System and method for distribution management including special pricing determination

Abstract

To provide automated stock keeping unit (SKU) management.SOLUTION: A system includes a user interface for receiving diverse catalog files, a catalog transformation module, a Real-Time Data Mesh (RTDM) module, a Master Data Governance (MDG) module, a Global Data Repository (GDR), and a search platform. The catalog transformation module, through iterative learning, transforms catalog files to a standard format and predicts categorization and attribute mapping. The RTDM module is configured to perform real-time data exchange. The MDG module validates the transformed catalogs. The GDR stores validated catalogs. The system can include a dynamic SKU creation module and a global pricing engine for real-time pricing, to improve data accuracy and SKU management, thereby facilitating integrated order processing and fulfillment.SELECTED DRAWING: None

Description

[Technical field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims the benefit of U.S. Provisional Patent Application No. 63 / 513,073, filed July 11, 2023, and U.S. Provisional Patent Application No. 63 / 515,075, filed July 21, 2023, each of which is incorporated herein in its entirety.

[0002] The disclosed embodiments relate to aspects of a user interface (UI) method and system. The traditional global distribution industry faces numerous challenges including supply chain management, inventory control, stock keeping unit (SKU) management, compliance, and evolving consumer expectations.

[0003] In various industries, e.g., retail, e-commerce, and supply chain management, accurate determination and application of special pricing plays a key role in ensuring competitiveness, optimizing sales, and satisfying customer demand. Special pricing refers to customized pricing strategies applied to specific scenarios, e.g., seasonal promotions, volume discounts, customer-specific agreements, and targeted marketing campaigns. Typically, these pricing strategies are based on a combination of factors including manufacturer details, product category, type of solution, past purchasing patterns, and other relevant variables.

[0004] Traditionally, the process of determining and applying special pricing has been labor-intensive, time-consuming, and prone to errors. Manual intervention and disparate data sources often impede the ability to make near real-time pricing decisions, resulting in delays and inefficiencies.

[0003] Traditional approaches to determining and applying special pricing in distribution platforms have suffered from many significant challenges, which have significantly hindered the efficiency of pricing analysts and sales teams. One of the most prominent issues is the lack of a synchronous real-time solution, which severely impedes the ability to make timely pricing decisions. Thus, manual calculation and validation of pricing against predefined rules has become a labor-intensive and time-consuming task.

[0005] The lack of a synchronized real-time solution forces pricing analysts and sales teams to rely on disparate data sources and outdated information. This in turn introduces inaccuracies into the pricing process because the data used to determine special pricing may not reflect the latest changes in product availability, market conditions, or promotional campaigns. Delays in accessing and updating this information can lead to missed opportunities for competitive pricing, resulting in lost sales and market share.

[0006] Additionally, the lack of real-time capabilities exacerbates the complexities associated with managing seasonal promotions and time-sensitive marketing campaigns. Pricing analysts must rely on manual methods to implement such promotions, often resulting in missed deadlines or an inability to quickly adapt to changing market dynamics. As a result, sales teams struggle to meet customer demand during peak periods, which can lead to customer dissatisfaction and lost revenue opportunities.

[0007] Also, the labor-intensive and manual nature of determining and applying special pricing makes the entire process highly prone to error. Pricing analysts and sales teams are prone to making mistakes in their efforts to deal with the vast amount of products and pricing scenarios. These errors can range from pricing miscalculations, applying incorrect pricing rules, or overlooking certain customer agreements, ultimately leading to billing discrepancies and customer complaints.

[0008] Inefficiencies arising from the lack of a synchronous real-time solution result in inconsistent pricing across different channels and customer segments. Pricing strategies that should be consistently applied may unintentionally vary due to delayed price updates, creating confusion among customers and damaging a brand's reputation for fair and transparent pricing practices.

[0009] In addition, the manual nature of the pricing decision process poses significant challenges in scaling pricing operations to handle an increasing number of products, customers, and market demands. As a business expands, pricing analysts' workload increases and it becomes increasingly difficult to maintain the same level of accuracy and responsiveness in pricing decisions. As a result, lack of scalability can thwart a company's growth potential and impede its ability to compete effectively in the marketplace.

[0010] Furthermore, a disjointed approach to determining and applying special pricing introduces significant delays into the overall sales process. Pricing analysts must devote significant time to manually process each pricing request, verifying compliance with various rules, and then applying the determined pricing to individual or multiple orders. These delays can lead to customer disappointment, lost sales opportunities, and negative effects on customer loyalty.

[0011] These challenges result in negative effects on the overall efficiency of the distribution platform, undermining the ability of companies to rapidly respond to market fluctuations, adapt to customer demand, and remain competitive. In an increasingly dynamic business landscape, the lack of a synchronous, real-time solution for special pricing determination and application has proven to be a significant obstacle to success, driving the need for innovative, automated, and efficient approaches to effectively address these issues. The present disclosure seeks to provide a robust solution that overcomes these limitations, providing a streamlined, near real-time special pricing determination system integrated with order processing workflow, paving the way for improved competitiveness, accuracy, and customer satisfaction.

[0012] Efforts have been made in the prior art to automate the process of special pricing calculation and application. However, existing systems and methods have fallen short in terms of scalability, accuracy, and integration with order processing workflow. There is a need for improved systems and methods that can leverage intelligent algorithms, caching mechanisms, data exchange, and integration with order processing systems to automate near real-time special pricing calculation and application.

[0013] Thus, there is a need for innovative systems and methods that overcome the limitations of existing solutions and provide an automated, accurate and efficient approach to special pricing calculation and application. The present disclosure addresses these needs, providing a robust system architecture and computerized method for streamlined, near real-time special pricing determination and its integration with order processing workflow. Summary of the Invention

[0014] 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 difficulties, the most significant exist in the areas of supply chain management, inventory and compliance issues, SKU management, the shift to a direct-to-consumer model, and rapidly evolving consumer expectations and behaviors.

[0015] The first key challenge relates to managing the supply chain, which is a central part of any distributor's operations, but which is usually outside the distributor'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 distributor'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, which involved 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.

[0016] A 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 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.

[0017] 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 OEMs, each with their own unique systems and processes, adds complexity. Additionally, addressing localization requirements aligned with the laws and regulations of different jurisdictions increases the potential for inefficiencies and errors.

[0018] Finally, the landscape of consumer behavior and expectations is rapidly changing. The shift to ecosystem commerce necessitates 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 that businesses evolve to meet these new customer expectations.

[0019] 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 after-sales support, installation, and training that enhance the overall customer experience.

[0020] However, to realise 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.

[0021] Single Pane of Glass The Single Pane of Glass (SPoG) disclosed herein can provide a comprehensive solution aimed at addressing these challenges: it can be configured to provide a holistic, user-friendly and efficient platform that streamlines the distribution process.

[0022] According to some embodiments, SPoG can be configured to address supply chain management by enhancing visibility and control over the supply chain process. Through real-time tracking and analysis, SPoG can provide useful insight into inventory levels and product status, ensuring that supply chain management processes are addressed efficiently.

[0023] According to some embodiments, SPoG can consolidate multiple communication channels (i.e., touch points) onto a single platform and emulate direct consumer channels into a distribution platform. This consolidation provides a unified direct channel for consumers to interact with distributors, significantly reducing supply chain complexity and improving the overall customer experience.

[0024] SPoG provides innovative solutions for improved inventory management through advanced forecasting capabilities. These predictive analytics can highlight demand trends and guide businesses to manage their inventory more efficiently and reduce the risk of stock-outs or overstocks.

[0025] According to some embodiments, the SPoG can include a global compliance database that is updated in real-time, allowing distributors to stay abreast of the latest international regulations, a feature that significantly reduces the burden of manual tracking and ensures smooth and compliant cross-border trade.

[0026] According to some embodiments, to streamline SKU management and product localization, SPoG consolidates data from various OEMs into a single platform, which not only ensures data consistency but also significantly reduces the potential for errors, further providing the ability to efficiently manage and distribute localized SKUs, thereby aligning with the needs and requirements of specific markets.

[0027] 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.

[0028] Additionally, 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 businesses to stay ahead of the curve and adapt to changing market conditions.

[0029] 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.

[0030] 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, distributors can expect to see increased efficiency, reduced errors and improved customer satisfaction, leading to sustained growth in an ever-evolving global marketplace.

[0031] Real-time Data Mesh (RTDM) According to some embodiments, the platform may include an implementation of Real-Time Data Mesh (RTDM). RTDM provides an innovative solution to address these challenges. RTDM can provide a distributed data architecture and enable real-time data availability across multiple sources and touchpoints. This aspect improves supply chain visibility, enables efficient management, and allows distributors to more effectively address disruptions.

[0032] RTDM enables the generation of predictive analytics and the ability to provide solutions for efficient inventory control, helping companies manage their inventory by providing insight into trends in demand and reducing the risk of overstocking or out-of-stocking.

[0033] RTDM can also facilitate global compliance and provide real-time updated compliance data to ensure distributors are up-to-date with international regulations, significantly reducing the burden of manual tracking and enabling integrated cross-border trade.

[0034] RTDM also simplifies SKU management and localization by consolidating data from various OEMs, ensuring data consistency and reducing the chance of errors. The capabilities to manage and distribute localized SKUs efficiently align with specific market needs.

[0035] With its centralized and intuitive interface, RTDM enhances customer experience for all parties, enabling easier access to and transact with technology, and meeting the expectations of technology partners in a consumer-driven generation.

[0036] Benefits of SPoG and RTDM Integration The integration of SPoG UI platform with RTDM allows for a centralized and holistic approach to the technical challenges encountered in distribution platforms. By integrating RTDM capabilities, SPoG can improve supply chain visibility, streamline inventory management, ensure compliance, simplify SKU management, and deliver superior customer experience. The real-time tracking and analytics provided by RTDM improves SPoG's ability to effectively manage supply chain and inventory. It provides accurate and up-to-date information, enabling distributors to make faster, more informed decisions. The integration of SPoG with RTDM also ensures data consistency, reducing errors and delays in SKU management and pricing. It simplifies product localization by providing a centralized platform for management of data from various OEMs, helping them to align with market needs.

[0037] Determining special pricing The present disclosure provides an innovative system and method for automated, near real-time special pricing calculation and application, addressing the limitations of existing solutions. The system leverages intelligent algorithms, caching mechanisms, data exchange, and integration with order processing workflows to streamline the pricing decision process, minimize manual effort, and reduce the potential for error.

[0038] In the prior art, the process of determining and applying special pricing was labor-intensive, time-consuming, and prone to error. Manual intervention and disparate data sources often hindered the ability to make timely pricing decisions, resulting in delays and inefficiencies. Existing automated systems were unable to provide the necessary scalability, accuracy, and integration with order processing workflows.

[0039] The present disclosure overcomes these limitations by introducing a synchronous system architecture that incorporates special pricing algorithms, caching, integration modules, and pricing validation and maintenance modules. These components work synergistically to ensure synchronized and accurate pricing decisions, validate pricing programs against predefined rules, and automate the creation and application of special pricing in near real-time.

[0040] The system also integrates and synchronizes with order processing workflows through APIs, facilitating accurate and timely application of special pricing for individual or multiple orders. Additionally, XML and EDI integration enables data exchange between different systems involved in the order fulfillment process, ensuring accurate and industry-standard compliant data transmission.

[0041] The present disclosure revolutionizes the process of calculating and applying special pricing in near real-time, reducing manual effort, minimizing errors, and improving overall sales effectiveness and customer satisfaction. By automating pricing decisions, the system enables rapid response to market conditions and customer requirements, improving competitiveness and optimizing sales.

[0042] Overall, the present disclosure provides a comprehensive solution that addresses the shortcomings of existing systems and methods, resulting in a streamlined, accurate and efficient approach for the calculation and application of near real-time special pricing. The systems and methods of the present invention empower businesses across a variety of industries to make data-driven pricing decisions, improve operational efficiencies and enhance customer experiences. [Brief description of the drawings]

[0043] [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 an embodiment of a system for supply chain 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 distribution 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] FIG. 1 is a flow diagram of a method for performing comprehensive supply chain management operations using a SPoG UI, according to some embodiments of the present disclosure. [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] 1 depicts an embodiment of a system for determining special pricing in a distribution network, according to some embodiments. [Figure 12] FIG. 2 is a flow diagram of a method for determining special pricing in a distribution network according to some embodiments of the present disclosure. [Figure 13] FIG. 1 is a diagram of a system for determining special pricing in a distribution network, according to some embodiments. [Figure 14] FIG. 2 is a block diagram of example components of a device according to some embodiments of the present disclosure. [Fig. 15A-14Q] Renders various screens and functionality of the SPoG UI, according to some embodiments. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0044] 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.

[0045] 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.

[0046] 1 illustrates a distribution platform operating environment 100, 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 the distribution platform.

[0047] Customers 120 within the operating environment of system 110 represent businesses or individuals seeking IT solutions that 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.

[0048] End customers 130 are the ultimate beneficiaries of the IT solutions provided by the system 110. They may include businesses or individuals who utilize IT products and services to improve their operations, productivity, or daily activities. End customers rely on the system 110 to access a wide range of IT solutions, ensuring access to the latest technologies and innovations in the market. The system 110 allows end customers to track their orders, receive delivery status updates, and access customer support services, thereby enhancing their overall experience.

[0049] 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.

[0050] 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.

[0051] 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.

[0052] Scalability and flexibility are key attributes of the system 110. It can accommodate the growing demands of the IT distribution model, whether that be with a growing customer base, an increasing number of vendors, or a broad range of IT products and services. The system 110 is designed to handle large-scale data processing, storage, and analysis, ensuring that it can support the evolving needs of the distribution platform. In addition, the system 110 leverages a technology stack that includes .NET, Java, and other suitable technologies to provide a robust foundation for its operations.

[0053] 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 transaction 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.

[0054] 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 integrated data flows and connections between various customer systems 220, ancillary systems 230, vendor systems 240, reseller systems 250, and other entities involved in the distribution process. The diagram illustrates the interconnectivity and mechanisms that enable efficient collaboration and data-driven decision making.

[0055] 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 250, 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:

[0056] Customer system integration: Integration points 210 can enable system 110 to connect with customer systems 220, allowing for efficient data exchange and synchronization. Customer systems 220 can include various entities, such as customer system 221, customer system 222, customer system 223. Integration with customer systems 220 empowers customers with access to real-time inventory information, pricing details, order tracking, and other relevant data, improving their visibility and decision-making capabilities.

[0057] Ancillary System Integration: Integration points 210 can enable system 110 to connect with ancillary systems 230, allowing for efficient data exchange and synchronization. Ancillary systems 230 can include various entities, such as ancillary systems 231, ancillary systems 233, and ancillary systems 233. Integration with ancillary systems 220 empowers customers with access to real-time inventory information, pricing details, order tracking, and other relevant data, improving their visibility and decision-making capabilities.

[0058] 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.

[0059] Reseller System Integration: Integration point 210 provides the functionality for reseller systems 250 to connect to system 110. Reseller systems 250 may include entities representing sales systems, customer management systems, and service distribution platforms employed by a reseller, such as reseller system 251, reseller system 252, and reseller system 253. Integration with reseller system 250 empowers the reseller to access up-to-date product information, manage customer accounts, track sales performance, and provide value-added services to customers.

[0060] 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 unified communication and data exchange, facilitating collaboration and an efficient distribution process.

[0061] The integration point 210 also allows for connectivity with systems of record 280 for additional data management and integration. Representing systems of record 280 can represent enterprise resource planning (ERP) systems or customer relationship management (CRM) systems, including both legacy ERP systems, e.g., SAP, Impulse, META, I-SCALA, and others, as well as future systems. The systems of record can include one or more storage repositories of critical and legacy business data. This facilitates integration of data exchange and synchronization between the distribution platform, the system 110, and the ERP, allowing real-time updates and ensuring availability of accurate and up-to-date information. The integration point 210 establishes connectivity between the systems of record 280 and the distribution platform, allowing stakeholders to leverage the wealth of data stored in the ERP for efficient collaboration, data-driven decision making, and streamlined distribution processes. These systems represent internal systems utilized by customers, vendors, and others.

[0062] 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 integrated data exchange.

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

[0064] 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 efficiently between customer systems 220, vendor systems 240, reseller systems 250, and other entities. This data exchange facilitates real-time visibility, enables data-driven decision making, and improves operational efficiency throughout the distribution platform.

[0065] 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 unified communication within the operating environment 200. These technologies provide a robust foundation for the system 110, ensuring scalability, flexibility, and efficient data processing capabilities. Furthermore, 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 participants to operate within a connected ecosystem. Data generated at various touchpoints, including customer orders, inventory updates, pricing changes, or delivery status, flows efficiently between different entities, systems, and components. The unified data is processed, harmonized, and provided in real-time through the system 110 to the appropriate participants. This real-time access to accurate and up-to-date information empowers the participants to make informed decisions, optimize supply chain operations, and improve customer experience.

[0066] 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.

[0067] 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.

[0068] 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.

[0069] 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 systems of other entities to interact with and process web pages, applications, and other data can be downloaded and stored on a hard disk or any other volatile or non-volatile storage medium or device, such as 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.

[0070] 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, and others.

[0071] 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.

[0072] 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.

[0073] 3 illustrates a system 300 for supply chain management. System 300 is a supply chain management solution designed to address the challenges faced by a fragmented supply chain ecosystem in the global distribution industry. System 300 can include several interconnected components and modules working in harmony to optimize supply chain operations, improve collaboration, and drive business efficiency.

[0074] In some embodiments, the SPoG UI 305 serves as a centralized user interface, providing stakeholders with a unified view of the entire supply chain. It 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 305 allows users to access relevant information and tools, empowering them to make data-driven decisions and efficiently manage supply chain activities.

[0075] For example, logistics managers can use the SPoG UI305 to monitor shipment status, track delivery routes, and see real-time inventory levels across multiple warehouses. They can visualize the data through interactive charts and graphs, such as a map showing the current location of each 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.

[0076] In some embodiments, the SPoG UI 305 is integrated 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 an event-driven architecture, the SPoG UI 305 ensures smooth information flow and enables collaborative decision-making across the supply chain ecosystem.

[0077] For example, when a purchase order is generated in the SPoG UI 305, the system 300 automatically updates inventory levels, triggers notifications to the warehouse management system, and initiates the shipping process. This integration enables efficient order fulfillment, reduces manual errors, and improves overall supply chain visibility.

[0078] In some embodiments, a real-time data mesh (RTDM) module 310 can be configured to provide an integrated flow of data within a supply chain ecosystem, collecting and harmonizing data from multiple sources and ensuring its availability in real-time.

[0079] In one non-limiting example, the RTDM module 310 can collect data from systems of record 280, which can represent a variety of systems including disparate inventory management systems, point of sale terminals, or customer relationship management systems. This data is harmonized by aligning formats, standardizing units of measurement, and reconciling discrepancies. The harmonized data is then made available in real time, allowing stakeholders across the supply chain to access accurate, up-to-date information.

[0080] In some embodiments, 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 or changes. The CDC component is specifically designed to work with a variety of transactional systems, which may include future and legacy ERP systems, customer relationship management (CRM) systems, and other enterprise-wide systems, ensuring compatibility and flexibility for businesses in diverse environments.

[0081] By providing continuous access to real-time data, stakeholders can make timely decisions and respond quickly to changing market conditions. For example, if the RTDM module 310 detects a sudden spike in demand for a particular product, it can trigger an alert to the production team so that they can adjust production schedules to prevent stockouts.

[0082] In some embodiments, the RTDM module 310 facilitates data management within supply chain operations, enabling real-time reconciliation of data from multiple sources and freeing vendors, resellers, customers, and end customers from the constraints imposed by legacy ERP systems. This increased flexibility supports improved efficiency, improved customer service, and increased innovation.

[0083] The system 300 can also include an Advanced Analytics and Machine Learning (AAML) module 315. The AAML module 315 can leverage powerful analytics tools and algorithms, such as Apache Spark, TensorFlow, or scikit-learn, to extract valuable insights from the collected data. It performs advanced analytics, predictive modeling, anomaly detection, and other machine learning operations.

[0084] For example, the AAML module 315 can analyze historical sales data to identify seasonal patterns and predict future demand. Forecasts can be generated that help optimize inventory levels, ensure inventory availability during busy periods, and minimize excess inventory costs. By leveraging machine learning algorithms, the AAML module 315 automates repetitive tasks, predicts customer preferences, and optimizes supply chain processes.

[0085] In addition to forecasting demand, the AAML module 315 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.

[0086] Additionally, the AAML module 315 can analyze data from various sources, such as social media feeds, customer reviews, and market trends, to gain a deeper understanding of consumer intentions and preferences. This information can be used to inform product development decisions, identify emerging market trends, and adapt business strategies to meet evolving consumer expectations.

[0087] The system 300 can perform integration and interoperability functions to connect with existing enterprise systems, e.g., ERP systems, warehouse management systems, customer relationship management systems. By establishing connections and data flows between these systems, the system 300 enables smooth data exchange, process automation, and end-to-end visibility across the supply chain. Integration protocols, APIs, and data connectors facilitate unified communication and interoperability between different modules and components, creating a holistic and connected supply chain ecosystem.

[0088] The implementation and deployment of system 300 can be tailored to meet specific business needs. In some non-limiting examples, 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.

[0089] The system 300 for supply chain management is a comprehensive and innovative solution that addresses the challenges faced by a fragmented supply chain 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 specific industry requirements to achieve efficiency and success in supply chain management.

[0090] FIG. 4 illustrates an embodiment of an advanced distribution platform including a system 400 for managing a complex distribution network, which can be an embodiment of system 300, providing a technology distribution platform for optimizing the management and operation of 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 can include a SPoG UI 405, a customer interaction module (CIM) 410, a RTDM module 415, an AI module 420, an interface display module 425, a personalized interaction module 430, a document hub 435, a catalog management module 440, a performance and prospect marker display 445, a predictive analytics module 450, a recommendation system module 455, a notification module 460, a self-onboarding module 465, and a communication module 470.

[0091] System 400, as one embodiment of system 300, utilizes a wide range of technologies and algorithms to integrate and consolidate supply chain management. These technologies and algorithms facilitate efficient data processing, personalized interactions, real-time analytics, secure communications, and effective management of documents, catalogs, and performance metrics.

[0092] In some embodiments, the SPoG UI 405 serves as a central interface within the system 400, providing stakeholders with a unified view of the entire distribution network. Front-end technologies such as ReactJS, TypeScript, Node.js, etc. are utilized to create an interactive and responsive user interface. These technologies enable the SPoG UI 405 to deliver a user-friendly experience, allowing stakeholders to access relevant information, navigate through different modules, and perform tasks efficiently.

[0093] In some embodiments, CIM 410, the customer interaction module, employs algorithms and technologies such as Oracle® Eloqua®, Adobe® Target, and Okta® to manage customer relationships within the distribution network. These technologies enable the module to handle customer data securely, personalize the customer experience, and provide unified access control to stakeholders.

[0094] In some embodiments, 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 supply chain 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 functionality allows stakeholders to access the latest and accurate information to make informed decisions.

[0095] In some embodiments, 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.

[0096] In some embodiments, the interface display module 425 focuses on presenting data and information in a clear and user-friendly manner. It utilizes technologies such as HTML, CSS, and JavaScript frameworks such as ReactJS to create interactive and responsive user interfaces. These technologies allow stakeholders to visualize data using various data visualization techniques, e.g., graphs, charts, and tables, allowing for efficient data understanding, comparison, and trend analysis.

[0097] In some embodiments, the personalized interaction module 430 utilizes customer data, trend history, and machine learning algorithms to generate personalized recommendations for products or services. In some non-limiting examples, it can be implemented using 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.

[0098] In some embodiments, document hub 435 serves as a central repository for storing and managing documents within system 400. In some non-limiting examples, it can be implemented utilizing SeeBurger® and Elastic Cloud for efficient document management, storage, and retrieval. For example, 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 search for relevant documents as needed.

[0099] In some embodiments, 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 latest product information, including specifications, pricing, availability, and promotions. In some non-limiting examples, it can be implemented using Kentico® and Akamai® to consolidate and aggregate catalog updates, content delivery, and caching. For example, the module can leverage Akamai's content delivery network (CDN) to quickly and efficiently deliver catalog information to stakeholders regardless of geographic location.

[0100] In some embodiments, the performance and outlook marker display 445 collects, analyzes, and visualizes real-time performance metrics and outlook related to supply chain operations. It utilizes tools such as Splunk® and Datadog® to enable effective performance monitoring and provide actionable insights. For example, the module can utilize Splunk's log analysis capabilities to identify performance bottlenecks in the supply chain and enable stakeholders to take proactive measures to optimize operations.

[0101] In some embodiments, predictive analytics module 450 employs machine learning algorithms and forecasting models to forecast demand patterns, optimize inventory levels, and improve overall supply chain efficiency. It utilizes technologies such as Apache Spark and TensorFlow for data analysis, modeling, and forecasting. For example, the module can use TensorFlow's deep learning capabilities to analyze historical sales data and forecast future demand, allowing stakeholders to optimize inventory levels and minimize costs.

[0102] In some embodiments, the recommendation system module 455 focuses on providing intelligent recommendations to stakeholders within the distribution network. It generates personalized recommendations for products or services based on customer data, historical trends, and machine learning algorithms. In some non-limiting examples, it can be implemented using 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.

[0103] In some embodiments, the notification module 460 enables delivery of real-time notifications to participants regarding important events, updates, or alerts within the supply chain. In some non-limiting examples, it can be implemented using message queues, event-driven architecture, and Apigee® X and TIBCO® for unified notification delivery. For example, the module can utilize TIBCO's messaging infrastructure to send notifications to participants' devices in real-time to ensure timely distribution of relevant information.

[0104] In some embodiments, 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 functionality. In some non-limiting examples, it can be implemented using technologies such as Okta® and Kentico® to ensure secure user authentication, access control, and self-learning resources. For example, the module can utilize Okta's identity and access management capabilities to securely onboard new participants, grant them appropriate access permissions, and guide them through the system's functionality.

[0105] In some embodiments, the communications module 470 enables unified and aggregated communication and collaboration within the system 400. It provides channels for participants to interact, exchange messages, share documents, and collaborate on projects. In some non-limiting examples, it can be implemented using Apigee® Edge and Adobe® Launch to facilitate secure and efficient communication, document sharing, and version control. For example, the module can leverage API management capabilities of Apigee Edge to ensure secure and reliable communication between participants to enable effective collaboration.

[0106] This allows the system 400 to incorporate various modules that utilize a diverse range of technologies and algorithms to optimize supply chain 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 experience, and effective performance monitoring, contributing to improved operational efficiency and success in supply chain management.

[0107] 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.

[0108] In some embodiments, the RTDM module 500 represents an effective data mesh and change capture component within an 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 management domain.

[0109] 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, as well as other data sources. The integration layer 510 can handle data exchange and synchronization between the RTDM module 500 and these systems. Data feeds are established to retrieve relevant information from the systems of record, such as sales orders, purchase orders, inventory data, and customer information. These feeds enable real-time data updates, ensuring that the RTDM module operates with the most current and accurate data.

[0110] The RTDM module 500 can include a data layer 520 configured to process and translate data for search and analysis. The RTDM module 500 creates a data mesh as 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. A PDS is configured to store a specific type 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.

[0111] 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 harmonized on the fly, and converted 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 insight and decision making.

[0112] 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 supply chain 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 referred to as PDS 524.1 through 524.N. These components work in unison to ensure efficient data management, reconciliation, and real-time availability.

[0113] In some embodiments, the data layer 520 includes a data lake 522, a novel storage and processing infrastructure designed to accommodate the ever-increasing volume, variety, and velocity of data generated within the supply chain. Built on a scalable distributed file system, such as Apache® Hadoop® Distributed File System (HDFS) or Amazon® S3, the data lake can provide 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 aggregate and moderate the influx of data from diverse sources.

[0114] In association with the data lake 522, multiple 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, pricing, and inventory levels. These purpose-specific data stores enable efficient data retrieval, analysis, and processing to serve the diverse needs of supply chain participants.

[0115] 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, and other enterprise-wide systems. The CDC constantly monitors and captures any updates, changes, or new transactions in these systems in real time. By capturing these changes, the data layer 520 ensures that the data within the data lake 522 and PDS 524 remains up-to-date, providing stakeholders with real-time insight into the supply chain ecosystem.

[0116] 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.

[0117] In some embodiments, to facilitate data processing and analysis, data layer 520 may include one or more distributed computing frameworks, such as Apache Spark or Apache Flink, in some non-limiting examples. These frameworks may enable parallel processing and distributed computing across large data sets stored in the data lake and PDS. By leveraging these frameworks, supply chain actors may perform complex analytical tasks, apply machine learning algorithms, and derive valuable insights from the data. For example, data layer 520 may leverage Apache Spark's machine learning libraries to develop predictive models for demand forecasting, optimizing inventory levels, and identifying potential supply chain risks.

[0118] 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.

[0119] 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 and leverage their managed services and scalable storage options. This allows for efficient scaling of resources based on demand, minimizes operational overhead, and provides an elastic infrastructure for managing supply chain data.

[0120] The Data Layer 520 of the RTDM module 500 can incorporate a highly scalable data lake, Data Lake 522, along 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 such as 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 efficient integration with existing systems, the Data Layer 520 empowers supply chain actors to make data-driven decisions to optimize operations and drive business success in dynamic and complex supply chain environments.

[0121] 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 can continuously learn from new data inputs and adapt its models to provide accurate and up-to-date insights. The AI ​​module 530 can generate predictions, recommendations, and alerts and publish such insights to a dedicated data feed.

[0122] The data engine layer 540 can comprise a set of interconnected systems responsible for the ingestion, processing, transformation, and integration of specialized data. Within the RTDM module 500, these systems include a collection of autonomously operating headless engines 540 that represent distinct functionality. These engines represent distinct functionality within the system and can include, for example, one or more recommendation engines, forecasting engines, and subscription management engines. Non-limiting examples of these headless engines include engines for subscriptions, solutions / bundles, ITAD (IT asset disposal), renewals, marketing, special pricing, financing, returns / claims, end users, order tracking, supermarket chains, search, vendor management, professional services, and ESG (environmental, social, and governance). These headless engines leverage harmonized data stored in the data mesh to provide specific business logic and services. The engines 540 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 functionality.

[0123] 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.

[0124] Additionally, a data distribution mechanism 545 can be employed to facilitate integration and access into 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.

[0125] 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 functionality. Through this layer, users can search and analyze real-time data related to various supply chain metrics, such as inventory levels, sales performance, and customer demand. The user experience layer supports personalized data feeds, allowing users to customize views and receive relevant updates based on their roles and responsibilities. Users can subscribe to specific data updates, such as inventory changes, pricing updates, or new SKU notifications, tailored to their preferences and roles.

[0126] Thus, in some embodiments, the RTDM module 500 for supply chain management may include integration with systems of record and may include one or more data layers with data mesh and purpose-built data stores, an AI component, a data engine layer, and a user experience layer. These components work together to provide users with intuitive access to real-time supply chain data, efficient data processing and analysis, and efficient integration with existing enterprise systems. Technical 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 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. The module provides distinct advantages of replicating and harmonizing data from diverse ERPs while maintaining the ability to maintain auditable and repeatable transactions, enabling a unified view for vendors, resellers, customers, end customers, and other entities in the distribution system, including IT distribution systems.

[0127] Single Pane of Glass UI 6 illustrates an SPoG UI according to one embodiment, generally designated as SPoG UI 600. In some embodiments, SPoG UI 600 can be an embodiment of SPoG UI 305 and represents a comprehensive, intuitive user interface designed to provide stakeholders with a unified, customizable view of the entire supply chain ecosystem. It combines a variety of features and functionality to enable users to gain a comprehensive understanding of their supply chains and efficiently manage their operations.

[0128] 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, whitespace-driven design.

[0129] The SPoG UI 600 can integrate functionality with the RTDM module 310 / 500 to provide stakeholders with a powerful and user-friendly interface for supply chain management. In some embodiments, the SPoG UI 600 can include a unified view (UV) module 605 to provide a customizable and holistic view of the supply chain, and a real-time data exchange module 610 to ensure accurate and up-to-date data synchronization based on the RTDM module 310 / 500. The collaborative decision-making module 615 fosters effective communication and collaboration among a diverse group of populations. The RBAC module 620 can be configured to secure access control. The customization module 625, the data visualization module 630, and the mobile and cross-platform accessibility module 635 can be configured to enhance user experience, data analysis, and accessibility, respectively. In some embodiments, the above-mentioned modules can enable stakeholders to make informed decisions, optimize supply chain operations, and promote business efficiency within the supply chain ecosystem.

[0130] The SPoG UI 600 can include a 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 supply chain ecosystem. The UV module 605 acts as a single entry point for users, providing a holistic and comprehensive view of supply chain operations and empowering them to make data-driven decisions. The UV module 605 can be configured to efficiently manage real-time data and maintain a visually clean 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.

[0131] UV Module 605 is built around a grid-based layout system and can leverage 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.

[0132] The SPoG UI 600 integrates the UV module 605 with a real-time data exchange module 610 to facilitate the continuous exchange of data between the SPoG UI 600 and the RTDM module 310, leveraging one or more data sources, which may include one or more ERP, CRM, or other sources. Through this module, stakeholders can access 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.

[0133] In some embodiments, the collaborative decision-making module 615 in the SPoG UI 600 fosters real-time collaboration and communication between participants. This module 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 participants can effectively collaborate based on accurate and synchronized data. This fosters overall operational efficiency and collaboration within the supply chain ecosystem.

[0134] To ensure secure and controlled access to functionality 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 can ensure that only authenticated users have access to certain features and information, protecting data privacy, security, and compliance within the supply chain ecosystem.

[0135] In some embodiments, 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.

[0136] The SPoG UI 600 can include a data visualization module 630, which enables stakeholders to analyze and interpret supply chain data through interactive dashboards, charts, graphs, and visual representations. 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.

[0137] 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 unified access to real-time data and functionality across a variety of devices.

[0138] 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.

[0139] 7 is a flow diagram of a method 700 for performing comprehensive supply chain management operations using an SPoG UI, according to some embodiments of the present disclosure. In some embodiments, the method 700 provides operational steps for streamlining supply chain processes, improving decision making, and optimizing operations within a supply chain ecosystem. In some embodiments, the method 700 performs real-time data search, visualization, customization, collaboration, access control, and cross-platform accessibility functionality through the SPoG UI. Based on the disclosure herein, the operations of the method 700 may be performed in different orders and / or with variations to suit particular implementation requirements.

[0140] In operation 705, the computing device receives user input through the SPoG UI representing a wide range of requests and commands related to supply chain management. User input encompasses actions such as selecting a particular data visualization, accessing a different module or functionality, initiating a workflow, configuring an interface, performing data-driven analysis, etc. This interactive input mechanism enables stakeholders to effectively engage with the SPoG UI and gain relevant insights to support the decision-making process.

[0141] In operation 710, the computing device processes the user input and leverages integration capabilities with the RTDM module to interact with the real-time data exchange module. This integration ensures efficient data retrieval and synchronization, allowing the computing device to access up-to-date and accurate information from diverse data sources within the supply chain ecosystem. By establishing a connection with the RTDM module and leveraging real-time data exchange, the computing device ensures that the perspectives presented in the SPoG UI reflect the latest developments and provide a comprehensive view of supply chain operations.

[0142] At operation 715, the computing device uses a data visualization module to generate visually appealing, interactive representations of the retrieved supply chain data. This module utilizes advanced visualization techniques to create dynamic dashboards, charts, graphs, and other visual elements to effectively communicate key performance indicators, trends, patterns, anomalies, and correlations within the supply chain ecosystem. Through these visualizations, stakeholders can gain valuable insights, identify critical areas, and assess the overall health of supply chain operations.

[0143] At operation 720, the computing device allows the user to personalize the dashboard and tailor the SPoG UI interface to their particular preferences and needs. The customization module empowers stakeholders to arrange widgets, charts, data visualizations, and other UI components to prioritize information most relevant to their roles and responsibilities. This flexibility ensures a user-centric experience, allowing stakeholders to focus on important data points and streamline decision-making processes within the SPoG UI.

[0144] In operation 725, the computing device facilitates real-time collaboration and communication between the participants through a collaborative decision-making module, which provides features that enable participants to exchange information, share insights and recommendations, initiate workflows, and participate in discussions within the SPoG UI interface. By integrating with the RTDM module, the collaborative decision-making module ensures that participants can effectively collaborate based on accurate and synchronized data, fostering a cohesive and agile supply chain ecosystem.

[0145] In operation 730, the computing device implements a secure access control mechanism through a role-based access control (RBAC) module integrated into the SPoG UI. This module allows administrators to define roles, grant permissions, and control user access based on their responsibilities and organizational hierarchy. By implementing RBAC, the computing device protects data privacy, ensures confidentiality, and maintains regulatory compliance within the supply chain ecosystem. Authorized parties can access specific features, functionality, and information based on the roles granted to them, minimizing the risk of unauthorized access to or misuse of data.

[0146] In operation 735, the computing device optimizes the SPoG UI for integrated accessibility across multiple devices and platforms through a mobile and cross-platform accessibility module. This module ensures that stakeholders can access the SPoG UI interface from desktop computers, laptops, smartphones, and tablets, allowing them to stay connected, informed, and engaged with supply chain operations while on the go. The interface is optimized to provide a consistent and intuitive user experience across different screen sizes, resolutions, and operating systems, facilitating real-time data access and improving stakeholder productivity.

[0147] In act 740, the computing device leverages a fast data module in a data-rich environment to efficiently deal with real-time data and maintain a visually clear interface. The module incorporates the unique composition of the SPoG UI structure, responsive data visualization, real-time data handling methods, adaptive information architecture, and optimization techniques. Act 740 can include processing large volumes of dynamic supply chain data in an understandable, efficient, and visually appealing manner. The SPoG UI's grid-based layout system is provided by CSS® Grid and Flexbox® technologies, allowing UI elements to fluidly adapt to available space and content while allowing HTML5, CSS3, and JavaScript (particularly React.js) to manage the dynamic aspects of the interface.

[0148] In summary, the method 700 depicted in FIG. 7 outlines a comprehensive approach to supply chain management through the SPoG UI. By leveraging real-time data search, visualization, customization, collaboration, access control, and cross-platform accessibility capabilities, stakeholders gain valuable insight into supply chain operations and can make informed decisions. The method facilitates efficient integration with RTDM modules to ensure accurate and up-to-date data synchronization. Through personalized dashboards, interactive data visualization, collaborative decision-making, secure access control, and cross-device accessibility, the SPoG UI empowers stakeholders to optimize supply chain operations, improve collaboration, and drive efficiency in a dynamic and complex supply chain ecosystem.

[0149] 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 capabilities of the SPoG UI to facilitate vendor onboarding into a supply chain ecosystem. By integrating real-time data, collaborative decision-making, and role-based access control functionality, 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.

[0150] In operation 805, the process begins when a vendor indicates an interest in joining the supply chain ecosystem. The computing device utilizes the SPoG UI to receive the vendor's information and related details, which may include company profile, contact information, product catalog, certifications, and any other relevant data required for the vendor onboarding process.

[0151] 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.

[0152] 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 efficient communication, file sharing, and workflow initiation, enabling stakeholders to collectively evaluate a vendor's suitability and efficiently progress through the onboarding steps.

[0153] 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 have access only 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.

[0154] 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 documentation requirements, and access real-time updates to ensure efficient and timely completion of onboarding tasks.

[0155] 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.

[0156] In act 835, the computing device facilitates integrated collaboration among 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.

[0157] 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.

[0158] At act 845, the computing device captures and records vendor onboarding activities within the audit trail module of 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.

[0159] 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 supply chain ecosystem. The SPoG UI provides stakeholders with a summary of the onboarding process, allowing them to confirm completion of all requirements and initiate further actions such as signing contracts, listing products, and collaboration.

[0160] 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 functionality, the SPoG UI empowers stakeholders to successfully onboard vendors into the supply chain 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 customizable features and notifications, streamlines the onboarding process, reduces manual effort, and optimizes vendor integration within a dynamic and complex supply chain environment.

[0161] 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 capabilities of the SPoG UI to facilitate onboarding of resellers into a supply chain ecosystem. By integrating real-time data, collaborative decision-making, and role-based access control functionality, 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.

[0162] At operation 905, the process begins when a reseller indicates an interest in joining the supply chain 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.

[0163] At operation 910, the computing device uses its integration 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.

[0164] 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 integrated communication, file sharing, and workflow initiation, enabling the parties to collectively assess the reseller's suitability and efficiently progress through the onboarding steps.

[0165] 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 have access only 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 reseller onboarding process, fostering a transparent and compliant environment.

[0166] 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 documentation requirements, and access real-time updates to ensure efficient and timely completion of onboarding tasks.

[0167] 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.

[0168] In operation 935, the computing device facilitates efficient collaboration among 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.

[0169] At operation 940, the computing device records and maintains an audit trail of the reseller onboarding activities within the audit trail module of 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.

[0170] 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 supply chain 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.

[0171] In conclusion, method 900 depicted in FIG. 9 highlights 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 functionality, SPoG UI empowers stakeholders to successfully onboard resellers into the supply chain 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-orchestrated workflow, optimizing reseller integration and fostering business success in a dynamic supply chain environment.

[0172] 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 supply chain 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 efficient and 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.

[0173] In operation 1005, the process begins when a potential customer or end customer indicates an interest in joining the supply chain 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, company profile, industry specific preferences, and any other relevant data.

[0174] In operation 1010, the computing device validates the customer or end-customer information using real-time data integration capabilities with external systems. By synchronously accessing data from various sources, such as customer relationship management (CRM) systems, or other enterprise-wide solutions, the computing device ensures the accuracy and completeness of the customer or end-customer details. This validation step helps establish a trusted foundation for the onboarding process and improves data integrity.

[0175] In operation 1015, the computing device initiates a customer or end-customer onboarding workflow through a collaborative decision-making module. This module facilitates integrated communication and collaboration between the stakeholders involved in the onboarding process, such as sales representatives, account managers, and customer support teams. The SPoG UI provides a centralized platform for stakeholders to collectively assess customer requirements, define personalized onboarding journeys, and make informed decisions throughout the onboarding process.

[0176] At operation 1020, the computing device uses 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 the onboarding process, fostering a transparent and compliant onboarding environment.

[0177] 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 the onboarding process. Stakeholders can monitor status, review documentation, and access real-time updates to ensure an efficient and integrated onboarding experience.

[0178] At operation 1030, the computing device utilizes the data visualization module of the SPoG UI to provide dynamic visualization and analytics related to the onboarding process. Through interactive charts, graphs, and reports, stakeholders gain insight into key onboarding metrics, 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.

[0179] In operation 1035, the computing device enables the participants to interact with the collaborative decision-making module to facilitate integrated collaboration during the onboarding process. Parties 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.

[0180] 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 align with the customer's or end customer's preferences, industry-specific requirements, and strategic goals. The customization feature increases customer satisfaction and engagement during the onboarding journey.

[0181] 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.

[0182] At act 1050, the computing device finishes the customer or end customer onboarding process within the SPoG UI. Once all required tasks, reviews, and approvals are completed, the customer or end customer is officially onboarded into the supply chain ecosystem. The SPoG UI provides stakeholders with a summary of the onboarding process, ensures all requirements have been met, and facilitates further actions such as account activation, service delivery, personalized customer engagement, etc.

[0183] In conclusion, method 1000 depicted in FIG. 10 illustrates the customer and end-customer onboarding process facilitated by the SPoG UI. By leveraging real-time data integration, collaborative decision-making, role-based access control, comprehensive visualization, customization, and audit trail functionality, the SPoG UI empowers stakeholders to successfully onboard customers and end-customers into the supply chain ecosystem. The SPoG UI's intuitive interface, personalized features, and robust collaboration capabilities streamline the onboarding process, improve transparency, and foster efficient communication between stakeholders. The 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 the SPoG UI, the customer and end-customer onboarding process becomes an integrated workflow, optimizing customer and end-customer integration and fostering business success within a dynamic supply chain environment.

[0184] FIG. 11 depicts the system architecture of system 1100, an advanced solution designed to automate the near real-time special pricing calculation and application process. The system incorporates various components that work synergistically to ensure precision pricing, minimize manual effort, and reduce the chance of errors, ultimately streamlining pricing workflow and improving overall sales effectiveness.

[0185] The system 1100 includes a special pricing algorithm 1110 that is specifically designed to address a wide range of variables that are essential for accurate pricing decisions. These variables include manufacturer details, specific products or product combinations, product categories, type of solution, time or specific dates, product approvals, end users, and solution providers. By leveraging these variables, the special pricing algorithm 1110 can make real-time calculations based on past buying patterns or predefined rules established by pricing analysts. This dynamic approach allows the system to quickly respond to changing market conditions and meet specific customer requirements.

[0186] In some embodiments, these algorithms are specifically designed to address a wide range of variables that are essential to accurate pricing decisions, including manufacturer details, specific products or combinations, product categories, type of solution, time or specific dates, product approvals, end users, and solution providers. By incorporating insights from a RTDM module with CDC (e.g., RTDM500), the special pricing algorithms 1110 can be configured to make real-time calculations based on past buying patterns or predefined rules established by pricing analysts. This dynamic approach allows the system to quickly respond to changing market conditions and meet specific customer requirements.

[0187] To ensure efficient retrieval of pricing and availability information, the system 1100 utilizes a caching mechanism 1140. In some embodiments, the APIgee Cache serves as the caching mechanism 1140 and stores Everest-enabled vendor codes. This cache facilitates fast access to vendor-specific data required for accurate pricing calculations. By utilizing cached information, the system ensures that the most up-to-date pricing and availability details are available for computation, resulting in accurate pricing recommendations. In some embodiments, by utilizing cached information and harmonized data from the RTDM module, the system: It ensures that the most up-to-date pricing and availability details are available for calculations, resulting in precise pricing recommendations.

[0188] The system 1100 incorporates an integration module 1115, which enables data and document exchange with external partners. In this embodiment, the integration module utilizes SEEBURGER integration technology to comply with industry standard data exchange protocols, ensuring smooth collaboration between different parties. Through SEEBURGER integration 1115, the system can securely and efficiently transmit pricing-related information and documents to external entities, streamlining the entire pricing workflow.

[0189] To maintain data integrity and accuracy, the system 1100 includes a pricing validation and maintenance module 1120. This module applies intelligent validation rules and algorithms to validate the correctness of pricing programs. By validating pricing data against predefined business rules, the system minimizes the risk of errors and inconsistencies. The pricing validation and maintenance module 1120 ensures that pricing programs remain accurate and up-to-date, improving the reliability of overall pricing.

[0190] The system 1100 incorporates a special pricing creation and application module 1125, which automates the creation and application of near real-time special pricing. This module retrieves special pricing algorithms 1110 and validated pricing data from pricing validation and maintenance module 1120. This ensures that the correct pricing and associated terms are accurately applied to individual or multiple orders. By automating this process, the system reduces manual effort and eliminates potential delays in the sales process, resulting in improved efficiency and customer satisfaction.

[0191] The system 1100 interacts with the order processing flow through integrations with V7 Create Order API Async, V6 Create Order API, and Order Entry PCG API, 1130. These integrations facilitate the transfer of pricing information into the ordering system and ensure accurate and timely application of special pricing to the order stream. This integration streamlines the fulfillment process and ensures that customers receive fair pricing for their orders.

[0192] To enable efficient data exchange between the SEEBURGER system and the Order Entry system, the system 1100 incorporates XML and EDI integration 1135. This integration ensures smooth transmission of pricing related data, order details, and other related information between the different systems involved in the order fulfillment process. The XML and EDI integration 1135 ensures accurate, secure, and industry standards compliant data exchange.

[0193] By combining these components and their interactions, the system 1100 automates the process of calculating and applying special pricing in near real-time. It leverages intelligent algorithms, caching mechanisms, data exchange, and integration with ordering systems to ensure accurate pricing, validate pricing programs, and apply pricing efficiently. The automation provided by the system 1100 reduces manual effort, minimizes errors, and improves overall sales effectiveness and customer satisfaction.

[0194] Figure 12 depicts a detailed process flow of the Special Pricing functionality, a solution that addresses the challenges associated with determining, creating, and applying fair pricing in the industry. By leveraging advanced algorithms and intelligent rules, the system significantly reduces the time, effort, and potential for error associated with the pricing process, ultimately streamlining operations and improving customer experience.

[0195] In operation 1205, the system begins the process by receiving various inputs that are important to the pricing decision. These inputs encompass a wide range of factors that influence the pricing decision, including manufacturer details, specific products or product families, product categories, type of solution, time or specific dates, product approvals, end users of the solution, and solution providers. Each input provides valuable context and contributes to the overall pricing calculation.

[0196] The system collects this information from multiple sources, such as user input through the platform or API. These inputs serve as the basis for subsequent steps in the special pricing process. The data received is carefully parsed and organized to ensure its relevance and accuracy.

[0197] At operation 1210, the system applies a special pricing calculation algorithm. Once the inputs have been received and verified, the system proceeds to operation 1210 where it applies a special pricing calculation algorithm. These algorithms are designed to perform complex calculations taking into account specific inputs previously received to determine the proper pricing for a given scenario.

[0198] The special pricing calculation algorithm can be based on intelligent rules and historical buying patterns. These rules define pricing models and criteria for different scenarios. By incorporating these rules into the algorithm, the system can effectively calculate the most accurate and appropriate pricing for each unique combination of inputs.

[0199] The algorithms can take into account factors such as manufacturer preferences, product category, historical sales data, and any other relevant variables. This comprehensive approach ensures that the calculated pricing is aligned with the specific requirements and business rules defined by the organization.

[0200] At operation 1215, the calculated pricing is validated using the master data. After the special pricing calculation algorithm determines the pricing for a particular scenario, the system proceeds to operation 1215, which involves validating the calculated pricing using the master data.

[0201] Master data refers to the authenticated and reliable data that serves as a reference for various processes within a system. In the context of special pricing, master data includes information such as pricing rules, discount structures, vendor contracts, and other relevant data points.

[0202] During this step, the system cross-references the calculated pricing with master data elements to ensure its accuracy and compliance with predefined rules and standards. The validation process involves checking for any discrepancies or exceptions that may require further review or adjustment.

[0203] By validating calculated pricing against master data, the system mitigates the risk of pricing errors and ensures that applied pricing is consistent with the organization's pricing policies and agreements.

[0204] Once the calculated pricing is verified and confirmed, the system moves to operation 1220, which may include applying the determined pricing and associated terms to the quote or order.

[0205] During this step, the system integrates with various modules and systems to facilitate the application of pricing, ensuring that the determined pricing is accurately applied to the quote or order, along with any applicable discounts, promotions, or special terms.

[0206] Application of pricing and terms involves updating relevant data fields and systems to reflect the correct pricing information. This step ensures that pricing calculations are translated into actionable terms, ready for further processing and fulfillment.

[0207] The system takes into account any specific requirements, contractual obligations, or business rules attached to pricing and terms, and by applying them correctly, enables the system to generate accurate quotes and orders that are aligned with customer expectations and the organization's pricing strategy.

[0208] In operation 1225, the system may include retrieving vendor codes for pricing and availability to complete the special pricing process. These codes play a key role in determining the pricing of different vendors and their respective products.

[0209] The system interacts with a Pricing and Availability (P&A) module, which serves as a centralized source of pricing and availability data. The P&A module retrieves pricing and availability vendor codes from the platform, which contain the details necessary to determine pricing for Everest-enabled vendor products.

[0210] Once the pricing and availability vendor codes are obtained, the system uses them to generate the correct pricing for those specific products. The pricing information is then routed to the appropriate systems, such as the Global Price Engine (GPE) or Global Data System (GDS), to ensure accurate pricing display for those products. For non-Everest enabled vendor products, the pricing information is sent directly to Impulse, the fulfillment module of the system.

[0211] This step ensures that the special pricing process takes into account the unique pricing structures associated with different vendors and products. By looking up and utilizing vendor codes for pricing and availability, the system ensures accuracy and consistency of pricing information throughout the system.

[0212] At operation 1230, the system may include triggering an orchestration of integrations to facilitate searching for products (e.g., SeeBurger integration). The orchestration serves as a mechanism for retrieving relevant product information based on specific search criteria.

[0213] In a non-limiting example, when a product search request is initiated, the system communicates with SeeBurger, which acts as an EDI translation tool and is responsible for orchestrating data searches from various sources, including GPE or GDS (potentially leveraging Elastic) to pull the required product information.

[0214] The orchestration process involves sending search criteria to SeeBurger, which then interacts with the appropriate modules or systems to gather the requested data. This ensures that the system has accurate and up-to-date product information for subsequent steps in the special pricing process.

[0215] In operation 1235, the system can utilize the APIgee cache to store and access data required for the special pricing process. The APIgee cache serves as a storage mechanism that allows for efficient and fast retrieval of information required during pricing calculations and order processing.

[0216] APIgee cache serves as a central repository where relevant data such as platform orders and SeeBurger orchestration outputs are stored. This caching mechanism ensures that frequently accessed data is readily available, reducing the need for repetitive data retrieval from external systems.

[0217] By utilizing APIgee cache, the system optimizes performance and response times by allowing data to be retrieved from the cache instead of relying solely on real-time data fetches from various sources. This caching approach significantly improves the overall efficiency of the special pricing process and improves system responsiveness.

[0218] At operation 1240, the system processes the order using V7 Create Order API Async and V6 Create Order API. These APIs play a key role in enabling the creation and management of orders within the system. V7 Create Order API Async and V6 Create Order API provide the necessary interface to receive order related inputs, whether from platform orders or via API integration. These APIs ensure order data flow into the system for further processing.

[0219] When an order is received, the system leverages these APIs to generate a corresponding order object inside the system. The APIs handle the necessary validations, data transformations, and system interactions required to create a comprehensive order representation.

[0220] The asynchronous nature of the V7 Create Order API allows for parallel processing of multiple orders, improving the scalability and performance of the system. This asynchronous feature ensures that order creation and processing can occur simultaneously, reducing the possibility of bottlenecks and improving overall efficiency. Once the order is processed and created within the system, operation 1240 can further include routing the processed order to Impulse. This step ensures that the order is forwarded to Impulse for further processing and fulfillment.

[0221] To facilitate this transfer, the system utilizes XML and EDI transmissions via the SeeBurger integration. These transmissions encapsulate the necessary order data including customer details, product information, pricing, and any additional order-specific requirements.

[0222] The integration between the system and Impulse allows for a smooth handover of order data, ensuring processed orders are accurately reflected in Impulse's system. This integration plays a key role in orchestrating the fulfillment process, allowing Impulse to facilitate order fulfillment, inventory management, shipping, and other related operations.

[0223] By routing processed orders to Impulse, the system ensures an end-to-end workflow, where order data flows from initial processing, through various stages, to the final fulfillment phase.

[0224] In some embodiments, SeeBurger serves as a key tool for converting XML and EDI data into the proper format, allowing interoperability between the different components. The converted EDI data is then sent to Impulse, while the XML data is sent to the Order Entry PCG API. These operations ensure accurate capture, processing, and management of pricing and order information, contributing to the overall efficiency and effectiveness of the special pricing process.

[0225] Figure 13 depicts a system 1300, which may be an embodiment of Figure 11, with an advanced automation framework designed to optimize the creation and application of special pricing. As previously described, the system in operation 1205 integrates the special pricing creation and application module 1125. This integration facilitates near real-time retrieval of special pricing algorithms and validated pricing data, leading to a streamlined sales process with significantly reduced manual effort.

[0226] Interacting with the order processing flow through the V7 Create Order API Async, V6 Create Order API and Order Entry PCG API, 1130 in operation 1210, the system enables the transfer of pricing information to the ordering system. This ensures accurate and timely application of special pricing.

[0227] The system 1300, as previously described, incorporates XML and EDI to provide for the exchange of pricing related data and order details between various systems, adhering to industry standards while maintaining data accuracy and security. The system 1300 begins the pricing process by collecting a variety of inputs that are important to the pricing decision. The system 1300 then applies specialized pricing calculation algorithms. These algorithms use intelligent rules and historical buying patterns to arrive at the most accurate pricing for a given scenario.

[0228] Once pricing is calculated, the system 1300 validates the calculated pricing against trusted master data, applies the validated pricing and associated terms to the quote or order, and integrates with various modules and systems to ensure accuracy.

[0229] The system 1300 retrieves vendor codes for pricing and availability to generate correct pricing for a particular product. The system 1300 triggers orchestration of integrations to facilitate product searches and retrieves relevant product information based on specific search criteria. The system 1300 employs APIgee cache for efficient storage and access of data during the pricing process. The system 1300 utilizes V7 Create Order API Async and V6 Create Order API for order creation and management. Once an order is processed, the system routes the order to the Impulse system for further processing and fulfillment using XML and EDI transmission.

[0230] Thereby, the system 1300 performs dedicated processes for generating real-time or near real-time special pricing calculations and applications. The system 1300 achieves advantages in increasing sales efficiency, minimizing errors, and improving customer satisfaction.

[0231] 14 is a block diagram of example components of a device 1400. One or more computer systems 1400 may be used, for example, to implement any of the embodiments described herein, as well as combinations and subcombinations thereof. The computer system 1400 may include one or more processors (also referred to as central processing units or CPUs), such as processor 1404. The processor 1404 may be connected to a communications infrastructure, i.e., bus 1406.

[0232] The computer system 1400 may also include user input / output devices 1403 , such as a monitor, keyboard, pointing device, etc., which may communicate with a communications infrastructure 1406 through a user input / output interface 1402 .

[0233] One or more of the processors 1404 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.

[0234] The computer system 1400 may also include a main or primary memory 1408, such as a random access memory (RAM). The main memory 1408 may include one or more levels of cache. The main memory 1408 may have control logic (i.e., computer software) and / or data stored therein.

[0235] The computer system 1400 may also include one or more secondary storage devices or memories 1410. The secondary memory 1410 may include, for example, a hard disk drive 1412 and / or a removable storage device or drive 1414.

[0236] The removable storage drive 1414 may interface with a removable storage unit 1418. The removable storage unit 1418 may include a computer usable or readable storage device having computer software (control logic) and / or data stored thereon. The removable storage unit 1418 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 any other removable storage unit and associated interface. The removable storage drive 1414 may read from and / or write to the removable storage unit 1418.

[0237] The secondary memory 1410 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 1400. Such means, devices, components, intermediaries, or other approaches may include, for example, a removable storage unit 1422 and an interface 1420. Examples of removable storage units 1422 and interfaces 1420 may include a program cartridge and a 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 a USB port, a memory card and associated memory card slot, and / or any other removable storage unit and associated interface.

[0238] Computer system 1400 may further include a communication or network interface 1424. Communication interface 1424 may enable computer system 1400 to communicate and interact with any combination of external devices, external networks, external entities, etc. (individually and collectively referred to by reference number 1428). For example, communication interface 1424 may enable computer system 1400 to communicate with external or remote devices 1428 via communication paths 1426, 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 computer system 1400 via communication paths 1426.

[0239] Additionally, computer system 1400 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.

[0240] The computer system 1400 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.

[0241] Any applicable data structures, file formats, and schemas in computer system 1400 may be derived from standards including, but not limited to, JavaScript Object Notation (JSON), Extensible Markup Language (XML), 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.

[0242] 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) stored thereon may also be referred to herein as a computer program product or program storage device. This includes, but is not limited to, computer system 1400, main memory 1408, secondary memory 1410, and removable storage units 1418 and 1422, 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 1400), may cause such data processing devices to operate as described herein.

[0243] Figures 15A through 15Q depict various screens and functionalities of the SPoG UI related to vendor onboarding, partner dashboard, customer cart, order summary, SKU generation, order tracking, shipment tracking, subscription history, and subscription changes. A detailed description of each drawing is provided below.

[0244] FIG. 15A depicts the Vendor Onboarding Start Screen, which represents the first step in the vendor onboarding process. It provides a form or interface where vendors can express their interest in joining the supply chain ecosystem. Vendors can enter basic information such as company details, contact information, product catalog, etc.

[0245] FIG. 15B depicts a vendor onboarding guide that displays a step-by-step guide or checklist that a vendor follows during the onboarding process. It outlines the tasks and requirements necessary to ensure that the vendor has a clear understanding of the onboarding process and can proceed smoothly.

[0246] FIG. 15C depicts a vendor onboarding call scheduler that facilitates scheduling calls or meetings between vendors and platform associates or representatives who will guide them through the onboarding process. Vendors can select a preferred time slot or request a call, ensuring effective communication and assistance throughout the onboarding journey.

[0247] FIGURE 15D depicts a Vendor Onboarding Task List that presents a comprehensive task list or dashboard that outlines the specific steps and actions required for successful vendor onboarding. It provides an overview of pending tasks, completed tasks, and upcoming deadlines to help vendors track their progress and ensure the timely completion of each onboarding task.

[0248] 15E depicts a vendor onboarding completion screen confirming successful completion of the vendor onboarding process, which may display a congratulatory message or a summary of completed tasks indicating the vendor is now officially onboarded into the supply chain ecosystem.

[0249] Figure 15F depicts a Partner Dashboard that provides partners or stakeholders with a centralized view of relevant information and metrics related to their partnership with the supply chain ecosystem. It provides an overview of performance indicators, key data points, and actionable insights to facilitate effective collaboration and decision making.

[0250] FIG. 15G depicts a Customer Product Cart, which represents a customer's product cart, to which they can add items they wish to purchase. It displays a list of selected products, quantities, prices, and other relevant details. The customer can review and modify the contents of their cart before proceeding with the checkout process.

[0251] FIG. 15H depicts a customer subscription cart that allows customers to manage their subscription-based purchases. It displays the selected subscription plan, pricing, and duration. Customers can review and change subscription details before finalizing their selection.

[0252] FIG. 15I depicts a Customer Order Summary that provides a summary of a customer's order, including details such as the products or subscriptions purchased, quantities, pricing, and any discounts or promotions applied, allowing the customer to review their order before confirming the purchase.

[0253] 15J depicts a vendor SKU generation screen for generating unique stock keeping unit (SKU) codes for vendor products. This may include fields or options that allow the vendor to specify product details, attributes, and pricing, and the system will auto-generate the corresponding SKU code.

[0254] Figures 15K and 15L depict a dashboard Order Summary for displaying summary information about orders placed within the supply chain ecosystem. They present key order details such as order number, customer name, product or subscription information, quantity, and order status. The dashboard provides an overview of order activity and enables stakeholders to track and manage orders efficiently.

[0255] Figure 15M depicts a customer subscription cart that allows a customer to add, change, or delete subscription plans. It can display a list of selected subscriptions, pricing, and renewal dates. Customers can manage their subscriptions and make changes according to their preferences and requirements.

[0256] FIG. 15N depicts a customer order tracking screen that allows customers to track the status and progress of their orders within the supply chain. It displays real-time updates on order fulfillment including processing, packing, and shipping. Customers can monitor the movement of their orders and predict delivery times.

[0257] FIG. 15O depicts a customer directed shipment tracking that provides real-time shipment tracking information to customers, which may include details such as carrier, tracking number, current location, estimated delivery date, etc. Customers can stay informed regarding the whereabouts of their shipments.

[0258] Figure 15P depicts a Customer Subscription History that presents a historical record of a customer's subscription activity. It displays a list of previous subscriptions, including subscription plan, duration, and status. Customers can review their subscription history, track past payments, and view details of previous subscriptions.

[0259] Figure 15Q depicts the customer subscription change dialog, which allows customers to modify their existing subscriptions. It provides options to upgrade or downgrade subscription plans, change billing details, or adjust other subscription-related preferences. Customers can manage their subscriptions according to their evolving needs or preferences.

[0260] The depicted UI screens are not limiting. In some embodiments, the UI screens of Figures 15A through 15Q collectively represent the diverse functionality and features provided by the SPoG UI, providing participants with a comprehensive, user-friendly interface for vendor onboarding, partnership management, customer interaction, order management, subscription management, and tracking within the supply chain ecosystem.

[0261] One or more computer systems may be configured to perform particular operations or actions by having software, firmware, hardware, or a combination thereof installed on the system that, when operated, causes the system to perform the actions. One or more computer programs may be configured to perform particular operations or actions by containing instructions that, when executed by a data processing device, cause the device to perform the actions.

[0262] In one general aspect, a computer-implemented method may include integrating multiple communication channels (i.e., touch points) among a population of users into a unified interactive interface within a computer system, the unified interactive interface referred to herein as an SPoG UI, where the SPoG is a central interface component configured to aggregate user interactions, data, and / or functionality of the population of users, the SPoG UI being positioned to facilitate operations across a supply chain ecosystem. The computer-implemented method may further include utilizing the SPoG UI to manage an end-to-end lifecycle of user interactions. The method may further include collecting data from the user interactions within the SPoG UI. Additionally, the method may include analyzing the collected data to generate one or more forecasts for business growth. The method may further include executing one or more artificial intelligence and / or machine learning algorithms to improve business operations based on the analyzed data. The method may further include incorporating periodic updates and improvements to the SPoG UI based on the analyzed data. The method may further include groups having distributors, resellers, customers, end customers, vendors, suppliers, where the population of users may include users selected from two or more diverse groups. Other embodiments of this aspect include corresponding computer systems, apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the method.

[0263] Implementations may include one or more of the following features: The integration may include establishing communication links with multiple pre-existing business platforms; The aggregated interaction points may include one or more of websites, customer relationship management systems, vendor platforms, and supply chain management systems; The managing the end-to-end lifecycle may include one or more of initial contact, service fulfillment, and follow-up interactions; The collecting data may include monitoring and / or logging user activity within the SPoG UI; The analyzing the collected data is performed using advanced statistical algorithms; The artificial intelligence and machine learning algorithms include predictive analytics to identify market trends; The artificial intelligence and machine learning algorithms include a recommendation system to personalize user interactions; The improvements may be based on the analytics and / or analyzed user feedback received through the SPoG UI. Implementations of the described techniques may include hardware, methods or processes, or computer tangible media.

[0264] In one general aspect, the system may include a communication integration module configured to integrate multiple communication channels (i.e., touch points). The system may further include an aggregation module configured to combine the integrated communication channels into a unified interactive interface, referred to herein as an SPoG UI, where the SPoG is a central interface component configured to aggregate user interactions, data, and / or functionality of a population of users, the SPoG UI being arranged to facilitate operations across a supply chain ecosystem. The system may further include a lifecycle management module configured to manage an end-to-end lifecycle of user interactions within the SPoG. The system may additionally or alternatively include a data collection module configured to automatically collect data from user interactions within the SPoG. The system may further include a data analysis module configured to generate one or more forecasts based on the collected data. The system may further include an artificial intelligence module configured to execute one or more AI and / or ML algorithms based on the analyzed data. The system may further include groups having distributors, resellers, customers, end customers, vendors, suppliers, where the user population may include users selected from two or more diverse groups. Other embodiments of this aspect include corresponding computer systems, apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the method.

[0265] The system and method for automating SKU management may include a user interface configured to receive catalog files in two or more diverse formats from a user; a catalog transformation module configured to convert the catalog file into a standard format and predict a category taxonomy and attribute mapping for at least one catalog item, where the catalog transformation module utilizes iterative learning to suggest category taxonomy and attribute mapping; a Real-Time Data Mesh (RTDM) module for ensuring real-time data exchange and synchronization across system components, where the RTDM module facilitates real-time interaction between the user and the system; a Master Data Governance (MDG) module for validating the converted catalog and communicating any errors back to the vendor; and a Global Data Repository (GDR) for storing the validated catalog and maintaining data integrity of the stored catalog.

[0266] The system and method may include a search platform for indexing and searching the stored catalog. The user interface is a single pane of glass user interface (SPoG UI). A dynamic SKU creation module for generating SKUs for catalog items in an ERP system, where the catalog items represent one or more non-transactional products, and generating the SKU allows one or more products to be added to a customer's cart. The GDR is integrated with the RTDM module to support real-time data synchronization across systems. The MDG module generates a display indicating one or more errors made in the catalog conversion process associated with the one or more converted catalogs. A global pricing engine (GPE) for determining real-time pricing for the catalog items. The GPE determines the price of the product based on one or more of vendor price files, market trends, and historical pricing data. Implementations may include one or more of the following features: The interactive elements include links to various business platforms. The user interactions include actions may include one or more of click, hover, and input data. The collected data is analyzed using advanced statistical algorithms. The personalized content is generated based on recommendation algorithms. The method may further include updating the SPoG UI based on the user feedback and the data analysis results. Implementations of the described techniques may include hardware, a method or process, or a computer tangible medium.

[0267] 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 present disclosure contemplated by the inventors, and is thus not intended to limit the disclosure and the appended claims in any way.

[0268] The present disclosure has been described above with the aid of functional building blocks illustrating the implementation of certain functions and their relationships. 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 their relationships are appropriately performed.

[0269] The above description of the specific embodiments fully reveals the general nature of the present disclosure, 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 disclosure 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 description rather than limitation, and therefore the terms or terms used herein should be interpreted in light of the teachings and guidance provided by those skilled in the art.

[0270] The breadth and scope of the present disclosure 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 system for automated near real-time special pricing calculation and application, comprising: a special pricing algorithm module configured to receive first information comprising one or more of manufacturer details, product category, type of solution, past purchasing patterns, and time period, and apply the received first information to the special pricing determination; a caching module for storing vendor-specific data for accurate pricing calculations; an integration module configured to enable data exchange with external partners and with order processing systems for accurate pricing application; A pricing validation and maintenance module that applies intelligent validation rules to ensure pricing accuracy; a special pricing creation and application module that automates the creation and application of special pricing to individual or multiple orders and quotes based on the first information and the data; The system, wherein the integration module comprises data exchange technology for facilitating data exchange with external systems.

2. The system of claim 1 , further comprising a real-time data mesh (RTDM) module providing real-time data management and analysis capabilities.

3. The system of claim 2 , wherein the special pricing algorithm module is configured to retrieve one or more forecasts from the RTDM module for accurate pricing determinations.

4. The system of claim 1 , wherein the caching mechanism includes a cache that stores vendor-specific data.

5. The system of claim 1 , wherein the integration module utilizes integration techniques for data and document exchange.

6. The system of claim 1 , further comprising a data validation mechanism within said pricing validation and maintenance module that leverages insight from said RTDM module.

7. The system of claim 1 , wherein the data exchange techniques ensure accurate, secure and industry standards compliant data exchange with external systems.

8. 1. A computerized method for automated near real-time special pricing calculation and application, the method comprising: receiving inputs including manufacturer details, product category, type of solution, past buying patterns, and time of year; applying a special pricing algorithm to determine appropriate pricing based on the received inputs; validating the calculated pricing using predefined business rules; applying the determined pricing and associated terms to individual or multiple orders and quotes; integrating with order processing systems to ensure accurate pricing application; and exchanging data with external systems using data exchange technology.

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

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

11. 10. The computerized method of claim 8, wherein the validation of calculated pricing includes applying intelligent validation rules based on insight from the RTDM module.

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

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

14. 10. The computerized method of claim 8, wherein the data exchange techniques ensure accurate, secure and industry standards compliant data exchange with external systems.

15. 1. A computerized method for automated near real-time special pricing calculation and application, the method comprising: receiving inputs including manufacturer details, product category, type of solution, past buying patterns, and time of year; applying a special pricing algorithm to determine appropriate pricing based on the received inputs; validating the calculated pricing using predefined business rules; applying the determined pricing and associated terms to individual or multiple orders and quotes; integrating with order processing systems to ensure accurate pricing application; and exchanging data with external systems using data exchange technology.

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

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

18. 10. The computerized method of claim 8, wherein the validation of calculated pricing includes applying intelligent validation rules based on insight from the RTDM module.

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

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

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