration and reduces latency in the dealer login workflow.

[0172] In operation 940, the computing device records and maintains an audit trail of dealer login activities within the audit trail module of the SPoG UI. This module captures detailed information about the actions taken, decisions made, and documents viewed during the login process. The audit trail enhances transparency, accountability, and compliance, serving as a valuable reference for future audits, reviews, and evaluations.

[0173] In operation 945, the computing device terminates the dealer login process within the SPoG UI. Once all the necessary tasks, reviews, and approvals are completed, the dealer is officially logged into the supply chain ecosystem. The SPoG UI provides a summary of the login process to the stakeholders, ensuring that all requirements are met and facilitating further actions, such as contract signing, product listing, and collaboration with the dealer.

[0174] In summary, Figure 9 The method 900 depicted emphasizes a streamlined and efficient dealer login process using the SPoG UI. By leveraging real-time data integration, collaborative decision-making, role-based access control, comprehensive visualization, and audit trail capabilities, the SPoG UI enables stakeholders to successfully onboard dealers into the supply chain ecosystem autonomously. The intuitive interface, customizable features, and robust collaboration capabilities of the SPoG UI streamline the login process, enhance transparency, and facilitate efficient communication among the stakeholders. The data visualization capabilities of the SPoG UI promote data-driven decision-making, while the audit trail ensures compliance and provides a reliable record of login activities. By effectively utilizing the SPoG UI, the dealer login process becomes a well-orchestrated workflow, optimizing dealer integration and promoting business success within a dynamic supply chain environment.

[0175] Figure 10FIG. 1000 is a flowchart of a method 1000 for customer and end - customer login using SPoGUI according to some embodiments of the present disclosure. Method 1000 outlines a comprehensive and user - centric approach for efficiently logging in customers and end - customers into a supply - chain ecosystem. By leveraging the capabilities of SPoGUI, including real - time data integration, collaborative decision - making, and personalized user experiences, stakeholders can successfully onboard customers and engage them, providing them with an efficient and customized login experience. Based on the disclosure herein, the operations in method 1000 can be performed in a different order and / or varied to accommodate specific implementation requirements.

[0176] In operation 1005, the process begins when a potential customer or end - customer expresses an interest in joining the supply - chain ecosystem. Using SPoGUI, the computing device captures the customer's or end - customer's information, preferences, and requirements necessary for the login process. This includes contact information, business profiles, industry - specific preferences, and any other relevant data.

[0177] In operation 1010, the computing device uses the real - time data integration capabilities with external systems to verify the customer's or end - customer's information. By synchronizing and accessing data from various sources, such as customer relationship management (CRM) systems or other enterprise - wide solutions, the computing device ensures the accuracy and integrity of the customer's or end - customer's information. This verification step helps establish a reliable foundation for the login process and enhances data integrity.

[0178] In operation 1015, the computing device initiates the customer or end - customer login workflow through a collaborative decision - making module. This module facilitates integrated communication and collaboration among stakeholders involved in the login process, such as sales representatives, account managers, and customer support teams. SPoGUI provides a centralized platform for stakeholders to jointly assess customer needs, define personalized login itineraries, and make informed decisions throughout the login process.

[0179] In operation 1020, the computing device utilizes a role - based access control (RBAC) module to manage access control and permissions during the login process. The RBAC module ensures that stakeholders have appropriate access to the customer's or end - customer's data based on their roles and responsibilities. This control mechanism protects sensitive information, maintains data privacy, and complies with regulatory requirements. Authorized stakeholders can securely view, update, and track the login progress, thus promoting a transparent and compliant login environment.

[0180] In operation 1025, the computing device makes full use of the Unified View (UV) module of the SPoG UI to provide stakeholders with a comprehensive and customizable dashboard layout of the customer or end - customer login process. This module combines relevant information, tasks, and milestones associated with the login journey, providing stakeholders with an overall view of the login progress. Stakeholders can monitor the status, view documents, and access real - time updates to ensure an efficient and integrated login experience.

[0181] In operation 1030, the computing device utilizes the data visualization module of the SPoG UI to present dynamic visualizations and analytics related to the login process. Through interactive charts, graphs, and reports, stakeholders gain insights into key login metrics, customer engagement levels, and potential bottlenecks. The data - driven approach enables stakeholders to make informed decisions autonomously, optimize login strategies, and personalize the login experience for each customer or end - customer.

[0182] In operation 1035, the computing device enables stakeholders to interact with the collaborative decision - making module to facilitate integrated collaboration during the login process. Stakeholders can share files, initiate workflows, and exchange information in real - time. The SPoG UI promotes effective communication, reduces latency, and ensures consistency among the stakeholders involved in the customer or end - customer login.

[0183] In operation 1040, the computing device employs a customization module to allow stakeholders to personalize the login experience for each customer or end - customer. Stakeholders can customize the interface, workflows, and communication to align with the preferences of the customer or end - customer, industry - specific requirements, and strategic goals. The customization capabilities enhance customer satisfaction and engagement during the login journey.

[0184] In operation 1045, the computing device utilizes the audit trail module within the SPoG UI to maintain a detailed record of customer or end - customer login activities. This module captures information about the actions taken, decisions made, and documents viewed throughout the login process. The audit trail enhances transparency, accountability, and compliance, serving as a valuable reference for future audits, reviews, and evaluations.

[0185] In operation 1050, the computing device terminates the customer or end - customer login process within the SPoG UI. Once all necessary tasks, reviews, and approvals are completed, the customer or end - customer is officially logged into the supply chain ecosystem. The SPoG UI provides stakeholders with a summary of the login process, ensuring that all requirements are met and facilitating further actions such as account activation, service onboarding, or personalized customer engagement.

[0186] In summary, Figure 10The method 1000 depicted illustrates the customer and end - customer login processes facilitated by SPoGUI. By leveraging real - time data integration, collaborative decision - making, role - based access control, comprehensive visualization, customization, and audit - trail capabilities, SPoGUI enables stakeholders to successfully on - board customers and end - customers into the supply - chain ecosystem autonomously. The intuitive interface, personalized features, and robust collaboration capabilities of SPoGUI streamline the login process, enhance transparency, and facilitate efficient communication among stakeholders. The data - visualization capabilities of SPoGUI promote data - driven decision - making, while the customization and audit - trail modules ensure a customized and compliant login experience. By effectively utilizing SPoGUI, the customer and end - customer login processes become integrated workflows, thus optimizing customer and end - customer integration and promoting business success within a dynamic supply - chain environment.

[0187] Figure 11 A system 1100 for automated SKU management according to some embodiments is depicted. In some embodiments, the system 1100 can provide a comprehensive solution for SKU management, leveraging the functionality of the UI 1105, data layer 1110, and catalog - transformation module 1120. The system 1100 can be configured to enhance the accuracy and efficiency of the SKU - management process by enabling data exchange, real - time SKU creation, and dynamic pricing. The system 1100 can also be configured to empower sellers and entities to effectively manage SKUs, optimize inventory levels, and provide an excellent customer experience in a rapidly evolving market.

[0188] System 1100 is provided to effectively manage a large number of SKUs in a distribution platform. The system is designed to automate SKU management and integrate with vendors to facilitate easy self-service. In some embodiments, one or more SKUs can be generated as virtual SKUs until an order is placed, at which point they become actual SKUs. The entire process is designed to be synchronous to ensure an efficient and integrated experience for the customer. The system can handle a large number of SKUs by presenting a large number of SKUs in the customer platform without overwhelming the backend system. The method achieves scalability and avoids operational overhead. The SKU creation process utilizes AI / ML algorithms and proprietary algorithms within the distribution platform to transform catalogs and create virtual SKUs. Data integrity and verification are key aspects addressed by the system, particularly in the Master Data Governance (MDG) module. The MDG module ensures data accuracy, consistency, and quality control. The system also includes a pricing engine to extract and calculate the prices of SKUs, including special pricing. The architecture of the system allows for dynamic SKU creation, efficient data processing, and integration with vendors, enabling scalability and aggregation of products from multiple vendors. The virtual SKU concept, supported by AI / ML algorithms and a rule engine, is a significant paradigm shift that helps overcome scaling challenges and enables handling a wider range of SKUs. Through the self-service feature, vendors can upload their catalogs without manual intervention, reducing operational overhead. The system incorporates a caching mechanism and data governance practices to manage the SKU lifecycle, identify inactive SKUs, and retain historical data for compliance and security purposes. In summary, the system's innovative approach to SKU management, virtual SKU creation, and pricing optimization drives scalability, operational efficiency, and an enhanced customer experience.

[0189] In some embodiments, system 1100 can include one or more interconnected modules and subsystems, each serving a specific function and contributing to the management of SKUs. In some embodiments, these components can include a catalog transformation module, a Real-Time Data Mesh (RTDM) module, a Master Data Governance (MDG) module, a Global Data Repository (GDR), a search platform, a dynamic SKU creation module, and a Global Pricing Engine (GPE). In some embodiments, the Real-Time Data Mesh (RTDM) module (reference numeral 1110) and the engine layer (reference numeral 1140) play important roles in facilitating real-time data management and processing.

[0190] In some embodiments, the UI 1105 can serve as the central point of interaction for users within the system 1100. In one non-limiting example, the UI 1105 can be an embodiment of the SPoG UI, such as the SPoG UI 305, 410, 600, or any other UI that allows users (such as one or more vendors) to easily navigate through different modules, access relevant information, and perform various tasks related to SKU management. The UI 1105 is designed to be intuitive, user-friendly, and responsive, enabling users to interact with the system efficiently.

[0191] In some embodiments, the data layer 1110 can be configured to enable efficient data flow across the SKU management ecosystem. The data layer 1110, which can be an embodiment of the RTDM module 310, 415, 500 or any other data layer, can include a data lake that serves as a scalable and robust storage infrastructure for storing structured and unstructured data related to SKUs. In some embodiments, the data layer 1110 is integrated with the RTDM module to enable real-time data exchange and synchronization. This integration ensures that the data within the data layer 1110 is up-to-date and readily available for SKU management operations.

[0192] In some embodiments, the data layer 1110 can be an RTDM module, such as an embodiment of the RTDM module 310 or the RTDM module 415 or the RTDM module 500. In some embodiments, the data layer 1110 can be a separate data layer that interacts with the RTDM module. As described above, the RTDM module can be configured to act as an ERP-agnostic real-time data grid. In some embodiments, the RTDM module collects data from systems in the record layer (including data from various enterprise systems such as ERP) and merges the data into the data lake within the data layer 1110.

[0193] The data layer 1110 serves as a repository for the coordinated and standardized data obtained from the RTDM module. Within the data layer 1110, various purpose-built data stores are deployed to store specific types of data, such as customer data, product data, financial data, etc. These purpose-built data stores optimize data retrieval based on specific use cases and requirements, ensuring efficient SKU management.

[0194] During the dynamic SKU process, when a customer adds a non-transactional product to their shopping cart, the data layer 1110 interacts with the RTDM module to facilitate real-time SKU creation. The data layer 1110, with its access to the most recent and accurate data, provides the necessary information to the dynamic SKU creation module. This module utilizes the data from the data layer 1110 in conjunction with other relevant data sources to generate SKUs in real-time within the ERP system.

[0195] By leveraging the data available within the data layer 1110 and the real-time capabilities of the RTDM module, system 1100 is configured to implement an efficient and accurate SKU creation process that is consistent with the most current information. This interaction between the data layer 1110 and the RTDM module facilitates an integrated data flow and enables the dynamic SKU creation process to be effectively executed across the entire SKU management ecosystem.

[0196] In some embodiments, the catalog conversion module 1120 may be an AAML module such as AAML module 315, or an embodiment such as AI module 420. In some embodiments, the catalog conversion module 1120 may be a separate data layer that interacts with the AAML or AI module. In a non-limiting example, the catalog conversion module 1120 may employ advanced AI / ML algorithms, thus leveraging frameworks such as TensorFlow and PyTorch. These algorithms are trained on a large dataset of existing vendor catalog files classified based on an integrated data governance process. By leveraging deep learning techniques and neural networks, the module gains the ability to accurately propose classifications and attribute mappings for new catalogs with 80% accuracy or higher.

[0197] In some embodiments, the catalog conversion module 1120 operates by processing the vendor catalog file received from the UI 1105 of system 1100. It applies a trained AI / ML model that may cover hierarchical and clustering algorithms to predict the most suitable classification and attribute mapping for each item within the catalog.

[0198] To achieve optimal results, the catalog conversion module 1120 may consider multiple data points, including product descriptions, keywords, and historical mapping patterns. It may employ natural language processing (NLP) techniques to analyze and extract meaningful information from text data, thus enabling accurate classification and attribute mapping.

[0199] Additionally, the catalog conversion module 1120 may be configured to promote flexibility and adaptability in the catalog mapping process. It may provide a feedback mechanism that allows vendor users or internal stakeholders to view and refine the proposed classifications and attribute mappings. This feedback can be incorporated into the iterative learning process of the module, thus further enhancing its accuracy and performance over time.

[0200] In a non-limiting example, consider a vendor catalog that includes electronic and / or IT products such as laptops, servers, and other computing devices. The catalog conversion module 1120 may analyze the product descriptions, identify relevant keywords, and apply clustering algorithms to group similar products together. Then, it may assign appropriate categories and map relevant attributes (such as brand, model, specifications, and pricing) to each item.

[0201] In some embodiments, the Catalog Transformation Module 1120 collaborates with the Master Data Governance (MDG) Module 1120 to ensure data integrity and validation. The Catalog Transformation Module 1120 can be configured to communicate with the MDG Module to validate the transformed catalog and identify any errors or inconsistencies in the classification and attribute mapping processes. Indicators or notifications such as flags are generated to alert suppliers, managers, etc. of these issues, thereby allowing them to make the necessary corrections and updates.

[0202] The Catalog Transformation Module 1120 can be configured to operate within the broader System 1100 architecture to interact with other components such as the Real-Time Data Mesh (RTDM) Module. Through this interaction, the transformed catalog is synchronized in real time to ensure the availability of accurate and up-to-date product information across the systems.

[0203] The Catalog Transformation Module 1120 uses advanced AI / ML algorithms and an integrated technology stack to convert vendor catalog files into a standardized format. By applying deep learning techniques and an iterative learning process, it accurately predicts the classification and attribute mapping for each catalog item. This module improves the efficiency of SKU management and streamlined operations and provides enterprises with the ability to effectively manage their product catalogs.

[0204] The AI / ML Module (AAML) 1115 uses advanced analytics and machine learning algorithms to enhance the SKU management capabilities within the System 1100. In some non-limiting examples, AAML 1115 can utilize technologies such as Apache Spark, TensorFlow, and scikit-learn to extract valuable insights from the data. These algorithms enable AAML 1115 to automate repetitive tasks, predict demand patterns, optimize inventory levels, and improve overall SKU management efficiency.

[0205] In one non-limiting example, the Catalog Transformation Module within the System 1100 uses the Data Layer 1110 and AAML 1115 to convert different catalog files into a standardized format. AAML 1115, trained with existing catalog data sets, predicts the classification and attribute mapping for the new catalog, ensuring an accurate and efficient transformation. The transformed catalog is then validated by the MDG Module and stored in the GDR to ensure data integrity and consistency.

[0206] The RTDM Module integrated with the Data Layer 1110 enables real-time data synchronization across the SKU management ecosystem. It facilitates data exchange and ensures that all system components have access to the latest information for SKU management operations. This real-time data exchange capability provides accuracy and efficiency in the SKU management process.

[0207] The Dynamic SKU Creation module collaborates with the GDR and RTDM modules to enable real-time SKU creation for non-trade products. When customers add these products to their shopping carts, the Dynamic SKU Creation module generates SKUs in the ERP system, thus facilitating order processing and fulfillment. The GPE integrated with the data layer 1110 determines the real-time pricing of catalog items based on the seller price file, market trends, and historical pricing data.

[0208] In some embodiments, the SPoG UI 1105 presents a unified view of various system elements and functions, thus delivering a user-friendly experience by consolidating multiple system components onto a single platform. It supports multiple data inputs, including seller catalogs in different formats. In some embodiments, the SPoG UI 1105 receives the catalog data and interfaces with other system components to enable efficient SKU management.

[0209] In some embodiments, the RTDM module 1110 is a crucial component that facilitates real-time data synchronization across system components. It works in tandem with the SPoG UI 1105 to ensure that the interface displays accurate real-time data during SKU creation and inventory management. In some embodiments, the RTDM 1110 enables real-time interaction between the user and the system, thus allowing instantaneous updates to be reflected across the system.

[0210] The system includes the AAML module 1115 that uses machine learning models to convert catalog files into a standardized format. This module predicts and proposes catalog item classification and attribute mapping, thus maintaining a feedback loop to enhance prediction accuracy over time. The AAML 1115 is trained with existing datasets and uses iterative learning to improve its predictions, thus contributing to its robustness and versatility in handling different catalog formats.

[0211] The MDG module 1120 validates the transformed catalog to ensure data accuracy and reliability. In some embodiments, the MDG 1120 conveys errors back to the seller, enabling the corrected catalog data to be corrected and re-uploaded via the SPoG UI 1105. This continuous feedback loop further enriches the data input into the AAML module 1115, thus improving the overall system performance and accuracy.

[0212] The GDR 1125 plays an important role in storing the validated catalogs and maintaining data integrity. It collaborates with the RTDM 1110 to support real-time data synchronization, thus ensuring that all components have access to the most accurate and current data. The GDR 1125 also facilitates the MDG 1120 by storing records of previously validated catalogs, thus providing an integrated repository for the system.

[0213] The search platform 1130 is integrated with the retrieval and indexing of the stored catalogs. It works in conjunction with the SPoG UI 1105 to provide users with a product exploration experience. The search platform 1130 also interacts with the RTDM 1110 for real-time data updates, which enhances the relevance and accuracy of search results.

[0214] In some embodiments, the system 1100 may include additional modules, such as a dynamic SKU creation module 1135 and a Global Pricing Engine (GPE) 1140. The dynamic SKU creation module 1135 dynamically generates SKUs for non-transactional products during checkout, enabling these products to be added to the customer's shopping cart. On the other hand, the GPE 1140 determines the real-time pricing of catalog items based on various factors such as vendor price files, market trends, and historical pricing data.

[0215] As described above, the data layer 1110 may be an embodiment of an RTDM module (e.g., 310, 415, 500) that utilizes the principles of a data grid to integrate interconnected components, processes, and subsystems for efficient real-time data management and analysis. In a non-limiting example, the RTDM module 1110 may include a cloud-based infrastructure that includes a data lake (e.g., the data lake 522 depicted as Figure 5 in) and a Purposeful Data Store (PDS) (e.g., PDSs 524.1 to 524.N). The data lake may be configured to provide scalable and fault-tolerant data storage capabilities, while the PDS is an optimized repository for storing specific types of data related to the SKU management domain.

[0216] The engine layer 1140 within the system 1100 represents a collection of interconnected systems responsible for specialized data ingestion, processing, transformation, and integration. As Figure 5 depicted in, the engine layer 1140 may be an embodiment of one or more engines 540. For example, the engine layer 1140 may include one or more engines configured to operate autonomously and perform different functions within the system, such as an engine configured to determine the real-time pricing of one or more catalog items based on various factors such as vendor price files, market trends, and historical pricing data.

[0217] In a non-limiting example, the engine layer 1140 may include a recommendation engine, an insights engine, a subscription management engine, and various other specialized engines customized to meet the specific needs of SKU management. These engines utilize the reconciled data stored in the data grid to deliver targeted business logic and services.

[0218] The engine layer 1140 is designed to receive data from multiple sources, including trading systems, IoT devices, and external data providers. It processes the input data, applies algorithms for data cleansing, aggregation, and enrichment, and prepares the data for further analysis and integration within the system.

[0219] In some embodiments, the engine layer 1140 also includes a data distribution mechanism (reference numeral 1145), similar to Figure 5 the data distribution mechanism 545 in [[reference]]. This mechanism includes one or more APIs to facilitate the distribution of data from the data grid and the engine to various endpoints, including user interfaces and external systems.

[0220] Embodiments of the engine layer 1140 and the headless engine 540 can perform operations to facilitate the SKU management system and contribute functions and advanced algorithms for category assignment, pricing, and other tasks related to SKU management. The engine layer 1140 can be operatively connected to other engines and the interconnection elements of the RTDM module 500, which operate autonomously to deliver dedicated services and enable efficient processing of incoming catalog items.

[0221] For example, the engine layer 1140 may include a Category Assignment Engine (CAE) that is responsible for assigning categories to catalog items based on the attributes and characteristics of the category items. In some embodiments, the CAE can be configured to perform one or more techniques, including natural language processing (NLP), machine learning, and rule-based systems, to perform accurate and automated classification.

[0222] Using NLP algorithms, the CAE analyzes the text description of the catalog item to extract relevant keywords and phrases. Then, taking into account synonyms, abbreviations, and variations in language usage, it can map the terms to a predefined category hierarchy. Machine learning models trained on a large amount of historical data help identify patterns and relationships between attributes and categories, thus enhancing the accuracy of classification.

[0223] In addition, the CAE utilizes a rule-based system to incorporate specific business rules and logic. These rules can be customized to match the unique requirements of the SKU management system, allowing for fine-grained control over category assignment. For example, rules can be defined to prioritize certain attributes or consider specific criteria when assigning categories, ensuring consistent and accurate results.

[0224] To support the pricing aspect of the SKU management system, the engine layer 1140 includes a Pricing Engine (PE) that determines the optimal pricing of catalog items based on various factors. The PE considers inputs such as the seller price file, market trends, historical pricing data, and predefined pricing algorithms or rules.

[0225] PE uses advanced algorithms to analyze pricing inputs and generate competitive and profitable prices. For example, it can employ machine learning algorithms such as regression models or neural networks to predict the optimal price range based on historical sales data and market dynamics. Alternatively, it can utilize a rule-based system that combines pricing strategies and guidelines set by the enterprise.

[0226] The engine layer 1140 also interfaces with a Real-Time Data Mesh (RTDM) module, a scalable and fault-tolerant data storage infrastructure that ensures real-time data management and analysis. The RTDM module integrates with various enterprise systems such as ERP to capture real-time changes and coordinate data for efficient processing within the SKU management system.

[0227] By leveraging the coordinated and standardized data within the RTDM module, the engines in the engine layer 1140 make informed decisions during the category assignment and pricing processes. They retrieve relevant information such as customer data, product data, financial data, etc. from the RTDM module to improve accuracy and consistency.

[0228] In addition, the engine layer 1140 incorporates advanced data processing capabilities through technologies such as Apache Spark or Apache Flink. These distributed computing frameworks enable parallel processing and distributed computing across large-scale datasets, thus facilitating efficient analysis and computation for category assignment and pricing.

[0229] In some embodiments, the engine layer 1140 can also utilize machine learning algorithms such as clustering or hierarchical models to identify together catalog items that are similar in pattern or group. This can assist in accurate category assignment and facilitate dynamic pricing strategies based on product similarity or customer segmentation.

[0230] Furthermore, the engine layer 1140 includes fine-grained access control mechanisms and authentication protocols to ensure data security and prevent unauthorized access to sensitive information. Data lineage and audit trail mechanisms track the origin and history of data, thus ensuring compliance with regulatory requirements and maintaining data integrity.

[0231] When new data becomes available, the engine layer 1140 operates in real time, continuously monitoring incoming catalog items and updating category assignment and pricing information. It efficiently processes a large number of catalog items, thus ensuring scalability and performance within the SKU management system.

[0232] The engine layer 1140 is integrated with the RTDM module to utilize advanced algorithms and incorporate customizable business rules to ensure accurate classification and competitive pricing, ultimately driving improved productivity and customer satisfaction. By leveraging the capabilities of the engine layer 1140, the system 1100 enables sellers and other entities to automate and streamline category assignment and pricing operations, reducing manual labor and increasing overall efficiency. The integration with the RTDM module, advanced algorithms, and data processing technologies empowers the headless engine of the engine layer 1140 to deliver accurate and timely results, enhancing the effectiveness of the SKU management system within the system 1100.

[0233] The combination of the RTDM module 1110, the engine layer 1140, and other system components within the system 1100 enables the data flow, real-time insights, and efficient management of SKU-related information. By integrating different technology stacks, data processing frameworks, and distribution mechanisms, enterprises can leverage the power of the system 1100 to streamline their SKU management processes, optimize operations, and make data-driven decisions.

[0234] Overall, the RTDM module 1110 and the engine layer 1140 within the system 1100 provide the necessary infrastructure and capabilities to handle real-time data management, processing, and analysis, ensuring efficient SKU management operations.

[0235] Figure 11 An embodiment of a system for SKU management, referred to as system 1100, is depicted. In this implementation, the system 1100 includes specific components for achieving efficient and accurate SKU management, including a user interface (UI) 1105, a data layer 1110, and an AI / ML module (AAML) 1115. These components work together to streamline the processes of SKU creation, management, and pricing, thereby improving the overall efficiency of the system. Figure 11 An embodiment of a system for SKU management, referred to as system 1100, is depicted. In this implementation, the system 1100 includes specific components for achieving efficient and accurate SKU management, including a user interface (UI) 1105, a data layer 1110, and an AI / ML module (AAML) 1115. These components work together to streamline the processes of SKU creation, management, and pricing, thereby improving the overall efficiency of the system.

[0236] Figure 12 A process flow for SKU generation is depicted, which is an integrated part of the login process within the system 1200, as Figure 11 depicted therein. This process flow outlines the steps involved in efficiently generating SKUs based on the seller catalog file, ensuring accuracy, real-time synchronization, and integration within the SKU management ecosystem.

[0237] In operation 1205, the SKU generation process starts when the system receives the vendor catalog file. These catalog files contain information about the products offered by the vendor, including their descriptions, attributes, and pricing details. Triggering the SKU generation process allows the system to convert these catalogs into standardized and structured data, thus facilitating efficient SKU management.

[0238] Operation 1210 may include a catalog conversion step. In this step, advanced AI / ML algorithms are applied to the vendor catalog files to transform them into a standardized format. Using techniques such as deep learning and neural networks, the algorithms predict the classification and attribute mapping for each item in the catalog. These predictions are based on the analysis of product descriptions, keywords, historical mapping patterns, and other relevant data points. The catalog conversion module further improves the accuracy of the predictions by extracting meaningful information from the text data using natural language processing (NLP) techniques. The module also allows for feedback and refinement of the proposed classification and attribute mapping, thus incorporating an iterative learning process to continuously improve the prediction accuracy.

[0239] To ensure the real-time availability and synchronization of SKU information, operation 1215 may include real-time data synchronization. The transformed catalogs from the catalog conversion step are synchronized with the data layer within the system. This synchronization ensures that the SKU information remains up-to-date and readily available for SKU management operations. By integrating a real-time data mesh (RTDM) module, the system facilitates data exchange and synchronization, thus allowing stakeholders to access the most accurate current SKU information.

[0240] Operation 1220 may include a master data governance (MDG) step, which is crucial for maintaining data integrity and validation. The transformed catalogs are validated within the MDG module to ensure their accuracy, consistency, and compliance with data governance practices. This validation process identifies any errors or inconsistencies in the classification and attribute mapping of the catalog. These issues are notified to stakeholders (such as vendors or managers) through indications or notifications, enabling them to make the necessary corrections and updates. The MDG module plays an important role in maintaining data quality and reliability throughout the SKU generation process.

[0241] Operation 1225 may include data storage and management. The validated and transformed catalogs are stored within a global data repository (GDR) to ensure data integrity and accessibility. The GDR acts as a central repository for the standardized and harmonized data obtained from the real-time data mesh (RTDM) module. This data storage mechanism supports real-time data synchronization and facilitates the availability of accurate and up-to-date SKU information across systems. Additionally, the GDR retains historical data for compliance and security purposes, thus providing a comprehensive repository for SKU-related information.

[0242] Operation 1230 may include a dynamic SKU creation step. During this step, the system typically generates SKUs for non-traded products in real time during the checkout process. By leveraging the transformed and synchronized catalog data, the system dynamically creates SKUs within the ERP system. This is achieved by associating the appropriate SKUs with the selected products to enable efficient order processing and fulfillment. The real-time nature of this process ensures that customers can add non-traded products to their shopping carts and complete their purchases without delay.

[0243] Operation 1235 may include pricing determination. In this step, the system determines the real-time pricing of catalog items. A pricing engine integrated with the data layer retrieves relevant data such as vendor price files, market trends, and historical pricing data. Using advanced algorithms such as machine learning models or rule-based systems, the pricing engine analyzes this data to generate competitive and profitable prices for catalog items. The determination of real-time pricing enhances the overall pricing optimization process, ensuring accurate and up-to-date pricing information for SKU management operations.

[0244] Operation 1240 may include termination and verification of the SKU generation process. At this stage, the entire SKU generation process is completed, and the system verifies the accuracy of the generated SKUs and pricing information. Any necessary actions such as order processing or further SKU management operations can be initiated based on the generated SKUs. This step concludes the SKU generation process, which is designed to improve the efficiency, accuracy, and integration of SKU management within the system.

[0245] Figure 12 The process flow diagram depicted illustrates the steps involved in the SKU generation process related to the login process within system 1200. By leveraging advanced AI / ML algorithms, real-time data synchronization, and data governance practices, the system ensures the accurate and timely generation of SKUs based on the vendor catalog file. This process flow supports integration, scalability, and efficiency when managing a large number of SKUs, ultimately enhancing the overall SKU management capabilities within the system.

[0246] Figure 13 is a block diagram of an example component of device 1300. One or more computer systems 1300 can be used, for example, to implement any of the embodiments discussed herein, as well as any combinations and sub-combinations thereof. Computer system 1300 can include one or more processors (also referred to as central processing units or CPUs), such as processor 1304. Processor 1304 can be connected to a communication infrastructure or bus 1306.

[0247] Computer system 1300 may also include user input / output device(s) 1303, such as monitors, keyboards, pointing devices, etc., which can communicate with the communication infrastructure 1306 through user input / output interface(s) 1302.

[0248] One or more processors 1304 may be a graphics processing unit (GPU). In one embodiment, the GPU may be a processor that is a dedicated electronic circuit designed to process math-intensive applications. The GPU may have a parallel architecture that is efficient at performing parallel processing of large blocks of data (common math-intensive data such as for computer graphics applications, images, video, etc.).

[0249] The computer system 1300 may also include a main memory or main storage 1308, such as random access memory (RAM). The main memory 1308 may include one or more levels of cache. Control logic (i.e., computer software) and / or data may be stored in the main memory 1308.

[0250] The computer system 1300 may also include one or more auxiliary storage devices or memories 1310. The auxiliary memory 1310 may include, for example, a hard disk drive 1312 and / or a removable storage device or drive 1314.

[0251] The removable storage drive 1314 may interact with a removable storage unit 1318. The removable storage unit 1318 may include a computer-usable or readable storage device on which computer software (control logic) and / or data is stored. The removable storage unit 1318 may be 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 any other removable storage unit and associated interface. The removable storage drive 1314 may read from and / or write to the removable storage unit 1318.

[0252] The auxiliary memory 1310 may include other means, devices, components, tools, or other paths for allowing the computer system 1300 to access computer programs and / or other instructions and / or data. Such means, devices, components, tools, or other paths may include, for example, a removable storage unit 1322 and an interface 1320. Examples of the removable storage unit 1322 and the interface 1320 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 any other removable storage unit and associated interface.

[0253] The computer system 1300 may also include a communication or network interface 1324. The communication interface 1324 may enable the computer system 1300 to communicate and interact with any combination of external devices, external networks, external entities, etc. (collectively and individually denoted by the reference numeral 1328). For example, the communication interface 1324 may allow the computer system 1300 to communicate with an external or remote device 1328 via a communication path 1326, which may be wired and / or wireless (or a combination thereof), and may include any combination of LAN, WAN, the Internet, etc. Control logic and / or data may be transmitted to and from the computer system 1300 via the communication path 1326.

[0254] The computer system 1300 may also be any one of, or any combination of, a personal digital assistant (PDA), a desktop workstation, a laptop or notebook computer, a netbook, a tablet computer, a smart phone, a smart watch or other wearable device, an appliance, a part of the Internet of Things, and / or an embedded system (to name just a few non-limiting examples).

[0255] The computer system 1300 may be a client or a server that accesses or hosts any application and / or data via any delivery paradigm, including but not limited to remote or distributed cloud computing solutions; local or on-premises software (“local” 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), etc.); and / or a hybrid model that includes any combination of the foregoing examples or other services or delivery paradigms.

[0256] Any available data structures, file formats, and schemas in the computer system 1300 may be derived from standards including but not limited to JavaScript Object Notation (JSON), Extensible Markup Language (XML), another markup language (YAML), Extensible HyperText Markup Language (XHTML), Wireless Markup Language (WML), message packets, XML User Interface Language (XUL), or any other functionally similar representation (alone or in combination). Alternatively, proprietary data structures, formats, or schemas may be used exclusively or in combination with known or open standards.

[0257] In some embodiments, a tangible non-transitory device or article including a tangible non-transitory computer-usable or readable medium having control logic (software) stored thereon may also be referred to herein as a computer program product or a program storage device. This includes but is not limited to computer system 1300, main memory 1308, secondary memory 1310, and removable storage units 1318 and 1322, as well as tangible articles implementing any combination of the foregoing. When executed by one or more data processing devices such as computer system 1300, such control logic may cause such data processing devices to operate as described herein.

[0258] Figures 14A to 14Q Depicts various screens and functions of the SPoGUI related to vendor login, partner dashboard, customer shopping cart, order summary, SKU generation, order tracking, shipped goods tracking, subscription history, and subscription modification. A detailed description of each figure is provided below:

[0259] Figure 14A Depicts a vendor login startup screen representing the initial steps of the vendor login process. It provides a form or interface where vendors can express their interest in joining the supply chain ecosystem. Vendors can enter their basic information such as company details, contact information, and product catalog.

[0260] Figure 14B Depicts a vendor login guide that shows a step-by-step guide or checklist for vendors to follow during the login process. It outlines the necessary tasks and requirements to ensure that vendors have a clear understanding of the login process and can proceed smoothly.

[0261] Figure 14C Depicts a vendor login call scheduler that facilitates scheduling calls or meetings between vendors and platform affiliates or representatives responsible for guiding them through the login process. Vendors can select a suitable time slot or request a call to ensure effective communication and assistance throughout the login journey.

[0262] Figure 14D Depicts a vendor login task list that presents a comprehensive task list or dashboard outlining the specific steps and actions required for a successful vendor login. It provides an overview of pending tasks, completed tasks, and upcoming deadlines to help vendors track their progress and ensure the timely completion of each login task.

[0263] Figure 14E Depicts a vendor login completion screen that confirms the successful completion of the vendor login process. It may display a congratulatory message or summary of the completed tasks to indicate that the vendor is now officially logged into the supply chain ecosystem.

[0264] Figure 14F A partner dashboard that depicts a centralized view providing relevant information and metrics related to their partnership within the supply chain ecosystem to partners or stakeholders. It provides an overview of performance indicators, key data points, and actionable insights to facilitate effective collaboration and decision-making.

[0265] Figure 14G A customer product cart that depicts the customer's product cart representing the items they wish to purchase. It shows a list of the selected products, quantities, prices, and other relevant details. Customers can view and modify the contents of their cart before proceeding to the checkout process.

[0266] Figure 14H A customer subscription cart that allows customers to manage their subscription-based purchases. It shows the selected subscription plans, pricing, and duration. Customers can view and modify their subscription details before finalizing their selection.

[0267] Figure 14I A customer order summary that depicts a summary of the customer's order, including details such as the purchased products or subscriptions, quantities, pricing, and any applied discounts or promotions. It allows customers to view their order before confirming the purchase.

[0268] Figure 14J A seller SKU generation screen for generating unique stock keeping unit (SKU) codes for seller products. It may include fields or options where the seller can specify product details, attributes, and pricing, and the system automatically generates the corresponding SKU code.

[0269] Figure 14K and Figure 14L Dashboard order summaries that depict a summary of information about orders placed within the supply chain ecosystem. They 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 activities, enabling stakeholders to efficiently track and manage orders.

[0270] Figure 14M A customer subscription cart that allows customers to add, modify, or cancel subscription plans. It can show a list of the selected subscriptions, pricing, and renewal dates. Customers can manage their subscriptions and make changes according to their preferences and requirements.

[0271] Figure 14N A customer order tracking screen that enables customers to track the status and progress of their orders within the supply chain. It shows real-time updates on order fulfillment, including processing, packaging, and shipping. Customers can monitor the movement of their orders and anticipate delivery times.

[0272] Figure 14O Depicts customer shipment tracking that provides customers with real-time tracking information about their shipments. It can include details such as the means of transportation, tracking number, current location, and estimated delivery date. Customers can stay informed about the whereabouts of their shipments.

[0273] Figure 14P Depicts customer subscription history that presents a history of a customer's subscription activities. It shows a list of previous subscriptions, including subscription plans, durations, and statuses. Customers can view their subscription history, track past payments, and refer to previous subscription details.

[0274] Figure 14Q Depicts a customer subscription modification dialog that 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.

[0275] The depicted UI screens are not restrictive. In some embodiments, Figures 14A to 14Q the UI screens together represent the various functions and features provided by the SPoG UI, thus providing stakeholders with a comprehensive and user-friendly interface for vendor login, partnership management, customer interaction, order management, subscription management, and tracking within the supply chain ecosystem.

[0276] A system of one or more computers can be configured to perform particular operations or actions by having software, firmware, hardware, or a combination of them installed on the system, the software, firmware, hardware, or a combination of them causing the system to perform the actions in operation. One or more computer programs can be configured to perform particular operations or actions by including instructions that, when executed by a data processing apparatus, cause the apparatus to perform the actions.

[0277] In one general aspect, a computer-implemented method can include integrating a plurality of communication channels (i.e., contacts) among user groups into a unified interactive interface in a computer system, where the unified interactive interface is referred to herein as the SPoG UI, where the SPoG is a central interface component configured to merge user interactions, data, and / or functions of user groups, and where the SPoG UI is arranged to facilitate operations across a supply chain ecosystem. The computer-implemented method can further include managing the end-to-end lifecycle of user interactions using the SPoG UI. The method can further include collecting data from the user interactions within the SPoG UI. Additionally, the method can include analyzing the collected data to generate one or more insights for business growth. Additionally, the method can include performing one or more artificial intelligence and / or machine learning algorithms to enhance business operations based on the analyzed data. The method can further include incorporating routine updates and improvements into the SPoG UI based on the analyzed data. Additionally, the method can include the case where the user groups can include users selected from two or more different groups, the groups having distributors, resellers, customers, end customers, sellers, and suppliers. Other embodiments of this aspect include corresponding computer systems, devices, and computer programs recorded on one or more computer storage devices each configured to perform the actions of the method.

[0278] Embodiments can include one or more of the following features. A method where the integration includes establishing communication links with a plurality of pre-existing business platforms. A method where the merged interaction points can include one or more websites, customer relationship management systems, seller platforms, and supply chain management systems. A method where the management of the end-to-end lifecycle can include one or more of initial contact, service fulfillment, and subsequent interactions. A method where collecting data can include monitoring and / or formally recording user activities within the SPoG UI. A method where performing analysis of the collected data using advanced statistical algorithms. A method where the artificial intelligence and machine learning algorithms include predictive analytics to identify market trends. A method where the artificial intelligence and machine learning algorithms include a recommendation system for personalizing user interactions. The improvements can be based on the analysis received through the SPoG UI and / or the analyzed user feedback. Embodiments of the described techniques can include hardware, methods or processes, or computer tangible media.

[0279] In one general aspect, a system may include a communication integration module configured to integrate multiple communication channels (i.e., contacts). The system may also include a consolidation module configured to combine the integrated communication channels into a unified interactive interface, which is referred to herein as the SPoG UI, where SPoG is a central interface component configured to consolidate user interactions, data, and / or functionality across user populations, and where the SPoG UI is arranged to facilitate operations across a supply chain ecosystem. Additionally, the system may include a lifecycle management module configured to manage the end-to-end lifecycle of user interactions within SPoG. The system may additionally or alternatively include a data collection module configured to automatically collect data from user interactions within SPoG. Additionally, the system may include a data analysis module configured to generate one or more insights based on the collected data. The system may also include an artificial intelligence module configured to execute one or more AI and / or ML algorithms based on the analyzed data. Additionally, the system may include the following: the user population may include users selected from two or more different groups, the groups being distributors, resellers, customers, end customers, sellers, and suppliers. Other embodiments of this aspect include corresponding computer systems, devices, and computer programs recorded on one or more computer storage devices each configured to perform the actions of the method.

[0280] Systems and methods for automating SKU management may include: a user interface configured to receive from a user catalog files in two or more different formats; a catalog conversion module configured to convert the catalog files to a standard format, thereby predicting the classification and attribute mapping of at least one catalog item, where the catalog conversion module utilizes iterative learning to propose the classification and attribute mapping; a real-time data grid (RTDM) module for ensuring real-time data exchange and synchronization across system components, where the RTDM module facilitates real-time interaction between the user and the system; a master data governance (MDG) module for validating the converted catalog and providing any errors back to the seller; and a global data repository (GDR) for storing the validated catalog and maintaining the data integrity of the stored catalog.

[0281] The system and method may include a search platform for indexing and retrieving stored catalogs. The user interface is a single-pane glass user interface (SPoG UI). A dynamic SKU creation module is used to generate SKUs in the ERP system of catalog items, where the catalog items represent one or more non-trade products, and where generating the SKUs allows one or more products to be added to a customer shopping cart. The GDR is integrated with the RTDM module to support real-time data synchronization across systems. The MDG module generates indications to represent one or more errors that occur during a catalog transformation process associated with one or more transformed catalogs. A global pricing engine (GPE) is used to determine the real-time pricing of catalog items. The GPE determines the price of a product based on one or more of the following: a seller price file, market trends, and historical pricing data. Implementations may include one or more of the following features. A method in which the interaction elements include links to various business platforms. A method in which the user interactions include actions that may include one or more clicks, hovers, and methods of entering data. A method in which the data collected is analyzed using advanced statistical algorithms. A method in which personalized content is generated based on a recommendation algorithm. The method may include updating the SPoG UI based on user feedback and data analysis results. Implementations of the described technology may include hardware, methods or processes, or computer tangible media.

[0282] It should be understood that the detailed description section, rather than the summary and abstract sections, is intended to be used to interpret the claims. The summary and abstract sections may set forth one or more but not all exemplary embodiments of the invention as contemplated by the inventor, and are therefore not intended to limit the invention and the appended claims in any way.

[0283] The present invention has been described above by means of functional building blocks of embodiments that specify functions and their relationships. For ease of description, the boundaries of these functional building blocks are arbitrarily defined herein. Alternative boundaries may be defined as long as the specified functions and their relationships are appropriately performed.

[0284] The foregoing description of specific embodiments will so fully reveal the general nature of the invention that others can, without departing from the general concept of the invention, readily modify and / or adapt it for various applications such as specific embodiments, using the knowledge of those skilled in the art, without undue experimentation. Therefore, such adaptations and modifications are intended to be within the meaning and scope of the equivalent embodiments of the disclosed embodiments, based on the teachings and guidance presented herein. It should be understood that the language or terminology herein is for the purpose of description and not of limitation, such that the terminology or phraseology of this specification is to be interpreted by those skilled in the art in light of the teachings and guidance.

[0285] The breadth and scope of the present invention should not be limited by any of the above exemplary embodiments, but should be defined only in accordance with the appended claims and their equivalents.

Claims

1. A system for automating SKU management, the system comprising: a user interface configured to receive from a user catalog files in two or more different formats; a catalog conversion module configured to convert the catalog file into a standard format to predict a classification and attribute mapping of at least one catalog item, wherein the catalog conversion module proposes the classification and attribute mapping using iterative learning; a real-time data mesh (RTDM) module for ensuring real-time data exchange and synchronization across system components, wherein the RTDM module facilitates real-time interaction between users and the system; A Master Data Governance (MDG) module to validate the converted catalog and provide feedback to the vendor on any errors; The Global Data Repository (GDR), which stores the validated catalogs and maintains the data integrity of the stored catalogs; Search platform for indexing and retrieving stored catalogs.

2. The system according to claim 1, wherein: The user interface is a single pane of glass user interface (SPoG UI).

3. The system according to claim 1, wherein: The system also includes a dynamic SKU creation module for generating a SKU in the ERP system for the catalog item, wherein the catalog item represents one or more non-transactional products, and wherein generating the SKU allows the one or more products to be added to a customer shopping cart.

4. The system according to claim 1, wherein: The GDR is integrated with the RTDM module to support real-time data synchronization across the system.

5. The system according to claim 1, wherein: The MDG Module generates an indication to indicate one or more errors that occurred during a catalog conversion process associated with one or more converted catalogs.

6. The system according to claim 1, wherein: The system also includes a global pricing engine (GPE) for determining real-time pricing for the catalog items.

7. The system according to claim 6, wherein: The GPE determines the price of a product based on one or more of: vendor price files, market trends, and historical pricing data.

8. A computerized method for automating SKU management, the method comprising: receiving, via a user interface, from a user, catalog files in two or more different formats; converting the catalog file into a standard format using a catalog conversion module to predict a classification and attribute mapping of at least one catalog item using iterative learning; Use the Real-Time Data Grid (RTDM) module to ensure real-time data exchange and synchronization across system components and facilitate real-time interaction between users and the system; Validate the converted catalog using the Master Data Governance (MDG) module and report any errors to the vendor; Use the Global Data Repository (GDR) to store validated catalogs and maintain data integrity of the stored catalogs; Use the search platform to index and retrieve the stored catalog; generating a SKU in the ERP system for the catalog item using a dynamic SKU creation module, wherein the catalog item represents one or more non-transactional products, and adding the one or more products to a customer shopping cart; Real-time pricing for the catalog items is determined using a global pricing engine (GPE), wherein the prices are determined based on one or more of: vendor price files, market trends, and historical pricing data.

9. The computerized method of claim 8, wherein: The user interface is a single pane of glass user interface (SPoG UI).

10. The computerized method of claim 8, wherein: The method also includes generating a SKU in the ERP system for the catalog item, wherein the catalog item represents one or more non-transactional products, and wherein generating the SKU allows the one or more products to be added to a customer shopping cart.

11. The computerized method of claim 8, wherein: The global data repository (GDR) is integrated with the real-time data grid (RTDM) module to support real-time data synchronization across the system.

12. The computerized method of claim 8, wherein: The master data governance (MDG) module generates an indication to represent one or more errors that occurred during a catalog conversion process associated with one or more converted catalogs.

13. The computerized method of claim 8, wherein: The method also includes determining real-time pricing for the catalog item using a global pricing engine (GPE).

14. The computerized method of claim 13, wherein: The GPE determines the price of a product based on one or more of: vendor price files, market trends, and historical pricing data.

15. A non-transitory computer readable medium (CRM) having stored thereon a plurality of instructions which, when executed by a processor, cause the processor to: receiving, via a user interface, from a user, catalog files in two or more different formats; converting the catalog file into a standard format using a catalog conversion module to predict a classification and attribute mapping of at least one catalog item using iterative learning; Use the Real-Time Data Grid (RTDM) module to ensure real-time data exchange and synchronization across system components and facilitate real-time interaction between users and the system; Validate the converted catalog using the Master Data Governance (MDG) module and report any errors to the vendor; Use the Global Data Repository (GDR) to store validated catalogs and maintain data integrity of the stored catalogs; Use the search platform to index and retrieve the stored catalog; generating a SKU in the ERP system for the catalog item using a dynamic SKU creation module, wherein the catalog item represents one or more non-transactional products, and adding the one or more products to a customer shopping cart; Real-time pricing for the catalog items is determined using a global pricing engine (GPE), wherein the prices are determined based on one or more of: vendor price files, market trends, and historical pricing data.

16. The CRM of claim 15, wherein: The user interface is a single pane of glass user interface (SPoGUI).

17. The CRM of claim 15, wherein: Also included is generating a SKU in the ERP system for the catalog item, wherein the catalog item represents one or more non-transactional products, and wherein generating the SKU allows the one or more products to be added to a customer shopping cart.

18. The CRM of claim 15, wherein: The global data repository (GDR) is integrated with the real-time data grid (RTDM) module to support real-time data synchronization across the system.

19. The CRM of claim 15, wherein: The master data governance (MDG) module generates an indication to represent one or more errors that occurred during a catalog conversion process associated with one or more converted catalogs.

20. The CRM of claim 15, wherein: Also included is determining real-time pricing for the catalog items using a global pricing engine (GPE), and wherein the GPE determines prices for products based on one or more of: vendor price files, market trends, and historical pricing data.

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