System and method for distribution management including special pricing determination

Abstract

Systems and methods for automated SKU management are provided. Embodiments include a user interface for receiving different directory files, a directory translation module, a real-time data grid (RTDM) module, a master data management (MDG) module, a global data repository (GDR), and a search platform. The directory conversion module converts the 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 validated directory. Embodiments may include a dynamic SKU creation module for real-time pricing and a global pricing engine. Embodiments improve data accuracy and SKU management, thereby facilitating integrated order processing and fulfillment.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims the benefit of U.S. Provisional Patent Application No. 63 / 513,073, filed on July 11, 2023, and U.S. Provisional Patent Application No. 63 / 515,075, filed on July 21, 2023. Each application is incorporated herein in its entirety. Background Art

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

[0004] In various industries such as retail, e-commerce and supply chain management, the accurate determination and application of special pricing plays a key role in ensuring competitiveness, optimizing sales and satisfying customer needs. Special pricing refers to customized pricing strategies applied to specific scenarios, such as seasonal promotions, volume discounts, customer-specific agreements or targeted marketing campaigns. These pricing strategies are usually based on a combination of factors, including manufacturer details, product category, solution type, historical purchasing patterns and other relevant variables.

[0005] Conventionally, the special pricing determination and application process has been labor-intensive, time-consuming, and prone to errors. Manual intervention and disparate data sources often hinder the ability to make near real-time pricing decisions, resulting in delays and inefficiencies. Traditional approaches to special pricing determination and application in traditional distribution platforms have been plagued by a host of challenges, which have significantly hampered the efficiency of pricing analysts and sales teams. One of the most prominent issues is the lack of a synchronized real-time solution, which severely hinders the ability to make timely pricing decisions. As a result, manual calculation and verification of pricing for predefined rules becomes a laborious and time-consuming task.

[0006] The lack of a synchronized, real-time solution forces pricing analysts and sales teams to rely on disparate data sources and outdated information. This in turn introduces inaccuracies into the pricing process, as the data used to determine special pricing may not reflect the latest changes in product availability, market conditions, or promotions. Delays in accessing and updating this information result in missed opportunities for competitive pricing, leading to potential losses in sales and market share.

[0007] Additionally, the lack of real-time capabilities exacerbates the complexities associated with managing seasonal promotions and time-sensitive marketing campaigns. Pricing analysts must resort to manual methods to implement such promotions, often resulting in missed time frames and an inability to quickly adapt to changing market dynamics. As a result, sales teams may struggle to meet customer demand during peak periods, leading to dissatisfied customers and lost revenue opportunities.

[0008] The labor-intensive and manual nature of special pricing determination and application also makes the entire process highly error-prone. Pricing analysts and sales teams working to handle a large number of products and pricing scenarios are more prone to errors. These errors can be in the form of miscalculations in pricing, incorrect application of pricing rules, or ignoring the scope of a specific customer agreement, ultimately leading to billing discrepancies and customer complaints.

[0009] Inefficiencies resulting from the lack of a synchronized, real-time solution lead to inconsistencies in pricing across different channels and customer segments. Pricing strategies that should be applied consistently may change unintentionally due to delays in price updates, causing confusion among customers and damaging the brand's reputation for fair and transparent pricing.

[0010] Additionally, the manual nature of the pricing determination process presents substantial challenges in scaling pricing operations to handle an increasing number of products, customers, and market demands. As businesses scale, pricing analysts face increasing workloads, making it increasingly difficult to maintain the same level of accuracy and responsiveness in pricing decisions. As a result, the lack of scalability can hamper a company's growth potential and hinder its ability to compete effectively in the marketplace.

[0011] Additionally, the disjointed approach to special pricing determination and application causes significant delays throughout the sales process. Pricing analysts must spend considerable time manually processing each pricing request, verifying compliance with various rules, and then applying the determined pricing to individual or multiple orders. These delays can result in frustrated customers, missed sales opportunities, and a negative impact on customer loyalty.

[0012] These challenges result in impairment to the overall efficiency of the distribution platform, undermining the company's ability to quickly respond to market fluctuations, adapt to customer demands, and maintain a competitive edge. In an increasingly dynamic business landscape, the lack of a synchronized, real-time solution for special pricing determination and application is proving to be a significant impediment to success, and innovative, automated, and efficient methods are urgently needed to effectively address these issues. The present invention seeks to provide a robust solution that overcomes these limitations, providing a streamlined, near-real-time special pricing determination system that is integrated with the order processing workflow, paving the way for improved competitiveness, accuracy, and customer satisfaction.

[0013] Efforts have been made in the prior art to automate the special pricing calculation and application process. However, existing systems and methods fall far short in terms of scalability, accuracy, and integration with order processing workflows. An improved system and method is needed that can fully leverage intelligent algorithms, caching mechanisms, data exchange, and integration with order processing systems to automate near real-time special pricing calculation and application.

[0014] Therefore, there is a need for an innovative system and method that overcomes the limitations of existing solutions and provides an automated, accurate and efficient method for special pricing calculation and application. The present invention addresses these needs and provides a robust system architecture and computerized method for streamlined near real-time special pricing determination and its integration into the order processing workflow. Summary of the invention

[0015] The global distribution industry is at a critical juncture, grappling with a host of challenges across multiple sectors. These barriers, both historical and emerging, require the creation of innovative and effective solutions to shift the industry toward growth and efficiency. Among these numerous barriers, the most significant are in the areas of supply chain management, inventory and compliance issues, SKU management, the shift to a direct-to-consumer model, and rapidly evolving consumer expectations and behaviors.

[0016] The first key challenge involves the management of the supply chain, a central part of any distributor's operations, but which is typically not within the distributor's core capabilities. This gap creates inefficiencies in the system and exacerbates difficulties in managing disruptions, which in turn directly impacts the distributor's ability to deliver products and services effectively and in a timely manner. To add to these challenges, market trends are favoring a more direct-to-consumer model. Traditional distribution methods involving many middlemen are gradually being replaced. This evolving market dynamic requires a significant re-evaluation and repositioning of existing business models and strategies to ensure consistency with this new market reality.

[0017] An issue in the distribution space is inventory management. Given the fickle nature of market demands and trends, companies must ensure that they maintain a flexible supply chain without having to hold positions in inventory. This makes the task of promising and delivering goods to customers substantially more complex and challenging. Furthermore, the sheer necessity of navigating through the myriad compliance regulations used to transport goods and services across international borders adds an additional layer of complexity to the distribution process. This not only makes the distribution process more complex and challenging, but also imposes additional layers of vigilance and paperwork to maintain compliance.

[0018] To further address these challenges, issues surrounding product localization, changing distribution rights, and managing global SKUs also need to be addressed. The process of coordinating data from different OEMs adds complexity, each with its own unique systems and processes. In addition, addressing localization requirements that comply with laws and regulations in different jurisdictions increases the potential for inefficiencies and errors.

[0019] Finally, the landscape of consumer behavior and expectations is changing rapidly. The shift to ecosystem commerce requires the creation of user-friendly, efficient, and configurable platforms for purchasing technology. Traditional methods of customer interaction are quickly falling out of favor, forcing companies to evolve and cater to these new customer expectations.

[0020] Despite these challenges, the distribution model has many advantages over the direct-to-consumer model. First, it enables manufacturers to focus on their core capabilities, leaving the complexity of logistics and distribution to specialized entities. Second, distribution networks typically have a wide reach, allowing products to be available to customers in remote areas that may not be feasible for manufacturers to reach directly. Third, distributors often provide value-added services that enhance the overall customer experience, such as after-sales support, installation, and training.

[0021] However, in order for these benefits to materialize and for the distribution model to remain relevant and effective, it must evolve and adapt to emerging challenges. Current pain points need to be addressed and processes should be made more efficient and streamlined to ensure the sustainability of the distribution model in an evolving market landscape.

[0022] Single glass pane

[0023] The Single Pane of Glass (SPoG) disclosed herein can provide a comprehensive solution aimed at addressing these challenges. It can be configured to provide a holistic, user-friendly and efficient platform that streamlines the distribution process.

[0024] According to some embodiments, SPoG can be configured to address supply chain management by enhancing visibility and control over the supply chain process. Through real-time tracking and analysis, SPoG can provide valuable insights into inventory levels and goods status, thereby ensuring that the process of supply chain management is handled efficiently.

[0025] According to some embodiments, SPoG can integrate multiple communication channels (i.e., touch points) into a single platform to emulate consumer-facing channels into the distribution platform. This integration provides a unified direct channel for consumers to interact with distributors, significantly reducing the complexity of the supply chain and enhancing the overall customer experience.

[0026] SPoG provides innovative solutions for improved inventory management through advanced forecasting capabilities. These predictive analytics can highlight demand trends, thereby guiding companies to manage their inventory more effectively and mitigate the risk of out-of-stock or overstocking.

[0027] According to some embodiments, SPoG may include a global compliance database. Through real-time updates, the database enables distributors to stay in sync with the latest international laws and regulations. This feature significantly reduces the burden of manual tracking, thereby ensuring smooth and compliant cross-border transactions.

[0028] According to some embodiments, in order to streamline SKU management and product localization, SPoG integrates data from various OEMs into a single platform. This not only ensures data consistency, but also significantly reduces the possibility of errors. In addition, it provides the ability to efficiently manage and distribute localized SKUs in line with specific market needs and requirements.

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

[0030] In addition, SPoG's advanced analytical capabilities provide extremely useful insights that can drive strategy and decision making. It can track and analyze trends in real time, allowing companies to stay ahead of the curve and adapt to changing market conditions.

[0031] The flexibility and scalability of SPoG makes it a future-proof solution. It can adapt to changing business needs, allowing companies to expand or contract their operations as needed without significant infrastructure changes.

[0032] SPoG's innovative approach to solving challenges in the distribution industry makes it an extremely useful tool. By enhancing supply chain visibility, streamlining inventory management, ensuring compliance, simplifying SKU management and providing an excellent customer experience, it provides a comprehensive solution to the complex problems that have long plagued the distribution sector. Through its implementation, distributors can expect increased efficiency, reduced errors and improved customer satisfaction, thereby achieving continued growth in the evolving global market.

[0033] Real-Time Data Grid (RTDM)

[0034] According to some embodiments, the platform may include the implementation of a real-time data grid (RTDM). RTDM provides an innovative solution for addressing these challenges. RTDM can provide a distributed data architecture to enable real-time data availability across multiple sources and touchpoints. This aspect enhances supply chain visibility, allowing efficient management and enabling distributors to handle disruptions more effectively.

[0035] RTDM enables the generation of predictive analytics, a capability that provides solutions for efficient inventory control. By providing insights into demand trends, it helps companies manage inventory, thereby reducing the risk of overstocking or stockouts.

[0036] RTDM can also facilitate global compliance, providing real-time updated compliance data to ensure distributors are on top of international regulations. This significantly reduces the manual tracking burden, enabling integrated cross-border transactions.

[0037] RTDM also simplifies SKU management and localization by integrating data from various OEMs, ensuring data consistency and reducing the possibility of errors. Its ability to manage and distribute localized SKUs efficiently meets specific market needs.

[0038] RTDM enhances the customer experience for all stakeholders with its consolidated and intuitive interface, allowing easy access and transaction of technology, meeting the expectations of a consumer-driven generation of technology partners.

[0039] Advantages of SPoG and RTDM integration

[0040] Integrating the SPoG UI platform with RTDM enables a unified, holistic approach to the technical challenges encountered in the distribution platform. By integrating the capabilities of RTDM, SPoG can enhance supply chain visibility, streamline inventory management, ensure compliance, simplify SKU management, and provide an excellent customer experience. The real-time tracking and analysis provided by RTDM improves SPoG's ability to efficiently manage the supply chain and inventory. It provides accurate and up-to-date information, enabling distributors to make informed decisions quickly. Integrating SPoG with RTDM also ensures data consistency and reduces errors and delays in SKU management and pricing. By providing a centralized platform for managing data from various OEMs, it simplifies product localization and helps align with market needs.

[0041] Special pricing determination

[0042] The present invention provides an innovative system and method for automatic near real-time special pricing calculation and application, which solves the limitations of existing solutions. The system fully utilizes intelligent algorithms, caching mechanisms, data exchange, and integration with order processing workflow to simplify the pricing determination process, minimize manual efforts, and reduce potential errors.

[0043] In the prior art, the special pricing determination and application process has been labor-intensive, time-consuming, and prone to errors. Manual intervention and disparate data sources often hinder the ability to make timely pricing decisions, resulting in delays and inefficiencies. Existing automated systems fail to provide the necessary scalability, accuracy, and integration with order processing workflows.

[0044] The present invention overcomes these limitations by introducing a synchronous system architecture that includes a special pricing algorithm, a cache, an integration module, and a pricing validation and maintenance module. These components work together to ensure synchronous and accurate pricing determination, validate pricing procedures against predefined rules, and automate the creation and application of near real-time special pricing.

[0045] The system also integrates and synchronizes order processing workflows through APIs, facilitating the accurate and timely application of special pricing for single or multiple orders. In addition, XML and EDI integration enables data exchange between the different systems involved in the order fulfillment process, ensuring accurate and industry-standard data transfer.

[0046] The invention revolutionizes the near real-time special pricing calculation and application process, reduces manual effort, minimizes errors, and improves overall sales effectiveness and customer satisfaction. By automating pricing determination, the system enables faster response to market conditions and customer requirements, improves competitiveness and optimizes sales.

[0047] In summary, the present invention provides a comprehensive solution that addresses the shortcomings of existing systems and methods, thereby providing a streamlined, accurate and efficient method for near real-time special pricing calculation and application. The system and method of the present invention enable enterprises in various industries to make data-driven pricing decisions, improve operational efficiency, and enhance customer experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 One embodiment of an operating environment for a distribution platform, referred to in this embodiment as a system, is shown.

[0049] Figure 2 Shows the establishment of Figure 1 An embodiment of an operating environment of a distribution platform on which the elements are introduced.

[0050] Figure 3 One embodiment of a system for supply chain management is shown.

[0051] Figure 4 Embodiments of an advanced distribution platform including a system for managing a complex distribution network are depicted, which may be embodiments of a system and provide a technical distribution platform for optimizing the management and operation of a distribution network.

[0052] Figure 5 An RTDM module according to an embodiment is shown.

[0053] Figure 6 An SPoGUI according to an embodiment is shown.

[0054] Figure 7 is a flow chart of a method for performing integrated supply chain management operations using SPoGUI according to some embodiments of the present invention.

[0055] Figure 8 is a flowchart of a method for performing vendor login using SPoGUI according to some embodiments of the present invention.

[0056] Fig. 9 is a flowchart of a method for dealer login using SPoGUI according to some embodiments of the present invention.

[0057] Fig.10 is a flow chart of a method for customer and end-customer login using SPoGUI according to some embodiments of the present invention.

[0058] Fig.11 An embodiment of a system for determining special pricing in a distribution network is depicted in accordance with some embodiments.

[0059] Fig.12 is a flow chart of a method for determining special pricing in a distribution network according to some embodiments of the present invention.

[0060] Fig.13 is a schematic diagram of a system for determining special pricing in a distribution network, according to some embodiments.

[0061] Fig.14 is a block diagram of example components of a device according to some embodiments of the invention.

[0062] FIG. 15A to FIG. 15Q Depicted are various screens and functions of the SPoGUI according to some embodiments. DETAILED DESCRIPTION

[0063] 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 may be read and executed by one or more processors. A 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, a machine-readable medium may include a read-only memory (ROM); a random access memory (RAM); a disk storage medium; an optical storage medium; a flash memory device, etc. In addition, 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, a processor, a controller, or other devices that execute firmware, software, routines, instructions, etc.

[0064] It should be understood that the operations shown in the exemplary method are not exhaustive, and other operations may be performed before, after, or between any of the operations shown. In some embodiments of the present disclosure, the operations may be performed in a different order and / or varied.

[0065] Figure 1An operating environment 100 of a distribution platform, referred to in this embodiment as system 110, 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, vendors 140, distributors 150, and other entities involved in the distribution process. The operating environment includes a wide range of characteristics and dynamics that contribute to the success and efficiency of the distribution platform.

[0066] Customers 120 within the operating environment of the system 110 represent businesses 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 equipment, or cloud-based services. The system 110 provides a user-friendly interface to customers, allowing them to browse, search, and select the most appropriate IT solution based on their needs. Customers can also access real-time data and analysis through the system 110, empowering them to make informed decisions and optimize their IT infrastructure.

[0067] End customers 130 are the ultimate beneficiaries of IT solutions provided by system 110. They can include businesses or individuals who utilize IT products and services to improve their operations, productivity, or daily activities. End customers rely on system 110 to access a wide range of IT solutions, 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, thereby enhancing their overall experience.

[0068] Vendors 140 play a key role in the operating environment of system 110. These vendors include manufacturers, distributors, and suppliers that provide a variety of IT products and services. System 110 acts as a centralized platform for vendors to showcase their products, manage inventory, and facilitate transactions with customers and dealers. Vendors can fully utilize 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, vendors can expand their reach, access new markets, and enhance their overall visibility and competitiveness.

[0069] Resellers 150 are intermediaries within the distribution model that bridge the gap between sellers and customers. They play an important role in the IT distribution ecosystem by connecting customers with the right IT solutions from a variety of sellers. Resellers may include retailers, value-added resellers (VARs), system integrators, or managed service providers. System 110 enables resellers to access a comprehensive catalog of IT solutions, manage their sales channels, and provide value-added services to customers. By fully leveraging system 110, resellers can enhance their customer relationships, optimize their product offerings, and increase their revenue streams.

[0070] Within the operating environment of the 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. The system 110 ensures that relevant data is exchanged between stakeholders in real time, thereby enabling accurate decisions 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, thereby eliminating data silos and enabling end-to-end visibility.

[0071] Scalability and flexibility are key features of the system 110. It can adapt to the growing demands of the IT distribution model, whether it involves an expanded customer base, an increasing number of sellers, or a wider range of IT products and services. The 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. In addition, the system 110 makes full use of a technology stack including .NET, Java, and other suitable technologies, providing a robust foundation for its operation.

[0072] In summary, the operating environment of system 110 within the IT distribution model includes customers 120, end customers 130, vendors 140, distributors 150, and other entities involved in the distribution process. System 110 serves as a centralized platform that facilitates 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 customer experience, and drive business success within the IT distribution ecosystem.

[0073] Figure 2 Shows the establishment of Figure 1 2. An embodiment of an operating environment 200 of a distribution platform based on the elements introduced in FIG. Within the operating environment, integration points 210 facilitate integrated data flows and connectivity between various customer systems 220, associated systems 230, vendor systems 240, dealer systems 250, and other entities involved in the distribution process. The diagram illustrates the interconnectivity and mechanisms for enabling efficient collaboration and data-driven decision making.

[0074] The operating environment 200 may include a system 110 as a distribution platform that serves as a central hub for managing and facilitating the distribution process. The system 110 may be configured to perform functions and operations as a bridge between customer systems 220, vendor systems 240, dealer systems 250, and other entities within the ecosystem. It may integrate communications, data exchange, and transaction processing to provide a unified and streamlined experience for stakeholders. In addition, the operating environment 200 may include one or more integration points 210 to ensure smooth data flow and connectivity. These integration points include:

[0075] Customer system integration: Integration point 210 may enable system 110 to connect with customer system 220, thereby achieving efficient data exchange and synchronization. Customer system 220 may include various entities, such as customer system 221, customer system 222, and customer system 223. Integration with customer system 220 grants customers access to real-time inventory information, pricing details, order tracking, and other relevant data, thereby enhancing their visibility and decision-making capabilities.

[0076] Related system integration: Integration point 210 may enable system 110 to connect with related system 230, thereby achieving efficient data exchange and synchronization. Related system 230 may include various entities such as related system 231, related system 233, and related system 233. Integration with related system 220 authorizes customers to access real-time inventory information, pricing details, order tracking, and other relevant data, thereby enhancing their visibility and decision-making capabilities.

[0077] Vendor System Integration: Integration point 210 facilitates an integrated connection between system 110 and vendor system 240. Vendor system 240 may include entities such as vendor system 241, vendor system 242, and vendor system 243, thereby representing the inventory management system, pricing system, and product catalog employed by the vendor. Integration with vendor system 240 ensures that vendors can efficiently update their product offerings, manage pricing and promotions, and receive real-time order notifications and fulfillment details.

[0078] Dealer system integration: Integration point 210 provides dealer system 250 with the ability to connect to system 110. Dealer system 250 may include entities such as dealer system 251, dealer system 252, and dealer system 253, thereby representing sales systems, customer management systems, and service delivery platforms employed by dealers. Integration with dealer system 250 allows dealers to access the latest product information, manage customer accounts, track sales performance, and provide value-added services to their customers.

[0079] 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, thereby promoting a collaborative and efficient distribution process.

[0080] Integration point 210 also enables connectivity with system of record 280 for additional data management and integration. Representation system of record 280 may represent an enterprise resource planning (ERP) system or a customer relationship management (CRM) system, including future systems as well as legacy ERP systems such as SAP, Impulse, META, I-SCALA, etc. The system of record may include one or more repositories of critical and legacy business data. It facilitates data exchange and synchronization integration between the distribution platform, system 110, and ERP, thereby enabling real-time updates and ensuring the availability of accurate and up-to-date information. Integration point 210 establishes connectivity between system of record 280 and the distribution platform, allowing stakeholders to fully leverage the rich data stored in ERP for efficient collaboration, data-driven decision making, and streamlined distribution processes. These systems represent internal systems used by customers, vendors, etc.

[0081] Integration points 210 within the operating environment 200 are facilitated through standardized protocols, APIs, and data connectors. These mechanisms ensure compatibility, interoperability, and secure data transfer between the distribution platform and connected systems. The system 110 employs industry standard protocols such as RESTful APIs, SOAP, or GraphQL to establish communication channels and enable integrated data exchange.

[0082] 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 used to authenticate users, authorize data access, and maintain the integrity and confidentiality of exchanged information.

[0083] In some embodiments, integration points 210 and data flows within 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 between customer systems 220, vendor systems 240, dealer systems 250, and other entities. This data exchange facilitates real-time visibility, enables data-driven decision making, and improves operational efficiency across the distribution platform.

[0084] In some embodiments, the system 110 makes full use of advanced technologies such as Typescript, NodeJS, ReactJS, NETcore, C#, and other suitable technologies that support integration points 210 and enable integrated communications within the operating environment 200. These technologies provide a robust foundation for the system 110, ensuring scalability, flexibility, and efficient data processing capabilities. In addition, the integration points 210 can also use algorithms, data analysis, and machine learning techniques to derive valuable insights, optimize distribution processes, and personalize customer experiences. 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 between different entities, systems, and components. The integrated data is processed, coordinated, and made available to relevant stakeholders in real time through the 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 customer experience.

[0085] Figure 2 The several elements in the operating environment depicted in the figure may include conventional, well-known elements that are only briefly explained here. For example, each client system (such as client system 220) may include a desktop personal computer, a workstation, a laptop computer, a PDA, a cellular phone, or any device that supports wireless access protocol (WAP) enablement, or any other computing device that can directly or indirectly connect to the Internet or other network. Each client system can typically run an HTTP client, such as Microsoft's Edge browser, Google's Chrome browser, Opera's browser, or a WAP-enabled browser for mobile devices, thereby allowing the client system to access, process, and view information, pages, and applications that can be obtained from the distribution platform through the network.

[0086] In addition, each client system may typically be equipped with a user interface device, such as a keyboard, mouse, trackball, touchpad, touch screen, pen, or similar device, for interacting with a graphical user interface (GUI) provided by the browser. These user interface devices enable users of the client systems to navigate the GUI, interact with pages, forms, and applications, and access data and applications hosted by the distribution platform.

[0087] The client system and its components may be operator-configurable using an application (including a web browser) running on a central processing unit such as an Intel Pentium processor or similar processor. Similarly, the distribution platform (system 110) and its components may be operator-configurable using an application running on a central processing unit (such as a processor system) that may include an Intel Pentium processor or similar processor and / or multiple processor units.

[0088] Computer program product embodiments include machine-readable storage media containing instructions for programming a computer to perform the processes described herein. Computer code for operating and configuring the distribution platform and customer systems, vendor 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, micro drive, magneto-optical disk, magnetic or optical card, nano system, or any suitable medium for storing instructions and data.

[0089] In addition, computer code for implementing the embodiments may 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 may also be transmitted over an extranet, VPN, LAN or other network and executed on a client system, server or server system using programming languages ​​such as C, C++, HTML, Java, JavaScript, ActiveX, VBScript, etc.

[0090] It should be appreciated that embodiments may be implemented in a variety of programming languages ​​executed on a client system, server, or server system, and that the choice of language may depend on the specific requirements and environment of the distribution platform.

[0091] Thus, operating environment 200 may couple the distribution platform with one or more integration points 210 and data flows to enable efficient collaboration and a streamlined distribution process.

[0092] Figure 3 A system 300 for supply chain management is shown. System 300 is a supply chain management solution designed to address the challenges faced by a decentralized supply chain ecosystem in the global distribution industry. System 300 may include several interconnected components and modules that work in concert to optimize supply chain operations, enhance collaboration, and drive business efficiency.

[0093] In some embodiments, the SPoG UI 305 serves as a centralized user interface, providing stakeholders with a unified view of the entire supply chain. It merges information from various sources and presents real-time data, analysis, and functionality customized for the user's specific role and responsibilities. By providing a customizable and intuitive dashboard-style 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.

[0094] For example, logistics managers can use SPoG UI 305 to monitor the status of goods, track delivery routes, and view real-time inventory levels across multiple warehouses. They can visualize data through interactive charts and graphs, such as a map showing the current location of each shipped shipment or a bar chart showing inventory levels by product category. By having a unified view of the supply chain, logistics managers can identify bottlenecks, optimize routes, and ensure timely delivery of goods.

[0095] In some embodiments, SPoG UI 305 is integrated with other modules of system 300 to facilitate real-time data exchange, synchronized operations, and streamlined workflow. Through API integration, data synchronization mechanisms, and event-driven architecture, SPoG UI 305 ensures smooth information flow and enables collaborative decision-making across the supply chain ecosystem.

[0096] For example, when a purchase order is generated in SPoG UI 305, system 300 automatically updates inventory levels, triggers 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.

[0097] In some embodiments, the real-time data grid (RTDM) module 310 can be configured to provide integrated data flow within the supply chain ecosystem. It aggregates data from multiple sources, coordinates it, and ensures its availability in real time.

[0098] In one non-limiting example, the RTDM module 310 can collect data from the system of record 280, which can represent a variety of systems, including different inventory management systems, point-of-sale terminals, and customer relationship management systems. It reconciles this data by aligning formats, standardizing units of measurement, and reconciling any differences. The unified data is then made available in real time, allowing stakeholders across the supply chain to access accurate and up-to-date information.

[0099] In some embodiments, the RTDM module 310 can be configured to capture changes in data across multiple transaction systems in real time. It uses an advanced change data capture (CDC) mechanism that constantly 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 may include future and traditional ERP systems, customer relationship management (CRM) systems, and other enterprise-wide systems, thereby ensuring compatibility and flexibility of businesses operating in a variety of environments.

[0100] By providing continuous access to real-time data, stakeholders can make timely decisions and respond quickly to changing market conditions. For example, if the RTDM module 310 detects a sudden surge in demand for a particular product, it can trigger an alert to the production team, enabling them to adjust the manufacturing schedule and prevent stock-outs.

[0101] 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 limitations imposed by traditional ERP systems. This enhanced flexibility supports improved efficiency, customer service, and innovation.

[0102] The system 300 may also include an advanced analytics and machine learning (AAML) module 315. The AAML module 315 may take advantage of 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.

[0103] For example, the AAML module 315 can analyze historical sales data to identify seasonal patterns and predict future demand. It can generate forecasts that help optimize inventory levels, ensure inventory availability during peak seasons, and minimize excess inventory costs. By leveraging machine learning algorithms, the AAML module 315 automates repetitive tasks, predicts customer preferences, and optimizes supply chain processes.

[0104] In addition to demand forecasting, the AAML module 315 can provide insights into customer behavior to enable targeted marketing campaigns and personalized customer experiences. For example, by analyzing customer data, the module can identify cross-sell or up-sell opportunities and recommend relevant products to individual customers.

[0105] In addition, 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 to make informed product development decisions, identify emerging market trends, and adjust business strategies to meet evolving customer expectations.

[0106] 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, 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, thereby creating a holistic and connected supply chain ecosystem.

[0107] The implementation and deployment of system 300 can be customized to meet specific business needs. In some non-limiting examples, it can be deployed using containerization technology (such as ) and orchestration frameworks (such as ) cloud-native solution. This approach ensures scalability, easy 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.

[0108] The system 300 for supply chain management is a comprehensive and innovative solution that addresses the challenges faced by the fragmented supply chain ecosystem. It combines the functionality of the SPoG UI 305, the RTDM module 310, and the AAML module 315 and 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 success in supply chain management.

[0109] Figure 4An embodiment of an advanced distribution platform including a system 400 for managing complex distribution networks is depicted, which may be an embodiment of the system 300 and provides a technical distribution platform for optimizing the management and operation of distribution networks. The system 400 includes several interconnected modules, each having a specific function and contributing to the overall efficiency of supply chain operations. In some embodiments, these modules may include an SPoG UI 405, a customer interaction module (CIM) 410, an RTDM module 415, an AI module 420, an interface display module 425, a personalized interaction module 430, a document hub 435, a catalog management module 440, a performance and insight tag display 445, a predictive analysis module 450, a recommendation system module 455, a notification module 460, a self-login module 465, and a communication module 470.

[0110] System 400, as an embodiment of system 300, utilizes a series of technologies and algorithms to integrate and merge 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.

[0111] In some embodiments, SPoG UI 405 is used as a central interface within the 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.

[0112] In some embodiments, CIM 410 or a customer interaction module uses a Target and The company uses state-of-the-art algorithms and technologies to manage customer relationships within its distribution network. These technologies enable the module to securely process customer data, personalize the customer experience, and provide integrated access controls for stakeholders.

[0113] In some embodiments, the RTDM module 415 or real-time data grid module is a key component of the system 400 that ensures smooth data flow across the supply chain ecosystem. or Pulsar's technologies are used for data ingestion, processing, and stream management. These technologies enable the RTDM module 415 to process real-time data streams, handle large amounts of data, and ensure low-latency data processing. In addition, the module uses a change data capture (CDC) mechanism to capture real-time data updates from various transaction systems (such as traditional ERP systems and CRM systems). This feature allows stakeholders to access the latest and accurate information for informed decision making.

[0114] 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 modules to automate repetitive tasks, predict demand patterns, optimize inventory levels, and improve overall supply chain efficiency. For example, AI module 420 can utilize predictive models to predict demand, allowing stakeholders to optimize inventory management and minimize out-of-stock or over-stock situations.

[0115] 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), thereby enabling efficient data understanding, comparison, and trend analysis.

[0116] 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 be implemented for data analysis, modeling, and delivering targeted recommendations. For example, this module can analyze customer preferences and purchase history to provide personalized product recommendations, enhance customer satisfaction, and boost sales.

[0117] In some embodiments, document hub 435 acts as a centralized repository for storing and managing documents within system 400. In some non-limiting examples, it may utilize and elastic cloud for efficient document management, storage, and retrieval. For example, the document hub 435 can employ SeeBurger's document management capabilities to classify and organize documents based on their type (e.g., contracts, invoices, product specifications, or compliance documents), thereby allowing stakeholders to easily access and retrieve relevant documents when needed.

[0118] 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, the and to integrate and merge catalog updates, content delivery, and caching. For example, the module can leverage Akamai's content delivery network (CDN) to quickly and efficiently deliver catalog information to stakeholders regardless of their geographic location.

[0119] In some embodiments, the performance and insight badge display 445 collects, analyzes, and visualizes real-time performance metrics and insights related to supply chain operations. and Datadog’s tools to enable effective performance monitoring and provide actionable insights. For example, the module can leverage Spunk’s log analysis capabilities to identify performance bottlenecks in the supply chain, enabling stakeholders to take proactive steps to optimize operations.

[0120] In some embodiments, the predictive analysis module 450 uses machine learning algorithms and forecasting models to predict demand patterns, optimize inventory levels, and improve overall supply chain efficiency. Spark and For example, the module can use TensorFlow’s deep learning capabilities to analyze historical sales data and predict future demand, allowing stakeholders to optimize inventory levels and minimize costs.

[0121] 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 is used for data analysis, modeling, and delivering targeted recommendations. For example, this module can leverage Adobe Target’s recommendation engine to analyze customer preferences and behaviors and deliver personalized product recommendations across various channels to enhance customer engagement and boost sales.

[0122] In some embodiments, the notification module 460 enables real-time notifications to be distributed to stakeholders regarding important events, updates, or alerts within the supply chain. In some non-limiting examples, this can be accomplished using X and for message queuing, event-driven architecture, and integrated notification delivery. For example, this module can leverage TIBCO’s messaging infrastructure to send real-time notifications to stakeholders’ devices, ensuring timely and relevant information dissemination.

[0123] In some embodiments, the self-login module 465 facilitates the login process for new stakeholders entering the distribution network. It provides guided steps, tutorials, or documentation to help users become familiar with the system and its functions. In some non-limiting examples, it can utilize a user interface such as and For example, the module can leverage Okta’s identity and access management capabilities to securely onboard new stakeholders, provide them with appropriate access rights, and guide them through the system’s capabilities.

[0124] In some embodiments, the communication module 470 enables integrated and merged communication and collaboration within the system 400. It provides a channel for stakeholders to interactively exchange messages, share documents, and collaborate on projects. In some non-limiting examples, it can utilize Edge and Launch to facilitate secure and efficient communication, document sharing, and version control. For example, this module can leverage the API management capabilities of APIege Edge to ensure secure and reliable communication between stakeholders, enabling them to collaborate effectively.

[0125] Therefore, the system 400 may include various modules that utilize various technologies and algorithms to optimize supply chain management. These modules (including SPoG UI 405, 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 insight tag display 445, predictive analysis 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 experience, and effective performance monitoring, which contributes to the improvement of operational efficiency and the success of supply chain management.

[0126] Real-time data grid

[0127] Figure 5 An RTDM module 500 is shown according to an embodiment. The RTDM module 500 may be an embodiment of the RTDM module 310, which may include interconnected components, processes, and subsystems configured to implement real-time data management and analysis.

[0128] In some embodiments, Figure 5 As shown, the RTDM module 500 represents an effective data grid and change capture component within the overall system architecture. The module is designed to provide real-time data management and coordination capabilities to achieve efficient operations within the supply chain management domain.

[0129] The RTDM module 500 may include an integration layer 510 (also referred to as a "system of record") that integrates with various enterprise systems. These enterprise systems may include ERP, such as Impulse, META, and I-SCALA, among other data sources. The integration layer 510 can handle data exchange and synchronization between the RTDM module 500 and these systems. Data feedback is established to retrieve relevant information from the record system, such as sales orders, purchase orders, inventory data, and customer information. These feedbacks enable real-time data updates and ensure that the RTDM module operates with the latest and accurate data.

[0130] 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. PDS is configured to store specific types of data, such as customer data, product data, financial data, etc. These PDSs are used as repositories for coordinated and standardized data, ensuring data consistency and integrity across systems.

[0131] In some embodiments, the RTDM module 500 implements a data replication mechanism to capture real-time changes in multiple data sources, including ERP (e.g., Impulse, META, I-SCALA) transaction systems. The captured data is then processed and reconciled on the fly, transforming it into a standardized format suitable for analysis and integration. This process ensures that data is readily available and up-to-date within the data grid, facilitating real-time insights and decisions.

[0132] 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 can include a highly scalable and robust data lake, which can be referred to as a data lake 522, and a set of purpose-built data stores (PDS), which can be represented as PDS 524.1 to 524.N. These components work in harmony to ensure efficient data management, coordination, and real-time availability.

[0133] In some embodiments, data layer 520 includes data lake 522, a novel storage and processing infrastructure designed to handle the increasing volume, variety, and velocity of data generated within the supply chain. 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 adapt to data inflows from different sources.

[0134] In association with the data lake 522, a plurality of purposeful data stores, PDS 524.1 to 524.N, may be employed. Each PDS 524 may be used as a dedicated repository optimized for storing and retrieving a specific type of data related to the supply chain domain. In some non-limiting examples, PDS 524.1 may be dedicated to customer data, storing information such as customer profiles, preferences, and transaction history. PDS 524.2 may focus on product data, including details about SKU codes, descriptions, pricing, and inventory levels. These purposeful data stores allow for efficient data retrieval, analysis, and processing, thereby meeting the different needs of supply chain stakeholders.

[0135] To ensure real-time data synchronization, the data layer 520 can be configured to use one or more complex change data capture (CDC) mechanisms. These CDC mechanisms are integrated with transaction systems, such as Impulse, META, and I-SCALA’s traditional ERP, as well as other enterprise-wide systems. CDC continuously monitors these systems for any updates, modifications, or new transactions 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, providing stakeholders with real-time insights into the supply chain ecosystem.

[0136] In some embodiments, the data layer 520 can be implemented to facilitate integration with existing enterprise systems using one or more frameworks such as .NET or Java, thereby ensuring compatibility with various existing systems and providing flexibility for customization and extensibility. For example, the data layer 520 can utilize the Java technology stack, including Spring and The framework facilitates integration with systems of record for groups with multiple ERP systems and other enterprise-wide solutions. This facilitates smooth data exchange, process automation, and end-to-end visibility across the supply chain.

[0137] In some embodiments, to facilitate data processing and analysis, in some non-limiting examples, the data layer 520 may include one or more distributed computing frameworks, such as Spark or Flink. These frameworks enable parallel processing and distributed computing across large-scale data sets stored in data lakes and PDSs. 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 leverage Apache Spark's machine learning library to develop predictive models for demand forecasting, optimize inventory levels, and identify potential supply chain risks.

[0138] 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 data within the data lake and PDS. At rest and in transit, data encryption technology protects sensitive supply chain information from unauthorized access. In addition, the data layer 520 can implement data lineage and audit trail mechanisms, allowing stakeholders to track the origin and history of data, ensuring data integrity and compliance with regulatory requirements.

[0139] In some embodiments, the data layer 520 can utilize containerization technology (such as ) and orchestration frameworks (such as ) in a cloud-native environment. This approach ensures scalability, elasticity, and efficient resource allocation. For example, the data layer 520 can be deployed on a or This allows for efficient scaling of resources based on demand, minimizing operational overhead and providing an elastic infrastructure for managing supply chain data.

[0140] The data layer 520 of the RTDM module 500 can combine a highly scalable data lake (data lake 522) and a dedicated PDS (PDS 524.1 to 524.N), and adopt a complex CDC mechanism, the data layer 520 ensures efficient data management, coordination and real-time availability. Various technology stacks such as .NET or Java and The integration of Spark's distributed computing framework enables powerful data processing, advanced analytics, and machine learning capabilities. Leveraging robust data governance and security measures, Data Layer 520 ensures data integrity, confidentiality, and compliance. Through its scalable infrastructure and efficient integration with existing systems, 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.

[0141] The RTDM module 500 may include an AI module 530 that is configured to implement one or more algorithms and machine learning models to analyze the data stored in the data layer 520 and derive meaningful insights. In some non-limiting examples, the AI ​​module 530 may apply predictive analysis, anomaly detection, and optimization algorithms to identify patterns, trends, and potential risks within the supply chain. The AI ​​module 530 may continuously learn from new data inputs and adapt its models to provide accurate and up-to-date insights. The AI ​​module 530 may generate predictions, recommendations, and alerts, and publish such insights to dedicated data feeds.

[0142] The data engine layer 540 may include a group of interconnected systems responsible for specialized data ingestion, processing, conversion, and integration. Within the RTDM module 500, these systems include a collection of autonomous headless engines 540, 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 / bundling, ITAD (IT asset disposal), renewals, marketing, special pricing, financing, returns / claims, end users, order tracking, hyperlinks, search, vendor management, professional services, and ESG (environment, society, and governance). These headless engines utilize the coordination data stored in the data grid to deliver specific business logic and services. The engine 540 can utilize the coordination data stored in the data grid to deliver specific business logic and services. Each engine 5 is designed to be pluggable, thereby allowing flexibility and future expansion of module capabilities.

[0143] These systems can be configured to ingest 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, making it ready for further analysis and integration.

[0144] Additionally, to facilitate integration and access to the RTDM module 500, a data distribution mechanism 545 may be employed. The data distribution mechanism 545 may be configured to include one or more APIs to facilitate distribution of data from the data grid and engine to various endpoints, including user interfaces, micro frontends, and external systems.

[0145] The experience layer 550 focuses on delivering an intuitive and user-friendly interface for interacting with supply chain data. The experience layer 550 can 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 customized to their preferences and roles, such as inventory changes, pricing updates, or new SKU notifications.

[0146] Therefore, in some embodiments, the RTDM module 500 for supply chain management may include integration with a system of record, and include one or more of a data layer, an AI component, a data engine layer, and a user experience layer with a data grid and purposeful data storage. 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. Therefore, the RTDM module 500 promotes 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 analytical capabilities. The module's ability to replicate and coordinate data from different ERPs while maintaining auditable and repeatable transactions provides significant advantages in achieving a unified view of sellers, dealers, customers, end customers, and other entities in the distribution system including the IT distribution system.

[0147] Single pane UI

[0148] Figure 6 An SPoG UI according to an embodiment is shown, generally indicated as SPoG UI 600. In some embodiments, SPoG UI 600 may be an embodiment of SPoG UI 305, which represents a comprehensive and intuitive user interface designed to provide stakeholders with a unified and customizable view of the entire supply chain ecosystem. It combines a range of features and functions that enable users to gain a comprehensive understanding of the supply chain and effectively manage its operations.

[0149] SPoG 600 can incorporate high-velocity data in data-rich environments. In contemporary data-rich environments, conventional UI designs often struggle by presenting large amounts of information in an understandable, efficient, and visually appealing manner. The challenge is heightened when data is dynamic, changes in real time, and needs to be effectively displayed in a single-pane environment that emphasizes clean, white-space-oriented designs.

[0150] SPoG UI 600 can integrate capabilities with RTDM module 310 / 500 and stakeholders to provide a powerful and user-friendly interface for supply chain management. In some embodiments, SPoG UI 600 may 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 module 310 / 500. Collaborative decision-making module 615 promotes effective communication and collaboration between different groups. RBAC module 620 can be configured to protect access control. Customization module 625, data visualization module 630, and mobile and cross-platform accessibility module 635 can be configured to enhance 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.

[0151] The SPoG UI 600 may include a UV module 605 that provides stakeholders with a centralized and customizable dashboard-style layout. The module allows users to access real-time data, analysis, and functionality 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 a holistic 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, thereby maintaining a visually clean interface without compromising performance. The innovative approach includes unique configuration of UI structure, responsive data visualization, real-time data processing methods, adaptive information architecture, and white space optimization.

[0152] UV module 605 can be constructed around a grid-based layout system using CSS grid and Flexbox technology. This structure provides the flexibility to create fluid layouts with elements that automatically adjust to available space and content. HTML5 and CSS3 are used as the underlying technologies for creating the UI, while JavaScript, specifically React.js, manages the dynamic aspects of the UI.

[0153] The SPoG UI 600 can integrate the UV module 605 with the real-time data exchange module 610 to facilitate the continuous exchange of data between the SPoG UI 600 and the RTDM module 310 to utilize one or more data sources, which may include one or more ERP, CRM 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.

[0154] In some embodiments, the collaborative decision module 615 within the SPoG UI 600 facilitates real-time collaboration and communication between stakeholders. The module enables the exchange of information, the initiation of workflows, and the sharing of insights and recommendations. By integrating with the RTDM module 310 / 500, the collaborative decision module 615 ensures that stakeholders can collaborate effectively based on accurate and synchronized data. This promotes overall operational efficiency and collaboration within the supply chain ecosystem.

[0155] To ensure secure and controlled access to functions and data, SPoG UI 600 includes 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.

[0156] In some embodiments, the customization module 625 allows users to personalize their dashboards and tailor the interface 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 supply chain operations, providing a user-centric experience that enhances productivity and usability.

[0157] The SPoG UI 600 may include a data visualization module 630 that enables stakeholders to analyze and interpret supply chain data through interactive dashboards, charts, graphs, and visual representations. Using advanced visualization techniques, the 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.

[0158] The SPoG UI 600 may 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 go. The module optimizes the user experience for different screen sizes, resolutions, and operating systems, ensuring integrated access to real-time data and functionality across a variety of devices.

[0159] It should be understood that the operations shown in the exemplary method are not exhaustive, and other operations may be performed before, after, or between any of the operations shown. In some embodiments of the present disclosure, the operations may be performed in a different order and / or varied.

[0160] Figure 7is a flow chart of a method 700 for performing integrated supply chain management operations using the SPoG UI according to some embodiments of the present invention. In some embodiments, the method 700 provides operational steps to simplify supply chain processes, enhance decision making, and optimize operations within a supply chain ecosystem. In some embodiments, the method 700 performs real-time data retrieval, visualization, customization, collaboration, access control, and cross-platform accessibility functions through the SPoG UI. Based on the disclosure herein, the operations in the method 700 may be performed in a different order and / or varied to accommodate specific implementation requirements.

[0161] At operation 705, the computing device receives user input through the SPoG UI, representing various requests and commands related to supply chain management. The user input includes actions such as selecting a specific data visualization, accessing different modules or functions, initiating a workflow, configuring the interface, and performing data-driven analysis. This interactive input mechanism enables stakeholders to effectively interact with the SPoG UI and gain relevant insights to support their decision-making process.

[0162] At operation 710, the computing device utilizes its integration capabilities with the RTDM module to process user input and interact with the real-time data exchange module. 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 utilizing real-time data exchange, the computing device ensures that the insights presented in the SPoG UI reflect the latest developments and provide a comprehensive view of supply chain operations.

[0163] At operation 715, the computing device employs a data visualization module to generate a visually appealing and interactive representation of the retrieved supply chain data. The 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.

[0164] At operation 720, the computing device enables the user to personalize their dashboard and customize the SPoG UI 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 simplify their decision-making process within the SPoG UI.

[0165] At operation 725, the computing device facilitates real-time collaboration and communication between stakeholders through the collaborative decision-making module. The module provides features that enable stakeholders to exchange information, share insights and recommendations, initiate workflows, and participate in discussions within the SPoGUI interface. By integrating with the RTDM module, the collaborative decision-making module ensures that stakeholders can collaborate effectively based on accurate and synchronized data, thereby promoting a cohesive and agile supply chain ecosystem.

[0166] 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. The 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, thereby minimizing the risk of unauthorized data access or misuse.

[0167] 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. The module ensures that stakeholders can access the SPoG UI interface from desktop computers, laptops, smart phones, 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, thereby facilitating real-time data access and improving stakeholder productivity.

[0168] At operation 740, the computing device utilizes high-speed data in the data enrichment environment module to efficiently process real-time data and maintain a visually clean interface. The module combines a unique configuration of the SPoG UI structure, responsive data visualization, real-time data processing methods, adaptive information architecture, and optimization techniques. Operation 740 may include processing large amounts of dynamic supply chain data in an understandable, efficient, and visually appealing manner. Grid and Technology Powering SPoG UI's grid-based layout system enables UI elements to fluidly adapt to available space and content, while HTML5, CSS3, and JavaScript (particularly React.js) manage the dynamic aspects of the interface.

[0169] In conclusion, Figure 7The method 700 depicted in outlines a comprehensive approach to supply chain management through SPoG UI. By leveraging real-time data retrieval, visualization, customization, collaboration, access control, and cross-platform accessibility capabilities, stakeholders gain valuable insights into supply chain operations and can make informed decisions. The method facilitates efficient integration with RTDM modules, thereby ensuring accurate and up-to-date data synchronization. Through personalized dashboards, interactive data visualization, collaborative decision making, secure access control, and cross-device accessibility, SPoG UI empowers stakeholders to optimize their supply chain operations, enhance collaboration, and drive efficiency in dynamic and complex supply chain ecosystems.

[0170] Figure 8 800 is a flow chart of a method 800 for vendor onboarding using the SPoG UI according to some embodiments of the present disclosure. In some embodiments, the method 800 outlines a simplified and efficient process for facilitating vendors to enter the supply chain ecosystem using the capabilities of the SPoG UI. 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 vendor onboarding process. Based on the disclosure herein, the operations in the method 800 may be performed in a different order and / or varied to accommodate specific implementation requirements.

[0171] At operation 805, the process is initiated when a vendor expresses interest in joining the supply chain ecosystem. The computing device receives the vendor's information and relevant details using the SPoG UI. This may include company profiles, contact information, product catalogs, certificates, and any other relevant data required for the vendor login process.

[0172] At operation 810, the computing device uses the integration capability with the real-time data exchange module to verify the vendor's information. By utilizing 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 login process.

[0173] At operation 815, the computing device initiates the vendor onboarding workflow through the collaborative decision module. The module allows stakeholders involved in the onboarding process (such as procurement personnel, 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 suitability of the vendor and efficiently proceed through the onboarding steps.

[0174] At operation 820, the computing device employs a role-based access control (RBAC) module to manage access controls and permissions throughout the vendor onboarding process. The RBAC module ensures that stakeholders can only access specific information and functionality required for their roles. This control mechanism protects sensitive data, maintains privacy, and is consistent with regulatory requirements. Authorized stakeholders can securely view and contribute to the vendor onboarding process, thereby promoting a transparent and compliant environment.

[0175] At operation 825, the computing device provides stakeholders with a comprehensive view of the vendor onboarding process through the Unified View (UV) module of the SPoG UI. The module presents an intuitive and customizable dashboard-style layout that consolidates relevant information, milestones, and tasks associated with the vendor onboarding process. Stakeholders can monitor progress, track documentation requirements, and access real-time updates to ensure efficient and timely completion of onboarding tasks.

[0176] In operation 830, the computing device enables stakeholders to interact with a data visualization module of the SPoG UI that provides dynamic visualization and analysis related to the vendor onboarding 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 onboarding process. This data-driven approach enables stakeholders to autonomously make informed decisions, effectively allocate resources, and optimize onboarding workflows.

[0177] In operation 835, the computing device facilitates integrated collaboration among stakeholders involved in the vendor onboarding process through a collaborative decision-making module. The module enables real-time communication, file sharing, and workflow coordination, allowing stakeholders to streamline the onboarding process. By providing a centralized platform for discussion, feedback, and approval, the SPoG UI facilitates efficient collaboration and reduces delays in the vendor onboarding workflow.

[0178] In operation 840, the computing device uses the workflow management module of the SPoG UI to ensure effective management and tracking of the vendor onboarding process. This module enables stakeholders to define and manage the order of tasks, approvals, and reviews required for successful vendor onboarding. Workflow templates can be configured to allow standardization and repeatability in the onboarding process. Stakeholders can monitor the status of each task, track completion, and receive notifications to ensure timely progress.

[0179] In operation 845, the computing device captures and records the vendor login activity within the audit trail module of the SPoG UI. The 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.

[0180] In operation 850, the computing device completes the vendor login process within the SPoG UI. Once all necessary steps, reviews, and approvals are completed, 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.

[0181] In conclusion, Figure 8 The method 800 depicted in outlines a streamlined and efficient vendor onboarding process using SPoG UI. By leveraging real-time data integration, collaborative decision making, role-based access control, comprehensive visualization, and workflow management capabilities, SPoG UI enables stakeholders to autonomously and successfully onboard vendors into the supply chain ecosystem. The process ensures data accuracy, promotes transparency, enhances collaboration, and facilitates informed decision making throughout the vendor onboarding workflow. SPoG UI's intuitive interface, combined with its customizable features and notifications, streamlines the onboarding process, reduces manpower, and optimizes vendor integration within dynamic and complex supply chain scenarios.

[0182] Fig. 9 is a flow chart of a method 900 for dealer onboarding using SPoGUI according to some embodiments of the present disclosure. Method 900 outlines a streamlined and efficient process that fully leverages the capabilities of SPoG UI to facilitate onboarding dealers into the supply chain ecosystem. By integrating real-time data, collaborative decision-making, and role-based access control capabilities, SPoGUI enables stakeholders to effectively manage and optimize the dealer onboarding process. Based on the disclosure herein, the operations in method 900 may be performed in a different order and / or varied to accommodate specific implementation requirements.

[0183] In operation 905, the process begins when a dealer expresses interest in joining the supply chain ecosystem. The computing device receives the dealer's information and relevant details using the SPoG UI. This includes company profile, contact information, business certification, dealer agreement, and any other relevant data required for the dealer login process.

[0184] In operation 910, the computing device uses the integration capability with the real-time data exchange module to verify the dealer's information. 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.

[0185] In operation 915, the computing device initiates the dealer onboarding workflow through the collaborative decision-making module. The module allows stakeholders involved in the onboarding 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 evaluate the suitability of the dealer and efficiently proceed through the onboarding steps.

[0186] In operation 920, the computing device employs a role-based access control (RBAC) module to manage access controls and permissions throughout the dealer onboarding process. The RBAC module ensures that stakeholders can only access specific information and functions necessary for their roles. This control mechanism protects sensitive data, maintains privacy, and aligns with regulatory requirements. Authorized stakeholders can securely view and contribute to the dealer onboarding process, thereby promoting a transparent and compliant environment.

[0187] In operation 925, the computing device provides stakeholders with a comprehensive view of the dealer onboarding process through the Unified View (UV) module of the SPoG UI. The module presents an intuitive and customizable dashboard-style layout that consolidates relevant information, milestones, and tasks associated with the dealer onboarding process. Stakeholders can monitor progress, track documentation requirements, and access real-time updates to ensure that onboarding tasks are completed efficiently and in a timely manner.

[0188] In operation 930, the computing device enables stakeholders to interact with the data visualization module of the SPoG UI, which provides dynamic visualization and analysis related to the dealer onboarding process. Through interactive charts, graphs, and reports, stakeholders can evaluate key performance indicators, identify bottlenecks, and gain insights into the overall efficiency of the onboarding process. This data-driven approach enables stakeholders to autonomously make informed decisions, effectively allocate resources, and optimize the dealer onboarding workflow.

[0189] In operation 935, the computing device facilitates efficient collaboration among stakeholders involved in the dealer onboarding process through a collaborative decision-making module. The module enables real-time communication, file sharing, and workflow coordination, allowing stakeholders to streamline the onboarding process. By providing a centralized platform for discussion, feedback, and approval, the SPoG UI facilitates efficient collaboration and reduces delays in the dealer onboarding workflow.

[0190] In operation 940, the computing device records and maintains an audit trail of the dealer login activity within the audit trail module of the SPoG UI. The module captures detailed information about actions taken, decisions made, and documents viewed during the login process. The audit trail enhances transparency, accountability, and compliance, thereby serving as a valuable reference for future audits, reviews, and assessments.

[0191] In operation 945, the computing device terminates the dealer onboarding process within the SPoG UI. Once all necessary tasks, reviews, and approvals are completed, the dealer is officially onboarded into the supply chain ecosystem. The SPoG UI provides stakeholders with an overview of the onboarding process, ensuring that all requirements are met and facilitating further actions such as contract signing, product listing, and collaboration with the dealer.

[0192] In conclusion, Fig. 9 The method 900 depicted in the disclosure emphasizes a streamlined and efficient dealer onboarding 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 autonomously and successfully onboard dealers into the supply chain ecosystem. The SPoG UI's intuitive interface, customizable features, and robust collaboration capabilities streamline the onboarding process, enhance transparency, and promote efficient communication among stakeholders. The SPoG UI's data visualization capabilities promote data-driven decision-making, while the audit trail ensures compliance and provides a reliable record of onboarding activities. By effectively leveraging the SPoG UI, the dealer onboarding process becomes a well-coordinated workflow, thereby optimizing dealer integration and promoting business success within a dynamic supply chain environment.

[0193] Fig.10is a flow chart of a method 1000 for customer and end-customer onboarding using SPoG UI according to some embodiments of the present disclosure. Method 1000 outlines a comprehensive and user-centric approach to efficiently onboarding customers and end-customers into a supply chain ecosystem. By fully leveraging the capabilities of SPoG UI, including real-time data integration, collaborative decision making, and personalized user experience, stakeholders can successfully onboard and engage customers, providing them with an efficient and customized onboarding experience. Based on the disclosure herein, the operations in method 1000 may be performed in a different order and / or varied to accommodate specific implementation requirements.

[0194] In operation 1005, the process begins when a potential customer or end customer expresses interest in joining the supply chain ecosystem. Using the SPoG UI, the computing device captures the customer's or end customer's information, preferences, and requirements necessary for the login process. This includes contact details, business profile, industry specific preferences, and any other relevant data.

[0195] In operation 1010, the computing device uses real-time data integration capabilities with external systems to verify the client or end-customer's information. By synchronizing and accessing data from various sources (such as a customer relationship management (CRM) system or other enterprise-wide solutions), the computing device ensures the accuracy and completeness of the client or end-customer's information. This verification step helps to establish a reliable foundation for the login process and enhance data integrity.

[0196] In operation 1015, the computing device initiates a customer or end-customer onboarding workflow through a collaborative decision-making module. The module facilitates integrated communication and collaboration between stakeholders involved in the onboarding process, such as sales representatives, account managers, and customer support teams. The SPoG UI provides a centralized platform for stakeholders to jointly assess customer needs, define personalized onboarding journeys, and make informed decisions throughout the onboarding process.

[0197] In operation 1020, the computing device manages access controls and permissions during the login process using a role-based access control (RBAC) module. The RBAC module ensures that stakeholders have appropriate access to customer or end-customer data based on their roles and responsibilities. This control mechanism protects sensitive information, maintains data privacy, and aligns with regulatory requirements. Authorized stakeholders can securely view, update, and track login progress, thereby promoting a transparent and compliant login environment.

[0198] 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 onboarding process. The module consolidates relevant information, tasks, and milestones associated with the onboarding journey, providing stakeholders with an overall view of the onboarding progress. Stakeholders can monitor status, view documents, and access real-time updates to ensure an efficient and integrated onboarding experience.

[0199] In operation 1030, the computing device utilizes the data visualization module of the SPoG UI to present dynamic visualizations and analytics related to the onboarding process. Through interactive charts, graphs, and reports, stakeholders gain insights into key onboarding metrics, customer engagement levels, and potential bottlenecks. The data-driven approach enables stakeholders to make informed decisions autonomously, optimize onboarding strategies, and personalize the onboarding experience for each customer or end-customer.

[0200] In operation 1035, the computing device enables stakeholders to interact with the collaborative decision-making module to facilitate integrated collaboration during the onboarding process. Stakeholders can share files, initiate workflows, and exchange information in real time. The SPoGUI facilitates efficient communication, reduces latency, and ensures alignment between stakeholders involved in a client or end-client onboarding.

[0201] In operation 1040, the computing device employs a customization module to allow stakeholders to personalize the onboarding experience for each customer or end customer. Stakeholders can customize interfaces, workflows, and communications to align with customer or end customer preferences, industry specific requirements, and strategic goals. The customization capability enhances customer satisfaction and engagement during the onboarding journey.

[0202] In operation 1045, the computing device utilizes an audit trail module within the SPoG UI to maintain a detailed record of the customer or end-customer login activities. The module captures information about actions taken, decisions made, and files viewed throughout the login process. The audit trail enhances transparency, accountability, and compliance, thereby serving as a valuable reference for future audits, reviews, and assessments.

[0203] In operation 1050, the computing device terminates the customer or end-customer onboarding process within the SPoG UI. Once all necessary tasks, reviews, and approvals are completed, the customer or end-customer is officially onboarded into the supply chain ecosystem. The SPoG UI provides stakeholders with an overview of the onboarding process, ensuring that all requirements are met and facilitating further actions, such as account activation, service provisioning, or personalized customer engagement.

[0204] In conclusion, Fig.10The method 1000 depicted in illustrates a customer and end-customer onboarding process facilitated by the SPoG UI. By leveraging real-time data integration, collaborative decision making, role-based access control, comprehensive visualization, customization, and audit trail capabilities, the SPoG UI enables stakeholders to autonomously and successfully onboard customers and end-customers into the supply chain ecosystem. The SPoG UI's intuitive interface, personalization features, and robust collaboration capabilities streamline the onboarding process, enhance transparency, and promote efficient communication among stakeholders. The SPoG UI's data visualization capabilities facilitate data-driven decision making, while the customization and audit trail modules ensure a customized and compliant onboarding experience. By effectively leveraging the SPoG UI, the customer and end-customer onboarding process becomes an integrated workflow, thereby optimizing customer and end-customer integration and promoting business success within a dynamic supply chain environment.

[0205] Fig.11 Depicting the system architecture of system 1100, a high-level solution designed to automate the process of calculating and applying special pricing in near real-time. The system includes various components that work together to ensure accurate pricing, minimize manual effort, and reduce potential errors, ultimately streamlining the pricing workflow and improving overall sales efficiency.

[0206] The system 1100 includes a special pricing algorithm 1110 that is specifically designed to handle a wide range of variables necessary for accurate pricing determination. These variables include manufacturer details, specific products or combinations of products, product categories, solution types, time of year or specific dates, product authorizations, end users, and solution providers. By fully leveraging these variables, the special pricing algorithm 1110 can perform real-time calculations based on historical buying patterns or predefined rules established by pricing analysts. This dynamic approach enables the system to respond quickly to changing market conditions and meet specific customer requirements.

[0207] In some embodiments, these algorithms are specifically designed to handle a wide range of variables necessary for accurate pricing determination, including manufacturer details, specific products or combinations, product categories, solution types, time of year or specific dates, product authorizations, end users, and solution providers. By combining insights from an RTDM module with a CDC (e.g., RTDM 500), special pricing algorithms 1110 can be configured to perform real-time calculations based on historical buying patterns or predefined rules established by pricing analysts. This dynamic approach enables the system to respond quickly to changing market conditions and meet specific customer requirements.

[0208] To ensure efficient retrieval of pricing and availability information, the system 1100 utilizes a caching mechanism 1140. In some embodiments, an APIgee cache is used as the caching mechanism 1140, storing Everest-enabled vendor codes. The cache facilitates fast access to vendor-specific data required for accurate pricing calculations. By fully leveraging cached information, the system ensures that the latest pricing and availability details are available for calculations, resulting in accurate pricing recommendations. In some embodiments, by leveraging cached information and reconciling data from the RTDM module, the system ensures that the latest pricing and availability details are available for calculations, resulting in accurate pricing recommendations.

[0209] System 1100 includes an integration module 1115 that enables data and document exchange with external partners. In this embodiment, the integration module utilizes SEEBURGER integration technology to adhere to industry standard data exchange protocols, ensuring smooth collaboration between different stakeholders. Through SEEBURGER integration 1115, the system can securely and efficiently transfer pricing-related information and documents to external entities, thereby streamlining the entire pricing workflow.

[0210] To maintain data integrity and accuracy, the system 1100 includes a pricing validation and maintenance module 1120. This module applies intelligent validation rules and algorithms to verify the correctness of pricing procedures. By validating pricing data against predefined business rules, the system minimizes the risk of errors and discrepancies. The pricing validation and maintenance module 1120 ensures that pricing procedures remain accurate and up-to-date, thereby improving overall pricing reliability.

[0211] The system 1100 includes a special pricing creation and application module 1125 that automates the creation and application of special pricing in near real time. The module retrieves the special pricing algorithm 1110 and validates pricing data from the pricing validation and maintenance module 1120. This ensures that the correct pricing and associated conditions are accurately applied to single or multiple orders. By automating the process, the system reduces manpower and eliminates potential delays in the sales process, thereby increasing efficiency and customer satisfaction.

[0212] The system 1100 interacts with the order processing flow through integrations with the V7 Create Order API Async, the V6 Create Order API, and the Order Incoming PCG API 1130. These integrations facilitate the delivery of pricing information into the order system, ensuring accurate and timely application of specific pricing to the order flow. This integration streamlines the fulfillment process and ensures that customers receive the correct pricing for their orders.

[0213] Fig.11Depicted is the system architecture of system 1100, a high-level solution designed to automate the near real-time special pricing calculation and application process. The system includes various components that work together to ensure accurate pricing, minimize manual effort, and reduce potential errors, ultimately streamlining pricing workflows and improving overall sales efficiency.

[0214] The system 1100 includes a special pricing algorithm 1110 that is specifically designed to handle a wide range of variables necessary for accurate pricing determination. These variables include manufacturer details, specific products or combinations of products, product categories, solution types, time of year or specific dates, product authorizations, end users, and solution providers. By fully leveraging these variables, the special pricing algorithm 1110 can perform real-time calculations based on historical buying patterns or predefined rules established by pricing analysts. This dynamic approach enables the system to respond quickly to changing market conditions and meet specific customer requirements.

[0215] In some embodiments, these algorithms are specifically designed to handle a wide range of variables necessary for accurate pricing determination, including manufacturer details, specific products or combinations, product categories, solution types, time of year or specific dates, product authorizations, end users, and solution providers. By combining insights from an RTDM module with a CDC (e.g., RTDM 500), special pricing algorithms 1110 can be configured to perform real-time calculations based on historical buying patterns or predefined rules established by pricing analysts. This dynamic approach enables the system to respond quickly to changing market conditions and meet specific customer requirements.

[0216] To ensure efficient retrieval of pricing and availability information, the system 1100 utilizes a caching mechanism 1140. In some embodiments, an APIgee cache is used as the caching mechanism 1140, storing Everest-enabled vendor codes. The cache facilitates fast access to vendor-specific data required for accurate pricing calculations. By fully leveraging cached information, the system ensures that the latest pricing and availability details are available for calculations, resulting in accurate pricing recommendations. In some embodiments, by leveraging cached information and reconciling data from the RTDM module, the system ensures that the latest pricing and availability details are available for calculations, resulting in accurate pricing recommendations.

[0217] System 1100 includes an integration module 1115 that enables data and document exchange with external partners. In this embodiment, the integration module utilizes SEEBURGER integration technology to adhere to industry standard data exchange protocols, ensuring smooth collaboration between different stakeholders. Through SEEBURGER integration 1115, the system can securely and efficiently transfer pricing-related information and documents to external entities, thereby streamlining the entire pricing workflow.

[0218] To maintain data integrity and accuracy, the system 1100 includes a pricing validation and maintenance module 1120. This module applies intelligent validation rules and algorithms to verify the correctness of pricing procedures. By validating pricing data against predefined business rules, the system minimizes the risk of errors and discrepancies. The pricing validation and maintenance module 1120 ensures that pricing procedures remain accurate and up-to-date, thereby improving overall pricing reliability.

[0219] The system 1100 includes a special pricing creation and application module 1125 that automates the creation and application of special pricing in near real-time. The module retrieves the special pricing algorithm 1110 and validated pricing data from the pricing validation and maintenance module 1120. This ensures that the correct pricing and associated conditions are accurately applied to single or multiple orders. By automating the process, the system reduces manpower and eliminates potential delays in the sales process, thereby increasing efficiency and customer satisfaction.

[0220] The system 1100 interacts with the order processing flow through integrations with the V7 Create Order API Async, the V6 Create Order API, and the Order Incoming PCG API 1130. These integrations facilitate the delivery of pricing information into the order system, ensuring accurate and timely application of specific pricing to the order flow. This integration streamlines the fulfillment process and ensures that customers receive the correct pricing for their orders.

[0221] To enable efficient data exchange between the SEEBURGER system and the order entry system, system 1100 incorporates XML and EDI integration 1135. This integration ensures smooth transfer of pricing-related data, order details and other relevant information between the different systems involved in the order fulfillment process. XML and EDI integration 1135 ensures accurate, secure and industry-standard data exchange.

[0222] By combining these components and their interactions, the system 1100 automates the near real-time special pricing calculation and application process. It utilizes intelligent algorithms, caching mechanisms, data exchange, and integration with order systems to ensure accurate pricing, validate pricing procedures, and efficiently apply pricing. The automation provided by the system 1100 reduces manpower, minimizes errors, and enhances overall sales effectiveness and customer satisfaction.

[0223] Fig.12 Describes the detailed process flow of solutions for specialized pricing functions that address the challenges associated with determining, creating, and applying correct pricing in the industry. By leveraging advanced algorithms and intelligent rules, the system significantly reduces the time, effort, and potential errors involved in the pricing process, ultimately streamlining operations and enhancing the customer experience.

[0224] In operation 1205, the system initiates the process by receiving various inputs that are critical to pricing determination. These inputs cover a range of factors that influence pricing decisions, including manufacturer details, specific product or products, product category, solution type, time of year or specific date, product authorization, end user of the solution, and solution provider. Each input provides valuable context and contributes to the overall pricing calculation.

[0225] The system collects this information from multiple sources, such as user input through the platform or API. These inputs are used as the basis for subsequent steps in the special pricing process. The received data is carefully analyzed and organized to ensure its relevance and accuracy.

[0226] In operation 1210, the system applies a special pricing calculation algorithm. Once the input is received and validated, the system proceeds to operation 1210 where it applies a special pricing calculation algorithm. These algorithms are designed to take into account specific inputs received earlier and perform complex calculations to determine the correct pricing for a given scenario.

[0227] Special pricing calculation algorithms can be based on intelligent rules and historical purchasing patterns. These rules define pricing models and criteria for different scenarios. By incorporating these rules into the algorithm, the system can effectively calculate the most accurate and appropriate pricing for each unique combination of inputs.

[0228] The algorithm can take into account factors such as manufacturer preferences, product category, historical sales data, and any other relevant variables. This comprehensive approach ensures that the calculated pricing is consistent with the specific requirements and business rules defined by the organization.

[0229] In operation 1215, the calculated pricing is validated using the master data. After the special pricing calculation algorithm has determined the pricing for the particular scenario, the system proceeds to operation 1215, which involves validating the calculated pricing using the master data.

[0230] Master data refers to authoritative and reliable data used as a reference for various processes within the system. In the context of special pricing, master data includes information such as pricing rules, discount structures, supplier agreements, and other relevant data points.

[0231] During this step, the system cross-references the calculated pricing with master data elements to ensure its accuracy and compliance with predefined rules and standards. The validation process involves checking for any discrepancies or anomalies that may require further review or adjustment.

[0232] By validating calculated pricing against master data, the system mitigates the risk of pricing errors and ensures that the pricing applied is consistent with the organization's pricing policies and agreements.

[0233] Where the calculated pricing is verified and validated, the system moves to operation 1220 which may include applying the determined pricing and associated conditions to the quote or order.

[0234] During this step, the system integrates with various modules and systems to facilitate the application of pricing. It ensures that the determined pricing along with any applicable discounts, promotions or special conditions are accurately applied to the quote or order.

[0235] The application of pricing and conditions involves updating relevant data fields and systems to reflect the correct pricing information. This step ensures that pricing calculations are converted into actionable terms and are ready for further processing and fulfillment.

[0236] The system takes into account any specific requirements, contractual obligations or business rules associated with pricing and conditions. By accurately applying them, the system enables the generation of accurate quotes and orders that are consistent with the customer's expectations and the organization's pricing strategy.

[0237] The system may include retrieving pricing and availability vendor codes to complete the special pricing process in operation 1225. These codes play an important role in determining pricing for different vendors and their corresponding products.

[0238] The system interacts with the Price and Availability (P&A) module, which acts as a centralized source of information for pricing and availability data. The P&A module retrieves pricing and availability vendor codes from the platform, which contain the necessary details to determine pricing for Everest-enabled vendor products.

[0239] Once the pricing and availability vendor codes are obtained, the system utilizes them to generate the correct pricing for these specific products. The pricing information is then forwarded to the appropriate system, such as the Global Pricing Engine (GPE) or Global Data System (GDS), to ensure accurate pricing representation for these products. For non-Everest enabled vendor products, pricing information is sent directly to Impulse, the fulfillment module of the system.

[0240] This step ensures that the special pricing process takes into account the unique pricing structures associated with different vendors and products. By retrieving and utilizing pricing and availability vendor codes, the system ensures accuracy and consistency of pricing information throughout the system.

[0241] In operation 1230, the system may include an orchestration that triggers an integration to facilitate product searches (eg, a SeebBurger integration). The orchestration serves as a mechanism to retrieve relevant product information based on specific search criteria.

[0242] In a non-limiting example, when a product search request is initiated, the system communicates with SeebBurger, which acts as an EDI conversion tool. SeebBurger is responsible for coordinating the retrieval of data from various sources, including GPE or GDS (possibly leveraging Elastic) to obtain the necessary product information.

[0243] The orchestration process involves sending the search criteria to SeebBurger, which then interacts with the appropriate modules or systems to collect the requested data. This ensures that the system obtains accurate and up-to-date product information for subsequent steps in the special pricing process.

[0244] The system may utilize the APIgee cache to store and access data required for a particular pricing process in operation 1235. The APIgee cache serves as a storage mechanism that enables efficient and rapid retrieval of necessary information during pricing calculations and order processing.

[0245] APIgee cache acts as a central repository where relevant data such as platform orders and SeebBurger orchestration outputs are stored. This caching mechanism ensures that frequently accessed data is readily available, reducing the need for repeated data retrieval from external systems.

[0246] By leveraging APIgee caching, the system optimizes performance and response time because data can be retrieved from the cache rather than relying solely on fetching real-time data from various sources. This caching approach significantly enhances the overall efficiency of the special pricing process and improves the responsiveness of the system.

[0247] In operation 1240, the system uses the V7 Create Order API Asynchronous and the V6 Create Order API to process the order. These APIs play a key role in enabling the creation and management of orders within the system. The V7 Create Order API Asynchronous and the V6 Create Order API provide the necessary interfaces for receiving order-related input, whether from the platform order or through API integration. These APIs ensure that order data flows into the system for further processing.

[0248] When an order is received, the system uses these APIs to generate a corresponding order object within the system. The API handles the necessary validation, data conversion, and system interactions required to create a comprehensive order representation.

[0249] The asynchronous nature of the V7 Create Order API allows for multiple orders to be processed in parallel, thereby enhancing the scalability and performance of the system. This asynchronous capability ensures that order creation and processing can occur simultaneously, thereby reducing potential bottlenecks and improving overall efficiency. Once the order has been processed and created within the system, operation 1240 may also include transmitting the processed order to Impulse. This step ensures that the order is transmitted to Impulse for further processing and fulfillment.

[0250] To facilitate this transfer, the system utilizes XML and EDI submissions via SeebBurger integration. These submissions encapsulate the necessary order data, including customer details, product information, pricing, and any additional order-specific requirements.

[0251] The integration between the system and Impulse enables a smooth handoff of order data, ensuring that processed orders are accurately reflected in the Impulse system. This integration plays a vital role in coordinating the fulfillment process, allowing Impulse to continue with order processing, inventory management, shipping and other related operations.

[0252] By transferring processed orders to Impulse, the system ensures an end-to-end workflow where order data flows from initial processing through various stages to the final fulfillment stage.

[0253] In some embodiments, SeebBurger serves as an important tool to convert XML and EDI data into the appropriate format, thereby allowing interoperability between different components. The converted EDI data is then sent to Impulse, while the XML data is sent to the Order Entry PCG API. These operations ensure accurate capture, processing, and management of pricing and order information, thereby contributing to the overall efficiency and effectiveness of a particular pricing process.

[0254] Fig.13 Represents system 1300, which may be Fig.11 The system is an embodiment of the invention and includes an advanced automation framework designed to optimize the creation and application of special pricing. As described above, the system integrates the special pricing creation and application module 1125 in operation 1205. This integration facilitates near real-time retrieval of special pricing algorithms and validated pricing data, thereby streamlining the sales process and significantly reducing manual operations.

[0255] In operation 1210, the system enables pricing information to be communicated to the order system, interacting with sequential processing flows through the V7 Create Order API asynchronously, the V6 Create Order API, and the Order Entry PCG API 1130. This ensures accurate and timely application of special pricing.

[0256] As described above, the system 1300 combines XML and EDI to provide the exchange of pricing-related data and order details between various systems, in compliance with industry standards while maintaining data accuracy and security. The system 1300 begins the pricing process by collecting different inputs that are critical to pricing determination. Subsequently, the system 1300 applies special pricing calculation algorithms. These algorithms use intelligent rules and historical purchasing patterns to arrive at the most accurate pricing for a given scenario.

[0257] Once pricing is calculated, the system 1300 validates the calculated pricing against authoritative and reliable master data and applies the validated pricing and associated conditions to the quote or order, integrating with various modules and systems to ensure accuracy.

[0258] The system 1300 retrieves pricing and availability vendor codes for generating the correct pricing for a particular product. The system 1300 triggers integrated orchestration to facilitate product searches, retrieving relevant product information based on specific search criteria. The system 1300 employs APIgee caching to efficiently store and access data during the pricing process. The system 1300 utilizes the V7 Create Order API Asynchronous and the V6 Create Order API to create and manage orders. Once an order is processed, the system transmits it to the Impulse system using XML and EDI submissions for further processing and fulfillment.

[0259] The system 1300 thereby executes a dedicated process to generate real-time or near real-time calculations and apply special pricing. The system 1300 achieves the advantages of increasing sales efficiency, minimizing errors, and increasing customer satisfaction.

[0260] Fig.14 1400. The computer system 1400 may be used, for example, to implement any of the embodiments discussed herein, as well as any of the combinations and sub-combinations thereof. The computer system 1400 may include one or more processors 1404 (also referred to as central processing units or CPUs), such as processor 1404. The processor 1104 may be connected to a communication infrastructure or bus 1406.

[0261] The computer system 1400 may also include user input / output device(s) 1403 , such as a monitor, keyboard, pointing device, etc., which may communicate with the communication infrastructure 1406 via the user input / output interface(s) 1402 .

[0262] One or more processors 1404 may be a graphics processing unit (GPU). In one embodiment, a GPU may be a processor that is a dedicated electronic circuit designed to process mathematically intensive applications. A GPU may have a parallel structure that efficiently performs parallel processing of large blocks of data (common mathematically intensive data such as computer graphics applications, images, videos, etc.).

[0263] The computer system 1400 may also include a main memory or main storage 1408, such as a random access memory (RAM). The main storage 1408 may include one or more levels of cache. The main storage 1408 may store control logic (ie, computer software) and / or data.

[0264] The computer system 1400 may also include one or more secondary storage devices or memories 1410. The secondary memory 1410 may include, for example, a hard drive 1412 and / or a removable storage device or drive 1414.

[0265] Removable storage drive 1414 can interact with removable storage unit 1418. Removable storage unit 1418 can include a computer-usable or readable storage device having computer software (control logic) and / or data stored thereon. Removable storage unit 1418 can be a program cartridge and cartridge interface (such as the program cartridge and cartridge interface present in a video game device), a removable memory chip (such as an EPROM or PROM) and associated sockets, a memory stick and USB port, a memory card and associated memory card slots, and / or any other removable storage unit and associated interface. Removable storage drive 1414 can read from and / or write to removable storage unit 1418.

[0266] Secondary storage 1410 may include other means, devices, components, tools, or other pathways for allowing computer system 1400 to access computer programs and / or other instructions and / or data. Such means, devices, components, tools, or other pathways may include, for example, a removable storage unit 1422 and an interface 1420. Examples of removable storage unit 1422 and interface 1420 may include a program cartridge and cartridge interface (such as that found in video game devices), a removable memory chip (such as an EPROM or PROM) and associated sockets, a memory stick and USB port, a memory card and associated memory card slot, and / or any other removable storage unit and associated interface.

[0267] The computer system 1400 may also include a communication or network interface 1424. The communication interface 1424 may enable the computer system 1400 to communicate and interact with any combination of external devices, external networks, external entities, and the like (individually and collectively denoted by reference numeral 1428). For example, the communication interface 1424 may allow the computer system 1400 to communicate with an external or remote device 1428 via a communication path 1426, which may be wired and / or wireless (or a combination thereof) and may include any combination of a LAN, a WAN, the Internet, and the like. Control logic and / or data may be transmitted to and from the computer system 1400 via the communication path 1426.

[0268] Computer system 1400 may also be any one 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, part of the Internet of Things, and / or an embedded system, to name a few non-limiting examples, or any combination thereof.

[0269] Computer system 1400 can be a client or server accessing or hosting any application and / or data through 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), hosted 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 including any combination of the foregoing examples or other services or delivery paradigms.

[0270] Any available data structures, file formats, and schemas in computer system 1400 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 packages, 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.

[0271] In some embodiments, a tangible non-transitory device or article of manufacture 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 program storage device. This includes, but is not limited to, computer system 1400, main memory 1408, auxiliary memory 1410, and removable storage units 1418 and 1422, and tangible articles of manufacture implementing any combination of the foregoing. When executed by one or more data processing devices (such as computer system 1400), such control logic may cause such data processing devices to operate as described herein.

[0272] FIG. 15A to FIG. 15Q Depicts various screens and functions of the SPoGUI related to seller login, partner dashboard, customer shopping cart, order summary, SKU generation, order tracking, shipment tracking, subscription history, and subscription modification. A detailed description of each figure is provided below:

[0273] Fig.15A Depicted is a vendor login splash 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.

[0274] Fig. 15B A vendor login guide is depicted, which shows a step-by-step guide or checklist for the vendor to follow during the login process. It outlines the necessary tasks and requirements, ensuring that the vendor has a clear understanding of the login process and can proceed smoothly.

[0275] Fig. 15C Depicted is a vendor onboarding call scheduler that facilitates scheduling calls or meetings between vendors and a platform associate or representative responsible for guiding them through the onboarding process. Vendors can select a suitable time slot or request a call, thereby ensuring efficient communication and assistance throughout the onboarding journey.

[0276] Fig.15D Depicted is 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, helping vendors track their progress and ensure timely completion of each login task.

[0277] Fig.15E A vendor login completion screen is depicted, which confirms the successful completion of the vendor login process. It may display a congratulatory message or summary of the completed task, indicating that the vendor is now officially logged into the supply chain ecosystem.

[0278] Fig.15F Depicts a partner dashboard that provides partners or stakeholders with a centralized view of relevant information and metrics related to their partnership with the supply chain ecosystem. It provides an overview of performance indicators, key data points, and actionable insights to facilitate effective collaboration and decision making.

[0279] Figure 15G Depicted is a customer product shopping cart representing a customer's product shopping cart in which they can add items they wish to purchase. It displays a list of selected products, quantities, prices, and other relevant details. Customers can view and modify their shopping cart contents before proceeding to the checkout process.

[0280] Fig.15H Depicts a customer subscription shopping cart that allows customers to manage their subscription-based purchases. It displays the selected subscription plan, pricing, and duration. Customers can review and modify their subscription details before finalizing their selection.

[0281] Fig.15I Depicts a customer order summary that provides a summary of the customer's order, including details such as the product or subscription purchased, quantity, pricing, and any applied discounts or promotions. It allows customers to review their order before confirming the purchase.

[0282] Fig.15J A vendor SKU generation screen for generating unique stock keeping unit (SKU) codes for vendor products is depicted. It may include fields or options where the vendor can specify product details, attributes, and pricing, and the system automatically generates a corresponding SKU code.

[0283] Figure 15K and 15L Depicts dashboards that display aggregate information about orders placed within a supply chain ecosystem. Order summaries. 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 activity, enabling stakeholders to track and manage orders efficiently.

[0284] Figure 15M Depicts a customer subscription shopping cart that allows customers to add, modify, or cancel subscription plans. It can display a list of selected subscriptions, pricing, and renewal dates. Customers can manage their subscriptions and make changes based on their preferences and requirements.

[0285] Fig.15N Depicted is a customer order tracking screen that enables customers to track the status and progress of their orders within the supply chain. It displays real-time updates on order fulfillment, including processing, packaging, and shipping. Customers can monitor the movement of their orders and anticipate delivery times.

[0286] Fig.15O Depicted is customer shipment tracking that provides customers with real-time tracking information about their shipments. It can include details such as the carrier, tracking number, current location, and estimated delivery date. Customers can stay informed of where their shipments are going.

[0287] Figure 15P Depicts a customer subscription history that presents a history of a customer's subscription activity. It displays a list of previous subscriptions, including subscription plans, duration, and status. Customers can view their subscription history, track past payments, and reference previous subscription details.

[0288] Figure 15QDepicts 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 based on their evolving needs or preferences.

[0289] The UI screens depicted are not limiting. In some embodiments, Figures 15A to 15Q The UI screens collectively represent the various functions and features provided by SPoG UI, providing stakeholders with a comprehensive and user-friendly interface for seller onboarding, partner relationship management, customer interaction, order management, subscription management and tracking within the supply chain ecosystem.

[0290] A system of one or more computers may be configured to perform specific operations or actions by having software, firmware, hardware, or a combination thereof installed on the system, which in operation causes the system to perform the actions. One or more computer programs may be configured to perform specific operations or actions by including instructions that, when executed by a data processing device, cause the device to perform the actions.

[0291] In one general aspect, a computer-implemented method may include integrating multiple communication channels (i.e., touchpoints) between user groups into a unified interactive interface in a computer system, the unified interactive interface being referred to herein as an SPoG UI, wherein the SPoG is a central interface component configured to merge user interactions, data, and / or functionality of a user group, and wherein the SPoG UI is arranged to facilitate operations across a supply chain ecosystem. The computer-implemented method may also include managing the end-to-end lifecycle of user interactions using the SPoG UI. The method may also include collecting data from the user interactions within the SPoG UI. In addition, the method may include analyzing the collected data to generate one or more insights for business growth. In addition, the method may include executing one or more artificial intelligence and / or machine learning algorithms to enhance business operations based on the analyzed data. The method may also include incorporating regular updates and improvements into the SPoG UI based on the analyzed data. In addition, the method may include the following case: the user group may include users selected from two or more different groups, the groups having distributors, dealers, customers, end customers, sellers, and suppliers. Other embodiments of this aspect include corresponding computer systems, apparatuses, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the method.

[0292] Implementations may include one or more of the following features. Where integration includes methods for establishing communication links with multiple pre-existing business platforms. Where merged interaction points may include methods for one or more websites, customer relationship management systems, vendor platforms, and supply chain management systems. Where end-to-end lifecycle management may include methods for initial contact, service fulfillment, and one or more of subsequent interactions. Where data collection may include methods for monitoring and / or formally recording user activities within the SPoG UI. Where advanced statistical algorithms are used to perform methods for analyzing the collected data. Where artificial intelligence and machine learning algorithms include methods for predictive analysis to identify market trends. Where artificial intelligence and machine learning algorithms include methods for obtaining a recommendation system for personalizing user interactions. Improvements may be based on analysis received through the SPoG UI and / or analyzed user feedback. Implementations of the described technology may include hardware, methods or processes or computer tangible media.

[0293] In one general aspect, the system may include a communication integration module configured to integrate multiple communication channels (i.e., touchpoints). The system may also include a merging module configured to combine the integrated communication channels into a unified interactive interface, referred to herein as SPoG UI, wherein SPoG is a central interface component configured to merge user interactions, data and / or functions of a user group, and wherein the SPoG UI is arranged to facilitate operations across the supply chain ecosystem. In addition, the system may include a lifecycle management module configured to manage the end-to-end lifecycle of user interactions within the SPoG. The system may additionally or alternatively include a data collection module configured to automatically collect data from user interactions within the SPoG. In addition, 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. In addition, the system may include the following situation: the user group may include users selected from two or more different groups, and the group has distributors, dealers, customers, end customers, sellers and suppliers. Other embodiments of this aspect include corresponding computer systems, apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the method.

[0294] Systems and methods for automated SKU management may include: a user interface configured to receive catalog files in two or more different formats from a user; a catalog conversion module configured to convert the catalog files into a standard format; predicting a classification and attribute mapping for at least one catalog item, wherein 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, wherein the RTDM module facilitates real-time interaction between users and the system; a master data management (MDG) module for validating the converted catalog and feeding back any errors to the seller; and a global data repository (GDR) for storing the validated catalog and maintaining the data integrity of the stored catalog.

[0295] The system and method may include a search platform for indexing and retrieving stored catalogs. The user interface is a single pane of glass user interface (SPoG UI). A dynamic SKU creation module is used to generate SKUs in an ERP system for catalog items, wherein the catalog items represent one or more non-transactional products, and wherein the generated SKU 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 an indication to indicate one or more errors that occurred during a catalog conversion process associated with one or more conversion catalogs. A global pricing engine (GPE) for determining real-time pricing for catalog items. The GPE determines the pricing of products based on one or more of the following: vendor price files, market trends, and historical pricing data. Implementations may include one or more of the following features. A method in which the interactive elements include links to various business platforms. A method in which the actions included by the user interaction may include one or more clicks, hovers, and input data. A method in which the collected data 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.

[0296] 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 present invention as contemplated by the inventors, and thus are not intended to limit the present invention and the appended claims in any way.

[0297] The present invention has been described above by means of functional building blocks that illustrate the implementation of specified functions and their relationships. For ease of description, the boundaries of these functional building blocks are arbitrarily defined herein. As long as the specified functions and their relationships are properly performed, alternative boundaries can be defined.

[0298] The foregoing description of specific embodiments will fully reveal the general nature of the invention so that others can easily modify and / or adapt it for various applications (such as specific embodiments) by applying the knowledge of those skilled in the art without departing from the general concept of the invention without excessive experimentation. Therefore, based on the teachings and guidance presented herein, such adaptations and modifications are intended to be within the meaning and scope of equivalents of the disclosed embodiments. It should be understood that the wording or terminology herein is for the purpose of description rather than limitation, so that the terms or wording of this specification are interpreted by the technician according to the teachings and guidance.

[0299] The breadth and scope of the present invention should not be limited by any of the above-described exemplary embodiments, but should be defined only in accordance with the following claims and their equivalents.

Claims

1. A system for automatic near real-time special pricing calculation and application, the system comprising: a special pricing algorithm module configured to receive first information including one or more of manufacturer details, product category, solution type, historical purchasing patterns, and time of year, and to apply the received first information in determining special pricing; A cache module to store vendor-specific data for accurate pricing calculations; an integration module configured to enable data exchange with external partners and with order processing systems for accurate pricing applications; a pricing validation and maintenance module that applies intelligent validation rules to ensure pricing accuracy; A special pricing creation and application module automatically creates and applies special pricing to a single order or multiple orders and quotations based on the first information and data. Wherein, the integration module includes data exchange technology to facilitate data exchange with external systems.

2. The system of claim 1 further comprising a real-time data grid (RTDM) module that provides real-time data management and analysis capabilities.

3. The system according to claim 2, wherein: The special pricing algorithm module is configured to retrieve one or more insights from the RTDM module for accurate pricing determination.

4. The system according to claim 1, wherein: The caching mechanism includes a cache that stores vendor-specific data.

5. The system according to claim 1, wherein: The integration module utilizes integration technology to exchange data and documents.

6. The system of claim 1, further comprising a data validation mechanism within the pricing validation and maintenance module utilizing insights from the RTDM module.

7. The system according to claim 1, wherein: The data exchange technology ensures accurate, secure and industry-standard data exchange with external systems.

8. A computerized method for automated near real-time special pricing calculation and application, the method comprising: Receive inputs including manufacturer breakdown, product category, solution type, historical buying patterns, and time of year; Apply special pricing algorithms based on received input to determine correct pricing; validate calculated pricing using predefined business rules; apply determined pricing and associated conditions to single or multiple orders and quotes; integrate with order processing systems to ensure accurate pricing application; perform data exchange with external systems using data exchange technologies.

9. The computerized method of claim 8, further comprising utilizing real-time data management and analysis capabilities provided by the RTDM module.

10. The computerized method of claim 8, wherein: The input for the special pricing determination is obtained from a cache mechanism that stores vendor-specific data.

11. The computerized method of claim 8, wherein: The pricing of the validation calculations includes applying intelligent validation rules based on insights from the RTDM module.

12. The computerized method of claim 8, further comprising retrieving vendor specific data from the caching mechanism for accurate pricing determination.

13. The computerized method of claim 8, wherein: The data exchange with the external system is facilitated using data exchange technology.

14. The computerized method of claim 8, wherein: The data exchange technology ensures accurate, secure and industry-standard data exchange with external systems.

15. A computerized method for automated near real-time special pricing calculation and application, the method comprising: Receive inputs including manufacturer breakdown, product category, solution type, historical buying patterns, and time of year; Apply special pricing algorithms based on received input to determine correct pricing; validate calculated pricing using predefined business rules; apply determined pricing and associated conditions to single or multiple orders and quotes; integrate with order processing systems to ensure accurate pricing application; perform data exchange with external systems using data exchange technologies.

16. The computerized method of claim 8, further comprising utilizing real-time data management and analysis capabilities provided by the RTDM module.

17. The computerized method of claim 8, wherein: The input for the special pricing determination is obtained from a cache mechanism that stores vendor-specific data.

18. The computerized method of claim 8, wherein: The pricing of the validation calculations includes applying intelligent validation rules based on insights from the RTDM module.

19. The computerized method of claim 8, further comprising retrieving vendor specific data from the caching mechanism for accurate pricing determination.

20. The computerized method of claim 8, wherein: The data exchange with the external system is facilitated using data exchange technology.

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