System and method for supply chain management including erp agnostic realtime data mesh with change data capture

Abstract

To provide system and methods for dynamically consolidating interaction points in a distribution ecosystem.SOLUTION: An operating environment 200 includes a system 110 as a distribution platform that serves as a central hub for managing and facilitating a distribution process. The system 110 can be configured to perform functions and operations as a bridge between customer systems 220, vendor systems 240, reseller systems 260, and other entities within an ecosystem. It can integrate communication, data exchange, and transactional processes, providing stakeholders with a unified and streamlined experience. Moreover, the operating environment 200 can include one or more integration points 210 to ensure smooth data flow and connectivity. The integration point can enable the system connect with customer systems, vendor systems, or reseller systems, enabling efficient data exchange, synchronization, and integration or connection.SELECTED DRAWING: Figure 2

Description

[Technical field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application is a continuation-in-part (CIP) of application Ser. No. 18 / 341,714, filed Jun. 26, 2023, entitled "Systems and Methods for Distribution Management Including Single Pane of Glass User Interface," the disclosure of which is incorporated herein by reference in its entirety.

[0002] The present invention relates to aspects of a real-time data mesh method and system encompassing distribution, supply chain management, and related functions.

[0003] The global distribution industry faces numerous challenges encompassing distribution management, supply chain management, inventory control, SKU management, compliance, and evolving consumer expectations. Historically, distribution and supply chain management have not been core competencies for many distributors, leading to inefficiencies. Inventory control has long been a major concern, and market fluctuations have called for flexible distribution and supply chain models. SKU management and localization add layers of complexity due to discrepancies in data from various OEMs and differing jurisdictional requirements. Compliance with international regulations also requires additional vigilance and paperwork. Finally, traditional ways of interacting with customers are quickly becoming obsolete with the shift toward ecosystem commerce.

[0004] An ERP (Enterprise Resource Planning) system is a software system that integrates and manages various core business processes and functions within an organization. It acts as a centralized database and platform that allows 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 distribution ecosystem, managing and optimizing the flow of goods, services, and information is essential for businesses to remain competitive and meet customer demands. However, traditional systems often face a number of challenges that hinder efficiency, visibility, and decision-making capabilities. These challenges include data fragmentation, limited integration, data inconsistencies, inefficient data processing, and data security concerns.

[0005] Data fragmentation is a common issue in distribution and supply chain systems, where data is stored across different systems and departments, often in legacy systems such as ERP, leading to information silos. As a result, stakeholders struggle to access real-time and holistic insights into key distribution and supply chain metrics, hindering their ability to make informed decisions and quickly respond to changing market trends. In addition, when information is stored in different formats, data inconsistencies arise, making it difficult to maintain data integrity and ensure accurate analysis.

[0006] Additionally, a lack of integration capabilities between disparate systems slows the flow of data across distribution and supply chains. Integrating data from multiple sources, including ERP, legacy systems, and external providers, is a complex and time-consuming process. This limitation limits the ability to gain a comprehensive view of the distribution and supply chain, inhibiting operational optimization. Additionally, inefficient data processing and analysis introduces another major challenge in distribution and supply chain management. Legacy systems often struggle to cope with the volume, variety, and velocity of distribution and supply chain data. Extracting meaningful insights and actionable information from this vast amount of data becomes a daunting task. The inability to efficiently process and analyze data inhibits trend identification, forecasting, optimization, and decision-making.

[0007] In addition, data security and governance concerns are key factors in distribution and supply chain management. Distribution and supply chain data often contains sensitive information, including customer data, pricing details, and contractual agreements. Ensuring the security, privacy, and compliance of this data is paramount to protect against unauthorized access and intrusion. Compliance with industry regulations and maintaining data integrity further complicates the situation in data management. Summary of the Invention

[0008] The global distribution industry is at a critical juncture, grappling with a series of challenges across multiple domains. These obstacles are both historical and emerging, requiring the devising of innovative and effective solutions to propel the sector towards growth and efficiency. Among these numerous challenges, the most significant exist in the areas of distribution management, supply chain management, inventory and compliance issues, SKU (stock keeping unit) management, the shift towards a direct-to-consumer model, and rapidly evolving consumer expectations and behaviors.

[0009] The first key challenge relates to managing the distribution process, which is a central part of any seller's operations, and yet, paradoxically, is usually outside the seller's core remit. This gap creates inefficiencies in the system and exacerbates the difficulty in managing disruptions, which in turn has a direct bearing on the seller's ability to deliver products and services efficiently and on time. In addition to these challenges, market trends are leaning more towards a direct-to-consumer model. Previous distribution methodologies, involving a significant number of intermediaries, are gradually being replaced. This evolving market force requires a significant re-evaluation and re-adaptation of existing business models and strategies to align with this new market reality.

[0010] A typical problem in the distribution sector 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 the need to hold inventory positions. This makes the task of fulfilling and delivering goods to customers substantially more complex and difficult. Furthermore, the sheer need to go through a myriad of compliance regulations to transport goods and services across international borders further complicates the distribution process. This not only makes the distribution process more involved and difficult, but also imposes an additional layer of vigilance and paperwork to remain compliant.

[0011] To further complicate these challenges, issues surrounding product localization, varying distribution rights, and global SKU management must also be addressed. The process of reconciling data from different original equipment manufacturers (OEMs), each with their own systems and processes, adds complexity. Additionally, dealing with localization requirements aligned with the laws and regulations of different jurisdictions increases the potential for inefficiencies and errors.

[0012] Ultimately, to ensure the sustainability of distribution models in evolving market conditions, processes need to become more efficient and streamlined. This entails shifting the focus of distribution platforms from supply chain management to encompass subscription management, customer visibility, and other key distribution-oriented capabilities. The landscape of consumer behavior and expectations is rapidly changing. The shift toward ecosystem commerce requires creating a user-friendly, efficient, and configurable platform for purchasing technology. Traditional ways of interacting with customers are rapidly falling out of favor, making it imperative for companies to evolve to meet these new customer expectations.

[0013] Despite these challenges, the distribution model offers a number of 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, the distribution network is often widespread, making products available to far-flung customers that would be unfeasible for the manufacturer to reach directly. Third, distributors often offer value-added services such as post-sales support, installation, and training that enhance the overall customer experience.

[0014] However, to realize these benefits and keep the distribution model relevant and effective, it is imperative that it evolves and adapts to new challenges. Current trouble spots need to be addressed and processes must be made more efficient and streamlined to ensure the sustainability of the distribution model in evolving market conditions. The systems and methods described herein are directed towards addressing these challenges. Additionally, the systems described herein can be configured to encompass functions such as subscription management and other customer-centric areas not effectively managed by previous distribution platforms.

[0015] Single Pane of Glass Single Pane of Glass (SPoG) can provide a comprehensive solution aimed at addressing these multifaceted challenges. It can be configured to provide a holistic, user-friendly and efficient platform that streamlines the distribution process.

[0016] 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 useful insight into inventory levels and product status to ensure supply chain and distribution management processes are addressed efficiently.

[0017] According to some embodiments, SPoG can consolidate multiple touch points onto a single platform and emulate a direct consumer channel into a distribution platform. This consolidation provides a unified direct channel for consumers to interact with merchants, significantly reducing supply chain complexity and improving the overall customer experience.

[0018] SPoG provides innovative solutions for improved inventory management through advanced forecasting capabilities. These predictive analytics highlight demand trends and guide businesses to manage their inventory more efficiently, reducing the risk of stock-outs or overstocks.

[0019] According to some embodiments, the SPoG can include a global compliance database that is updated in real-time, allowing merchants to stay abreast of the latest international regulations. This functionality significantly reduces the burden of manual tracking and ensures smooth and compliant cross-border transactions.

[0020] According to some embodiments, to streamline SKU management and product localization, SPoG consolidates data from various OEMs into a single platform. This not only ensures data consistency but also significantly reduces the potential for errors. Furthermore, it provides the ability to efficiently manage and distribute localized SKUs, thereby aligning with the needs and requirements of specific markets.

[0021] 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.

[0022] In addition, SPoG's advanced analytical capabilities provide valuable insights that can drive strategy and decision-making: trends can be tracked and analyzed in real-time, enabling companies to stay ahead of the curve and adapt to changing market conditions.

[0023] SPoG's flexibility and scalability make it a future-proof solution, able to adapt to changing business needs and allowing companies to scale their operations up or down as needed without having to make major changes to their infrastructure.

[0024] SPoG's innovative approach to solving distribution industry challenges makes it an invaluable tool. It provides a comprehensive solution to the complex problems that have long plagued the distribution sector by increasing supply chain visibility, streamlining inventory management, ensuring compliance, simplifying SKU management and delivering a superior customer experience. Through its implementation, merchants can expect increased efficiency, reduced errors and improved customer satisfaction, leading to sustained growth in an ever-evolving global marketplace.

[0025] Real-time Data Mesh (RTDM) According to some embodiments, the platform may include an implementation of Real-Time Data Mesh (RTDM). RTDS provides an innovative solution to address these challenges. RTDM is a distributed data architecture that enables real-time data availability across multiple sources and touchpoints. This feature improves visibility into the supply chain and distribution, enables efficient management, and allows merchants to respond to disruptions more effectively.

[0026] RTDM's predictive analytics capabilities provide solutions for efficient inventory control. By providing insight into demand trends, it helps companies manage their inventory and reduces the risk of overstocking or out-of-stocking.

[0027] RTDM's global compliance database is updated in real time and ensures merchants are up to date with international regulations, significantly reducing the burden of manual tracking and enabling integrated cross-border trade.

[0028] RTDM also simplifies SKU management and localization by consolidating data from various OEMs, ensuring data consistency and reducing the chance of errors. The ability to manage and distribute localized SKUs efficiently aligns with specific market needs.

[0029] RTDM enhances customer experience with its intuitive interface, making technology easier to access and purchase, meeting the expectations of a new generation of technology buyers.

[0030] Benefits of SPoG and RTDM Integration Integrating the SPoG platform with RTDM offers numerous benefits. First, it provides a holistic solution to a long-standing challenge in the distribution industry. RTDM's capabilities enable SPoG to improve supply chain visibility, streamline inventory management, ensure compliance, simplify SKU management, and deliver a superior customer experience.

[0031] The real-time tracking and analytics provided by RTDM improves SPoG's ability to effectively manage its supply chain and inventory, providing accurate and up-to-date information that enables sellers to make faster, more informed decisions.

[0032] Integrating SPoG with RTDM also ensures data consistency and reduces errors in SKU management, and helps simplify product localization and align with market needs by providing a centralized platform for managing data from various OEMs.

[0033] RTDM's global compliance database is integrated with SPoG to facilitate compliant cross-border trade, while reducing the burden of manual tracking, resulting in significant time and resource savings.

[0034] In some embodiments, a distribution platform incorporates SPoG and RTDM to provide an improved, comprehensive distribution system that can leverage the advantages of the distribution model, address existing challenges, and position for sustained growth in a constantly evolving global marketplace. [Brief description of the drawings]

[0035] [Figure 1] Illustrated is one embodiment of an operating environment for a distribution platform, referred to in this embodiment as a system. [Diagram 2]2 illustrates one embodiment of a distribution platform operating environment that builds upon the elements introduced in FIG. 1. [Diagram 3] 1 illustrates one embodiment of a system for supply chain and distribution management. [Figure 4] 1 depicts an embodiment of an advanced distribution platform that includes a system for managing a complex distribution network, which can be an embodiment of a system, providing a technology delivery platform for optimizing the management and operation of a distribution network. [Diagram 5] 1 illustrates an RTDM module according to one embodiment. [Figure 6] 1 illustrates a SPoG UI according to one embodiment. [Figure 7] 1 illustrates an RTDM module according to one embodiment. [Figure 8] FIG. 2 is a flow diagram of a method for vendor onboarding using SPoG UI according to some embodiments of the present disclosure. [Figure 9] FIG. 1 is a flow diagram of a method for reseller onboarding using a SPoG UI, according to some embodiments of the present disclosure. [Figure 10] FIG. 1 is a flow diagram of a method for customer and end customer onboarding using a SPoG UI, according to some embodiments of the present disclosure. [Figure 11] FIG. 2 is a block diagram of example components of a device according to some embodiments of the present disclosure. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0036] The present embodiments may be implemented in hardware, firmware, software, or any combination thereof. The present embodiments may also be implemented as instructions stored on a machine-readable medium, which may be read and executed by one or more processors. A machine-readable medium may include any mechanism for storing or transmitting information in a form readable by a machine (e.g., a computing device). For example, a machine-readable medium may include a read-only memory (ROM), a random access memory (RAM), a magnetic disk storage medium, an optical storage medium, a flash memory device, and others. Furthermore, firmware, software, routines, instructions may be described herein as performing certain actions. However, it should be understood that such description is merely for convenience and that such actions are in fact the results obtained by a computing device, processor, controller, or other device executing the firmware, software, routines, instructions, etc.

[0037] It should be understood that the acts shown in the example methods are not exhaustive and that other acts can be performed before, after, or between any of the acts shown. In some embodiments of the present disclosure, acts can be performed in different orders and / or variations.

[0038] 1 illustrates an operating environment 100 of a distribution platform, referred to in this embodiment as system 110. System 110 operates within the context of an information technology (IT) distribution model and serves the needs of various participants, such as customers 120, end customers 130, vendors 140, resellers 150, and other entities involved in the distribution process. This operating environment encompasses a wide range of characteristics and dynamics that contribute to the success and efficiency of a distribution platform.

[0039] Customers 120 within the operating environment of system 110 represent companies or individuals seeking IT solutions to meet their specific needs. These customers may require a diverse range of IT products, such as hardware components, software applications, network equipment, or cloud-based services. System 110 provides customers with a user-friendly interface, allowing them to browse, search, and select the most suitable IT solution based on their requirements. Customers can also access real-time data and analytics through system 110, empowering them to make informed decisions and optimize their IT infrastructure.

[0040] End customers 130 are the ultimate beneficiaries of the IT solutions provided by system 110. They may include companies or individuals who utilize IT products and services to improve their operations, productivity, or daily activities. End customers rely on system 110 to access a wide range of IT solutions, ensuring access to the latest technologies and innovations in the market. System 110 allows end customers to track their orders, receive delivery status updates, and access customer support services, thereby enhancing their overall experience.

[0041] Vendors 140 play a key role within the operating environment of system 110. These vendors encompass manufacturers, distributors, and suppliers that offer a diverse range of IT products and services. System 110 serves as a centralized platform for vendors to showcase their products, manage inventory, and facilitate business with customers and resellers. Vendors can leverage system 110 to streamline supply chain operations, manage pricing and promotions, and gain insight into customer preferences and market trends. By integrating with system 110, vendors can expand their reach, access new markets, and improve their overall visibility and competitiveness.

[0042] Resellers 150 are the middlemen in the distribution model, bridging the gap between vendors and customers. They play a key role in the IT distribution ecosystem by connecting customers with the right IT solutions from various vendors. Resellers may include retailers, value-added resellers (VARs), system integrators, or managed service providers. The system 110 enables resellers to access a comprehensive catalog of IT solutions, manage their sales pipeline, and provide value-added services to customers. By leveraging the system 110, resellers can improve relationships with their customers, optimize their product offerings, and increase revenue streams.

[0043] Within the operating environment of the system 110, there are various dynamics and characteristics 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 the parties, enabling accurate decision-making and timely actions. Integration with existing enterprise systems, such as enterprise resource planning (ERP) systems, customer relationship management (CRM) systems, warehouse management systems, etc., allows for unified communication and interoperability, eliminating data silos and enabling end-to-end visibility.

[0044] Scalability and flexibility are key features of the system 110. It can accommodate the growing demands of the IT distribution model, such as a growing customer base, an increasing number of vendors, and a wide range of IT products and services. The system 110 is designed to handle large-scale data processing, storage, and analysis, ensuring it can support the evolving needs of the distribution platform. In addition, the system 110 leverages a technology stack that includes .NET, Java, and other suitable technologies to provide a robust foundation for its operations.

[0045] In summary, the operating environment of the system 110 within the IT distribution model encompasses customers 120, end customers 130, vendors 140, resellers 150, and other entities involved in the distribution process. The system 110 serves as a centralized platform that facilitates efficient collaboration, communication, and transactional processes among these parties. By leveraging real-time data exchange, integration, scalability, and flexibility, the system 110 empowers the parties to optimize their operations within the IT distribution ecosystem, improve customer experience, and drive business success.

[0046] Figure 2 illustrates a distribution platform operating environment 200 built from the elements introduced in Figure 1. Within this operating environment, integration points 210 facilitate data flow and connectivity between various customer systems 220, vendor systems 240, reseller systems 260, and other entities involved in the distribution process. The diagram showcases the interconnectivity and mechanisms that enable efficient collaboration and data-driven decision making.

[0047] The operating environment 200 can include the system 110 as a distribution platform that serves as a central hub for managing and facilitating the distribution process. The system 110 can be configured to function and operate as a bridge between the customer system 220, the vendor system 240, the reseller system 260, and other entities within the ecosystem. The communication, data exchange, and transaction process can be integrated to provide a unified and streamlined experience for the participants. Additionally, the operating environment 200 can include one or more integration points 210 to ensure smooth data flow and connectivity. These integration points include the following:

[0048] Customer system integration: Integration points 210 can enable system 110 to connect with customer systems 220, allowing efficient data exchange and synchronization. Customer systems 220 can include various entities, such as customer system 221, customer system 222, 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 systems 220 empowers customers to access real-time inventory information, pricing details, order tracking, and other relevant data, improving customer visibility and decision-making capabilities.

[0049] Vendor System Integration: Integration point 210 facilitates an integration connection between system 110 and vendor system 240. Vendor system 240 may include entities representing inventory management systems, pricing systems, and product catalogs employed by a vendor, e.g., vendor system 241, vendor system 242, vendor system 243. Integration with vendor system 240 ensures that vendors can efficiently update their product offerings, manage pricing and promotions, and receive real-time order notifications and fulfillment details.

[0050] Reseller System Integration: Integration point 210 provides the capability for reseller systems 260 to connect to system 110. Reseller systems 260 may include entities representing sales systems, customer management systems, and service delivery platforms employed by a reseller, such as reseller system 261, reseller system 262, and reseller system 263. Integration with reseller systems 260 empowers resellers to access up-to-date product information, manage customer accounts, track sales performance, and provide value-added services to customers.

[0051] Other Entity System Integration: Integration point 210 further enables connectivity with other entities involved in the distribution process. These entities may include entities such as entity system 271, entity system 272, and entity system 273. Integration with these systems ensures communication and data exchange, facilitating collaboration and an efficient distribution process.

[0052] 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, e.g., RESTful API, SOAP, or GraphQL, to establish communication channels and enable data exchange.

[0053] In some embodiments, the system 110 can incorporate authentication and authorization mechanisms to ensure secure access and data protection. Technologies such as OAuth or JSON Web Token (JWT) can be employed to authenticate users, authorize data access, and maintain the integrity and confidentiality of exchanged information.

[0054] In some embodiments, integration points 210 and data flows within operating environment 200 enable participants to operate within a connected ecosystem. Data generated at various stages of the distribution process, including customer orders, inventory updates, shipping details, and sales analytics, flows between customer systems 220, vendor systems 240, reseller systems 260, and other entities. This data exchange facilitates real-time visibility, enables data-driven decision making, and improves operational efficiency across the distribution platform.

[0055] In some embodiments, the system 110 leverages advanced technologies such as Typescript, NodeJS, ReactJS, .NET Core, C#, and other suitable technologies to support the integration points 210 and enable communication within the operating environment 200. These technologies provide a robust foundation for the system 110, ensuring scalability, flexibility, and efficient data processing capabilities. Additionally, the integration points 210 can also employ algorithms, data analytics, and machine learning techniques to derive valuable insights, optimize the distribution process, and personalize the customer experience. The integration points 210 and data flows within the operating environment 200 enable the parties to operate within a connected ecosystem. Data generated at various touchpoints, including customer orders, inventory updates, price changes, or delivery status, flows between different entities, systems, and components. The integrated data is processed, harmonized, and provided in real-time through the system 110 to the appropriate parties. This real-time access to accurate and up-to-date information empowers the parties to make informed decisions, optimize supply chain operations, and improve customer experience.

[0056] Some elements in the operating environment depicted in Figure 2 may include conventional, well-known elements that are only briefly described herein. For example, each of the customer systems, such as customer system 220, may include a desktop personal computer, a workstation, a laptop, a PDA, a mobile phone, or any Wireless Access Protocol (WAP)-enabled device, or any other computing device capable of interfacing directly or indirectly with the Internet or other network connection. Each of the customer systems is typically capable of running an HTTP client, such as Microsoft's Edge browser, Google's Chrome browser, Opera's browser, or a WAP-enabled browser for mobile devices, and the customer systems are capable of accessing, processing, and displaying information, pages, and applications available from the distribution platform over a network.

[0057] Additionally, each of the customer systems may typically be equipped with a user interface device, such as a keyboard, mouse, trackball, touchpad, touchscreen, pen, or similar device for interacting with the graphical user interface (GUI) provided by the browser. These user interface devices enable users of the customer systems to navigate the GUI, interact with pages, forms, and applications, and access data and applications hosted by the distribution platform.

[0058] The customer system and its components may be operator configurable using applications including a web browser running on a central processing unit, such as an Intel Pentium processor or similar processor. Similarly, the distribution platform (system 110) and its components may be operator configurable using applications running on a central processing unit, such as an Intel Pentium processor or similar processor, and / or a processor system that may include multiple processor units.

[0059] An embodiment of a computer program product includes a machine-readable storage medium containing instructions for programming a computer to perform the processes described herein. The computer code for operating and configuring the distribution platform and customer systems, vendor systems, reseller systems, and other entity systems to process intercommunications, 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 a ROM, RAM, floppy disk, optical disk, DVD, CD, microdrive, magneto-optical disk, magnetic card, optical card, nanosystem, or any suitable medium for storing instructions and data.

[0060] Furthermore, computer code for implementing the present embodiments can be transmitted and downloaded from a software source over the Internet or any other conventional network connection using communications media and protocols such as TCP / IP, HTTP, HTTPS, Ethernet, etc. The code can also be transmitted over an extranet, VPN, LAN, or other network and executed on a client system, server, or server system using programming languages ​​such as C, C++, HTML, Java, JavaScript, ActiveX, VBScript.

[0061] It will be appreciated that the present embodiment can be implemented in a variety of programming languages ​​executed on the client system, server, or server system, and the choice of language may depend on the particular requirements and environment of the distribution platform.

[0062] This enables the operating environment 200 to couple the distribution platform with one or more integration points 210 and data flows to enable efficient collaboration and a streamlined distribution process.

[0063] Figure 3 illustrates a system 300 for supply chain and distribution management. System 300 (Figure 3) is a supply chain and distribution management solution designed to address the challenges faced by a fragmented distribution ecosystem in the global distribution industry. System 300 can include several interconnected components and modules that work in harmony to optimize supply chain and distribution operations, improve collaboration, and drive business efficiency.

[0064] The Single Pane of Glass (SPoG) UI 305 serves as a centralized user interface, providing stakeholders with a unified view of the entire supply chain. It aggregates information from various sources and presents real-time data, analytics, and functionality tailored to the user's specific roles and responsibilities. By providing a customizable and intuitive dashboard-style layout, the SPoG UI allows users to access relevant information and tools, empowering them to make data-driven decisions and efficiently manage supply chain and distribution activities.

[0065] For example, logistics managers can use the SPoG UI to monitor shipment status, track delivery routes, and see real-time inventory levels across multiple warehouses. They can visualize the data through interactive charts, such as a map showing the current location of each shipment, or a bar graph 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.

[0066] SPoG UI 305 integrates with other modules of the 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 entire distribution ecosystem.

[0067] For example, when a purchase order is generated in the SPoG UI, the system automatically updates inventory levels, triggers notifications to the warehouse management system, and initiates the shipping process. This integration enables efficient order fulfillment, reduces manual errors, and improves overall supply chain visibility.

[0068] The Real-Time Data Mesh (RTDM) module 310 is another key component of the system 300, responsible for ensuring the data flow within the distribution ecosystem, collecting and harmonizing data from multiple sources and ensuring its availability in real-time.

[0069] To illustrate the power of the RTDM module, consider an example. In a distribution network, the RTDM module collects data from various systems, including inventory management systems, point of sale terminals, or customer relationship management systems. It harmonizes this data by harmonizing formats, standardizing units of measurement, and reconciling discrepancies. The harmonized data is then made available in real time, enabling stakeholders across the supply chain to access accurate, up-to-date information.

[0070] The RTDM module 310 can be configured to capture data changes across multiple transactional systems in real time. It employs advanced Change Data Capture (CDC) mechanisms that constantly monitor transactional systems to detect updates and changes. The CDC component module is specifically designed to work with a variety of transactional systems, including legacy ERP systems, customer relationship management (CRM) systems, and other enterprise-wide systems, ensuring compatibility and flexibility for businesses in diverse environments.

[0071] Having access to real-time data allows stakeholders to make timely decisions and respond quickly to changing market conditions. For example, if the RTDM module detects a sudden spike in demand for a particular product, it can trigger an alert to the production team so they can adjust production schedules to prevent stockouts.

[0072] The RTDM module 310 facilitates data management within supply chain operations. It enables real-time reconciliation of data from multiple sources, freeing vendors, resellers, customers, and end customers from the constraints of legacy ERP systems. This increased flexibility supports improved efficiency, customer service, and innovation.

[0073] Another component of the system 300 is the Advanced Analytics and Machine Learning (AAML) module 315. Leveraging powerful analytics tools and algorithms such as Apache Spark, TensorFlow, or scikit-learn, the AAML module extracts valuable insights from the collected data. It enables advanced analytics, predictive modeling, anomaly detection, and other machine learning capabilities.

[0074] For example, the AAML module can analyze historical sales data to identify seasonal patterns and forecast future demand. It can generate forecasts that help optimize inventory levels, ensure stock availability during busy periods, and minimize excess inventory costs. By leveraging machine learning algorithms, the AAML module automates repetitive tasks, predicts customer preferences, and optimizes supply chain processes.

[0075] In addition to forecasting demand, the AAML module can provide insight into customer behavior, enabling targeted marketing campaigns and personalized customer experiences. For example, by analyzing customer data, the module can identify cross-selling or up-selling opportunities and recommend relevant products to individual customers.

[0076] Additionally, the AAML module can analyze data from various sources, such as social media feeds, customer reviews, and market trends, to gain a deeper understanding of customer intent and preferences. This information can be used to inform product development decisions, identify emerging market trends, and adapt business strategies to meet evolving consumer expectations.

[0077] System 300 emphasizes integration and interoperability to connect with existing enterprise systems, e.g., 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 holistic and connected distribution ecosystem.

[0078] The implementation and deployment of system 300 can be tailored to meet specific business needs. It can be deployed as a cloud-native solution using containerization technologies such as Docker and orchestration frameworks such as Kubernetes. This approach ensures scalability, easy management, and efficient updates across different environments. The implementation process involves configuring the system to meet specific supply chain requirements, integrating with existing systems, and customizing modules and components based on business needs and preferences.

[0079] The system 300 for supply chain and distribution management is a comprehensive and innovative solution that addresses the challenges faced by the fragmented distribution ecosystem. It combines the power of the SPoG UI 305, RTDM module 310, and AAML module 315 along with integration with existing systems. By leveraging a diverse technology stack, scalable architecture, and robust integration capabilities, the system 300 provides end-to-end visibility, data-driven decision-making, and optimized supply chain operations. The examples and options provided herein are non-limiting and can be customized to meet the requirements of a particular industry, driving efficiency and success in supply chain and distribution management.

[0080] FIG. 4 illustrates an embodiment of an advanced distribution platform including a system 400 for managing a complex distribution network, which may be an embodiment of system 300, providing a technology distribution platform for optimizing the management and operation of a distribution network. System 400 includes several interconnected modules, each performing a specific function and contributing to the overall efficiency of supply chain operations. In some embodiments, these modules may include SPoG UI 405, CIM 410, RTDM module 415, AI module 420, interface display module 425, personalized interaction module 430, document hub 435, catalog management module 440, performance and prospect marker display 445, predictive analytics module 450, recommendation system module 455, notification module 460, self-onboarding module 465, and communication module 470.

[0081] System 400, as one embodiment of system 300, utilizes a wide range of technologies and algorithms to enable supply chain and distribution management that facilitate efficient data processing, personalized interactions, real-time analytics, secure communications, and effective management of documents, catalogs, and performance metrics.

[0082] SPoG UI 405 acts as a central interface inside the system 400, providing stakeholders with a unified view of the entire distribution network. It leverages front-end technologies such as ReactJS, TypeScript, and Node.js to create an interactive and responsive user interface. These technologies enable SPoG UI 405 to deliver a user-friendly experience, allowing stakeholders to access relevant information, navigate through different modules, and perform tasks efficiently.

[0083] CIM410, the Customer Interaction Module, employs algorithms and technologies from Oracle Eloqua, Adobe Target, and Okta to manage customer relationships within the distribution network. These technologies enable it to handle customer data securely, personalize the customer experience, and provide access control to interested parties.

[0084] The RTDM module 415, i.e., real-time data mesh module, is a key component of the system 400 and ensures smooth data flow across the entire distribution ecosystem. For data ingestion, processing, and stream management, it utilizes technologies such as Apache Kafka, Apache Flink, and Apache Pulsar. These technologies enable the RTDM module 415 to handle real-time data streams, process large volumes of data, and ensure low latency data processing. In addition, the module employs a change data capture (CDC) mechanism to capture real-time data updates from various transactional systems, e.g., legacy ERP and CRM systems. This capability allows stakeholders to access the latest and accurate information and make informed decisions.

[0085] The AI ​​module 420 within the system 400 leverages advanced analytics and machine learning algorithms, including Apache Spark, TensorFlow, and scikit-learn, to extract valuable insights from data. These algorithms enable the module to automate repetitive tasks, forecast demand patterns, optimize inventory levels, and improve overall supply chain efficiency. For example, the AI ​​module 420 can utilize predictive models to forecast demand, allowing stakeholders to optimize inventory management and minimize out-of-stock and overstock situations.

[0086] The Interface Display module 425 focuses on presenting data and information in a clear and user-friendly manner. It leverages technologies such as HTML, CSS, and JavaScript frameworks such as ReactJS to create interactive and responsive user interfaces. These technologies allow stakeholders to visualize data using various data visualization techniques, e.g., graphs, charts, and tables, allowing for efficient data understanding, comparison, and trend analysis.

[0087] The personalized interaction module 430 utilizes customer data, trend history, and machine learning algorithms to generate personalized recommendations for products or services. It employs technologies such as Adobe Target, Apache Spark, and TensorFlow for data analysis, modeling, and delivery of targeted recommendations. For example, the module can analyze customer preferences and purchase history to provide personalized product recommendations to improve customer satisfaction and drive sales.

[0088] The document hub 435 serves as a central repository for storing and managing documents within the system 400. It employs technologies such as SeeBurger and Elastic Cloud for efficient document management, storage, and retrieval. For example, the document hub 435 can use SeeBurger's document management capabilities to categorize and organize documents based on type, such as contracts, invoices, product specifications, compliance documents, etc., allowing stakeholders to easily access and retrieve relevant documents as needed.

[0089] The catalog management module 440 enables the creation, management, and distribution of up-to-date product catalogs. This ensures that stakeholders have access to the most up-to-date product information, including specifications, pricing, availability, and promotions. It employs technologies such as Kentico and Akamai to facilitate catalog updates, content distribution, and caching. For example, the module can leverage Akamai's content delivery network (CDN) to quickly and efficiently deliver catalog information to stakeholders, regardless of geographic location.

[0090] The Performance and Outlook Marker Display 445 collects, analyzes, and visualizes real-time performance metrics and outlooks related to supply chain and distribution operations. It utilizes tools such as Splunk and Datadog to enable effective performance monitoring and provide actionable insights. For example, the module leverages Splunk's log analysis capabilities to identify performance bottlenecks in the supply chain and enable stakeholders to take proactive measures to optimize operations.

[0091] The predictive analytics module 450 employs machine learning algorithms and forecasting models to forecast demand patterns, optimize inventory levels, and increase overall supply chain efficiency. It utilizes technologies such as Apache Spark and TensorFlow for data analysis, modeling, and forecasting. For example, the module can use the deep learning capabilities of TensorFlow to analyze historical sales data and forecast future demand, allowing stakeholders to optimize inventory levels and minimize costs.

[0092] The Recommendation System module 455 focuses on providing intelligent recommendations to stakeholders within the distribution network. It generates personalized recommendations for products or services based on customer data, historical trends, and machine learning algorithms. It employs technologies such as Adobe Target and Apache Spark to analyze data, model, and deliver targeted recommendations. For example, the module can leverage Adobe Target's recommendation engine to analyze customer preferences and behaviors and deliver personalized product recommendations across various channels to improve customer engagement and drive sales.

[0093] The notification module 460 enables delivery of real-time notifications to stakeholders regarding important events, updates, or alerts within the supply chain. It employs technologies such as Apigee X and TIBCO for message queues, event-driven architecture, and notification delivery. For example, the module can leverage TIBCO's messaging infrastructure to send notifications in real-time to stakeholders' devices, ensuring timely dissemination of relevant information.

[0094] The self-onboarding module 465 facilitates the onboarding process for new participants entering the distribution network. It provides guided steps, tutorials, or documentation to help users become familiar with the system and its features. It employs technologies such as Okta and Kentico 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 participants, grant them appropriate access permissions, and guide them through the system's features.

[0095] The communications module 470 enables unified and aggregated communication and collaboration within the system 400. It provides channels for stakeholders to interact, exchange messages, share documents, and collaborate on projects. In some non-limiting examples, it employs technologies such as Apigee Edge and Adobe Launch to facilitate secure and efficient communication, document sharing, and version control. For example, the module leverages the API management capabilities of Apigee Edge to ensure secure and reliable communication between stakeholders to enable effective collaboration.

[0096] This allows the system 400 to incorporate various modules that utilize a diverse range of technologies and algorithms to optimize supply chain and distribution management. These modules 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 and prospect marker display 445, predictive analytics module 450, recommendation system module 455, notification module 460, self-onboarding module 465, and communication module 470, which work together to provide end-to-end visibility, data-driven decision-making, personalized interactions, real-time analytics, and streamlined communication within the distribution network. The incorporation of specific technologies and algorithms allows for efficient data management, secure communication, personalized experiences, and effective performance monitoring, contributing to improved operational efficiency and success in supply chain and distribution management.

[0097] Real-time Data Mesh 5 illustrates an RTDM module 500 according to one embodiment. The RTDM module 500 may be one embodiment of the RTDM module 310 and may include interconnected components, processes, and subsystems configured to enable real-time data management and analysis.

[0098] The RTDM module 500 represents an effective data mesh and change capture component within the overall system architecture as depicted in Figure 5. The module is designed to provide real-time data management and reconciliation capabilities to enable efficient operations within the supply chain and distribution management domain.

[0099] The RTDM module 500 can include an integration layer 510 (also called "systems of record") that integrates with various enterprise systems. These enterprise systems can include ERPs, such as SAP, Impulse, META, and I-SCALA, among others, and other data sources. The integration layer 510 can handle data exchange and synchronization between the RTDM module 500 and these systems. Data feeds are established to retrieve relevant information from the systems of record, such as sales orders, purchase orders, inventory data, and customer information. These feeds enable real-time data updates, ensuring that the RTDM module operates with the most current and accurate data.

[0100] The RTDM module 500 can include a data layer 520 configured to process and translate data for search and analysis. At the heart of the data layer is the data mesh, a cloud-based infrastructure designed to provide scalable and fault-tolerant data storage capabilities. Within the data mesh, multiple purpose-built data stores (PDSs) are deployed to store specific types of data, e.g., customer data, product data, or inventory data. Each PDS is optimized for efficient data retrieval based on specific use cases and requirements. PDSs are configured to store specific types of data, e.g., customer data, product data, financial data, etc. These PDSs act as repositories of harmonized and standardized data, ensuring data consistency and integrity across systems.

[0101] In some embodiments, the RTDM module 500 implements a data replication mechanism to capture real-time changes from multiple data sources, including transactional systems like ERP (e.g., SAP, Impulse, META, I-SCALA). The captured data is then processed and reconciled on the fly and transformed into a standardized format suitable for analysis and integration. This process ensures that data is readily available and up-to-date within the data mesh, facilitating real-time insights and decision making.

[0102] More specifically, data layer 520 within RTDM module 500 can be configured as a powerful and flexible foundation for managing and processing data within a distribution ecosystem. In some embodiments, data layer 520 can encompass a highly scalable and robust data lake, which may be referred to as data lake 522, along with a set of purpose-built data stores (PDS), which may be denoted as PDS 524.1 through 524.N. These components work in unison to ensure efficient data management, reconciliation, and real-time availability.

[0103] At the heart of the data layer 520 sits data lake 522, a state-of-the-art storage and processing infrastructure designed to accommodate the ever-growing volume, variety, and velocity of data generated within the supply chain. Built on a scalable distributed file system, such as the Apache Hadoop Distributed File System (HDFS) or Amazon S3, the data lake provides a unified, scalable platform for storing both structured and unstructured data. Leveraging the elasticity and fault-tolerance of cloud-based storage, the data lake 522 can accommodate the influx of data from diverse sources.

[0104] In association with the data lake 522, a population of purpose-specific data stores PDS 524.1 through 524.N may be employed. Each PDS 524 may act as a dedicated repository optimized for storing and retrieving a particular 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, transaction history, etc. PDS 524.2 may focus on product data encompassing details regarding SKU codes, descriptions, prices, and inventory levels. These purpose-specific data stores enable efficient data retrieval, analysis, and processing to serve the diverse needs of supply chain participants.

[0105] To ensure real-time data synchronization, the data layer 520 can be configured to employ one or more advanced change data capture (CDC) mechanisms. These CDC mechanisms integrate with transactional systems such as legacy ERPs such as SAP, Impulse, META, I-SCALA, etc., as well as other enterprise-wide systems. CDC constantly monitors and captures any updates, changes, and 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, providing stakeholders with a real-time view into the distribution ecosystem.

[0106] In some embodiments, data layer 520 can be implemented using one or more frameworks, such as .NET or Java, to facilitate integration with existing enterprise systems, ensuring compatibility with a wide range of existing systems and providing flexibility for customization and extensibility. For example, data layer 520 can utilize a Java technology stack, including frameworks such as Spring and Hibernate, to facilitate integration with systems of record with a diverse population of ERP systems and other enterprise-wide solutions. This can facilitate smooth data exchange, process automation, and end-to-end visibility across the supply chain.

[0107] In terms of data processing and analytics, the data layer 520 leverages the power of distributed computing frameworks, such as Apache Spark or Apache Flink, in some non-limiting examples. These frameworks can enable parallel processing and distributed computing across large data sets stored in the data lake and PDS. By leveraging these frameworks, supply chain actors 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 libraries to develop predictive models for demand forecasting, optimizing inventory levels, and identifying potential supply chain risks.

[0108] 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. Data encryption techniques protect sensitive supply chain information from unauthorized access, both at rest and in transit. Additionally, the data layer 520 can implement data lineage and audit trail mechanisms to enable stakeholders to track data provenance and history, ensuring data integrity and compliance with regulatory requirements.

[0109] In some embodiments, the data layer 520 can be deployed in a cloud-native environment, leveraging containerization technologies such as Docker and orchestration frameworks such as Kubernetes. This approach ensures scalability, resilience, and efficient resource allocation. For example, the data layer 520 can be deployed on cloud infrastructure provided by AWS, Azure, or Google Cloud to take advantage of their managed services and scalable storage options. This allows for scaling of resources based on demand, minimizes operational overhead, and provides an elastic infrastructure for managing supply chain data.

[0110] The Data Layer 520 of the RTDM Module 500 integrates the highly scalable data lake, Data Lake 522, with purpose-built PDSs, PDS 524.1 to 524.N, and by employing high-performance CDC mechanisms, the Data Layer 520 ensures efficient data management, reconciliation, and real-time availability. The integration of diverse technology stacks, e.g., .NET or Java, with distributed computing frameworks like Apache Spark enables powerful data processing, advanced analytics, and machine learning capabilities. With robust data governance and security measures, the Data Layer 520 ensures data integrity, confidentiality, and compliance. With its scalable infrastructure and integration with existing systems, the Data Layer 520 empowers supply chain stakeholders to make data-driven decisions to optimize operations and drive business success in a dynamic and complex distribution environment.

[0111] The RTDM module 500 can 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 to derive meaningful insights. In some non-limiting examples, the AI ​​module 530 can apply predictive analytics, anomaly detection, and optimization algorithms to identify patterns, trends, and potential risks within the supply chain. The AI ​​module 530 continuously learns from new data inputs and adapts its models to provide accurate and up-to-date insights. The AI ​​module 530 can generate predictions, recommendations, and alerts and publish such insights to a dedicated data feed.

[0112] The data engine layer 540 comprises a set of interconnected systems responsible for data ingestion, processing, transformation, and integration. The data engine layer 540 of the RTDM module 500 can include a collection of autonomously operating headless engines 540.1 to 540.N. These engines represent distinct functions within the system and can include, for example, one or more recommendation engines, forecast engines, and subscription management engines. The engines 540.1 to 540.N can leverage harmonized data stored in the data mesh to provide specific business logic and services. Each engine is designed to be pluggable, allowing flexibility and future expansion of the module's capabilities. Exemplary engines are shown in FIG. 5 and are not intended to be limiting. Any additional headless engines can be included in the data engine layer 540 or other exemplary layers of the disclosed system.

[0113] These systems can be configured to receive data from multiple sources, e.g., transactional systems, IoT devices, external data providers. The data ingestion process involves extracting data from these sources and converting it into a standardized format. Data processing algorithms are applied to cleanse, aggregate, and enrich the data, making it ready for further analysis and integration.

[0114] Additionally, a data distribution mechanism can be employed to facilitate integration and access to the RTDM module 500. The data distribution mechanism 545 can include one or more APIs and be configured to facilitate data distribution from the data mesh and engine to various endpoints including user interfaces, micro-frontends, and external systems.

[0115] The experience layer 550 focuses on providing an intuitive and user-friendly interface for interacting with supply chain data. The experience layer 550 can include data visualization tools, interactive dashboards, and user-centric features. Through this layer, users can obtain and analyze real-time data related to various supply chain metrics, e.g., inventory levels, sales performance, customer demand. The user experience layer supports personalized data feeds, allowing users to customize views and receive relevant updates based on their roles and responsibilities. Users can take advantage of specific data updates, e.g., inventory changes, price updates, new SKU notifications, tailored to their preferences and roles.

[0116] Thus, in some embodiments, the RTDM module 500 for supply chain and distribution management may include integration with systems of record and may include one or more data layers with data mesh and purpose-built data stores, AI components, a data engine layer, and a user experience layer. These components work together to provide users with intuitive access to real-time supply chain data, efficient data processing and analysis, and integration with existing enterprise systems. Technical feeds and searches within the module ensure that users can search relevant and up-to-date information and insights to make informed decisions and optimize supply chain operations. Thus, the RTDM module 500 facilitates supply chain and distribution management by providing a scalable, real-time data management solution. Its innovative architecture allows for rich integration of disparate data sources, efficient data reconciliation, and advanced analytical capabilities. With the ability to replicate and reconcile data from diverse ERPs while maintaining auditable and repeatable transactions, the module offers distinct advantages in enabling a unified view for vendors, resellers, customers, end customers, and other entities in the distribution system, including IT distribution systems.

[0117] Single Pane of Glass UI 6A illustrates an SPoG UI according to one embodiment. SPoG UI 600. In some embodiments, SPoG UI 600 can be an embodiment of SPoG UI 305, which represents a comprehensive and intuitive user interface designed to provide stakeholders with a unified, customizable view of the entire distribution ecosystem. It combines various functions and features that enable users to comprehensively understand their supply chains and efficiently manage their operations.

[0118] The SPoG UI 600 can include a unified view (UV) module 605, which provides stakeholders with a centralized, customizable, dashboard-style layout. This module allows users to access real-time data, analytics, 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 holistic and comprehensive view of supply chain operations and empowering them to make data-driven decisions.

[0119] The SPoG UI 600 integrates with a real-time data exchange module 610 to facilitate the continuous exchange of data between the SPoG UI 600 and the RTDM 310, leveraging one or more data sources, which may include one or more ERP, CRM, or other sources. Through this module, stakeholders have access to up-to-date, accurate, and harmonized data. The real-time data synchronization ensures that the information presented in the SPoG UI 600 reflects the latest outlook and developments across the supply chain. This integration enables stakeholders to make informed decisions based on accurate and synchronized data.

[0120] The Collaborative Decision-Making Module 615 within the SPoG UI 600 facilitates real-time collaboration and communication between stakeholders. It allows for the exchange of information, initiation of workflows, and sharing of insights and recommendations. By integrating with the RTDM Module 310 / 500, the Collaborative Decision-Making Module 615 ensures that stakeholders can effectively collaborate based on accurate and synchronized data. This fosters overall operational efficiency and collaboration in the distribution ecosystem.

[0121] To ensure secure and controlled access to functions and data, the SPoG UI 600 incorporates a Role-Based Access Control (RBAC) module 620. Administrators can define roles, grant permissions, and control user access based on their responsibilities and organizational hierarchy. The RBAC module 620 ensures that only authenticated users can access specific functions and information, protecting data privacy, security, and compliance within the distribution ecosystem.

[0122] 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 their supply chain operations, providing a user-centric experience that improves productivity and ease of use.

[0123] SPoG UI 600 integrates a powerful data visualization module 630, which enables stakeholders to analyze and interpret supply chain data through interactive dashboards, charts, graphs, and visual representations. Leveraging advanced visualization techniques, this module presents complex data in a clear and intuitive manner. Users can gain insight into key performance indicators (KPIs), trends, patterns, and anomalies, facilitating data-driven decision-making and strategic planning.

[0124] The SPoG UI 600 can include a mobile and cross-platform accessibility module 635 to ensure accessibility across multiple devices and platforms. Stakeholders can access the interface from desktop computers, laptops, smartphones, and tablets, allowing them to stay connected and informed while on the go. This 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.

[0125] By integrating these reference elements / modules within the SPoG UI 600 and leveraging its integration capabilities through the RTDM module 310 / 500, stakeholders can benefit from a powerful and user-friendly interface for supply chain and distribution management. The Unified View (UV) module 605 provides a customizable and holistic 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 fosters effective communication and collaboration, while the RBAC module 620 ensures secure access control. The Customization module 625, Data Visualization module 630, and Mobile and Cross-Platform Accessibility module 635 enhance user experience, data analytics, and accessibility, respectively. Together, these modules enable stakeholders to make informed decisions, optimize supply chain operations, and drive business efficiency within the distribution ecosystem.

[0126] SPoG600 is capable of incorporating high velocity data in data-rich environments. In today's data-rich environments, traditional UI designs often struggle to present large amounts of information in a way that is understandable, efficient and visually appealing. This challenge is amplified when data is dynamic, changes in real-time and must be effectively displayed in a single-pane environment that prioritizes clean, white space-driven design.

[0127] The SPoG605 UI can be configured to efficiently manage real-time data and maintain a visually clear interface without compromising performance. This innovative approach includes a unique composition of UI structure, responsive data visualization, real-time data handling methods, adaptive information architecture, and white space optimization.

[0128] SPoG605UI is built around a grid-based layout system, leveraging CSS Grid and Flexbox technologies. This structure provides the flexibility to create fluid layouts with elements that automatically adapt to the available space and content. HTML5 and CSS3 serve as the underlying technologies for creating the UI, while JavaScript, specifically React.js, manages the dynamic aspects of the UI.

[0129] It should be understood that the operations shown in the exemplary methods are not exhaustive and that other operations may similarly be performed before, after, or between any of the illustrated operations. In some embodiments of the present disclosure, operations may be performed in different orders and / or may be varied.

[0130] FIG. 7 illustrates an embodiment of RTDM 700, an ERP-agnostic real-time data mesh with change data capture. In some embodiments, RTDM 700 can be operatively connected to a UI, which can be an embodiment of SPoG UI 405 (shown in FIG. 7) or another embodiment of a SPoG UI described herein. RTDM 700 can include one or more headless engines 705, which can include one or more of an onboarding engine 705.A, a document hub engine 705.B, a connectivity engine 705.C, a supply and demand engine 705.D, a customer service engine 705.E, a performance engine 705.F, a business planning engine 705.G, a go-to-market engine 705.H, and a vendor management engine 705.I. In some embodiments, RTDM 700 can additionally include API connectivity 710 and a data layer 720, which can include a global data lake 722, a data governance module 724, and a system of record 725.

[0131] According to some embodiments, the RTDM 700 is configured to perform processes related to change data capture (CDC) in an ERP-agnostic real-time data mesh. A 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 reconciled on the fly and transformed into a standardized format suitable for analysis and integration. This process ensures that data is kept up to date within the data mesh, enabling real-time insight and decision making.

[0132] CDC may include one or more of the following steps:

[0133] Data Source Monitoring: The CDC mechanism continuously monitors transactional systems such as ERPs, legacy systems, and other enterprise-wide solutions for any updates, modifications, or new transactions. This monitoring can be achieved through various techniques, e.g., log-based capture, triggers, and polling mechanisms.

[0134] Change detection: Once a change is detected in the data source, the CDC mechanism captures the specific change made to the data, which can include inserts, updates, deletes, or other changes.

[0135] Data Capture: The CDC mechanism captures changed data in its raw form and preserves the original data structure and format. This ensures that the captured data can be accurately transformed and integrated.

[0136] Transformation and Harmonization: The captured raw data undergoes a transformation and harmonization process. This involves converting the data into a standardized format suitable for analysis and integration. At this stage, data cleansing, normalization, and enrichment techniques may be applied to ensure data quality and consistency.

[0137] Integration and storage: The transformed and harmonized data is then integrated into the data mesh, specifically the global data lake and associated PDS. The data is stored in a structured manner, easily accessible, and available for real-time analysis and decision making.

[0138] Depending on the data source and requirements, there are several approaches to CDC that can be utilized based on the RTDM 700.

[0139] Log-Based CDC: This approach leverages the transaction or change logs of the source systems to capture and extract changes made to the data. Algorithms are used to parse and interpret the log files to identify specific changes and extract the relevant data for further processing.

[0140] Trigger-based CDC: In this approach, triggers are set on the source database tables to capture and track changes made to the data. When a change occurs, the trigger is fired and the CDC mechanism captures the relevant data changes for further processing.

[0141] Polling-based CDC: In some cases, when real-time data capture is not feasible or necessary, a polling mechanism can be employed. The CDC mechanism periodically polls the source system for any new or modified data. Data is then captured and processed based on the polling interval.

[0142] Such algorithms can be implemented using a combination of programming languages, database query languages, and data integration tools, 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.

[0143] According to aspects of the present disclosure, the CDC ensures that data within the ERP-agnostic real-time data mesh is continuously updated and harmonized, enabling stakeholders to access real-time insights, make informed decisions, and optimize supply chain operations.

[0144] In some embodiments, headless engines 705 can be included in the RTDM 700, each performing a specific function within the system.

[0145] In some embodiments, the onboarding engine 705.A simplifies the process of onboarding new vendors into the distribution ecosystem. It addresses tasks such as sign-up and account creation, vendor profiling, product evaluation, and contract execution. By streamlining the onboarding process, the engine ensures an integrated experience, reduces friction and improves collaboration between vendors and the supply chain.

[0146] In some embodiments, the document hub engine 705.B serves as a central repository for managing contracts and related documents. It provides functionality for creating, storing, and searching contracts, ensuring easy access and effective contract management. By centralizing contract-related information, the document hub engine enables parties to maintain a comprehensive view of contractual agreements and related documents.

[0147] In some embodiments, the connectivity engine 705.C facilitates communication and data exchange between various entities within the distribution ecosystem. This enables connectivity for catalog management, order status and tracking, and rich content delivery. Through standardized APIs and protocols, the connectivity engine ensures efficient and reliable data transfer, improving overall connectivity and collaboration between the parties.

[0148] 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. It provides real-time insight into supply and demand patterns, enabling stakeholders to make informed decisions and optimize operations. The supply and demand engine acts as a central hub for supply chain visibility, ensuring timely order processing and efficient inventory management.

[0149] In some embodiments, the customer service engine 705.E handles general problem resolution and customer support within the distribution ecosystem. It provides functionality for managing customer inquiries, complaints, and returns. By streamlining customer service operations, the engine improves customer satisfaction and strengthens customer relationships.

[0150] In some embodiments, the performance engine 705.F focuses on monitoring and analysis of key performance and business health indicators. It provides stakeholders with visibility into sales performance, pipeline visibility, and alerts and forecasts. The performance engine facilitates data-driven decision making, enabling stakeholders to identify improvement opportunities and optimize business processes.

[0151] In some embodiments, the business planning engine 705.G enables stakeholders to capture and track business objectives and goals. It provides functionality for strategic planning, forecasting, and resource allocation. By aligning business goals with operational strategies, the business planning engine supports effective decision making and helps drive business success.

[0152] In some embodiments, the go-to-market engine 705.H encompasses the core marketing services within the distribution ecosystem. It facilitates the creation and distribution of marketing content, training resources, and promotional materials. The go-to-market engine supports product launches, marketing campaigns, and channel partner assistance to ensure an effective go-to-market strategy.

[0153] In some embodiments, the vendor management engine 705.I focuses on managing vendor relationships and related activities. It addresses contractual agreements, supply and demand management, customer service, and go-to-market initiatives related to vendors and partners. The vendor management engine ensures effective collaboration and coordination between the distribution ecosystem and its vendors and partners.

[0154] In some embodiments, the API connectivity layer 710 enables integration and communication between RTDM 700 and external systems, such as ERP, legacy systems, and other enterprise-wide solutions. Through well-defined APIs, RTDM 700 can exchange data and interact with these systems, ensuring data consistency and real-time updates.

[0155] The data layer 720 can be configured as a suite of interconnected systems and infrastructure designed to manage, process, and analyze supply chain data. This facilitates real-time data management, harmonization, and availability, enabling stakeholders to derive actionable insights and make informed decisions. In some embodiments, the data layer 720 includes a global data lake 722 and a data governance module 724.

[0156] 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 one non-limiting example, the global data lake 722 can be formed as an implementation of cloud-based storage technologies such as Apache Hadoop Distributed File System (HDFS) or Amazon S3 to accommodate the diverse data requirements of the distribution ecosystem. Within the global data lake 722, multiple purpose-built data stores (PDSs) can be configured and deployed. These PDSs are dedicated repositories optimized to store and retrieve specific types of data related to the supply chain domain. Each PDS within the global data lake 722 serves as a storage entity dedicated to a specific category of data, such as customer data, product data, financial data, etc.

[0157] In some embodiments, the global data lake 722 serves as a scalable, fault-tolerant storage infrastructure within the RTDM 700. It encompasses a variety of purpose-built data stores (PDS) optimized for efficient data acquisition 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 serves as a central repository of harmonized and standardized data, enabling real-time insights and analytics.

[0158] In some embodiments, the PDS provides several advantages within the global data lake 722. Each PDS can be optimized for efficient data retrieval based on specific use cases and requirements. By organizing data into dedicated data stores, the global data lake 722 allows stakeholders to quickly access relevant data, facilitating real-time insights and decision-making. The PDS within the global data lake 722 can enable for data harmonization and standardization across the distribution ecosystem. By enforcing consistent data formats, structures, and semantics, the PDS ensures data integrity and enables data integration across disparate systems and processes.

[0159] The global data lake 722, along with its PDS, allows for leveraging cloud-based storage infrastructure, enabling scalability and adaptability. As supply chain data volumes grow, the global data lake 722 can scale to accommodate growing data requirements while ensuring system performance and responsiveness are maintained.

[0160] A PDS also allows for segmenting data based on specific data types, ensuring data privacy and confidentiality. By organizing data within a dedicated PDS, sensitive information can be isolated and access controls can be applied to protect data privacy and comply with regulatory requirements.

[0161] In some embodiments, the data governance module 724 is configured to be operatively connected to the global data lake 722 and the PDS and configured to ensure data integrity, security, and compliance within the RTDM 600. The data governance module 724 can establish policies, processes, and controls for governing data management activities within the distribution ecosystem.

[0162] In some embodiments, the data governance module 724 can implement robust data security measures, including access controls, authentication mechanisms, and encryption techniques, to protect the confidentiality and integrity of supply chain data, ensuring that only authorized users can access specific data and protecting against unauthorized data breaches. The data governance module 724 can also enforce data quality standards and conduct data validation and cleansing processes, ensuring that data stored in the global data lake 722 and the PDS is accurate, consistent, and trustworthy, allowing stakeholders to rely on high-quality data for decision-making and analysis.

[0163] The data governance module 724 can facilitate compliance with relevant regulations and industry standards governing data management within the supply chain domain, establishing controls and processes to ensure data privacy, retention, and lawful use, and mitigating the risk of non-compliance and associated penalties.

[0164] The data governance module 724 can incorporate data lineage and audit trail mechanisms, allowing stakeholders to trace the provenance, history, and applied transformations of data in the global data lake 722 and the PDS. This ensures data traceability, improves 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. This ensures data integrity, compliance with regulatory requirements, and data privacy and security. The data governance module incorporates fine-grained access controls, authentication protocols, data encryption techniques, and audit trail mechanisms to protect data and ensure accountability.

[0165] In some embodiments, the data governance module 724 can manage several functions implemented to manage different types of data within the supply chain. These functions include:

[0166] Catalog Management: The data governance module 724 facilitates management of transactional catalog data, ensuring that catalog information such as product details, pricing, and availability is accurate, up-to-date, and consistent across the entire distribution ecosystem.

[0167] Pimcore Product Data: This function within the Data Governance module 724 focuses on managing product data using Pimcore-based solutions. It allows for the creation, storage, and maintenance of product-related information including descriptions, specifications, images, and other attributes. By centralizing product data management, the module ensures data consistency and accessibility across the supply chain.

[0168] Order Status and Tracking (OST): The data governance module 724 incorporates the ability to track and manage order status across the supply chain. It provides real-time visibility into the progress of an order including ordering, fulfillment, and delivery. This capability increases transparency and allows stakeholders to monitor and address issues related to order processing and delivery.

[0169] Quote Management: This module enables management of quotes within the distribution ecosystem. It allows stakeholders to create, revise, and track quotes for products or services. Quote management functionality ensures efficient handling of quote-related information, facilitating a streamlined quoting process and accurate pricing.

[0170] Special Pricing Management: This function within the Data Governance module 724 focuses on managing special pricing within the supply chain. It allows for the maintenance and enforcement of special pricing agreements, such as bid prices or contract specific pricing. By ensuring consistent and accurate special pricing, the module supports pricing transparency and contract compliance.

[0171] Renewal Management: The data governance module 724 includes functionality for managing renewals within the distribution ecosystem. It facilitates tracking and management of service contract renewals, license agreement renewals, and associated renewal schedules. The renewal management functionality ensures timely renewal notifications and accurate contract management.

[0172] Contract Management: This function within the Data Governance Module 724 enables management and tracking of contracts within the distribution ecosystem. It provides a central 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 contracting processes.

[0173] Subscription Management: This module incorporates functionality to manage subscriptions within the supply chain. It enables tracking of subscription-based services, usage / metering information, and billing / payment details. The subscription management functionality ensures accurate subscription tracking, billing accuracy, and customer satisfaction.

[0174] Vendor Partner Management: This feature focuses on managing vendor partnerships within the distribution ecosystem. It facilitates management of strategic partner information, partner program details, partner certifications, and partner level information. Vendor Partner Management capabilities improve collaboration and coordination with key vendors and partners, enabling mutually beneficial relationships.

[0175] Marketing Data Management: The Data Governance module 724 includes functionality for managing marketing related data within the distribution ecosystem. This facilitates the storage, retrieval, and distribution of marketing content, such as marketing materials, training resources, and promotional assets. Marketing data management functionality ensures effective marketing operations, content sharing, and improved go-to-market strategies.

[0176] Within its comprehensive set of capabilities, the Data Governance module 724 can accommodate other functions, additional or alternative (not listed), to ensure data integrity, security, and compliance within the distribution ecosystem. It protects the confidentiality and integrity of supply chain data by implementing fine-grained access controls, authentication mechanisms, and encryption methods. It enforces data quality standards, performs validation processes, and ensures accurate and consistent data across different functions.

[0177] Additionally, the Data Governance module 724 supports compliance with regulatory requirements and industry standards governing data management within the supply chain domain. It establishes controls and processes to ensure data privacy, retention, and lawful use, mitigating the risk of non-compliance and associated penalties. It also incorporates data lineage and audit trail mechanisms to enable stakeholders to trace data origins, history, and transformations applied.

[0178] The system of record 725 represents the integration layer within RTDM 700. It connects with various enterprise systems, including ERP and other data sources, allowing 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.

[0179] The Data Layer 720, which includes a Global Data Lake 722 and a Data Governance Module 724, is at the heart of RTDM 600. The Global Data Lake 722 provides a scalable, fault-tolerant storage infrastructure while the PDS optimizes data retrieval, harmonization, and integration. The Data Governance Module 724 ensures data integrity, security, and compliance, fostering sound data management practices within the distribution ecosystem. The combination of these components empowers the SPoG to leverage real-time data insights to drive efficient and informed decision-making within a dynamic and complex distribution environment.

[0180] Overall, RTDM700 combines the enumerated elements, headless engine, API connectivity, global data lake, data governance module, and system of record to provide a comprehensive, scalable, real-time data management solution that enables efficient supply chain operations, data harmonization, advanced analytics, and integration with existing enterprise systems. RTDM700 empowers stakeholders with timely insights and actionable intelligence to optimize supply chain processes and drive business success.

[0181] 8 is a flow diagram of a method 800 for vendor onboarding using the SPoG UI, according to some embodiments of the present disclosure. In some embodiments, the method 800 outlines a streamlined and efficient process that leverages the power of the SPoG UI to facilitate vendor onboarding into the distribution ecosystem. By integrating real-time data, collaborative decision-making, and role-based access control capabilities, the SPoG UI enables stakeholders to effectively manage and optimize the vendor onboarding process. Based on the disclosure herein, the operations of the method 800 may be performed in different orders and / or varied to suit particular implementation requirements.

[0182] In operation 805, the process begins when a vendor indicates an interest in joining the distribution ecosystem. The computing device utilizes the SPoG UI to receive the vendor's information and related details, including company profile, contact information, product catalog, certifications, and any other relevant data required for the vendor onboarding process.

[0183] At operation 810, the computing device uses its integration capabilities with the real-time data exchange module to verify the vendor's information. By leveraging real-time data synchronization and access to external systems, the computing device ensures that the vendor details are accurate and up-to-date. This verification step helps maintain data integrity, minimize errors, and establish a trusted foundation for the vendor onboarding process.

[0184] In operation 815, the computing device initiates a vendor onboarding workflow through a collaborative decision-making module. This module enables 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 collectively assess a vendor's suitability and efficiently progress through the onboarding steps.

[0185] 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 participants only have access to the specific information and functionality required for their role. This control mechanism protects sensitive data, maintains privacy, and meets regulatory requirements. Authorized participants can securely review and contribute to the vendor onboarding process, fostering a transparent and compliant environment.

[0186] At act 825, the computing device provides stakeholders with a comprehensive view of the vendor onboarding process through a unified view (UV) module of the SPoG UI. This module presents an intuitive, customizable dashboard-style layout that aggregates relevant information, milestones, and tasks pertaining to the vendor onboarding process. Stakeholders can monitor progress, track document requirements, and access real-time updates to ensure efficient and timely completion of onboarding tasks.

[0187] At operation 830, the computing device enables stakeholders to interact with a data visualization module of the SPoG UI, which provides dynamic visualization and analytics related to the vendor onboarding process. Through interactive charts, graphs, and reports, stakeholders can evaluate key performance indicators, identify bottlenecks, and gain insight into the overall efficiency of the vendor onboarding process. This data-driven approach empowers stakeholders to make informed decisions, effectively allocate resources, and optimize onboarding workflows.

[0188] In operation 835, the computing device facilitates collaboration between the parties involved in the vendor onboarding process through a collaborative decision-making module. This module enables real-time communication, document sharing, and workflow coordination, allowing the parties to streamline the onboarding process. By providing a centralized platform for discussion, feedback, and approvals, the SPoG UI fosters efficient collaboration and reduces delays in the vendor onboarding workflow.

[0189] At operation 840, the computing device uses the workflow management module of the SPoG UI to ensure effective management and tracking of the vendor onboarding process. This module allows participants to define and manage the sequence of tasks, approvals, and reviews required for successful vendor onboarding. Workflow templates can be configured, allowing for standardization and repeatability of the onboarding process. Participants can monitor the status of each task, track completion, and receive notifications to ensure timely progress.

[0190] At act 845, the computing device captures and records vendor onboarding activities in an audit trail module within the SPoG UI. This module maintains a detailed history of the onboarding process, including actions taken, documents reviewed, and decisions made. The audit trail increases transparency, accountability, and compliance, providing stakeholders with a reliable record for future reference and potential audits.

[0191] At operation 850, the computing device finishes the vendor onboarding process within the SPoG UI. Once all necessary steps, reviews, and approvals are complete, the vendor is officially onboarded into the distribution ecosystem. The SPoG UI can provide stakeholders with a summary of the onboarding process, allowing them to verify completion of all requirements and initiate further actions such as signing contracts, listing products, and collaboration.

[0192] In conclusion, method 800 depicted in FIG. 8 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, fosters transparency, improves 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 manual effort, and optimizes vendor integration within a dynamic and complex distribution environment.

[0193] 9 is a flow diagram of a method 900 for reseller onboarding using the SPoG UI, according to some embodiments of the present disclosure. The method 900 outlines a streamlined and efficient process that leverages the power of the SPoG UI to facilitate onboarding of resellers into a distribution ecosystem. By integrating real-time data, collaborative decision-making, and role-based access control capabilities, the SPoG UI enables stakeholders to effectively manage and optimize the reseller onboarding process. Based on the disclosure herein, the operations of the method 900 may be performed in different orders and / or varied to suit particular implementation requirements.

[0194] At operation 905, the process begins when a reseller indicates an interest in joining the distribution ecosystem. The computing device utilizes the SPoG UI to receive the reseller's information and related details, including company profile, contact information, corporate credentials, reseller agreement, and any other relevant data required for the reseller onboarding process.

[0195] At operation 910, the computing device uses its integration capabilities with the real-time data exchange module to verify the reseller's information. By leveraging real-time data synchronization and access to external systems, the computing device ensures that the reseller's details are accurate and up-to-date. This verification step helps maintain data integrity, minimizing errors and establishing a trusted foundation for the reseller onboarding process.

[0196] In operation 915, the computing device initiates a reseller onboarding workflow through a collaborative decision-making module. This module enables the parties involved in the onboarding process, e.g., sales representatives, legal teams, account managers, to collaborate and make informed decisions based on the reseller's information. The SPoG UI facilitates communication, file sharing, and workflow initiation, allowing the parties to collectively evaluate the reseller's suitability and efficiently progress through the onboarding steps.

[0197] At operation 920, the computing device employs a role-based access control (RBAC) module to manage access control and permissions throughout the reseller onboarding process. The RBAC module ensures that participants only have access to the specific information and functionality required for their respective roles. This control mechanism protects sensitive data, maintains privacy, and meets regulatory requirements. Authorized participants can securely review and contribute to the reseller onboarding process, fostering a transparent and compliant environment.

[0198] At act 925, the computing device provides stakeholders with a comprehensive view of the reseller onboarding process through a unified view (UV) module of the SPoG UI. This module presents an intuitive, customizable dashboard-style layout that aggregates relevant information, milestones, and tasks pertaining to the reseller onboarding process. Stakeholders can monitor progress, track document requirements, and access real-time updates to ensure efficient and timely completion of onboarding tasks.

[0199] At operation 930, the computing device enables stakeholders to interact with a data visualization module of the SPoG UI, which provides dynamic visualization and analytics related to the reseller onboarding process. Through interactive charts, graphs, and reports, stakeholders can evaluate key performance indicators, identify bottlenecks, and gain insight into the overall efficiency of the onboarding process. This data-driven approach empowers stakeholders to make informed decisions, effectively allocate resources, and optimize the reseller onboarding workflow.

[0200] At operation 935, the computing device facilitates collaboration between the parties involved in the reseller onboarding process through a collaborative decision-making module. This module enables real-time communication, document sharing, and workflow coordination, allowing the parties to streamline the onboarding process. By providing a centralized platform for discussion, feedback, and approvals, the SPoG UI fosters efficient collaboration and reduces delays in the reseller onboarding workflow.

[0201] At operation 940, the computing device records the reseller onboarding activities and maintains an audit trail in an audit trail module within the SPoG UI. This module captures detailed information about actions taken, decisions made, and documents reviewed during the onboarding process. The audit trail increases transparency, accountability, and compliance and serves as a valuable reference for future audits, reviews, and assessments.

[0202] At act 945, the computing device finishes the reseller onboarding process within the SPoG UI. Once all required tasks, reviews, and approvals are completed, the reseller is officially onboarded into the distribution ecosystem. The SPoG UI provides stakeholders with a summary of the onboarding process, ensures that all requirements have been met, and facilitates further actions such as signing contracts, listing products, and collaborating with the reseller.

[0203] In conclusion, method 900 depicted in FIG. 9 outlines a streamlined and efficient reseller 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 resellers into the distribution ecosystem. SPoG UI's intuitive interface, customizable features, and robust collaboration capabilities streamline the onboarding process, improve transparency, and foster efficient communication between stakeholders. SPoG UI's data visualization capabilities facilitate data-driven decision-making, while the audit trail ensures compliance and provides a reliable record of onboarding activities. Through effective utilization of SPoG UI, the reseller onboarding process becomes a well-coordinated workflow that optimizes reseller integration and fosters business success in a dynamic distribution environment.

[0204] FIG. 10 is a flow diagram 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 for efficiently onboarding customers and end-customers into a distribution ecosystem. By leveraging the capabilities of the SPoG UI, including real-time data integration, collaborative decision-making, and personalized user experience, participants can successfully onboard and engage customers and provide an adapted onboarding experience. Based on the disclosure herein, the operations of the method 1000 can be performed in different orders and / or varied to suit specific implementation requirements.

[0205] In operation 1005, the process begins when a customer or end customer indicates an interest in joining the distribution ecosystem. The computing device utilizes the SPoG UI to capture the customer's or end customer's information, preferences, and requirements necessary for the onboarding process, including contact details, business profile, industry specific preferences, and any other relevant data.

[0206] In operation 1010, the computing device validates the customer or end-customer's information using its real-time data integration capabilities with external systems. By synchronously accessing data from various sources, such as a customer relationship management (CRM) system, or other enterprise-wide solutions, the computing device ensures the accuracy and completeness of the customer or end-customer's information. This validation step helps establish a trusted foundation for the onboarding process and improves data integrity.

[0207] In operation 1015, the computing device initiates a customer or end customer onboarding workflow through a collaborative decision-making module. This module facilitates communication and collaboration between the parties involved in the onboarding process, such as sales representatives, account managers, and customer support teams. The SPoG UI provides a centralized platform for the parties to collectively assess customer requirements, define a personalized onboarding journey, and make informed decisions throughout the onboarding process.

[0208] At operation 1020, the computing device employs a role-based access control (RBAC) module to manage access control and permissions during the onboarding process. The RBAC module ensures that participants have appropriate access to customer or end-customer data based on their roles and responsibilities. This control mechanism protects sensitive data, maintains data privacy, and meets regulatory requirements. Authorized participants can securely review, update, and track onboarding progress, fostering a transparent and compliant onboarding environment.

[0209] At act 1025, the computing device leverages the unified view (UV) module of the SPoG UI to provide stakeholders with a comprehensive, customizable, dashboard-style layout of the customer or end-customer onboarding process. This module aggregates relevant information, tasks, and milestones pertaining to the onboarding journey, providing stakeholders with a holistic view of onboarding progress. Stakeholders can monitor status, review documents, and access real-time updates to ensure an efficient onboarding experience.

[0210] At act 1030, the computing device utilizes the data visualization module of the SPoG UI to present dynamic visualizations and analytics related to the onboarding process. Through interactive charts, graphs, and reports, stakeholders gain insight into key performance indicators, customer engagement levels, and potential bottlenecks. This data-driven approach empowers stakeholders to make informed decisions, optimize onboarding strategies, and personalize the onboarding experience for each customer or end customer.

[0211] In operation 1035, the computing device enables the participants to interact with the collaboration decision-making module to facilitate collaboration during the onboarding process. The participants can share documents, initiate workflows, and exchange information in real-time. The SPoG UI fosters efficient communication, reduces latency, and ensures alignment between the participants involved in customer or end-customer onboarding.

[0212] At operation 1040, the computing device employs a customization module to enable participants to personalize the onboarding experience for each customer or end customer. Parties can tailor interfaces, workflows, and communications to match the customer's or end customer's preferences, industry-specific requirements, and strategic goals. The customization capabilities increase customer satisfaction and engagement during the onboarding journey.

[0213] At operation 1045, the computing device utilizes an audit trail module within the SPoG UI to maintain a detailed record of customer or end customer onboarding activities. This module captures information regarding actions taken, decisions made, and documents reviewed throughout the onboarding process. The audit trail increases transparency, accountability, and compliance and serves as a valuable reference for future audits, reviews, and assessments.

[0214] At act 1050, the computing device finishes 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 a summary of the onboarding process, ensures that all requirements have been met, and facilitates further actions such as account activation, service delivery, or personalized customer engagement.

[0215] In conclusion, method 1000 depicted in FIG. 10 illustrates the customer and end-customer onboarding process facilitated by SPoG UI. By leveraging real-time data integration, collaborative decision-making, role-based access control, comprehensive visualization, customization, and audit trail capabilities, SPoG UI empowers stakeholders to successfully onboard customers and end-customers into the distribution ecosystem. SPoG UI's intuitive interface, personalized features, and robust collaboration capabilities streamline the onboarding process, improve transparency, and foster efficient communication between stakeholders. SPoG UI's data visualization capabilities facilitate data-driven decision-making, while the customization and audit trail modules ensure a tailored and compliant onboarding experience. Through effective utilization of SPoG UI, the customer and end-customer onboarding process becomes a workflow, optimizing customer and end-customer integration and fostering business success within the dynamic distribution environment.

[0216] 11 is a block diagram of example components of a device 1100. One or more computer systems 1100 may be used to implement, for example, any of the embodiments described herein, as well as combinations and subcombinations thereof. The computer system 1100 may include one or more processors (also referred to as central processing units or CPUs), such as processor 1104. The processor 1104 may be connected to a communication infrastructure, i.e., bus 1106.

[0217] The computer system 1100 may also include user input / output devices 1103 , such as a monitor, keyboard, pointing device, etc., which may communicate with a communications infrastructure 1106 through a user input / output interface 1102 .

[0218] One or more of the processors 1104 may be a graphics processing unit (GPU). In one embodiment, a GPU may be a processor that is a specialized electronic circuit designed to handle mathematically intensive applications. A GPU may have a parallel structure that is efficient for parallel processing of large blocks of data, such as mathematically intensive data common in computer graphics applications, images, videos, etc.

[0219] The computer system 1100 may also include a main or primary memory 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 have control logic (i.e., computer software) and / or data stored therein.

[0220] The computer system 1100 may also include one or more secondary storage devices or memories 1110. The secondary memory 1110 may include, for example, a hard disk drive 1112 and / or a removable storage device or drive 1114.

[0221] The removable storage drive 1114 may interact with a removable storage unit 1118. The removable storage unit 1118 may include a computer usable or readable device having computer software (control logic) and / or data stored thereon. The removable storage unit 1118 may be a program cartridge and cartridge interface (e.g., as found in a video game device), a removable memory chip (such as an EPROM or PROM) and associated socket, a memory stick and USB port, a memory card and associated memory card slot, and / or other removable storage unit and associated interface. The removable storage drive 1114 may read from and / or write to the removable storage unit 1118.

[0222] The secondary memory 1110 may include other means, devices, components, intermediaries, or other approaches for allowing computer programs and / or other instructions and / or data to be accessed by the computer system 1100. Such means, devices, components, intermediaries, or other approaches may include, for example, a removable storage unit 1122 and an interface 1120. Examples of removable storage units 1122 and interfaces 1120 may include a program cartridge and cartridge interface (e.g., as found in a video game device), a removable memory chip (such as an EPROM or PROM) and associated sockets, a memory stick and a USB port, a memory card and associated memory card slot, and / or any other removable storage unit and associated interface.

[0223] The computer system 1100 may further include a communication or network interface 1124. The communication interface 1124 may enable the computer system 1100 to communicate and interact with any combination of external devices, external networks, external entities, etc. (individually and collectively referred to by reference number 1128). For example, the communication interface 1124 may enable the computer system 1100 to communicate with external or remote devices 1128 via communication paths 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 and from the computer system 1100 via the communication paths 1126.

[0224] Additionally, computer system 1100 may be a personal digital assistant (PDA), a desktop workstation, a laptop or notebook computer, a netbook, a tablet, a smartphone, a smart watch or other wearable, an appliance, part of the Internet of Things, and / or an embedded system, or any combination thereof, to name a few non-limiting examples.

[0225] The computer system 1100 may 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-premise software ("on-premise" cloud-based solutions), "as a service" models (e.g., Content as a Service (CaaS), Digital Content as a Service (DCaaS), Software as a Service (SaaS), Managed Software as a Service (MSaaS), Platform as a Service (PaaS), Desktop as a Service (DaaS), Framework as a Service (FaaS), Backend as a Service (BaaS), Mobile Backend as a Service (MBaaS), Infrastructure as a Service (IaaS), etc.), and / or hybrid models including any combination of the foregoing examples or other service or delivery paradigms.

[0226] Any applicable data structures, file formats, and schemas in computer system 1100 may be derived from standards including, but not limited to, JavaScript Object Notation (JSON), Extensible Markup Language (XML), Yet Another Markup Language (YAML), Extensible Hypertext Markup Language (XHTML), Wireless Markup Language (WML), MessagePack, XML User Interface Language (XUL), or any other functionally similar representations, alone or in combination. Alternatively, proprietary data structures, formats, or schemas may be used, either exclusively or in combination with known or open standards.

[0227] In some embodiments, a tangible, non-transitory apparatus or article of manufacture comprising a tangible, non-transitory computer usable or readable medium having 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, as well as tangible articles of manufacture embodying 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.

[0228] It is understood that the intention is to use the Detailed Description, and not the Abstract, to interpret the claims. The Abstract may set forth one or more, but not all, exemplary embodiments of the invention as contemplated by the inventors, and is thus not intended to limit the scope of the invention and the appended claims in any manner.

[0229] The present invention has been described above with the aid of functional building blocks illustrating the implementation of certain functions and relationships thereof. The boundaries of these functional building blocks have been arbitrarily defined herein for convenience of description. Alternative boundaries may be defined so long as the certain functions and relationships thereof are appropriately performed.

[0230] The foregoing description of specific embodiments fully reveals the general nature of the present invention, and by applying the knowledge of those skilled in the art, such specific embodiments can be easily modified and / or applied to various applications without departing from the general concept of the present invention without undue experimentation. Such applications and modifications are therefore intended to be within the meaning and range of equivalents of the disclosed embodiments, based on the teaching and guidance presented herein. It should be understood that the expressions or terms used herein are for the purpose of explanation, and thus should be interpreted by those skilled in the art in light of the teachings and guidance.

[0231] 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. 1. A computerized method for change data capture in an ERP-independent real-time data mesh, comprising: Monitoring transactional systems, including ERP, for real-time changes; capturing and processing the changed data using a change data capture mechanism; Transforming and harmonizing the captured data into a standardized format compatible with analysis and integration processes to ensure data consistency and compatibility across the data mesh; integrating the transformed and harmonized data into a data layer of the real-time data mesh that includes a global data lake comprising one or more purpose-built data stores (PDS) to enable real-time analytics and decision making based on the most up-to-date data within the data mesh.

2. The method of claim 1 , wherein the transformation and harmonization of the captured data involves data cleansing, normalization, and enrichment techniques to ensure data quality and consistency.

3. 2. The method of claim 1, wherein the data layer includes the global data lake configured as a scalable, fault-tolerant storage infrastructure to provide a central repository for the captured and transformed data within the real-time data mesh.

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

5. The method of claim 1 , wherein the data layer comprises a purpose-built data store (PDS) optimized for efficient retrieval and storage of a particular type of data.

6. The method of claim 1 , further comprising storing the transformed and harmonized data in a cloud-based storage infrastructure.

7. 2. The method of claim 1, further comprising applying artificial intelligence and / or machine learning models to enhance the change data capture process and facilitate automated analysis and decision-making within the real-time data mesh.

8. 1. A system for change data capture in an ERP-agnostic real-time data mesh, comprising: A transactional system including an ERP, one or more computers for monitoring said computerized system for real-time changes, said one or more computers comprising: one or more headless engines; and a data layer of a real-time data mesh operatively connected to the one or more headless engines, the data layer comprising a global data lake comprising one or more purpose-specific data stores (PDS) to enable real-time analytics based on real-time data within the data mesh, the one or more computers configured to capture and process the changed data using a change data capture mechanism, the global data lake configured to transform and harmonize the captured data into a standardized format compatible with analytics and integration processes to ensure data consistency and compatibility across the data mesh.

9. The system of claim 8 , further comprising a data governance module for ensuring data integrity, security, and compliance within the real-time data mesh.

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

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

12. 10. The system of claim 8, wherein the data governance module includes functionality for catalog management, Pimcore-based product data management, order status and tracking (OST) management, special pricing management, and quote management.

13. 9. The system of claim 8, wherein the data layer comprises a purpose-built data store (PDS) optimized for efficient retrieval and storage of specific types of data related to the supply chain domain.

14. The system of claim 8 , further comprising storing the transformed and harmonized data in a cloud-based storage infrastructure.

15. 10. The system of claim 8, further comprising artificial intelligence and / or machine learning models used to enhance the change data capture process and enable automated analysis and decision-making within the real-time data mesh.

16. When executed by the processor, monitoring transactional systems, including ERP, for real-time changes; capturing and processing the changed data using a change data capture mechanism; Transforming and harmonizing the captured data into a standardized format suitable for analysis and integration; and integrating the transformed and harmonized data into a data layer of a real-time data mesh for real-time analysis and decision-making.

17. 20. The computer-readable medium of claim 16, further comprising instructions for performing change data capture using one or more trigger-based, machine learning and / or polling-based CDC algorithms.

18. 20. The computer-readable medium of claim 16, wherein the data layer comprises a purpose-specific data store (PDS) optimized for efficient retrieval and storage of a particular type of data.

19. 20. The computer-readable medium of claim 16, wherein the instructions further comprise storing the transformed and harmonized data in a cloud-based storage infrastructure.

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

Images