Supply chain management system and method including ERP agnostic real-time data grid and change data capture

Abstract

The invention provides a system and a method for dynamically merging interaction points in a distribution ecosystem. The method involves integrating a plurality of communication contacts between distributors, dealers, end users, retailers, and vendors into a unified interactive interface. The interface enables end-to-end partner lifecycle management, system data collection, analysis using advanced statistical algorithms, deployment of artificial intelligence and machine learning algorithms, and continuous updates based on user feedback. The system includes modules for communication integration, merging, lifecycle management, data collection, data analysis, and artificial intelligence. According to the method and system disclosed by the invention, supply chain operation can be enhanced, actionable insights can be generated, and personalized user experience is provided, so that business growth is finally promoted and efficiency is improved.

Description

[0001] Priority Document

[0002] This application is a U.S. continuation-in-part application (CIP) with a filing date of June 26, 2023, named “Systems and Methods for Distribution Management Including Single Pane of Glass User Interface” and application number US18 / 341,714, and the entire text of its disclosure is incorporated herein by reference. Technical Field

[0003] The present invention relates to a real-time data grid method and system including distribution, supply chain management and related functions. Background Art

[0004] The traditional global distribution industry faces many challenges, including distribution management, supply chain management, inventory control, stock keeping unit (SKU) management, compliance and evolving consumer expectations. Traditionally, distribution and supply chain management have not been the core competencies of many distributors, and therefore have been inefficient. Inventory control has long been a focus, and market fluctuations require flexible distribution and supply chain models. SKU management and localization efforts have become increasingly complex due to different data from various original equipment manufacturers (OEMs) and different requirements in different jurisdictions. Compliance with international regulations also requires increased vigilance and additional paperwork. Finally, with the shift to ecosystem commerce, traditional customer interaction methods will soon become obsolete.

[0005] Enterprise Resource Planning (ERP) system is a software system that integrates and manages various core business processes and functions within an organization. It serves as a central database and platform, allowing different departments and functions such as finance, human resources, procurement, inventory management, production, sales and distribution to share and access information in real time. In a complex distribution and delivery ecosystem, managing and optimizing the flow of goods, services and information is essential for companies to remain competitive and meet customer needs. However, traditional systems often face many challenges that hinder efficiency, visibility and decision-making. These challenges include data fragmentation, limited integration, inconsistent data, inefficient data processing and data security issues.

[0006] Data fragmentation is a prevalent problem in distribution and supply chain systems, where data is stored in various systems and departments, often in traditional systems such as ERP, resulting in information silos. As a result, stakeholders struggle to gain a comprehensive understanding of key distribution and supply chain metrics in real time, hampering their ability to make informed decisions and respond quickly to changing market dynamics. In addition, when information is stored in different formats, data inconsistencies arise, which creates challenges in maintaining data integrity and ensuring accurate analysis.

[0007] In addition, the lack of integration capabilities between different systems also hinders the flow of data throughout the distribution and supply chain. Integrating data from multiple sources (including ERP, traditional systems and external providers) becomes a complex and time-consuming process. This limitation constrains the ability to fully understand the distribution and supply chain and hinders operational optimization. In addition, inefficient data processing and analysis is another major challenge faced in distribution and supply chain management. Traditional systems often struggle to handle the volume, variety and velocity of distribution and supply chain data. Extracting meaningful insights and actionable intelligence from massive amounts of data becomes a daunting task. The inability to efficiently process and analyze data hinders trend identification, forecasting, optimization and decision-making.

[0008] In addition, data security and governance issues are also key factors in distribution and supply chain management. Distribution and supply chain data often contain sensitive information, including customer data, pricing details, and contractual agreements. Ensuring the security, privacy, and compliance of this data is an important measure to prevent unauthorized access and data breaches. Complying with industry regulations and maintaining data integrity further complicates the data management landscape. Summary of the invention

[0009] The global distribution industry is at a critical juncture, facing a series of challenges across multiple sectors. These obstacles are both legacy and emerging, requiring innovative and effective technology solutions to guide the industry to achieve growth and improve efficiency. Among these many obstacles, the first are distribution management, supply chain management, inventory and compliance issues, stock keeping unit (SKU) management, the shift to direct-to-consumer models, and rapidly evolving consumer expectations and behaviors.

[0010] The first key challenge involves distribution process management, a core part of any distributor’s operations. Paradoxically, this is not usually a distributor’s core competency. This gap creates system inefficiencies, exacerbates the difficulty of managing disruptions, and directly impacts the distributor’s ability to deliver products and services efficiently and on time. In addition to these challenges, market trends are moving toward a more direct-to-consumer model. Traditional distribution methods involving numerous intermediaries are gradually being replaced. This evolving market dynamic requires a significant reassessment and repositioning of existing business models and strategies to ensure alignment with this new market reality.

[0011] A classic problem in the distribution space is inventory management. Given the volatile nature of market demands and trends, companies must ensure that they maintain a flexible distribution and supply chain without having to hold inventory. This greatly increases the complexity and challenges of promising and delivering goods to customers. In addition, there are numerous compliance regulations that must be adhered to when transporting goods and services across borders, which adds an additional layer of complexity to the distribution process. This not only makes the distribution process more complex and challenging, but also requires additional vigilance and paperwork to maintain compliance.

[0012] To further address these challenges, issues surrounding product localization, changes in distribution rights, and managing global SKUs also need to be addressed. Original Equipment Manufacturers (OEMs) each have their own unique systems and processes, and the coordination process of data from different OEMs adds complexity. In addition, addressing localization requirements that comply with laws and regulations in different jurisdictions also increases the potential for inefficiency and errors.

[0013] Finally, processes should be made more efficient and streamlined to ensure the sustainability of the distribution model in the evolving market landscape. This involves shifting the focus of the distribution platform from supply chain management to encompass subscription management, customer visibility, and other key distribution-oriented functions. The landscape of consumer behavior and expectations is changing rapidly. The shift to ecosystem commerce requires the creation of a user-friendly, efficient, and configurable technology procurement platform. Traditional customer interaction methods are rapidly falling out of favor, and companies must evolve to meet these new customer expectations.

[0014] Despite these challenges, the distribution model has many advantages over the direct-to-consumer model. First, it allows manufacturers to focus on their core competencies, leaving the complexities of logistics and distribution to specialized entities. Second, distribution networks often have a wide reach, allowing products to be available to customers in far-flung 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.

[0015] 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. The systems and methods described herein are directed to addressing these challenges. In addition, the systems described herein can be configured to include features such as subscription management and other customer-centric areas that traditional distribution platforms fail to effectively manage.

[0016] Single Pane of Glass (SPoG)

[0017] Single Pane of View (SPoG) provides a comprehensive technology solution designed to address these multifaceted challenges. SPog can be configured to provide a comprehensive, user-friendly, and efficient platform that streamlines the distribution process.

[0018] According to some embodiments, SPoG can be configured to address supply chain and distribution management by enhancing visibility and control over supply chain processes. Through real-time tracking and analysis, SPoG can provide valuable insights into inventory levels and goods status, thereby ensuring efficient handling of supply chain and distribution management processes.

[0019] According to some embodiments, SPoG can integrate multiple touch points into a single platform to emulate direct-to-consumer channels into distribution platforms. This integration provides a unified direct channel for consumers to interact with distributors, significantly reducing the complexity of the supply chain and improving the overall customer experience.

[0020] SPoG provides innovative technology solutions for improving inventory management through advanced forecasting capabilities. These predictive analytics can highlight demand trends and guide companies to manage their inventory more effectively, reducing the risk of out-of-stock or overstocking.

[0021] According to some embodiments, SPoG may include a global compliance database. Through real-time updates, the database enables distributors to keep up to date with the latest international laws and regulations. This feature significantly reduces the burden of manual tracking, thereby ensuring smooth compliance of cross-border transactions.

[0022] 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, SPoG provides the ability to efficiently manage and distribute localized SKUs to meet specific market needs and requirements.

[0023] 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 meeting the expectations of a new generation of technology buyers.

[0024] Additionally, SPoG’s advanced analytics capabilities provide valuable insights that drive strategy and decision making, tracking and analyzing trends in real time, allowing businesses to stay ahead and adapt to changing market conditions.

[0025] SPoG’s flexibility and scalability make it a forward-thinking technology solution that can adapt to changing business needs, allowing companies to expand or shrink operations as needed without major changes to infrastructure.

[0026] SPoG is an extremely useful tool that takes an innovative approach to solving the challenges facing the distribution industry. By enhancing supply chain visibility, streamlining inventory management, ensuring compliance, simplifying SKU management, and providing an excellent customer experience, SPoG provides a comprehensive technology solution to the complex problems that have long plagued the distribution industry. By implementing the system, distributors can look forward to increased efficiency, reduced errors, and improved customer satisfaction, thereby achieving continued growth in an evolving global market.

[0027] Real-Time Data Mesh (RTDM)

[0028] According to some embodiments, the platform may include the implementation of a real-time data grid (RTDM). RTDM provides an innovative technical solution to these challenges. RTDM is a distributed data architecture that enables real-time data availability across multiple sources and touchpoints. This feature enhances supply chain visibility, allowing efficient management and enabling distributors to handle disruptions more effectively.

[0029] RTDM’s predictive analysis capabilities provide a technical solution for efficient inventory control. By gaining insight into demand trends, it can help companies manage inventory and thus reduce the risk of overstocking or out-of-stock.

[0030] The real-time updated RTDM global compliance database ensures distributors are aware of international regulations. This significantly reduces the manual tracking burden, thus enabling cross-border transactions.

[0031] 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 can effectively meet specific market needs.

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

[0033] Advantages of SPoG and RTDM integration

[0034] The integration of the SPoG platform with RTDM brings many advantages. First, it provides a comprehensive technical solution to the long-standing problems in the distribution industry. With the power of RTDM, SPoG can enhance supply chain visibility, streamline inventory management, ensure compliance, simplify SKU management, and provide an excellent customer experience.

[0035] The real-time tracking and analysis provided by RTDM enhances SPoG’s ability to efficiently manage its supply chain and inventory, providing accurate, up-to-date information that enables distributors to make informed decisions quickly.

[0036] SPoG and RTDM integration also ensures data consistency and reduces errors in SKU management. By providing a centralized platform to manage data from various OEMs, it simplifies product localization and helps meet market requirements.

[0037] RTDM's global compliance database is integrated with SPoG to facilitate compliance in cross-border transactions and reduce the burden of manual tracking, saving a lot of time and resources.

[0038] In some embodiments, the distribution platform includes SPoG and RTDM to provide an improved integrated distribution system that can fully leverage the advantages of distribution models, address existing challenges, and achieve sustainable development in an evolving global market. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 A certain embodiment of a distribution platform operating environment, referred to as a system in this embodiment, is shown.

[0040] Figure 2 Shown Figure 1 A certain embodiment of a distribution platform operating environment based on elements.

[0041] Figure 3 A certain embodiment of a supply chain and distribution management system is shown.

[0042] Figure 4 Embodiments of an advanced distribution platform including a complex distribution network management system are depicted, which can be used as system embodiments to provide a technical distribution platform for optimizing distribution network management and operations.

[0043] Figure 5 An RTDM module according to an embodiment of the present disclosure is shown.

[0044] Figure 6 An SPoG UI according to an embodiment of the present disclosure is shown.

[0045] Figure 7 An RTDM module according to an embodiment of the present disclosure is shown.

[0046] Figure 8 is a flowchart of a method for performing merchant login using SPoG UI according to some embodiments of the present disclosure.

[0047] Fig. 9is a flowchart of a method for performing dealer login using SPoG UI according to some embodiments of the present disclosure.

[0048] Fig.10 is a flow chart of a method for performing customer and end-customer login using SPoG UI according to some embodiments of the present disclosure.

[0049] Fig.11 is a block diagram of example device components according to some embodiments of the present disclosure. DETAILED DESCRIPTION

[0050] The embodiments of the present disclosure may be implemented by hardware, firmware, software, or any combination thereof. The embodiments of the present disclosure 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, and instructions may be described herein as performing certain actions. However, it should be understood that such descriptions are only for the sake of narrative convenience, and such actions are actually due to a computing device, processor, controller, or other device executing firmware, software, routines, instructions, etc.

[0051] 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, operations may be performed in different orders and / or in different ways.

[0052] Figure 1 A distribution platform operating environment 100 is shown, referred to in this embodiment as system 110. System 110 operates in the context of an information technology (IT) distribution model for 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 range of features and dynamics that contribute to the success and efficiency of the distribution platform.

[0053] Customers 120 within the operating environment of system 110 represent businesses or individuals seeking IT solutions to meet their specific needs. These customers may need a variety of IT products, such as hardware components, software applications, network equipment, or cloud-based services. System 110 provides customers with a user-friendly interface, allowing customers to browse, search, and select the most appropriate IT solution based on their needs. Customers can also access real-time data and analysis through system 110, empowering customers to make informed decisions and optimize their IT infrastructure.

[0054] End customers 130 are the ultimate beneficiaries of the IT solutions provided by system 110. End customers 130 may include businesses or individuals who utilize IT products and services to improve their operations, productivity, or daily activities. End customers 130 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 130 to track their orders, receive updates on delivery status, and access customer support services, thereby enhancing the overall customer experience.

[0055] Suppliers 140 play a key role within the operating environment of system 110. These suppliers 140 include manufacturers, distributors, and suppliers that provide a variety of IT products and services. System 110 acts as a centralized platform for sellers to showcase their products, manage inventory, and facilitate transactions with customers and dealers. Suppliers can fully utilize system 110 to streamline their supply chain and distribution operations, manage pricing and promotions, and gain insights into customer preferences and market trends. By integrating with system 110, sellers can expand their reach, access new markets, and enhance their overall visibility and competitiveness.

[0056] Reseller 150 is a middleman within the distribution model, bridging the gap between sellers and customers. Reseller 150 plays 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.

[0057] 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 in real-time between stakeholders, 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 communication and interoperability, thereby eliminating data silos and enabling end-to-end visibility.

[0058] Scalability and flexibility are key features of System 110 to meet 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. System 110 is designed to handle large-scale data processing, storage, and analysis, ensuring that it can support the needs of the distribution platform evolution. In addition, System 110 makes full use of a technology stack including .NET, Java, and other suitable technologies to provide a robust foundation for its operation.

[0059] In summary, the operating environment of system 110 within the IT distribution model includes customers 120, end customers 130, sellers 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.

[0060] Figure 2 Shown Figure 1 200. Within the operating environment, integration points 210 facilitate data flows and connections between various customer systems 220, vendor systems 240, dealer systems 260, and other entities involved in the distribution process. This diagram illustrates the interconnectivity and mechanisms for achieving efficient collaboration and data-driven decision making.

[0061] The operating environment 200 may include a system 110 that serves as a distribution platform that acts as a central hub to manage and facilitate the distribution process. The system 110 may be configured to perform functions and operations that serve as a bridge between customer systems 220, vendor systems 240, dealer systems 260, and other entities within the ecosystem. The system 110 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:

[0062] Customer system integration: Integration point 210 may enable system 110 to connect with customer system 220, thereby enabling efficient data exchange and synchronization. Customer system 220 may include various entities, such as customer system 221, customer system 222, and customer system 223. These systems represent internal systems utilized by customers, such as enterprise resource planning (ERP) or customer relationship management (CRM) systems. Integration with customer system 220 empowers customers to access real-time inventory information, pricing details, order tracking, and other relevant data, thereby enhancing customer visibility and decision-making capabilities.

[0063] 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 the vendor can efficiently update its product offerings, manage pricing and promotions, and receive real-time order notifications and fulfillment details.

[0064] Dealer system integration: Integration point 210 provides dealer system 260 with the ability to connect to system 110. Dealer system 260 may include entities such as dealer system 261, dealer system 262, and dealer system 263, thereby representing sales systems, customer management systems, and service delivery platforms employed by dealers. Integration with dealer system 260 empowers dealers to access the latest product information, manage customer accounts, track sales performance, and provide value-added services to their customers.

[0065] Integration with other entity systems: 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 communication and data exchange, thereby promoting a collaborative and efficient distribution process.

[0066] 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 uses industry standard protocols such as RESTful API, SOAP, or GraphQL to establish communication channels and enable data exchange.

[0067] In some embodiments, the system 110 can combine 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.

[0068] 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 between customer systems 220, vendor systems 240, dealer systems 260, and other entities. This data exchange facilitates real-time visibility, enables data-driven decision making, and improves operational efficiency across the distribution platform.

[0069] In some embodiments, the system 110 makes full use of advanced technologies such as Typescript, NodeJS, ReactJS, NET Core, C#, and other suitable technologies that support integration points 210 and enable integrated communications within the operating environment 200. Such 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 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. Such real-time access to accurate and up-to-date information empowers stakeholders to make informed decisions, optimize supply chain operations, and improve customer experience.

[0070] Figure 2 Several elements in the depicted operating environment may include conventional well-known elements that are only briefly summarized herein. 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 the Wireless Access Protocol (WAP) or any other computing device that can directly or indirectly connect to the Internet or other network connections. Each client system can typically run an HTTP client, such as a Microsoft Edge browser, a Google Chrome browser, an Opera browser, or a mobile device browser that supports WAP, thereby allowing the client system to access, process, and view information, pages, and applications available from the distribution platform through the network.

[0071] 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.

[0072] The client system and its components may be operator configurable using applications (including web browsers) running on a central processing unit such as an Intel Pentium processor or similar processor. Similarly, the distribution platform (system 110) and its components may be operator configurable using applications running on a central processing unit such as a processor system, which may include an Intel Pentium processor or similar processor and / or multiple processor units.

[0073] 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.

[0074] 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.

[0075] It should be understood that the embodiments of the present disclosure may be implemented in a variety of programming languages ​​executed on a client system, server, or server system, and that the language selection may depend on the specific requirements and environment of the distribution platform.

[0076] 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.

[0077] Figure 3 A system 300 for supply chain and distribution management is shown. System 300 ( Figure 3 ) is a supply chain and distribution management solution designed to address the challenges of a fragmented distribution ecosystem in the global distribution industry. System 300 may include several interconnected components and modules that work in coordination to optimize supply chain and distribution operations, enhance collaboration, and drive business efficiency.

[0078] The Single Pane View (SPoG) UI 305 serves as a centralized user interface, providing stakeholders with a unified view of the entire supply chain. The SPoG UI consolidates information from various sources and presents real-time data, analysis, and functionality tailored to the user's specific role and responsibilities. By providing a customizable and intuitive dashboard-type layout, the SPoG UI enables users to access relevant information and tools, enabling users to make data-driven decisions and efficiently manage their supply chain and distribution activities.

[0079] For example, logistics managers can use SPoG UI 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 transported goods 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.

[0080] SPoG UI 305 integrates 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 distribution ecosystem.

[0081] For example, when a purchase order is generated in the SPoG UI, the system automatically updates inventory levels, triggers a notification to the warehouse management system, and initiates the shipping process. This integration enables efficient order fulfillment, reduces manual errors, and improves overall supply chain visibility.

[0082] The real-time data grid (RTDM) module 310 is another key component of the system 300 and is responsible for ensuring the data flow within the distribution system. This module aggregates data from multiple sources, coordinates the data, and ensures data availability in real time.

[0083] The present disclosure illustrates the capabilities of the RTDM module in light of the following example. In a distribution network, the RTDM module 310 collects data from various systems including different inventory management systems, point-of-sale terminals, and customer relationship management systems, and reconciles these data by aligning formats, standardizing measurement units, and reconciling any differences. The reconciled data is then made available in real time, allowing stakeholders to access accurate, up-to-date information across the supply chain.

[0084] The RTDM module 310 can be configured to capture data changes across multiple transaction systems in real time. The module uses an advanced Change Data Capture (CDC) mechanism that constantly monitors transaction systems to detect any updates or modifications. The CDC component is specifically designed to work with a variety of transaction systems, including traditional ERP systems, customer relationship management (CRM) systems, and other enterprise-level systems, ensuring compatibility and flexibility for operating businesses in a variety of environments.

[0085] With access to real-time data, stakeholders can make timely decisions and respond quickly to changing market conditions. For example, if the RTDM module detects a sudden spike in demand for a specific product, it can trigger an alert to the production team, allowing them to adjust the manufacturing schedule and prevent stock-outs.

[0086] The RTDM module 310 facilitates data management operations within the supply chain. The module 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 increased efficiency, improved customer service, and innovation.

[0087] Another component of the system 300 is the Advanced Analytics and Machine Learning (AAML) module 315. The AAML module extracts valuable insights from the collected data by leveraging powerful analytical tools and algorithms, such as Apache Spark, TensorFlow, or scikit-learn. The module implements advanced analytics, predictive modeling, anomaly detection, and other machine learning capabilities.

[0088] For example, the AAML module can analyze historical sales data to identify seasonal patterns and predict future demand. The module 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 automates repetitive tasks, predicts customer preferences, and optimizes supply chain processes.

[0089] In addition to demand forecasting, the AAML module can provide insights into customer behavior, enabling targeted marketing campaigns and personalized customer experience. For example, by analyzing customer data, the module can identify cross-sell or up-sell opportunities and recommend relevant products to individual customers.

[0090] In addition, the AAML module 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.

[0091] System 300 emphasizes integration and interoperability to connect existing enterprise systems such as ERP systems, warehouse management systems, and customer relationship management systems. By establishing connections and data flows between these systems, System 300 enables smooth data exchange, process automation, and end-to-end visibility across the supply chain. Integration protocols, APIs, and data connectors facilitate communication and interoperability between different modules and components, creating a comprehensive interconnected distribution ecosystem.

[0092] The implementation and deployment of system 300 can be customized to meet specific business needs. The system can be deployed as a cloud-native solution using containerization technologies (such as Docker) and orchestration frameworks (such as Kubernetes). This approach ensures scalability, easy management, and efficient updates across different environments. The implementation process involves configuring the system to meet specific supply chain and distribution requirements, integrating with existing systems, and customizing modules and components based on business needs and preferences.

[0093] The supply chain and distribution management system 300 is a comprehensive innovative technology solution that solves the challenges faced by the fragmented distribution ecosystem. The system combines the functions of SPoG UI 305, RTDM module 310 and AAML module 315 and integrates with existing systems. By leveraging different technology stacks, scalable architecture and robust integration capabilities, the system 300 provides end-to-end visibility, data-driven decision-making and optimized supply chain operations. Non-limiting examples and options are provided in this specification and can be customized to meet specific industry requirements and drive efficiency and success in supply chain and distribution management.

[0094] Figure 4 An embodiment of an advanced distribution platform including a complex distribution network management system 400 is depicted, which can be used as an embodiment of the system 300, providing a technical distribution platform for optimizing distribution network management and operations. The system 400 includes several interconnected modules, each module having a specific function and contributing to the overall efficiency of supply chain operations. In some embodiments, these modules may include 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 insight tag display 445, predictive analysis module 450, recommendation system module 455, notification module 460, self-login module 465 and communication module 470.

[0095] System 400, as an embodiment of system 300, makes full use of a series of technologies and algorithms to achieve supply chain and distribution management. Such technologies and algorithms promote efficient data processing, personalized interaction, real-time analysis, secure communication and effective management of documents, catalogs and performance metrics.

[0096] In some embodiments, SPoG UI 405 serves as a central interface within the system 400, providing stakeholders with a unified view of the entire distribution network. This module leverages front-end technologies such as ReactJS, TypeScript, and Node.js to create an interactive and responsive user interface. Such 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.

[0097] CIM 410 or the Customer Interaction Module employs algorithms and technologies such as Oracle Eloqua, Adobe Target, and Okta to manage customer relationships within the distribution network. Such technologies enable the module to securely process customer data, personalize the customer experience, and provide stakeholder access controls.

[0098] The RTDM module 415 or real-time data grid module is a key component of the system 400 to ensure smooth data flow across the distribution ecosystem. The module utilizes technologies such as Apache Kafka, Apache Flink, or Apache Pulsar for data ingestion, processing, and stream management. Such technologies enable the RTDM module 415 to handle real-time data streams, process large amounts of data, and ensure low-latency data processing. In addition, the module employs a change data capture (CDC) mechanism to capture real-time data updates from various transactional systems such as traditional ERP systems and CRM systems. This functionality allows stakeholders to access accurate and up-to-date information to make informed decisions.

[0099] The AI ​​module 420 within the system 400 utilizes advanced analytics and machine learning algorithms, including Apache Spark, TensorFlow, and scikit-learn, to extract valuable insights from the data. These algorithms enable the module to automate repetitive tasks, predict demand patterns, optimize inventory levels, and improve overall supply chain efficiency. For example, the AI ​​module 420 can utilize predictive models to predict demand, thereby allowing stakeholders to optimize inventory management and minimize out-of-stock or over-inventory situations.

[0100] The interface display module 425 focuses on presenting data and information in a clear, user-friendly manner. This module utilizes technologies such as HTML, CSS, and JavaScript frameworks such as ReactJS to create interactive, responsive user interfaces. Such 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.

[0101] The personalized interaction module 430 uses customer data, historical trends, and machine learning algorithms to generate personalized recommendations for products or services. This module uses technologies such as Adobe Target, Apache Spark, and TensorFlow to analyze data, model, and deliver targeted recommendations. For example, this module can analyze customer preferences and purchase history to provide personalized product recommendations, enhance customer satisfaction, and promote sales.

[0102] Document hub 435 acts as a centralized repository for storing and managing documents within system 400. This module leverages technologies such as SeeBurger and Elastic Cloud to enable efficient document management, storage, and retrieval. For example, document hub 435 can employ SeeBurger's document management capabilities to categorize and organize documents based on document type (e.g., contracts, invoices, product specifications, or compliance documents), thereby allowing stakeholders to easily access and retrieve relevant documents as needed.

[0103] The catalog management module 440 enables the creation, management, and distribution of up-to-date product catalogs. This module ensures that stakeholders have access to the latest product information, including specifications, pricing, availability, and promotions. Technologies such as Kentico and Akamai are used to facilitate catalog updates, content delivery, and caching. For example, this module can utilize Akamai's Content Delivery Network (CDN) to quickly and efficiently deliver catalog information to stakeholders regardless of their geographic location.

[0104] Performance Insights Tag Monitor 445 collects, analyzes, and visualizes real-time performance metrics and insights about supply chain operations. This module leverages tools such as Splunk and Datadog to enable effective performance monitoring and provide actionable insights. For example, this 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.

[0105] The predictive analytics module 450 employs machine learning algorithms and forecasting models to predict demand patterns, optimize inventory levels, and improve overall supply chain efficiency. This module utilizes technologies such as Apache Spark and TensorFlow for data analysis, modeling, and forecasting. For example, this module can utilize the deep learning capabilities of TensorFlow to analyze historical sales data and predict future demand, allowing stakeholders to optimize inventory levels and minimize costs.

[0106] The recommendation system module 455 focuses on providing intelligent recommendations to stakeholders within the distribution network. This module generates personalized recommendations for products or services based on customer data, historical trends, and machine learning algorithms. Technologies such as Adobe Target and Apache Spark are used to perform data analysis, modeling, and delivery of targeted recommendations. For example, this module can utilize Adobe Target's recommendation engine to analyze customer preferences and behaviors and deliver personalized product recommendations across various channels, thereby enhancing customer engagement and driving sales.

[0107] The notification module 460 enables the distribution of real-time notifications to stakeholders regarding important events, updates, or alerts within the supply chain. This module leverages technologies such as Apigee X and TIBCO to implement message queues, event-driven architectures, and integrated notification delivery. For example, this module can leverage TIBCO's messaging infrastructure to send real-time notifications to stakeholders' devices, thereby ensuring timely dissemination of relevant information.

[0108] The self-onboarding module 465 facilitates the onboarding process for new stakeholders entering the distribution network. The module provides guided steps, tutorials, or documentation to help users become familiar with the system and its functionality. Technologies such as Okta and Kentico are used to ensure secure user authentication, access control, and self-learning resources. 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 functionality.

[0109] The communication module 470 enables communication and collaboration within the system 400. This module provides a channel for stakeholders to interact, exchange messages, share documents, and collaborate on projects. Technologies such as Apigee Edge and Adobe Launch can be used to facilitate secure and efficient communication, document sharing, and version control. For example, this module can utilize the API management capabilities of Apiege Edge to ensure secure and reliable communication between stakeholders, enabling effective collaboration.

[0110] Therefore, the system 400 may include various modules that utilize various technologies and algorithms to optimize supply chain and distribution 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 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 to achieve efficient data management, secure communication, personalized experience, and effective performance monitoring helps to improve operational efficiency and the success of supply chain and distribution management.

[0111] Real-time data grid

[0112] Figure 5 RTDM module 500 according to an embodiment of the present disclosure is shown. RTDM module 500 may be an embodiment of RTDM module 310 and may include interconnected components, processes, and subsystems configured to implement real-time data management and analysis.

[0113] like 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 and distribution management domain.

[0114] The RTDM module 500 may include an integration layer 510 (also known as a "record system") that integrates with various enterprise systems. These enterprise systems may include ERPs (such as SAP, Impulse, META, and I-SCALA, etc.) and 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 accurate and up-to-date data.

[0115] The RTDM module 500 may include a data layer 520 configured to process and transform data for retrieval and analysis. The core of the data layer is a data grid, which is designed to provide a cloud-based infrastructure that provides scalable, fault-tolerant data storage capabilities. Within the data grid, multiple purpose data stores (PDS) are deployed to store specific types of data, such as customer data, product data, or inventory data. Each PDS is optimized based on specific use cases and requirements for efficient data retrieval. The PDS is configured to store specific types of data, such as customer data, product data, financial data, etc. These PDSs serve as repositories for coordinated and standardized data, ensuring data consistency and integrity across systems.

[0116] In some embodiments, the RTDM module 500 implements a data replication mechanism to capture real-time changes from multiple data sources, including transaction systems such as ERP (e.g., Impulse, META, I-SCALA). The captured data is then processed and coordinated on the fly, converting it into a standardized format suitable for analysis and integration. This process ensures that data is easily accessible and up-to-date within the data grid, thereby facilitating real-time insights and decisions.

[0117] 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 distribution ecosystem. In some embodiments, the data layer 520 can include a highly scalable and robust data lake (which can be referred to as data lake 522) and a set of purposeful data stores (PDS), which can be represented as PDS 524.1 to 524.N. These components work in coordination to ensure efficient data management, coordination, and real-time availability.

[0118] The core of the data layer 520 is the data lake, i.e., data lake 522, which is a state-of-the-art storage and processing infrastructure designed to handle the ever-increasing volume, variety, and velocity of data generated within the supply chain. Relying on a scalable distributed file system (such as Apache Hadoop Distributed File System (HDFS) or Amazon S3), the data lake provides a scalable and unified platform for storing structured and unstructured data. Leveraging the elasticity and fault tolerance of cloud storage, data lake 522 can adapt to the influx of data from different sources.

[0119] Associated with the data lake 522, multiple intentional data stores PDS 524.1 to 524.N may be employed. Each PDS 524 may act 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 intentional data stores allow for efficient data retrieval, analysis, and processing to meet the different needs of supply chain stakeholders.

[0120] To ensure real-time data synchronization, the data layer 520 can be configured to employ one or more sophisticated change data capture (CDC) mechanisms. These CDC mechanisms integrate with transactional systems such as traditional ERPs such as SAP, Impulse, META, and I-SCALA, as well as other enterprise-level systems. CDC continuously monitors and captures any updates, modifications, or new transactions in these systems in real time. By capturing these changes, the data layer 520 ensures that the data within the data lake 522 and PDS 524 remains up to date, thereby providing stakeholders with real-time insights into the distribution ecosystem.

[0121] 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 a variety of existing systems and providing flexibility for customization and expansion capabilities. For example, the data layer 520 can utilize the Java technology stack, including frameworks such as Spring and Hibernate, to integrate with a system of record with a variety of ERP systems and other enterprise-level solutions. This can facilitate smooth data exchange, process automation, and end-to-end visibility across the supply chain.

[0122] In terms of data processing and analysis, in some non-limiting examples, the data layer 520 takes full advantage of the capabilities of distributed computing frameworks such as Apache Spark or Apache Flink. These frameworks can 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.

[0123] 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.

[0124] In some embodiments, the data layer 520 can be deployed in a cloud-native environment using containerization technologies (such as Docker) and orchestration frameworks (such as Kubernetes). This approach ensures scalability, elasticity, and efficient resource allocation. For example, the data layer 520 can be deployed on a cloud infrastructure provided by AWS, Azure, or Google Cloud using its managed services and scalable storage options. This allows efficient expansion of resources based on demand, minimizes operational overhead, and provides a resilient infrastructure for managing supply chain data.

[0125] 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. The integration of various technology stacks such as .NET or Java and distributed computing frameworks such as Apache Spark enables powerful data processing, advanced analysis, and machine learning capabilities. With robust data governance and security measures, the data layer 520 ensures data integrity, confidentiality, and compliance. The data layer 520 enables supply chain stakeholders to make data-driven decisions, optimize operations, and drive business success in complex and dynamic distribution environments through its scalable infrastructure and integration with existing systems.

[0126] The RTDM module 500 may include an AI module 530 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 model to provide accurate, up-to-date insights. The AI ​​module 530 may generate predictions, recommendations, and alerts, and publish such insights to dedicated data feeds.

[0127] The data engine layer 540 may include a set of interconnected systems responsible for data ingestion, processing, conversion, and integration. The data engine layer 540 of the RTDM module 500 may include a series of autonomously operated headless engines 540.1 to 540.N. These engines represent different functions within the system, and may include, for example, one or more recommendation engines, insight engines, and subscription management engines. Engines 540.1 to 540.N can make full use of the coordinated data stored in the data grid to deliver specific business logic and services. Each engine is designed as a pluggable engine, allowing flexibility and future expansion of module capabilities. Figure 5 An exemplary engine is shown in , but is not intended to be limiting. Any additional headless engines may be included in the data engine layer 540 or other exemplary layers of the disclosed system.

[0128] These systems can be configured to receive data from multiple sources, such as transactional systems, IoT devices, and external data providers. The data ingestion process involves extracting data from these sources and converting the data into a standardized format. Data processing algorithms are applied to cleanse, aggregate, and enrich the data for further analysis and integration.

[0129] Additionally, a data distribution mechanism may be employed to facilitate integration and access to the RTDM module 500. 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.

[0130] The experience layer 550 focuses on delivering an intuitive, 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 the experience layer, users can retrieve and analyze real-time data on various supply chain metrics, such as inventory levels, sales performance, and customer demand. The user experience layer supports personalized data feedback, allowing users to customize their views and receive relevant updates based on their roles and responsibilities. Users can subscribe to specific data updates customized to their preferences and roles, such as inventory changes, pricing updates, or new SKU notifications.

[0131] Therefore, in some embodiments, the RTDM module 500 for supply chain and distribution management can be integrated with a system of record and include one or more data layers with a data grid and a data storage area, an AI component, a data engine layer, and a user experience layer. These components work together to provide users with intuitive access to real-time supply chain data, efficient data processing and analysis, and 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 facilitates supply chain and distribution management by providing a scalable real-time data management solution, and its innovative architecture allows for rich integration of different data sources, efficient data coordination, and advanced analytical capabilities. The module has the ability to replicate and coordinate data from different ERPs while maintaining the auditability and repeatability of transactions, providing a clear advantage in achieving a unified view of sellers, dealers, customers, end customers, and other entities in the distribution system (including IT distribution systems).

[0132] SINGLE PANE OF GLASS UI

[0133] Figure 6 An SPoG UI according to an embodiment of the present disclosure is shown, namely SPoG UI 600. In some embodiments, SPoG UI 600 can be an embodiment of SPoG UI 305, representing an intuitive and comprehensive user interface designed to provide stakeholders with a customizable unified view of the entire distribution ecosystem. The SPoG UI combines a series of features and functions that enable users to gain a comprehensive understanding of the supply chain and effectively manage its operations.

[0134] The SPoG UI 600 may include a Unified View (UV) module 605 that provides stakeholders with a customizable, centralized dashboard-type layout. The module allows users to access real-time data, analysis, and functionality tailored to their specific roles and responsibilities within the distribution ecosystem. The UV module 605 serves as a single entry point for users, providing a comprehensive, integrated view of supply chain operations and empowering users to make data-driven decisions.

[0135] The SPoG UI 600 is integrated with the Real-Time Data Exchange Module 610, facilitating continuous data exchange between the SPoG UI 600 and the RTDM 310 to fully leverage 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, synchronized data.

[0136] The collaborative decision module 615 within the SPoG UI 600 facilitates real-time collaboration and communication between stakeholders. The module enables exchanging information, initiating workflows, and sharing 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, synchronized data. This promotes overall operational efficiency and collaboration within the distribution ecosystem.

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

[0138] The customization module 625 empowers 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.

[0139] SPoG UI 600 includes a powerful 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.

[0140] The SPoG UI 600 may include a mobile and cross-platform access module 635 to ensure accessibility across multiple devices and platforms. Stakeholders can access the interface from desktop computers, laptops, smartphones, and tablet computers, 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 access to real-time data and functionality across a variety of devices.

[0141] By integrating these reference elements / modules within the SPoG UI 600 and leveraging its integration capabilities with the RTDM module 310 / 500, stakeholders can benefit from a powerful user-friendly interface for supply chain and distribution management. The Unified View (UV) module 605 provides a customizable, comprehensive view of the supply chain and distribution environment, while the Real-Time Data Exchange module 610 ensures accurate and up-to-date data synchronization. The Collaborative Decision Making module 615 promotes effective communication and collaboration, and the RBAC module 620 ensures secure access control. The Customization module 625, the Data Visualization module 630, and the Mobile and Cross-Platform Access module 635 enhance user experience, data analysis, and accessibility, respectively. In summary, these modules enable stakeholders to make informed decisions, optimize supply chain operations, and improve business efficiency within the distribution ecosystem.

[0142] SPoG 600 can contain high-velocity data in data-rich environments. In today’s data-rich environment, conventional UI design frequently struggles to present large amounts of information in a digestible, efficient, and visually appealing manner. The challenge is exacerbated when data is dynamic, changing in real time and needs to be effectively displayed in a single-pane environment that emphasizes a clean, white-space design.

[0143] SPoG 605UI can be configured to efficiently manage real-time data, thereby maintaining a visually clean interface without compromising performance. This innovative approach includes unique UI structure configuration, responsive data visualization, real-time data processing methods, adaptive information architecture, and white space optimization.

[0144] SPoG UI 605 is built around a grid-based layout system by making full use of CSS grid and Flexbox technology. This structure allows for the flexibility of creating fluid layouts that automatically adjust elements based on 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.

[0145] 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, operations may be performed in different orders and / or in different ways.

[0146] Figure 7 RTDM 700 is shown as an embodiment of ERP agnostic real-time data grid and change data capture. In some embodiments, RTDM 700 can be operably connected to a UI, which can be an embodiment of SPoG UI 405 (e.g., Figure 7) or another embodiment of the SPoG UI described herein. RTDM 700 may include one or more headless engines 705, which may include one or more login engines 705.A, document hub engines 705.B, connection engines 705.C, supply and demand engines 705.D, customer service engines 705.E, performance engines 705.F, business planning engines 705.G, market entry engines 705.H, and vendor management engines 705.I. In some embodiments, RTDM 700 may also include API connections 710 and data layers 720, which may include global data lakes 722, data governance modules 724, and record systems 725.

[0147] According to some embodiments, RTDM 700 is configured to perform change data capture (CDC) related processes in an ERP-agnostic real-time data grid. The CDC mechanism is implemented to capture real-time changes from multiple data sources, including transactional systems such as ERP. The captured data is then processed and coordinated on the fly, converting it into a standardized format suitable for analysis and integration. This process ensures that the data in the data grid remains up to date, thereby enabling real-time insights and decisions.

[0148] CDC can include one or more of the following steps:

[0149] Data source monitoring: CDC mechanisms continuously monitor transactional systems (such as ERP), traditional systems, and other enterprise-level solutions to obtain any updates, modifications, or new transactions. This monitoring can be achieved through various technologies, such as log-based capture, triggers, or polling mechanisms.

[0150] Change Detection: Once a change is detected in the data source, the CDC mechanism captures the specific changes made to the data. This can include inserts, updates, deletes, or other modifications.

[0151] Data Capture: The CDC mechanism captures changed data in its original format, preserving the original data structure and format. This ensures that the captured data can be accurately transformed and integrated.

[0152] Transformation and Harmonization: The captured raw data goes through a transformation and harmonization process. This includes converting the data into a standardized format suitable for analysis and integration. Data cleansing, normalization, and enrichment techniques can be applied at this stage to ensure data quality and consistency.

[0153] Integration and storage: The transformed and harmonized data is then integrated into the data grid, specifically into the global data lake and related PDS. The data is stored in a structured way, making it easily accessible and usable for real-time analysis and decision making.

[0154] There are several CDC methods available based on RTDM 700, depending on the data source and requirements:

[0155] Log-based CDC: This approach leverages the transaction log or change log of the source system to capture and extract changes made to the data. Algorithms are used to analyze and interpret the log files to identify specific changes and extract relevant data for further processing.

[0156] Trigger-based CDC: In this approach, triggers are set on the source database tables to capture and track any changes made to the data. When a change occurs, the trigger is triggered and the CDC mechanism captures the relevant data changes for further processing.

[0157] Polling-based CDC: In some cases, when real-time data capture is not feasible or necessary, a polling mechanism can be used. The CDC mechanism periodically polls the source system to check for any new or modified data. The data is then captured and processed based on the polling interval.

[0158] Such algorithms can be implemented using a combination of programming languages, database query languages, and data integration tools, leveraging technologies such as Apache Kafka, Apache Nifi, etc. In addition, AI and / or ML models can be generated and trained to implement CDC in real-time data processing pipelines.

[0159] According to various aspects of the present disclosure, the CDC ensures that data within the ERP-agnostic real-time data grid is continuously updated and coordinated, thereby enabling stakeholders to access real-time insights, make informed decisions, and optimize supply chain operations.

[0160] In some embodiments, headless engines 705 may be included in RTDM 700, with each engine serving a specific function within the system.

[0161] In some embodiments, the onboarding engine 705.A simplifies the process of onboarding new vendors to the distribution ecosystem. The engine handles tasks such as registration and account creation, vendor profile building, product evaluation, and contract fulfillment. By streamlining the onboarding process, the engine ensures an integrated experience, thereby reducing friction and enhancing collaboration between vendors and the supply chain.

[0162] In some embodiments, the document hub engine 705.B acts as a centralized repository for managing contracts and related documents. The engine provides the ability to create, store, and retrieve contracts, thereby ensuring easy access and efficient contract management. By centralizing contract-related information, the document hub engine enables stakeholders to maintain a comprehensive view of contract agreements and related documents.

[0163] In some embodiments, the connection engine 705.C facilitates communication and data exchange between various entities within the distribution ecosystem. The engine enables connectivity for catalog management, order status and tracking, and rich content delivery. Through standardized APIs and protocols, the connection engine ensures efficient and reliable data transmission, thereby enhancing overall connectivity and collaboration between stakeholders.

[0164] In some embodiments, the supply and demand engine 705.D focuses on managing supply chain dynamics, including order fulfillment, inventory visibility, and supply chain optimization. The engine provides real-time insights into supply and demand patterns, allowing stakeholders to make informed decisions and optimize their operations. The supply and demand engine acts as a central hub for supply chain visibility, ensuring timely processing of orders and efficient inventory management.

[0165] In some embodiments, the customer service engine 705.E handles general problem solving and customer support within the distribution ecosystem. The engine provides functionality for managing customer inquiries, complaints, and returns. By streamlining customer service operations, the engine improves customer satisfaction and strengthens customer relationships.

[0166] In some embodiments, the performance engine 705.F focuses on monitoring and analyzing key performance indicators and business operating status indicators. The engine provides stakeholders with visibility into sales performance, channel visibility, alerts, and insights. The performance engine facilitates data-driven decision making, allowing stakeholders to identify opportunities to improve and optimize their business processes.

[0167] In some embodiments, the business planning engine 705.G enables stakeholders to capture and track business goals and objectives. The engine provides strategic planning functions, forecasting functions, and resource allocation functions. By combining business goals with operational strategies, the business planning engine supports effective decision-making and helps drive business success.

[0168] In some embodiments, the go-to-market engine 705.H comprises core marketing services within the distribution ecosystem. The engine facilitates the creation and distribution of marketing content, training resources, and sales support materials. The go-to-market engine supports product launches, marketing campaigns, and enabling channel partners, thereby ensuring an effective go-to-market strategy.

[0169] In some embodiments, the vendor management engine 705.I focuses on managing vendor relationships and related activities. The engine handles contract projects, supply and demand management, customer service, and vendor and partner-related market planning. The vendor management engine ensures effective collaboration and coordination between the distribution ecosystem and its suppliers and partners.

[0170] In some embodiments, the API connection layer 710 enables integration and communication between RTDM 700 and external systems (such as ERP, traditional systems and other enterprise-level solutions). Through well-defined APIs, RTDM 700 can exchange data and interact with these systems to ensure data consistency and real-time updates.

[0171] The data layer 720 can be configured as a set of interconnected systems and infrastructure designed to manage, process, and analyze supply chain data, facilitating real-time data management, coordination, and availability, thereby enabling stakeholders to derive actionable insights and make informed decisions. In some embodiments, the data layer 720 can include a global data lake 722 and a data governance module 724.

[0172] In some embodiments, the global data lake 722 is configured as a scalable, fault-tolerant storage infrastructure that serves as a central repository for supply chain data within the RTDM 600. In a non-limiting example, the global data lake 722 can be formed as an implementation of cloud-based storage technology (such as Apache Hadoop Distributed File System (HDFS) or Amazon S3) to accommodate the different data needs of the distribution ecosystem. Multiple purposeful data stores (PDS) can be configured and deployed within the global data lake 722. These PDSs are specially designed repositories optimized for storing and retrieving specific types of data related to the supply chain field. Each PDS in the global data lake 722 acts as a dedicated storage entity for a specific data category (such as customer data, product data, financial data, etc.).

[0173] In some embodiments, the global data lake 722 is used as a scalable, fault-tolerant storage infrastructure within the RTDM 700. The global data lake contains various purpose data stores (PDS) optimized for efficient data retrieval and storage. These PDS include product data, non-transactional data, transactional data, order status and tracking (OST) data, renewal data, subscription data, etc. The global data lake acts as a central repository for coordinated and standardized data, enabling real-time insights and analysis.

[0174] In some embodiments, PDSs within the global data lake 722 provide several advantages: Each PDS can be optimized based on specific use cases and requirements to enable efficient data retrieval. By organizing data into purpose-built data stores, the global data lake 722 enables stakeholders to quickly access relevant data, thereby facilitating real-time insights and decision making. PDSs in the global data lake 722 can be enabled to coordinate and standardize data across the distribution ecosystem. By enforcing consistent data formats, structures, and semantics, PDSs ensure data integrity and enable data integration across different systems and processes.

[0175] The global data lake 722 and its PDS can be enabled to take full advantage of cloud-based storage infrastructure to achieve scalability and elasticity. As the volume of supply chain data increases, the global data lake 722 can scale to accommodate the growing data needs, ensuring that the system remains high-performance and responsive.

[0176] PDS also allows data segmentation based on specific data types, ensuring data privacy and confidentiality. By organizing data into dedicated PDS, sensitive information can be isolated and access controls can be applied to protect data privacy and comply with regulatory requirements.

[0177] In some embodiments, the data governance module 724 is configured to be operably connected to the global data lake 722 and the PDS and to ensure the integrity, security, and compliance of data within the RTDM 600. The data governance module 724 can establish policies, processes, and controls to govern data management activities in the distribution ecosystem.

[0178] In some embodiments, the data governance module 724 can implement robust data security measures, including access control, authentication mechanisms, and encryption techniques, to protect the confidentiality and integrity of supply chain data. The module ensures that only authorized users can access specific data, thereby preventing unauthorized data leakage. The data governance module 724 can also enforce data quality standards and implement data validation and cleansing processes. The module ensures the accuracy, consistency, and reliability of data stored in the global data lake 722 and PDS, so that stakeholders can rely on high-quality data for decision making and analysis.

[0179] The data governance module 724 can facilitate compliance with relevant regulations and industry standards for governing data management within the supply chain domain. This module establishes controls and processes to ensure data privacy, retention, and legal use, thereby reducing the risk of non-compliance and related penalties.

[0180] The data governance module 724 can incorporate data lineage and audit trail mechanisms, allowing stakeholders to track the origin, history, and transformations applied to data within the global data lake 722 and PDS. This ensures data traceability, enhances data transparency, and supports compliance and audit requirements. In some embodiments, the data governance module 724 provides robust data governance and management capabilities within the RTDM 700. The module ensures data integrity, compliance with regulatory requirements, data privacy, and security. The data governance module integrates fine-grained access control, authentication protocols, data encryption technology, and audit trail mechanisms to protect data and ensure accountability.

[0181] In some embodiments, the data governance module 724 can manage and implement several functions for managing different types of data within the supply chain. These functions include:

[0182] Catalog Management: The data governance module 724 facilitates the management of transaction catalog data. This module ensures that catalog information (such as product details, pricing, and availability) is accurate, up-to-date, and consistent across the distribution ecosystem.

[0183] Pimcore Product Data: This functionality within the Data Governance module 724 focuses on managing product data using a Pimcore-based solution. This module enables the creation, storage, and maintenance of product-related information, including descriptions, specifications, images, and other attributes. By centralizing product data management, this module ensures data consistency and accessibility across the supply chain.

[0184] Order Status and Tracking (OST): Data Governance Module 724 contains the functionality to track and manage the status of orders throughout the supply chain. This module provides real-time visibility into the progress of orders, including order placement, fulfillment, and delivery. This functionality enhances transparency and enables stakeholders to monitor and resolve any issues regarding order processing and delivery.

[0185] Quote Management: This module enables management of quotes within the distribution ecosystem. This module allows stakeholders to create, modify, and track quotes for products or services. Quote management functionality ensures efficient handling of quote-related information, thereby facilitating a streamlined quote process and accurate pricing.

[0186] Special Price Management: This functionality in the Data Governance module 724 focuses on managing special price arrangements within the supply chain. This module enables the maintenance and enforcement of special price agreements, such as bid pricing or contract-specific pricing. By ensuring accurate and consistent special prices, this module supports pricing transparency and contract compliance.

[0187] Renewal Management: The data governance module 724 includes functionality for managing renewals within the distribution ecosystem. This module facilitates tracking and managing service contract renewals, license contract renewals, and related renewal schedules. The renewal management functionality ensures timely renewal notifications and accurate contract management.

[0188] Contract Management: This functionality within the Data Governance module 724 enables the management and tracking of contracts within the distribution ecosystem. The module provides a centralized repository for storing and accessing contract-related information such as terms, conditions, and expiration dates. The contract management functionality ensures effective contract governance, compliance, and streamlined contract processes.

[0189] Subscription Management: This module contains functionality for managing subscriptions within the supply chain. This module enables tracking of subscription-based services, usage / metering information, and billing / payment details. Subscription management functionality ensures accurate subscription tracking, billing accuracy, and customer satisfaction.

[0190] Vendor Partner Management: This feature focuses on managing vendor partner relationships within the distribution ecosystem. This module helps maintain strategic partner information, partner program details, partner certifications, and partner-level information. The vendor partner management feature enhances collaboration and coordination with key vendors and partners to achieve mutually beneficial relationships.

[0191] Marketing Data Management: The Data Governance module 724 includes functionality for managing marketing-related data within the distribution ecosystem. This module facilitates the storage, retrieval, and distribution of marketing content, such as marketing materials, training resources, and sales support assets. Marketing data management functionality ensures effective marketing operations, content sharing, and improved go-to-market strategies.

[0192] The data governance module 724 may include additional or alternative functions (not listed) within its comprehensive set of functions to ensure data integrity, security, and compliance within the distribution ecosystem. By implementing fine-grained access controls, authentication mechanisms, and encryption techniques, this module can protect the confidentiality and integrity of supply chain data. This module enforces data quality standards, implements validation processes, and ensures accuracy and consistency of data across different functions.

[0193] In addition, the data governance module 724 supports compliance with regulatory requirements and industry standards for data management within the supply chain domain. The module establishes controls and processes to ensure data privacy, retention, and legitimate use, thereby reducing the risk of non-compliance and related penalties. The module also contains data lineage and audit trail mechanisms, thereby enabling stakeholders to track the source, history, and transformations applied to data.

[0194] System of record 725 represents the integration layer within RTDM 700. Connects various enterprise systems (including ERP and other data sources) to enable data exchange and synchronization. The system of record retrieves relevant information such as sales orders, purchase orders, inventory data, and customer information, ensuring real-time data updates and accurate insights.

[0195] The data layer 720, which includes the global data lake 722 and the data governance module 724, is the core of the RTDM 600. The global data lake 722 provides a scalable, fault-tolerant storage infrastructure, while the PDS optimizes data retrieval, coordination, and integration. The data governance module 724 ensures data integrity, security, and compliance, thereby promoting reliable data management practices in the distribution ecosystem. The combination of these components enables SPoG to fully leverage real-time data insights and drive efficient and informed decisions in complex and dynamic distribution environments.

[0196] In summary, RTDM 700 combines enumeration elements, headless engines, API connections, global data lakes, data governance modules, and record systems to provide a comprehensive and scalable real-time data management solution that enables efficient supply chain operations, data coordination, advanced analytics, and integration with existing enterprise systems. RTDM 700 enables stakeholders to gain timely insights and actionable intelligence to optimize their supply chain processes and drive business success.

[0197] Figure 8 800 is a flow chart of a method 800 for vendor login using SPoG UI according to some embodiments of the present disclosure. In some embodiments, method 800 outlines a streamlined and efficient process for vendor login to a distribution ecosystem by fully leveraging the capabilities of SPoG UI. By integrating real-time data, collaborative decision-making, and role-based access control capabilities, SPoG UI enables stakeholders to effectively manage and optimize the vendor login process. Based on the present disclosure, the operations in method 800 may be performed in a different order and / or in a different manner to accommodate specific implementation requirements.

[0198] At operation 805, the process is initiated when a vendor expresses interest in joining the distribution ecosystem. The computing device receives the vendor's information and relevant details using the SPoG UI. This may include company profile, contact information, product catalog, certifications, and any other relevant data required for the vendor login process.

[0199] At operation 810, the computing device uses the capability of integration with the real-time data exchange module to verify the vendor information. By utilizing real-time data synchronization and accessing external systems, the computing device ensures that the vendor 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.

[0200] At operation 815, the computing device initiates a vendor onboarding workflow through a collaborative decision-making module. This 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 vendor information. The SPoG UI facilitates communication, file sharing, and workflow initiation, enabling stakeholders to jointly evaluate the suitability of vendors and efficiently complete onboarding steps.

[0201] At operation 820, the computing device employs a role-based access control (RBAC) module to manage access control and permissions throughout the vendor onboarding process. The RBAC module ensures that stakeholders can only access specific information and functions required for their roles. This control mechanism protects sensitive data, maintains privacy, and meets regulatory requirements. Authorized stakeholders can securely review and contribute to the vendor onboarding process, thereby promoting a transparent and compliant environment.

[0202] 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 a customizable intuitive dashboard-type 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.

[0203] At operation 830, the computing device enables stakeholders to interact with the data visualization module of the SPoG UI, which provides dynamic visualization and analysis of 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 make informed decisions autonomously, allocate resources efficiently, and optimize onboarding workflows.

[0204] At operation 835, the computing device facilitates 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, thereby 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.

[0205] At operation 840, the computing device uses the workflow management module of the SPoG UI to ensure efficient 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 each task status, track completion, and receive notifications to ensure timely progress.

[0206] At 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 reviewed, and decisions made. The audit trail enhances transparency, accountability, and compliance, providing stakeholders with a reliable record for future reference and potential audits.

[0207] At operation 850, the computing device terminates the vendor onboarding process within the SPoG UI. Once all necessary steps, reviews, and approvals are completed, the vendor is officially onboarded into the distribution ecosystem. The SPoG UI may provide stakeholders with an onboarding process summary, allowing them to verify that all requirements are completed and initiate next steps, such as contract signing, product inventory, and collaboration.

[0208] In conclusion, Figure 8The depicted method 800 outlines a streamlined and efficient vendor onboarding process using the SPoG UI. By leveraging real-time data integration, collaborative decision making, role-based access control, comprehensive visualization, and workflow management capabilities, the SPoG UI empowers stakeholders to successfully onboard vendors into the distribution ecosystem. The process ensures data accuracy, improves transparency, enhances collaboration, and promotes informed decision making throughout the vendor onboarding workflow. The SPoG UI's intuitive interface combined with its customizable features and notifications streamlines the onboarding process, reduces labor, and optimizes vendor integration within a complex and dynamic distribution environment.

[0209] Fig. 9 900 is a flow chart of a method 900 for dealer onboarding using SPoG UI according to some embodiments of the present disclosure. Method 900 outlines a streamlined and efficient process for onboarding dealers into a distribution ecosystem by fully leveraging the capabilities of SPoG UI. By integrating real-time data, collaborative decision-making, and role-based access control capabilities, SPoG UI enables stakeholders to effectively manage and optimize the dealer onboarding process. Based on the present disclosure, the operations in method 900 may be performed in a different order and / or in a different manner to suit specific implementation requirements.

[0210] The process begins when a dealer expresses interest in joining the distribution ecosystem at operation 905. The computing device receives the dealer's information and related 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.

[0211] At operation 910, the computing device uses the ability to integrate with the real-time data exchange module to verify the dealer information. By leveraging real-time data synchronization and accessing external systems, the computing device ensures that the dealer 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.

[0212] At operation 915, the computing device initiates the dealer onboarding workflow through the collaborative decision-making module. This 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 dealer information. The SPoG UI facilitates communication, file sharing, and workflow initiation, enabling stakeholders to jointly evaluate the suitability of dealers and efficiently complete the onboarding steps.

[0213] At 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 required for their roles. This control mechanism protects sensitive data, maintains privacy, and complies with regulatory requirements. Authorized stakeholders can securely review and contribute to the dealer onboarding process, contributing to a transparent compliance environment.

[0214] At 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 a customizable intuitive dashboard-type 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.

[0215] At operation 930, the computing device enables stakeholders to interact with a data visualization module of the SPoG UI that provides dynamic visualization and analysis of 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 empowers stakeholders to make informed decisions, allocate resources efficiently, and optimize dealer onboarding workflows.

[0216] At operation 935, the computing device facilitates 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.

[0217] At 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. This module captures details about actions taken, decisions made, and documents reviewed during the login process. The audit trail enhances transparency, accountability, and compliance, thereby serving as a valuable reference for future audits, reviews, and assessments.

[0218] At 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 distribution ecosystem. The SPoG UI provides stakeholders with an overview of the onboarding process, ensuring that all requirements are met and facilitating next steps, such as contract signing, product listing, and dealer collaboration.

[0219] In conclusion, Fig. 9The method 900 depicted in the figure highlights a streamlined and efficient dealer onboarding process using SPoG UI. By leveraging real-time data integration, collaborative decision-making, role-based access control, comprehensive visualization and audit trail capabilities, SPoG UI empowers stakeholders to successfully onboard dealers into the distribution ecosystem. SPoG UI's intuitive interface, customizable features, and robust collaboration capabilities streamline the onboarding process, enhance transparency, and promote efficient communication among stakeholders. SPoG UI's data visualization capabilities promote data-driven decisions, while audit trails ensure compliance and provide a reliable record of onboarding activities. By effectively leveraging SPoG UI, the dealer onboarding process becomes a well-coordinated workflow, thereby optimizing dealer integration and promoting business success within a dynamic distribution environment.

[0220] Fig.10 is a flow chart of a method 1000 for customer and end-customer onboarding using the SPoG UI according to some embodiments of the present disclosure. The method 1000 outlines a comprehensive user-centric approach to efficiently onboard customers and end-customers into the distribution ecosystem. By leveraging the capabilities of the SPoG UI, including real-time data integration, collaborative decision making, and personalized user experience, stakeholders can enable successful customer onboarding and engagement, providing customers with a customized onboarding experience. Based on the present disclosure, the operations in the method 1000 may be performed in a different order and / or in a different manner to suit specific implementation requirements.

[0221] The process begins when a potential customer or end customer expresses interest in joining the distribution ecosystem at operation 1005. The computing device utilizes the SPoG UI to capture 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.

[0222] At operation 1010, the computing device uses the ability to integrate with external systems real-time data to verify the customer or end-customer information. By synchronizing and accessing data from various sources (such as a customer relationship management (CRM) system or other enterprise-level solutions), the computing device ensures the accuracy and completeness of the customer or end-customer information. This verification step helps to establish a reliable foundation for the login process and enhance data integrity.

[0223] At operation 1015, the computing device initiates a customer or end-customer onboarding workflow through a collaborative decision-making module. The module facilitates 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.

[0224] At operation 1020, the computing device manages access controls and permissions during the onboarding 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 complies with regulatory requirements. Authorized stakeholders can securely review, update, and track onboarding progress, thereby promoting a transparent and compliant onboarding environment.

[0225] At operation 1025, the computing device leverages the Unified View (UV) module of the SPoG UI to provide stakeholders with a comprehensive customizable dashboard-type layout of the customer or end-customer onboarding process. The module consolidates relevant information, tasks, and mileage associated with the onboarding journey, providing stakeholders with a comprehensive view of the onboarding progress. Stakeholders can monitor status, review documents, and access real-time updates to ensure an efficient onboarding experience.

[0226] At operation 1030, the computing device utilizes the data visualization module of the SPoG UI to present dynamic visualizations and analytics about 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 empowers stakeholders to make informed decisions, optimize onboarding strategies, and personalize the onboarding experience for each customer or end-customer.

[0227] At operation 1035, the computing device enables stakeholders to interact with the collaborative decision-making module to facilitate collaboration during the onboarding process. Stakeholders can share files, initiate workflows, and exchange information in real time. The SPoG UI facilitates efficient communication, reduces delays, and ensures compliance between stakeholders involved in a customer or end-customer onboarding.

[0228] At 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.

[0229] At operation 1045, the computing device utilizes the 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 documents reviewed throughout the login process. The audit trail enhances transparency, accountability, and compliance, thereby serving as a valuable reference for future audits, reviews, and assessments.

[0230] At 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 distribution ecosystem. The SPoG UI provides stakeholders with an overview of the onboarding process, ensuring that all requirements are met and facilitating next steps, such as account activation, service provisioning, or personalized customer engagement.

[0231] In conclusion, Fig.10 The method 1000 depicted therein 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 empowers stakeholders to successfully onboard customers and end-customers into the distribution 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 a workflow, thereby optimizing customer and end-customer integration and promoting business success within a dynamic distribution environment.

[0232] Fig.11 1100 is an example component block diagram of device 1100. One or more computer systems 1100 may be used, for example, to implement any of the embodiments described herein and combinations and sub-combinations thereof. Computer system 1100 may include one or more processors (also known as central processing units or CPUs), such as processor 1104. Processor 1104 may be connected to a communication infrastructure or bus 1106.

[0233] The computer system 1100 may also include user input / output devices 1103 , such as a monitor, keyboard, pointing device, etc., which may communicate with the communication infrastructure 1106 through the user input / output interface 1102 .

[0234] One or more processors 1104 may be a graphics processing unit (GPU). In one embodiment, a GPU may be a processor, i.e., a dedicated electronic circuit designed to handle math-intensive applications. A GPU may have a parallel architecture that efficiently processes large blocks of data (such as math-intensive data commonly found in computer graphics applications), images, videos, etc., in parallel.

[0235] The computer system 1100 may also include a main memory or primary storage 1108, such as a random access memory (RAM). The main memory 1108 may include one or more levels of cache. The main memory 1108 may store control logic (ie, computer software) and / or data.

[0236] The computer system 1100 may also include one or more secondary storage devices or memories 1110. The secondary storage 1110 may include a hard drive 1112 and / or a removable storage device or drive 1114, for example.

[0237] The removable storage drive 1114 can interact with the removable storage unit 1118. The removable storage unit 1118 can include a computer usable or readable storage device having stored therein computer software (control logic) and / or data. The removable storage unit 1118 can be a program cartridge and cartridge interface (such as a program cartridge and cartridge interface in a video game device), a removable memory chip (such as an EPROM or PROM) and an associated socket, a memory stick and a USB port, a memory card and an associated memory card slot, and / or any other removable storage unit and associated interface. The removable storage drive 1114 can read from and / or write to the removable storage unit 1118.

[0238] Secondary storage 1110 may include other devices, apparatuses, components, tools, or other means that allow computer system 1100 to access computer programs and / or other instructions and / or data. Such devices, apparatuses, components, tools, or other means may include, for example, a removable storage unit 1122 and an interface 1120. Examples of removable storage unit 1122 and interface 1120 may include a program cartridge and a cartridge interface (such as a program cartridge and cartridge interface in a video game device), a removable memory chip (such as an EPROM or PROM) and an associated socket, a memory stick and a USB port, a memory card and an associated memory card slot, and / or any other removable storage unit and an associated interface.

[0239] The computer system 1100 may also include a communication or network interface 1124. The communication interface 1124 may enable the computer system 1100 to communicate and interact with any combination of external devices, external networks, external entities, etc. (individually and collectively labeled with reference numeral 1128). For example, the communication interface 1124 may allow the computer system 1100 to communicate with an external or remote device 1128 via a communication path 1126, which may be wired and / or wireless (or a combination thereof) and may include any combination of a LAN, a WAN, the Internet, etc. Control logic and / or data may be transmitted to / from the computer system 1100 via the communication path 1126.

[0240] The computer system 1100 may also be any personal digital assistant (PDA), desktop workstation, laptop or notebook computer, netbook, tablet computer, smart phone, smart watch or other wearable device, appliance, local Internet of Things and / or embedded system (to name a few non-limiting examples), or any combination thereof.

[0241] Computer system 1100 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.

[0242] Any available data structures, file formats, and schemas in the computer system 1100 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 schemes may be used alone or in combination with known or open standards.

[0243] In some embodiments, a tangible non-transitory device or article of manufacture including a tangible non-transitory computer-usable or readable medium storing control logic (software) may also be referred to herein as a computer program product or program storage device. This includes, but is not limited to, computer system 1100, main memory 1108, secondary memory 1110, and removable storage units 1118 and 1122, and tangible articles of manufacture implementing any combination of the foregoing. Such control logic, when executed by one or more data processing devices (such as computer system 1100), may cause such data processing devices to operate as described herein.

[0244] It should be understood that the detailed description section, rather than the summary and abstract sections, is intended to be used to interpret the claims. The summary and abstract sections may set forth one or more but not all exemplary embodiments of the invention contemplated by the inventor, and are not intended to limit the invention and the appended claims in any way.

[0245] The present invention is described above by means of functional building blocks that illustrate implementation of specified functions and relationships thereof. For ease of description, the boundaries of these functional building blocks are arbitrarily defined herein. Alternative boundaries may be defined as long as the specified functions and relationships thereof are appropriately performed.

[0246] The above description of specific embodiments will fully reveal the overall nature of the present invention. Without departing from the overall concept of the present invention, others can easily modify and / or rectify various applications (such as specific embodiments) by applying common sense of those skilled in the art without excessive experimentation. Therefore, based on the teachings and guidance proposed herein, such rectifications and modifications are intended to fall within the meaning and scope of the equivalent schemes of the disclosed embodiments. It should be understood that the wording or terminology herein is for descriptive rather than restrictive purposes, so that the terms or wording of this specification should be interpreted by the technician according to teaching and guidance.

[0247] 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 computerized method for capturing change data in an ERP-agnostic real-time data grid, comprising: Monitor real-time changes in transaction systems including ERP; Use change data capture mechanisms to capture and process change data; Transforming and harmonizing the captured data into a standardized format compatible with analysis and integration processes, thereby ensuring data consistency and compatibility across the data grid; The transformed and coordinated data is integrated into the data layer of the real-time data grid, which includes a global data lake composed of one or more intentional data storage areas to enable real-time analysis and decision-making based on the latest data in the data grid.

2. The method according to claim 1, wherein: Transforming and harmonizing the captured data involves data cleansing, normalization, and enrichment techniques to ensure data quality and consistency.

3. The method according to claim 1, wherein: The global data lake contained in the data layer is configured as a scalable, fault-tolerant storage infrastructure that provides a central repository for data captured and transformed within the real-time data grid.

4. The method according to claim 1, further comprising: Implement change data capture using one or more trigger-based, machine learning, and / or polling-based CDC algorithms.

5. The method according to claim 1, wherein: The data layer includes purpose-built data storage areas optimized for efficient retrieval and storage of specific types of data.

6. The method according to claim 1, further comprising: Store the transformed and harmonized data in a cloud-based storage infrastructure.

7. The method according to claim 1, further comprising: Applying artificial intelligence and / or machine learning models to enhance the change data capture process facilitates automated analysis and decision making within the real-time data grid.

8. A system for capturing change data in an ERP-agnostic real-time data grid, comprising: One or more computers that monitor real-time changes to a transaction system including an ERP, wherein the transaction system is a computerized system, wherein the one or more computers include: one or more headless engines; A data layer of a real-time data grid is operably connected to the one or more headless engines, the data layer includes a global data lake composed of one or more intentional data storage areas to enable real-time analysis and decision-making based on the latest data in the data grid, wherein the one or more computers are configured to capture and process change data using a change data capture mechanism, and wherein the global data lake is configured to transform and reconcile the captured data into a standardized format compatible with analysis and integration processes, thereby ensuring data consistency and compatibility across the data grid.

9. The system according to claim 8, further comprising: A data governance module is used to ensure data integrity, security and compliance within the real-time data grid.

10. The system according to claim 8, wherein: The headless engine is connected to the data layer through an API connection, thereby enabling integration and communication between components.

11. The system according to claim 8, wherein: The system of record is integrated with external enterprise systems including ERP for data exchange and synchronization.

12. The system according to claim 8, wherein: The data governance module includes catalog management function, Pimcore-based product data management function, order status and tracking management function, special price management function and quotation management function.

13. The system according to claim 8, wherein: The data layer includes purpose-built data storage areas optimized for efficient retrieval and storage of specific types of data relevant to the supply chain domain.

14. The system of claim 8, further comprising: Cloud-based storage infrastructure for storing the transformed and harmonized data.

15. The system of claim 8, further comprising: Artificial intelligence and / or machine learning models to enhance the change data capture process, thereby enabling automated analysis and decision making within the real-time data grid.

16. A computer readable medium comprising instructions, which when executed by a processor perform the following steps: Monitor real-time changes in transaction systems including ERP; Use change data capture mechanisms to capture and process change data; Transform and harmonize captured data into standardized formats suitable for analysis and integration; Integrate the transformed and harmonized data into the data layer of a real-time data grid for real-time analysis and decision making.

17. The computer-readable medium of claim 16, wherein: The instructions also include implementing change data capture using one or more trigger-based, machine learning, and / or polling-based CDC algorithms.

18. The computer-readable medium of claim 16, wherein: The data layer includes purpose-built data storage areas optimized for efficient retrieval and storage of specific types of data.

19. The computer-readable medium of claim 16, wherein: The instructions also include storing the transformed and reconciled data in a cloud-based storage infrastructure.

20. The computer-readable medium of claim 16, wherein: The instructions also include applying artificial intelligence and / or machine learning models to enhance a change data capture process to facilitate automated analysis and decision making within the real-time data grid.

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