System and method for converting hardware-software-cloud to service (AAS)

Abstract

Computerized systems and methods are described for converting conventional technical products into "as a service" (AaS) models to facilitate transitions from capital expenditure (CapEx) to operational expenditure (OpEx). A method includes receiving a user input for a technical product selection and accessing a real-time data grid (RTDM) to retrieve data. An advanced analysis and machine learning (AAML) module analyzes user input and marketplace data to optimize transitions to subscription-based services. The process results are displayed to the user through a single glass pane user interface (SPoG UI). And the AaS conversion module converts the product into a customizable subscription packet. This approach emphasizes dynamic pricing based on the use, flexibility, and / or scalability of the service. Methods are provided for real-time reporting, subscription management, and vendor system integration, thereby implementing a comprehensive AaS conversion process suitable for modern technical products and services.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application is a continuation-in-part (CIP) of the following U.S. patent applications: U.S. Patent Application No. 18 / 341,714, filed June 26, 2023; U.S. Patent Application No. 18 / 349,836, filed July 10, 2023. This application also claims the benefit of U.S. Provisional Application No. 63 / 513,073, filed July 11, 2023; U.S. Provisional Application No. 63 / 513,078, filed July 11, 2023; U.S. Provisional Application No. 63 / 515,075, filed July 21, 2023; U.S. Provisional Application No. 63 / 515,076, filed July 21, 2023; and U.S. Application No. 18 / 614,517, filed March 22, 2024. Each of these applications is incorporated herein by reference in its entirety. Background Art

[0003] Traditional ordering processes within distribution and supply chain platforms are plagued by inefficiencies, delays, and inaccuracies. Traditionally, multiple systems and vendors perform each activity independently, from creating a bill of materials to registering transactions, applying pricing, generating quotes, and submitting orders. This approach leads to operational inefficiencies and increases the potential for errors.

[0004] Enterprise resource planning (ERP) systems have long served as the backbone for managing business processes, including distribution and supply chains. These systems serve as a central repository where different departments, such as finance, human resources, and inventory management, can access and share real-time data. While comprehensive, ERP systems present several challenges in today's complex distribution and supply chain environments. One of the primary challenges is data fragmentation. Data silos across different departments or even separate ERP systems make it difficult to achieve real-time visibility. Users lack a comprehensive understanding of key distribution and supply chain metrics, which negatively impacts decision-making.

[0005] Furthermore, ERP systems often lack effective data integration capabilities. Traditional ERP systems are not designed to effectively integrate with external systems, or even between different modules within the same ERP suite. This design leads to cumbersome, error-prone manual processes for transferring data between systems, impacting the flow of information throughout the supply chain. When information exists in different formats across systems, data inconsistencies arise, hindering accurate data analysis and leading to uninformed decisions.

[0006] Data inconsistency presents another challenge. When data exists in different formats or units across departments or ERP systems, standardizing that data for meaningful analysis becomes a laborious process. Businesses often resort to time-consuming manual processes for data conversion and validation, further delaying decision-making. Furthermore, traditional ERP systems often lack the ability to efficiently process large volumes of data. These systems struggle to provide timely insights for operational improvements, a particular problem for businesses operating complex and sprawling distribution and supply chain networks.

[0007] Data security is another concern, especially given the sensitive nature of supply chain data, which can include customer details, pricing, and contracts. Ensuring compliance with global regulations on data security and governance adds an extra layer of complexity. Traditional ERP systems often lack robust security features flexible enough to adapt to the evolving landscape of cybersecurity threats and compliance requirements. Summary of the Invention

[0008] The automated "as a service" (AaS) model transformation process aims to address shortcomings in the technology distribution industry by integrating various systems and activities into a unified interface, enabling the transition of technology products (hardware, software, cloud services) to a subscription-based AaS model. This shift from capital expenditure to operating expenditure facilitates the entire service selection and subscription process and improves the efficiency of activities such as service configuration, dynamic pricing application, and subscription management. The platform ensures data security and compliance while effectively integrating and accelerating the transformation process.

[0009] In the global distribution industry, challenges such as inefficient distribution management, SKU management, and the transition to a direct-to-consumer model require innovative solutions. Traditional distribution methods are increasingly inadequate, especially as consumer expectations and regulations shift. By integrating functionality for distribution management, supply chain management, and customer visibility, the platform supports the transition from traditional sales methods to a flexible, subscription-based service model.

[0010] According to some embodiments, the transformation module can be configured to incorporate algorithms to optimize product and service selection based on real-time market data and customer preferences. The system includes a transformation module that integrates with a real-time data grid (RTDM) and a single pane of glass user interface (SPoG UI) to optimize the provision of subscription-based services. By using advanced algorithms that adjust offerings based on real-time market data and customer usage patterns, the flexibility and scalability of service options are enhanced.

[0011] In one non-limiting example, a subscription recommendation engine employs sophisticated algorithms to provide users with dynamic, usage-based service options. A dynamic pricing engine uses models such as multivariate linear regression or random forests to predict and adjust subscription costs based on actual service usage, market conditions, and customer-specific factors.

[0012] In one embodiment, a subscription management and real-time pricing module, operatively connected to the RTDM and SPoG UI, manages the lifecycle of subscriptions. The module(s) optimize service options based on real-time data, using algorithms to dynamically adjust pricing and service configurations. The system includes a pricing engine for cost forecasting, adjusting for variables such as usage intensity and market trends.

[0013] In some embodiments, the system enables users to convert their selections to a subscription model with a single click via the SPoG UI. It includes modules for checking user permissions and aggregating configuration options based on current offerings, thereby facilitating the subscription management process.

[0014] Additionally, or alternatively, the system employs validation algorithms, such as support vector machines, to ensure the accuracy of subscription configurations. It synchronizes real-time data from various systems, ensuring consistent and up-to-date information across subscription models.

[0015] The embodiments disclosed herein integrate multiple systems, automate processes, and perform validation to automate the transition of technology products into subscription-based services. By implementing intelligent rules and validation, the system can efficiently perform complex tasks, reducing time and errors. The system's adaptability ensures it remains current and evolves to meet market and customer needs.

[0016] The system uses a data-driven approach to automatically create and manage subscription packages based on user consumption patterns. This includes assembling various technology products and services into consistent subscriptions that align with individual usage patterns and preferences. The system generates user profiles based on comprehensive data analysis, including aspects such as digital engagement and technology preferences. This data informs the creation of subscription packages that meet specific user requirements in areas such as software applications, cloud computing, and hardware requirements.

[0017] During this process, personas are automatically generated based on comprehensive market research and real user data, encompassing a broad range of attributes including demographics, purchasing patterns, digital engagement, and preferences across various product categories. The identification of personas allows for a nuanced understanding of customer needs in areas such as technology, software applications, cloud computing solutions, and hardware requirements.

[0018] The system incorporates advanced algorithms to analyze user data, including historical usage and interaction patterns, to identify preferences and anticipate needs. This facilitates the creation of highly relevant and attractive subscription packages. Automatic subscription bundling integrates products and services from different categories, ensuring that each package meets the user's technical and service needs. Automatically generated AaS subscriptions can include combinations of hardware with compatible software solutions and cloud services, designed to enhance user productivity and efficiency.

[0019] Single glass pane

[0020] Single Pane of Glass (SPoG) can provide a comprehensive solution configured to address these multifaceted challenges. It can be configured to provide a holistic, user-friendly and efficient platform that facilitates the distribution process.

[0021] According to some embodiments, SPoG can be configured to address supply chain and distribution management by enhancing visibility and control over supply chain processes. Through real-time tracking and analysis, SPoG can provide valuable insights into inventory levels and goods status, thereby ensuring that supply chain and distribution management processes are handled efficiently.

[0022] According to some embodiments, SPoG can integrate multiple touchpoints into a single platform to emulate a direct consumer channel into a distribution platform. This integration provides a unified direct channel for consumers to interact with distributors, significantly reducing the complexity of the supply chain and enhancing the overall customer experience.

[0023] SPoG provides innovative solutions for improved inventory management through advanced forecasting capabilities. These predictive analytics can highlight demand trends, leading companies to manage their inventory more effectively and mitigate the risk of out-of-stock or overstocking.

[0024] According to some embodiments, the SPoG may include a global compliance database. This database, updated in real time, enables distributors to stay up-to-date with the latest international laws and regulations. This feature significantly reduces the burden of manual tracking, ensuring smooth and compliant cross-border transactions.

[0025] According to some embodiments, to facilitate localization of as-a-service (AaS) transformations, SPoG integrates data from various OEMs into a single platform. This not only ensures data consistency but also significantly reduces the likelihood of errors. Furthermore, it provides the ability to efficiently manage AaS transformations in line with specific market needs and requirements.

[0026] According to some embodiments, SPoG is a highly configurable and user-friendly platform whose intuitive interface allows users to easily access and purchase technology, thereby aligning with the expectations of a new generation of technology buyers.

[0027] In addition, SPoG's advanced analytical capabilities provide extremely useful insights that can drive strategy and decision-making. It can track and analyze trends in real time, allowing companies to stay ahead and adapt to changing market conditions.

[0028] The flexibility and scalability of SPoG make it a future-proof solution that can adapt to changing business needs, allowing companies to expand or contract their operations as needed without requiring significant infrastructure changes.

[0029] SPoG's innovative approach to addressing distribution industry challenges makes it an extremely valuable tool. By enhancing supply chain visibility, facilitating inventory management, ensuring regulatory compliance, simplifying AaS transitions, and delivering an exceptional customer experience, it offers a comprehensive solution to the complex issues that have long plagued the distribution sector. Through its implementation, distributors can expect increased efficiency, reduced errors, and improved customer satisfaction, enabling continued growth in an evolving global marketplace.

[0030] Real-Time Data Grid (RTDM)

[0031] According to some embodiments, the platform may include an implementation of a real-time data grid (RTDM). RTDS offers an innovative solution to these challenges. RTDM (Distributed Data Architecture) enables real-time data availability across multiple sources and touchpoints. This feature enhances supply chain visibility, allows for efficient management, and enables distributors to more effectively handle disruptions.

[0032] RTDM's predictive analytics capabilities provide a solution for efficient inventory control. By providing insights into demand trends, it helps companies manage inventory, thereby reducing the risk of overstocking or stockouts.

[0033] RTDM’s global compliance database, updated in real time, ensures distributors are aware of current international regulations. This significantly reduces the burden of manual tracking, enabling cross-border transactions.

[0034] RTDM also simplifies AaS transformation by integrating data from various OEMs, ensuring data consistency and reducing the possibility of errors. Its ability to manage product and market data efficiently meets specific market needs.

[0035] RTDM enhances the customer experience with its intuitive interface, allowing easy access and purchase of technology, meeting the expectations of the next generation of technology buyers.

[0036] Advantages of SpoG and RTDM integration

[0037] Integrating the SPoG platform with RTDM offers numerous advantages. First, it provides a holistic solution to a long-standing problem in the distribution industry. Leveraging the power of RTDM, SPoG can enhance supply chain visibility, facilitate AaS transformation, and deliver a superior customer experience.

[0038] The real-time tracking and analysis provided by RTDM enhances SPoG’s ability to effectively manage its supply chain and inventory. It provides accurate and current information, enabling distributors to make informed decisions quickly.

[0039] Integrating SPoG with RTDM also ensures data consistency and reduces errors in AaS conversion. By providing a centralized platform for managing data from various OEMs, it simplifies product localization and helps align with market requirements.

[0040] RTDM’s global compliance database integrated with SPoG facilitates compliant cross-border transactions. It also reduces the burden of manual tracking, saving significant time and resources.

[0041] In some embodiments, the distribution platform incorporates SPoG and RTDM to provide an improved and comprehensive distribution system that can leverage the advantages of the distribution model, address its existing challenges, and position it for continued growth in an ever-evolving global market. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 One embodiment of an operating environment for a distribution platform, referred to in this embodiment as a system, is shown.

[0043] Figure 2 Shown in Figure 1 An embodiment of an operating environment for a distribution platform built on the elements introduced in.

[0044] Figure 3 One embodiment of a system for distribution management is shown.

[0045] Figure 4 A system for automating the AaS transition process is described according to one embodiment.

[0046] Figure 5 An SPoG UI according to one embodiment is shown.

[0047] Figure 6 An RTDM module according to one embodiment is shown.

[0048] Figure 7 A system for automated AaS transition according to one embodiment is shown.

[0049] Figure 8is a flow chart of a method for automating the AaS transition process according to some embodiments of the present disclosure.

[0050] Figure 9 is a flow chart for automatic service configuration in an AaS conversion system according to some embodiments of the present disclosure.

[0051] Figure 10 is a flow chart of a process for automated data management and analysis in an AaS conversion system according to some embodiments of the present disclosure.

[0052] Figure 11 is a block diagram of example components of a device according to some embodiments of the present disclosure.

[0053] Figures 12A to 12Q Depicted are various screens and functions of the SPoG UI according to some embodiments. DETAILED DESCRIPTION

[0054] Embodiments may be implemented in hardware, firmware, software, or any combination thereof. Embodiments may also be implemented as instructions stored on a machine-readable medium, which may be read and executed by one or more processors. A machine-readable medium may include any mechanism for storing or transmitting information in a form readable by a machine (e.g., a computing device). For example, a machine-readable medium may include a read-only memory (ROM); a random access memory (RAM); a magnetic disk storage medium; an optical storage medium; a flash memory device, etc. In addition, firmware, software, routines, instructions may be described herein as performing certain actions. However, it should be understood that such descriptions are merely for convenience, and that such actions are actually generated by a computing device, a processor, a controller, or other device that executes the firmware, software, routines, instructions, etc.

[0055] It should be understood that the operations shown in the exemplary methods are not exhaustive, and other operations may be performed before, after, or between any of the operations shown. In some embodiments of the present disclosure, operations may be performed in a different order and / or varied.

[0056] Figure 1 The operating environment 100 of a distribution platform, referred to in this embodiment as system 110, is shown. System 110 operates in the context of an information technology (IT) distribution model, catering to a variety of users such as customers 120, end customers 130, vendors 140, distributors 150, and other entities involved in the distribution process. The operating environment includes a wide range of characteristics and dynamics that contribute to the success and efficiency of the distribution platform.

[0057] Customers 120 within the operating environment of system 110 represent businesses or individuals seeking IT solutions to meet their specific needs. These customers may require a variety of IT products, such as hardware components, software applications, network equipment, or cloud-based services. System 110 provides customers with a user-friendly interface, allowing them to browse, search, and select the most appropriate IT solution based on their needs. Customers can also access real-time data and analytics through system 110, empowering them to make informed decisions and optimize their IT infrastructure.

[0058] End customers 130 may be the ultimate beneficiaries of the IT solutions provided by 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 system 110 to access a wide range of IT solutions, ensuring they have access to the latest technologies and innovations in the market. System 110 enables end customers to track their orders, receive updates on delivery status, and access customer support services, thereby enhancing their overall experience.

[0059] Vendors 140 play a vital role in the operating environment of system 110. These vendors can include manufacturers, distributors, and suppliers offering a variety of IT products and services. System 110 serves as a centralized platform for vendors to showcase their products, manage inventory, and facilitate transactions with customers and resellers. Vendors can leverage system 110 to facilitate their 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 enhance their overall visibility and competitiveness.

[0060] Distributors 150 can be intermediaries within the distribution model, bridging the gap between vendors and customers. They play a vital role in the IT distribution ecosystem by connecting customers with the right IT solutions from a variety of vendors. Distributors can include retailers, value-added resellers (VARs), system integrators, or managed service providers. System 110 enables distributors to access a comprehensive catalog of IT solutions, manage their sales channels, and provide value-added services to customers. By fully leveraging system 110, distributors can strengthen their customer relationships, optimize their product offerings, and increase their revenue streams.

[0061] Within the operating environment of system 110, various dynamics and features may exist that contribute to its effectiveness. These dynamics include real-time data exchange, integration with existing enterprise systems, scalability, and flexibility. System 110 ensures that relevant data can be exchanged between users in real time, enabling accurate decisions and timely actions. Integration with existing enterprise systems (such as enterprise resource planning (ERP) systems, customer relationship management (CRM) systems, and warehouse management systems) allows for communication and interoperability, eliminating data silos and enabling end-to-end visibility.

[0062] System 110 is designed to be scalable and flexible. It can adapt to the growing demands of the IT distribution model, whether it involves an expanding customer base, a growing number of vendors, or a wider range of IT products and services. System 110 can be configured to handle large-scale data processing, storage, and analysis, ensuring it can support the evolving needs of the distribution platform. In addition, system 110 leverages a technology stack including .NET, Java, and other suitable technologies, providing a robust foundation for its operation.

[0063] In summary, the operating environment of system 110 within the IT distribution model includes customers 120, end customers 130, vendors 140, distributors 150, and other entities involved in the distribution process. System 110 serves as a centralized platform that facilitates efficient collaboration, communication, and transaction processes among these users. By leveraging real-time data exchange, integration, scalability, and flexibility, system 110 empowers users to optimize their operations, enhance the customer experience, and drive business success within the IT distribution ecosystem.

[0064] Figure 2 Describes the operating environment 200 of the distribution platform, expanding Figure 1 The environment features integration points 210 that enable data flows and connections between various systems, such as customer systems 220, vendor systems 240, dealer systems 260, and other entities in the AaS transformation process. Figure 2 The interconnectivity of the network and the mechanisms that facilitate collaborative and data-driven decision-making for AaS transformation are shown. The operating environment 200 is configured to automate the AaS transformation process using AI and ML techniques to process and analyze data for service transformation.

[0065] Some embodiments of the AaS transformation process involve a systematic approach to converting a traditional product or service to a subscription-based model with minimal human intervention. The process includes several technical components: Various data, including product specifications, user-service interactions, and usage patterns, are collected. This data is aggregated from sources such as CRM systems and web analytics tools and fed into a real-time data grid (RTDM). The RTDM processes and normalizes the data, serving as a centralized repository for real-time data updates and retrieval. An AAML module analyzes this aggregated data to determine the optimal strategy for service transformation. It segments services based on data-driven insights and predicted user preferences. Informed by the insights from the AAML module, the AaS transformation module configures subscription services for each user or market segment. It applies predictive models and heuristic algorithms to determine service offerings that meet specific user requirements. Users interact with these services through the SPoG UI to customize and confirm their subscription choices. The system includes a feedback loop in which responses to subscription services are collected and analyzed to continuously refine service offerings.

[0066] AI algorithms in the AaS transformation process address inventory management, service customization, and optimization of user choices. Machine learning models, such as neural networks and decision trees, refine service offerings. The AaS process uses ML-based algorithms for real-time service configuration. Advanced analytics, such as ensemble learning or reinforcement learning, continuously optimize the AaS process. The AI ​​and ML technologies in the operating environment 200 use supervised and unsupervised learning algorithms, including convolutional neural networks for pattern recognition and logistic regression for decision-making. These components dynamically adapt to changing data inputs (such as user preferences and market conditions), thereby optimizing decision paths through reinforcement learning. ML components use predictive analytics to continuously refine outputs by absorbing new data to enhance the accuracy and relevance of service transformations.

[0067] Operating environment 200 includes system 110, which serves as the central hub for managing the AaS transformation process. System 110 acts as a bridge between customer system 220, vendor system 240, distributor system 260, and other entities. It integrates communication, data exchange, and transaction processes to provide a cohesive experience. Furthermore, environment 200 features integration points 210, utilizing a hybrid architecture combining RESTful APIs and WebSockets for real-time data exchange and synchronization. This architecture utilizes SSL / TLS protocols for security, protecting data during transmission.

[0068] Customer System Integration: Integration point 210 enables system 110 to connect with customer systems 220, facilitating efficient data exchange and synchronization. Customer systems 220 may include entities such as customer system 221, customer system 222, and customer system 223. These systems represent internal systems used by customers, such as ERP or CRM systems. Integration with customer systems 220 allows customers to access real-time information about their AaS subscriptions, including personalized bundles, pricing details, order tracking, and other relevant data, thereby enhancing their decision-making capabilities. This integration provides an automated, real-time solution for creating and managing AaS conversion processes, thereby improving operational efficiency for customers.

[0069] Data exchange between customer system 220, vendor system 240, and dealer system 260 is enabled through a robust ETL (extract, transform, load) process, described below with reference to the real-time data grid architecture, ensuring data consistency and reliability. This interaction is governed by predefined business rules and logic that dictate data flows and processing methods. Advanced mapping and transformation tools are employed to reconcile disparate data formats, enabling data integration and utilization across these systems. This coordinated data exchange supports synchronized operations, enabling efficient and informed decision-making across the distribution network.

[0070] Integration with related systems: Integration points 210 enable system 110 to connect with related systems 230, thereby facilitating efficient data exchange and synchronization. These systems contribute to the overall efficiency of the AaS transformation process by providing relevant market and product data.

[0071] Vendor System Integration: Integration point 210 facilitates connectivity between system 110 and vendor systems 240. Vendor systems 240 may include entities such as vendor system 241, vendor system 242, and vendor system 243, representing inventory management, pricing systems, and product catalogs. Integration with vendor systems 240 ensures that vendors can efficiently update their offerings and receive real-time notifications to facilitate the AaS transition process.

[0072] Dealer System Integration: Integration point 210 allows dealer system 260 to connect with system 110. Dealer system 260 includes entities such as dealer system 261, dealer system 262, and dealer system 263, thereby handling sales, customer management, and service delivery. Integration empowers dealers to access the latest product information and effectively manage customer relationships.

[0073] Integration with other entities: Integration point 210 also connects other entities involved in the distribution process, thereby promoting collaboration and efficient distribution. This integration ensures real-time data exchange for AaS transformation processing and decision-making within the distribution ecosystem.

[0074] The configuration of system 110 includes sophisticated AI and ML capabilities to automate the AaS conversion process based on individual preferences, thereby ensuring relevance and optimization in the distribution process.

[0075] Integration point 210 also enables connectivity to a system of record 280 for additional data management and integration. The system of record 280 may represent an enterprise resource planning (ERP) system or a customer relationship management (CRM) system, including both future systems as well as legacy ERP systems such as SAP, Impulse, META, I-SCALA, and the like. The system of record may include one or more repositories of critical and legacy business data. It facilitates integration for data exchange and synchronization between the distribution platform, system 110, and ERP, enabling real-time updates and ensuring the availability of accurate and up-to-date information. Integration point 210 establishes a connection between the system of record 280 and the distribution platform, allowing stakeholders to leverage the rich data stored in the ERP for efficient collaboration, data-driven decision-making, and a streamlined distribution process. These systems represent internal systems used by customers, vendors, and the like.

[0076] Integration points 210 within the operating environment 200 can be facilitated through standardized protocols, APIs, and data connectors. These mechanisms ensure compatibility, interoperability, and secure data transfer between the distribution platform and connected systems. The system 110 employs industry-standard protocols such as RESTful APIs, SOAP, or GraphQL to establish communication channels and enable integrated data exchange.

[0077] In some embodiments, the system 110 can incorporate authentication and authorization mechanisms to ensure secure access and data protection. Technologies such as OAuth or JSON Web Tokens (JWT) can be used to authenticate users, authorize data access, and maintain the integrity and confidentiality of exchanged information.

[0078] In some embodiments, integration points 210 and data flows within operating environment 200 enable users to operate within a connected ecosystem. Data generated at various stages of the distribution process (including customer orders, inventory updates, shipment details, and sales analytics) flows between customer systems 220, vendor systems 240, distributor systems 260, and other entities. This data exchange facilitates real-time visibility, enables data-driven decision-making, and improves operational efficiency across the entire distribution platform.

[0079] In some embodiments, system 110 leverages advanced technologies such as Typescript, NodeJS, ReactJS, NET core, C#, and other suitable technologies to support integration points 210 and enable communication within operating environment 200. These technologies provide a strong foundation for system 110, ensuring scalability, flexibility, and efficient data processing capabilities. In addition, integration points 210 can also employ algorithms, data analysis, and machine learning techniques to derive valuable insights, optimize distribution processes, and personalize customer experiences. Integration points 210 and data flows within operating environment 200 enable users to operate within a connected ecosystem. Data generated at various touchpoints (including customer orders, inventory updates, pricing changes, or delivery status) flows between different entities, systems, and components. Through system 110, integrated data can be processed, coordinated, and made available to relevant users in real time. This real-time access to accurate and current information empowers users to make informed decisions, optimize supply chain operations, and improve the customer experience.

[0080] Figure 2 The several elements in the operating environment depicted in the figure may include conventional, well-known elements that are only briefly explained here. For example, each client system (such as client system 220) may include a desktop personal computer, a workstation, a laptop computer, a PDA, a mobile phone, or any device that supports wireless access protocol (WAP) enablement, or any other computing device that can be directly or indirectly connected to the Internet or other network. Each client system can typically run an HTTP client, such as Microsoft's Edge browser, Google's Chrome browser, Opera's browser, or a WAP-enabled browser for mobile devices, thereby allowing the client system to access, process, and view information, pages, and applications available from the distribution platform over the network.

[0081] In addition, each client system may typically be equipped with a user interface device, such as a keyboard, mouse, trackball, touchpad, touch screen, pen, or similar device, for interacting with the graphical user interface (GUI) provided by the browser. These user interface devices enable users of the client system to navigate the GUI, interact with pages, forms, and applications, and access data and applications hosted by the distribution platform.

[0082] The client 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 a processor system) that may include an Intel Pentium processor or similar processor and / or multiple processor units.

[0083] Computer program product embodiments include 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, dealer systems, and other entity systems to communicate with each other 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 ROM, RAM, floppy disk, optical disk, DVD, CD, microdrive, magneto-optical disk, magnetic or optical card, nanosystem, or any suitable medium for storing instructions and data.

[0084] Furthermore, computer code for implementing the embodiments may be transmitted and downloaded from a software source over the Internet or any other conventional network connection using communication media and protocols such as TCP / IP, HTTP, HTTPS, Ethernet, etc. The code may also be transmitted over an extranet, VPN, local area network, or other network and executed on a client system, server, or server system using programming languages ​​such as C, C++, HTML, Java, JavaScript, ActiveX, VBScript, etc.

[0085] It should be understood that embodiments may be implemented in a variety of programming languages ​​executed on the client system, server, or server system, and that the choice of language may depend on the specific requirements and environment of the distribution platform.

[0086] Thus, the operating environment 200 can couple the distribution platform with one or more integration points 210 and data flows to enable efficient collaboration and a streamlined distribution process.

[0087] Figure 3 A system 300 for supply chain and distribution management is shown. System 300 ( Figure 3 ) is a supply chain and distribution management solution that is configured to address the challenges faced by the fragmented distribution ecosystem in the global distribution industry. System 300 may include several interconnected components and modules that work in concert to optimize supply chain and distribution operations, enhance collaboration, and drive business efficiency.

[0088] SPoG UI 305 serves as a centralized user interface, providing users with a centralized view of the entire supply chain. It consolidates information from various sources and presents real-time data, analytics, and functionality tailored to the user's specific role and responsibilities. By providing a customizable and intuitive summary tabular layout, SPoG UI enables users to access relevant information and tools, empowering them to make data-driven decisions and effectively manage their supply chain and distribution activities.

[0089] For example, logistics managers can use SPoG UI 305 to monitor the status of shipments, track delivery routes, and view real-time inventory levels across multiple warehouses. They can visualize data through interactive charts and graphs, such as a map showing the current location of each shipped shipment or a bar chart showing inventory levels by product category. By having a unified view of the supply chain, logistics managers can identify bottlenecks, optimize routes, and ensure timely delivery of goods.

[0090] The SPoG UI 305 is integrated with other modules of the system 300 to facilitate real-time data exchange, synchronized operations and workflows. Through API integration, data synchronization mechanisms and event-driven architecture, the SPoG UI 305 ensures a smooth flow of information and enables collaborative decision-making across the distribution ecosystem. The SPoG UI 305 is designed with a user-centric approach, featuring an intuitive and responsive layout. It utilizes front-end technology to present dynamic and interactive data visualizations. Customizable summary tables allow users to customize their views based on specific roles and requirements. The UI supports drag-and-drop functionality for ease of use, and its adaptive design ensures compatibility across various devices and platforms. Advanced filtering and search capabilities enable users to efficiently navigate to and access relevant supply chain data and insights.

[0091] For example, when a purchase order is generated in the SPoG UI, the system automatically updates inventory levels, triggers a notification to the warehouse management system, and initiates the shipping process. This integration enables efficient order fulfillment, reduces manual errors, and enhances overall supply chain visibility.

[0092] The Real-Time Data Grid (RTDM) module 310 is another component of the system 300 and is responsible for ensuring the flow of data within the distribution ecosystem. It aggregates data from multiple sources, coordinates it, and ensures its availability in real time.

[0093] Within a distribution network, the RTDM module collects data from various systems, including inventory management systems, point-of-sale terminals, and customer relationship management systems. It harmonizes this data by aligning formats, standardizing units of measurement, and reconciling any discrepancies. This unified data is then made available in real time, allowing users across the supply chain to access accurate and current information.

[0094] The RTDM module 310 can be configured to capture changes in data across multiple transaction systems in real time. It employs a sophisticated change data capture (CDC) mechanism that continuously monitors transaction systems to detect any updates or modifications. The CDC component can be specifically configured to work with a variety of transaction systems, which may include traditional ERP systems, customer relationship management (CRM) systems, and other enterprise-wide systems, thereby ensuring compatibility and flexibility for businesses operating in various environments.

[0095] By having access to real-time data, users can make timely decisions and respond quickly to changing market conditions. For example, if the RTDM module detects a sudden spike in demand for a specific product, it can trigger an alert to the production team, allowing them to adjust manufacturing schedules and prevent stockouts.

[0096] The RTDM module 310 facilitates data management operations within the supply chain. It enables real-time coordination of data from multiple sources, freeing vendors, distributors, customers, and end-customers from the constraints imposed by traditional ERP systems. This enhanced flexibility supports improved efficiency, customer service, and innovation.

[0097] Another component of system 300 is the Advanced Analytics and Machine Learning (AAML) module 315. Leveraging powerful analytical tools and algorithms such as Apache Spark, TensorFlow, or scikit-learn, the AAML module extracts valuable insights from collected data. It supports advanced analytics, predictive modeling, anomaly detection, and other machine learning capabilities.

[0098] For example, the AAML module can analyze historical sales data to identify seasonal patterns and predict future demand. It can generate forecasts that help optimize inventory levels, ensure inventory availability during peak seasons, and minimize excess inventory costs. By leveraging machine learning algorithms, the AAML module automates repetitive tasks, predicts customer preferences, and optimizes supply chain processes.

[0099] In addition to demand forecasting, the AAML module can provide insights into customer behavior, enabling targeted marketing campaigns and personalized customer experiences. For example, by analyzing customer data, the module can identify cross-sell or up-sell opportunities and recommend relevant products to individual customers.

[0100] In addition, the AAML module can analyze data from various sources, such as social media feedback, customer reviews, and market trends, to gain a deeper understanding of consumer sentiment and preferences. This information can be used to make informed product development decisions, identify emerging market trends, and adjust business strategies to meet evolving consumer expectations.

[0101] System 300 emphasizes integration and interoperability to connect with existing enterprise systems such as ERP systems, warehouse management systems, and customer relationship management systems. By establishing connections and data flows between these systems, System 300 enables smooth data exchange, process automation, and end-to-end visibility across the supply chain. Integration protocols, APIs, and data connectors facilitate communication and interoperability between different modules and components, creating a holistic and connected distribution ecosystem.

[0102] The implementation and deployment of system 300 can be customized to meet specific business needs. It can be deployed as a cloud-native solution using containerization technologies (such as Docker) and orchestration frameworks (such as Kubernetes). This approach ensures scalability across diverse environments, ease of management, and efficient updates. Implementation involves configuring the system to align with specific supply chain requirements, integrating with existing systems, and customizing modules and components based on business needs and preferences.

[0103] The system 300 for supply chain and distribution management is a comprehensive and innovative solution that addresses the challenges of a fragmented distribution ecosystem. It combines the functionality of the SPoG UI 305, the RTDM module 310, and the AAML module 315, as well as integration with existing systems. By leveraging a diverse technology stack, a scalable architecture, and strong integration capabilities, the system 300 provides end-to-end visibility, data-driven decision-making, and optimized supply chain operations. The examples and options provided in this specification are non-limiting and can be customized to meet specific industry requirements, drive efficiency, and achieve success in supply chain and distribution management.

[0104] Figure 4 An embodiment of a system 400 is depicted for automatically transforming any technology product into an "as a service" (AaS) model by interactively combining SPoG UI, RTDM, and AI / ML technologies to implement a comprehensive service transformation system. System 400 is configured to integrate with existing dealer systems, ensuring efficient data exchange and system synchronization.

[0105] The SPoG UI 405 serves as the primary user interface. Users interact with this interface to perform various tasks, providing straightforward interaction and customization. It displays information and options relevant to a distributor's different business models and customer demographics. It displays real-time data from a data grid 410 and provides controls for initiating actions within the system 400. For example, users can interact directly from the SPoG UI 405 with a dynamic display of service options, interactive elements for subscription customization, and real-time feedback tools regarding user selections. It integrates with other system components to reflect accurate service information and user customization options. The SPoG UI is developed using web-based technology, allowing access from a variety of devices, such as desktop computers, laptops, tablets, and smartphones. The SPoG UI 405 provides a comprehensive view of the entire distribution ecosystem, consolidating data and functionality from various modules into a centralized, easy-to-navigate platform. The SPoG UI 405 simplifies the management of complex distribution tasks, providing distributors with a streamlined experience. In some embodiments, the SPoG 405 includes a dynamic pricing tool that displays variable costs based on individual user spending patterns. Dynamic pricing tools enable users to consider how their usage affects the cost of their AaS subscription, thereby improving pricing transparency and adaptability.

[0106] Data grid 410 is a complex data management layer that aggregates and coordinates data from various sources, including ERP, vendor platforms, and third-party databases. This component ensures that all operational modules in system 400 access consistent and up-to-date information. System 400 can be synchronized with existing dealer systems, ensuring efficient data exchange and system functionality.

[0107] Data grid 410 aggregates and coordinates data from various systems, such as inventory management, point of sale, and CRM, ensuring real-time data availability. It employs change data capture (CDC) to track real-time changes in transaction systems. This module standardizes data formats and units, ensuring data consistency and accuracy for decision-making processes related to service offerings.

[0108] AI module 460 uses machine learning algorithms and predictive modeling to automate the transition of products and services to a subscription model. AI module 460 analyzes market trends, user preferences, and consumption data to dynamically adjust service offerings. AI module 460 is configured to dynamically adjust pricing and service options based on real-time usage data. This allows for a flexible subscription model that adapts to changing user needs and consumption habits.

[0109] AI module 460 includes a decision support system for customizing subscriptions based on complex data analysis. In some embodiments, AI module 460 employs deep learning neural networks, particularly convolutional neural networks (CNNs) and recurrent neural networks (RNNs), for pattern recognition and time series analysis. For example, CNNs can be used to identify trends and patterns in market data, while RNNs, particularly LSTM (long short-term memory) networks, can analyze sequential data, such as time-based user interaction patterns. In some embodiments, AI module 460 can use decision trees for classification and regression tasks. These trees analyze user data and market conditions to classify users into different categories based on their service preferences. Random forest and gradient boosting algorithms, along with ensembles of decision trees, provide improved accuracy and stability in predictions. In some embodiments, clustering, particularly K-means and hierarchical clustering, is employed to segment markets and user groups into different groups. Market / user segmentation helps AI module 460 understand different user preferences and customize AaS subscription models for different market segments.

[0110] In some embodiments, AI module 460 can use reinforcement learning (RL) to adjust service offerings based on user feedback. RL algorithms, particularly Q-learning and policy gradient methods, can adjust the model to maximize user satisfaction, thereby learning from each interaction to improve recommendation accuracy. The module integrates reinforcement learning algorithms to continuously adjust service offerings based on user feedback, thereby improving the accuracy and relevance of customized subscriptions over time. In addition, NLP techniques can be used to analyze user feedback and queries. By utilizing tokenization, sentiment analysis, and named entity recognition, AI module 460 interprets user feedback to improve the service customization process.

[0111] Real-time processing based on data grid 410 enables AI module 460 to dynamically adjust service offerings based on current usage patterns and immediate market feedback. Data grid 410 also enables accurate tracking of real-time usage data to implement usage-based pricing strategies. Data grid 410 can include collaborative filtering and content-based recommendation systems to analyze user behavior and preferences, comparing them with similar user profiles or content characteristics to suggest appropriate service adjustments.

[0112] In some embodiments, AI module 460 can integrate predictive analytics tools to employ time series forecasting methods (e.g., autoregressive integrated moving average, exponential smoothing, etc.) to predict future service demand. Optimization algorithms, such as linear programming and genetic algorithms, can facilitate optimal subscription configuration, considering factors such as cost, user preferences, and resource availability to recommend the most effective service bundle. AI module 460 can employ Monte Carlo simulation and scenario analysis for risk assessment and strategic planning, simulating different market scenarios and evaluating the potential impact of various subscription models under different conditions.

[0113] The Service Management Module 430 automatically oversees the service lifecycle, including subscription initiation, modification, and termination. It incorporates tools for service-level management, compliance monitoring, customer request handling, and service change processes. The Service Management Module 430 can leverage real-time data to ensure that service delivery meets user expectations and contractual agreements. The module can also be configured to manage multi-tier subscription environments, facilitating the efficient handling of complex arrangements involving diverse technology products.

[0114] The subscription billing and analytics module 440 processes financial transactions and provides analytical insights into service usage. Billing and analytics 440 includes functionality for invoice generation, payment processing, and financial reporting. Its analytics component analyzes consumption patterns, service popularity, and revenue trends, providing strategic insights for business decisions. The subscription billing and analytics module 440 can be configured to adjust variable billing based on actual service usage and provide deeper analytical insights into user consumption patterns and preferences.

[0115] The service customization engine 450 allows users to personalize their service packages. It is integrated with the SPoG UI 405 and the AI ​​module 460 to facilitate user-driven service configuration. The engine uses algorithms to recommend service combinations based on user input and historical data to provide a customized service experience. The service customization engine 450 can be configured to provide comprehensive personalization options, allowing users to personalize their AaS subscription packages using various components (e.g., hardware, software), and additional services.

[0116] The asset management module 470 automatically tracks and manages the allocation and utilization of physical and digital assets within a subscription model. It includes an inventory control system, asset tracking capabilities, and resource consistency tools. This module explicitly expresses the service provider's ownership of physical assets, consistent with the "as a service" model. This ensures that users have access to the latest technology without the burden of ownership. It also ensures that assets are optimally utilized and accurately accounted for during service delivery.

[0117] System 400 transforms traditional product sales into a flexible, subscription-based service model. System 400 leverages real-time data processing, AI-driven analytics, and user customization capabilities to deliver a seamless "as-a-service" experience.

[0118] Figure 5 An embodiment of an advanced distribution platform including a system 500 for managing complex distribution networks is depicted, which can be an embodiment of system 300 and provides a technology distribution platform for optimizing the management and operation of distribution networks. System 500 includes several interconnected modules, each of which has a specific function and contributes to the overall efficiency of supply chain operations. In some embodiments, these modules can include an SPoG UI 505, a customer interaction module CIM 510, a RTDM module 515, an AI module 520, an interface display module 525, a personalized interaction module 530, a document hub 535, a catalog management module 540, a performance and insight badge display 545, a predictive analysis module 550, a recommendation system module 555, a notification module 560, a self-login module 565, and a communication module 570.

[0119] System 500, as an embodiment of system 300, can implement supply chain and distribution management using a series of technologies and algorithms that facilitate efficient data processing, personalized interaction, real-time analysis, secure communication, and effective management of documents, catalogs, and performance metrics.

[0120] In some embodiments, the SPoG UI 505 serves as a central interface within the system 500, providing users with a unified view of the entire distribution network. It utilizes front-end technologies such as ReactJS, TypeScript, and Node.js to create an interactive and responsive user interface. These technologies enable the SPoG UI 505 to deliver a user-friendly experience, allowing users to access relevant information, navigate through different modules, and perform tasks efficiently.

[0121] The CIM 510, or Customer Interaction Module, uses 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 securely process customer data, personalize the customer experience, and provide access control for users.

[0122] The RTDM module 515, or real-time data grid module, is a key component of system 500 that ensures smooth data flow across the distribution ecosystem. It utilizes technologies such as Apache Kafka, Apache Flink, or Apache Pulsar for data ingestion, processing, and stream management. These technologies enable the RTDM module 515 to handle real-time data streams, process large amounts of data, and ensure low-latency data processing. Additionally, the module employs a change data capture (CDC) mechanism to capture real-time data updates from various transaction systems, such as traditional ERP and CRM systems. This functionality allows users to access current and accurate information for informed decision-making.

[0123] The AI ​​module 520 within the system 500 can use 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, predict demand patterns, optimize inventory levels, and improve overall supply chain efficiency. For example, the AI ​​module 520 can utilize predictive models to forecast demand, allowing users to optimize inventory management and minimize out-of-stock or overstock situations.

[0124] The interface display module 525 focuses on presenting data and information in a clear and user-friendly manner. It uses technologies such as HTML, CSS, and JavaScript frameworks (such as ReactJS) to create interactive and responsive user interfaces. These technologies allow users to visualize data using various data visualization techniques (such as graphs, charts, and tables), thereby enabling efficient data understanding, comparison, and trend analysis.

[0125] The personalized interaction module 530 leverages customer data, historical trends, and machine learning algorithms to generate personalized recommendations for products or services. It can employ technologies such as Adobe Target, Apache Spark, and TensorFlow for data analysis, modeling, and delivering targeted recommendations. For example, the module can analyze customer preferences and purchase history to provide personalized product recommendations, thereby increasing customer satisfaction and boosting sales.

[0126] Document Hub 535 serves as a centralized repository for storing and managing documents within System 500. It can leverage technologies such as SeeBurger and Elastic Cloud for efficient document management, storage, and retrieval. For example, Document Hub 535 can utilize SeeBurger's document management capabilities to categorize and organize documents based on their type (such as contracts, invoices, product specifications, or compliance documents), allowing users to easily access and retrieve relevant documents when needed.

[0127] The catalog management module 540 enables the creation, management, and distribution of up-to-date product catalogs. It ensures that users have access to current product information, including specifications, pricing, availability, and promotions. Technologies such as Kentico and Akamai can be utilized to facilitate catalog updates, content delivery, and caching. For example, the module can use Akamai's content delivery network (CDN) to quickly and efficiently deliver catalog information to users regardless of their geographic location.

[0128] Performance and Insights Monitor 545 collects, analyzes, and visualizes real-time performance metrics and insights related to supply chain operations. It leverages tools such as Splunk and Datadog to enable effective performance monitoring and provide actionable insights. For example, the module can leverage Splunk's log analysis capabilities to identify performance bottlenecks in the supply chain, enabling users to take proactive measures to optimize operations.

[0129] The predictive analytics module 550 employs machine learning algorithms and forecasting models to predict demand patterns, optimize inventory levels, and improve overall supply chain efficiency. It leverages technologies such as Apache Spark and TensorFlow for data analysis, modeling, and forecasting. For example, the module can leverage TensorFlow's deep learning capabilities to analyze historical sales data and predict future demand, allowing users to optimize inventory levels and minimize costs.

[0130] The recommendation system module 555 focuses on providing intelligent recommendations to users in the distribution network. It generates personalized recommendations for products or services based on customer data, historical trends, and machine learning algorithms. Technologies such as Adobe Target and Apache Spark can be used for data analysis, modeling, and delivery of targeted recommendations. For example, the module can use Adobe Target's recommendation engine to analyze customer preferences and behavior and deliver personalized product recommendations across various channels, thereby enhancing customer engagement and driving sales.

[0131] The notification module 560 enables the distribution of real-time notifications to users regarding important events, updates, or alerts within the supply chain. It leverages technologies such as Apigee X and TIBCO for message queuing, event-driven architecture, and notification delivery. For example, the module can leverage TIBCO's messaging infrastructure to send real-time notifications to users' devices, ensuring timely and relevant information dissemination.

[0132] The self-onboarding module 565 facilitates the onboarding process for new users entering the distribution network. It provides guided steps, tutorials, or documentation to help users familiarize themselves with the system and its functionality. Technologies such as Okta and Kentico can be leveraged to ensure secure user authentication, access control, and self-learning resources. For example, the module can leverage Okta's identity and access management capabilities to securely onboard new users, provide them with appropriate access rights, and guide them through the system's functionality.

[0133] The communication module 570 enables communication and collaboration within the system 500. It provides a channel for users to exchange messages, share documents, and collaborate on projects. Technologies such as Apigee Edge and Adobe Launch can be used to promote secure and efficient communication, document sharing, and version control. For example, the module can utilize the API management capabilities of Apigee Edge to ensure secure and reliable communication between users, enabling them to collaborate effectively.

[0134] Therefore, the system 500 can include various modules that utilize various technologies and algorithms to optimize supply chain and distribution management. These modules (including SPoG UI 505, CIM 510, RTDM module 515, AI module 520, interface display module 525, personalized interaction module 530, document hub 535, catalog management module 540, performance and insight tag display 545, predictive analysis module 550, recommendation system module 555, notification module 560, self-login module 565 and communication module 570) work together to provide end-to-end visibility, data-driven decision-making, personalized interaction, real-time analysis and streamlined communication within the distribution network. The combination of specific technologies and algorithms can achieve efficient data management, secure communication, personalized experience and effective performance monitoring, thereby contributing to improved operational efficiency and the success of supply chain and distribution management.

[0135] Real-time Data Grid

[0136] Figure 6 An RTDM module 600 is shown according to an embodiment. The RTDM module 600 may be an embodiment of the RTDM module 310, which may include interconnected components, processes, and subsystems configured to implement real-time data management and analysis.

[0137] like Figure 6 As shown, the RTDM module 600 represents an effective data grid and change capture component within the overall system architecture. This module can be configured to provide real-time data management and standardization capabilities, thereby enabling efficient operations within the supply chain and distribution management field.

[0138] The RTDM module 600 may include an integration layer 610 (also referred to as a "system of record") for integration with various enterprise systems. These enterprise systems may include ERP systems such as SAP, Impulse, META, and I-SCALA, as well as other data sources. The integration layer 610 may handle data exchange and synchronization between the RTDM module 600 and these systems. Data feeds may be established to retrieve relevant information from the system of record, such as sales orders, purchase orders, inventory data, and customer information. These feeds enable real-time data updates and ensure that the RTDM module operates with the latest and most accurate data.

[0139] The RTDM module 600 may include a data layer 620 configured to process and transform data for retrieval and analysis. The data layer 620 includes a data grid, which is a cloud-based infrastructure configured to provide scalable and fault-tolerant data storage capabilities. Within the data grid, multiple purchase data stores (PDSs) can be deployed to store specific types of data, such as customer data, product data, or inventory data. Each PDS can be optimized for efficient data retrieval based on specific use cases and requirements. The PDS can be configured to store specific types of data, such as customer data, product data, financial data, etc. These PDSs serve as repositories for normalized and / or standardized data, thereby ensuring data consistency and integrity across systems.

[0140] In some embodiments, the RTDM module 600 implements a data replication mechanism to capture real-time changes from multiple data sources, including transaction systems such as ERP (e.g., Impulse, META, I-SCALA). The captured data can then be processed and standardized on the fly, converting it into a standardized format suitable for analysis and integration. This process ensures that data is readily available and current within the data grid, facilitating real-time insights and decision-making.

[0141] More specifically, the data layer 620 within the RTDM module 600 can be configured as a robust and flexible foundation for managing and processing data within the distribution ecosystem. In some embodiments, the data layer 620 can include a highly scalable and robust data lake, which can be referred to as data lake 622, and a set of purpose-built data stores (PDSs), which can be denoted as PDSs 624.1 through 624.N. These components are integrated to ensure efficient data management, standardization, and real-time availability.

[0142] Data layer 620 includes data lake 622, a state-of-the-art storage and processing infrastructure configured to handle 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 provides a unified and scalable platform for storing both structured and unstructured data. Leveraging the elasticity and fault tolerance of cloud-based storage, data lake 622 can adapt to the influx of data from diverse sources.

[0143] Associated with data lake 622, a set of purpose-built data stores can be employed: PDSs 624.1 through 624.N. Each PDS 624 can serve as a specialized repository optimized for storing and retrieving a specific type of data related to the supply chain domain. In some non-limiting examples, PDS 624.1 can be dedicated to customer data, storing information such as customer profiles, preferences, and transaction history. PDS 624.2 can focus on product data, including details about SKU codes, descriptions, pricing, and inventory levels. These purpose-built data stores allow for efficient data retrieval, analysis, and processing, thereby meeting the diverse needs of supply chain users.

[0144] To ensure real-time data synchronization, the data layer 620 can be configured to employ one or more change data capture (CDC) mechanisms. These CDC mechanisms can integrate with transactional systems, such as traditional ERP systems like SAP, Impulse, META, and I-SCALA, as well as other enterprise-wide systems. CDC continuously monitors these systems for any updates, modifications, or new transactions and captures them in real time. By capturing these changes, the data layer 620 ensures that the data within the data lake 622 and PDS 624 remains current, providing users with real-time insights into the distribution ecosystem.

[0145] In some embodiments, the data layer 620 can be implemented to facilitate integration with existing enterprise systems using one or more frameworks such as .NET or Java, thereby ensuring compatibility with various existing systems and providing flexibility for customization and extensibility. For example, the data layer 620 can utilize the Java technology stack, including frameworks such as Spring and Hibernate, to facilitate integration with systems of record across a variety 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.

[0146] For data processing and analysis, data layer 620 can leverage the capabilities of distributed computing frameworks, such as Apache Spark or Apache Flink, as non-limiting examples. These frameworks enable parallel processing and distributed computing across large-scale datasets stored in data lakes and PDSs. By leveraging these frameworks, supply chain users can perform complex analytical tasks, apply machine learning algorithms, and derive valuable insights from the data. For example, data layer 620 can use Apache Spark's machine learning libraries to develop predictive models for demand forecasting, optimize inventory levels, and identify potential supply chain risks.

[0147] In some embodiments, the data layer 620 can incorporate strong 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 technologies, both at rest and in transit, protect sensitive supply chain information from unauthorized access. Furthermore, the data layer 620 can implement data lineage and audit trail mechanisms, allowing users to track the origin and history of data, ensuring data integrity and compliance with regulatory requirements.

[0148] In some embodiments, the data layer 620 can be deployed in a cloud-native environment using containerization technologies (such as Docker) and orchestration frameworks (such as Kubernetes). This approach ensures scalability, elasticity, and efficient resource allocation. For example, the data layer 620 can be deployed on cloud infrastructure provided by AWS, Azure, or Google Cloud, leveraging its managed services and scalable storage options. This allows for efficient resource scaling based on demand, minimizes operational overhead, and provides a resilient infrastructure for managing supply chain data.

[0149] The data layer 620 of the RTDM module 600 can combine a highly scalable data lake (data lake 622) and purpose-built PDSs (PDSs 624.1 to 624.N), and employ a CDC mechanism to ensure efficient data management, standardization, and real-time availability. In a non-limiting example, the data layer 620 can be implemented using any appropriate technology (such as .NET or Java) and / or a distributed computing framework (such as Apache Spark), enabling powerful data processing, advanced analytics, and machine learning capabilities. Utilizing strong data governance and security measures, the data layer 620 ensures data integrity, confidentiality, and compliance. Through its scalable infrastructure and integration with existing systems, the data layer 620 enables supply chain users to make data-driven decisions, optimize operations, and drive business success in dynamic and complex distribution environments.

[0150] The RTDM module 600 may include an AI module 630 configured to implement one or more algorithms and machine learning models to analyze the data stored in the data layer 620 and derive meaningful insights. In some non-limiting examples, the AI ​​module 630 may apply predictive analytics, anomaly detection, and optimization algorithms to identify patterns, trends, and potential risks within the supply chain. The AI ​​module 630 may continuously learn from new data inputs and adapt its models to provide accurate and current insights. The AI ​​module 630 may generate predictions, recommendations, and alerts, and publish such insights to dedicated data feeds.

[0151] The data engine layer 640 comprises a set of interconnected systems responsible for data ingestion, processing, conversion, and integration. The data engine layer 640 of the RTDM module 600 may include a collection of autonomously operating headless engines 640.1 to 640.N. These engines represent different functions within the system and may include, for example, one or more recommendation engines, insight engines, and subscription management engines. Engines 640.1 to 640.N can use standardized data stored in the data grid to deliver specific business logic and services. Each engine can be configured to be pluggable, allowing for flexibility and future expansion of module capabilities. Figure 5 An exemplary engine is shown in , which is not meant to be limiting. Any additional headless engines may be included in the data engine layer 640 or other exemplary layers of the disclosed system.

[0152] These systems can be configured to ingest data from multiple sources, such as transaction systems, IoT devices, and external data providers. The data ingestion process involves extracting data from these sources and converting it into a standardized format. Data processing algorithms can be applied to cleanse, aggregate, and enrich the data, making it ready for further analysis and integration.

[0153] Furthermore, to facilitate integration and access to the RTDM module 600, a data distribution mechanism 645 can be employed. The data distribution mechanism can be configured to include one or more APIs to facilitate distribution of data from the data grid and engine to various endpoints, including user interfaces, micro frontends, and external systems.

[0154] The Experience Layer 650 focuses on delivering an intuitive and user-friendly interface for interacting with supply chain data. This layer can include data visualization tools, interactive summary tables, and user-centric functionality. Through this layer, users can retrieve and analyze real-time data related to various supply chain metrics, such as inventory levels, sales performance, and customer demand. The User Experience Layer supports personalized data feedback, allowing users to customize their views and receive relevant updates based on their roles and responsibilities. Users can subscribe to specific data updates tailored to their preferences and roles, such as inventory changes, pricing updates, or new SKU notifications.

[0155] Thus, in some embodiments, the RTDM module 600 for supply chain and distribution management can include integration with systems of record and include one or more of a data layer with a data grid and purposeful data storage, an AI component, a data engine layer, and a user experience layer. These components work together to provide users with intuitive access to real-time supply chain data, data processing and analysis, and efficient integration with existing enterprise systems. Technical feedback and retrieval within the module ensure that users can retrieve relevant, current information and insights to make informed decisions and optimize supply chain operations. Thus, the RTDM module 600 facilitates supply chain and distribution management by providing a scalable, real-time data management solution. Its innovative architecture enables rich integration of disparate data sources, efficient data standardization, and advanced analytical capabilities. The module's ability to replicate and standardize data from different ERP systems while maintaining auditable and repeatable transactions offers significant advantages in achieving a unified view of vendors, distributors, customers, end customers, and other entities within the distribution system, including IT distribution systems.

[0156] Automatic AAS model conversion

[0157] In one embodiment, Figure 7 A system 700 for converting a technology product into an AaS model is depicted. The system 700 includes a real-time data grid 710, a single pane of glass user interface (SPoG UI) 705, an advanced analytics and machine learning (AAML) module 715, and an AaS conversion module 720.

[0158] In some embodiments, the SPoG UI 705 (which may be an embodiment of the SPoG UI described above) may be enhanced with a one-click conversion feature, allowing users to instantly convert their shopping cart to a subscription-based service model, thereby simplifying the transition to a monthly payment plan and enhancing the user experience.

[0159] RTDM 710 aggregates and standardizes real-time data from various sources, which is critical to the efficient operation of the AaS transformation module 720. This includes data about product specifications, subscription usage patterns, and market trends. RTDM 710 establishes a centralized, unified data hub that aggregates and standardizes data from multiple sources such as ERP, CRM systems, and market intelligence. It utilizes a combination of data warehouses and data lakes to efficiently process both structured and unstructured data. RTDM 710 uses ETL processes and data normalization techniques to ensure data uniformity and accessibility. This standardized data is critical to the function of the AaS transformation module 720, providing the necessary input for the accurate and efficient conversion of products into subscription-based services. RTDM 710 maintains data integrity and relevance, which is critical to the automated process of AaS transformation. In some embodiments, RTDM 710 is configured to interface with an asset management system, supporting service providers' ownership of physical assets while allowing users to access and utilize these assets under a comprehensive service agreement.

[0160] The AAML module 715 serves as the central processing unit of the AaS conversion process. It contains specialized rules and algorithms designed for key tasks in the AaS conversion, such as analyzing product compatibility for service conversion, optimizing subscription models, and applying dynamic pricing strategies. The AAML module 715 uses analytical tools for big data processing and deep learning. It performs sentiment analysis, trend forecasting, and behavioral analysis to understand and anticipate market and user needs. The AAML module 715 integrates and trains machine learning algorithms based on historical data sets to identify usage patterns and provide predictive recommendations for subscription models. It adjusts its algorithms based on a continuous feedback loop, thereby improving its accuracy over time. This module performs functions that are critical to automating the AaS conversion process, ensuring that services meet individual user preferences and market conditions.

[0161] In one embodiment, the AaS transformation module 720 processes data for converting traditional technology products into subscription-based services. It integrates data from RTDM 710 on product specifications, subscription usage patterns, and market trends. Module 720 analyzes product-service compatibility and leverages information from AAML 715 for dynamic pricing. It performs product-to-service conversion, ensuring consistency with market data and user preferences. The AaS transformation module 720 incorporates methods for converting traditional one-time purchase technology items into subscription-based services. For example, such a mechanism can convert a complete shopping cart into a service model, thereby facilitating the transition from capital expenditure (CapEx) to operating expenditure (OpEx). In some embodiments, the AaS transformation module 720 is configured to bundle various technology offerings (including hardware, software, cloud services, and warranties) into a single, unified subscription package, thereby enhancing the value proposition and streamlining the user experience.

[0162] Service Analysis Submodule 725: Submodule 725 evaluates a product's compatibility with subscription models. It processes data from RTDM 710, focusing on market demand analysis, user preference assessment, and technical feasibility studies. It applies analytical tools to assess a product's suitability within the subscription model and evaluate service quality standards.

[0163] The subscription pricing engine 730 uses an algorithm to dynamically price subscription services. It processes market data and user engagement metrics from RTDM 710 and applies predictive models from AAML 715. The engine adjusts prices for different customer profiles, taking into account market conditions, to maintain the financial viability of the subscription services. In some embodiments, the subscription pricing engine 730 can implement a dynamic pricing algorithm that adjusts fees based on actual service usage, such as reducing costs during periods of reduced usage (e.g., vacation time), thereby reflecting a true consumption-based pricing model.

[0164] The service configuration engine 735 facilitates subscription service selection and customization for users. It integrates with the SPoG UI 705 and provides configuration options based on RTDM 710 data, including real-time service availability and user preferences. In some embodiments, the service configuration engine 735 may include a flexibility module that enables users to switch between different services and tiers, such as modifying subscription levels or transitioning between various streaming services. This module ensures adaptability to user needs and market changes.

[0165] The subscription package generator 740 compiles service packages by matching the inventory and service capabilities from the RTDM 710 with user requirements. It uses RESTful APIs and data connectors for real-time data synchronization to optimize subscription package configuration.

[0166] The dynamic pricing calculator 745 calculates the subscription package cost. It retrieves pricing data and promotion details from RTDM 710 to apply user-specific discounts. The calculator ensures competitive and personalized pricing for each subscription package.

[0167] The Error Checking Integrator 780 validates subscription configurations and selections against predefined criteria from the AAML 715. It ensures that subscription packages are error-free and consistent before their finalization, employing algorithms for accuracy verification.

[0168] System 700 can be configured to integrate multiple layers of management modules capable of handling complex interactions across hardware, software, and cloud service layers. This can include robust API integration and efficient lifecycle management to ensure seamless service delivery. In some embodiments, system 700 can include advanced tools for managing the technical and administrative challenges of subscriptions (including billing management, service term synchronization (co-terming), and dynamic adjustments to changes in subscription terms and conditions) via SPoG 705.

[0169] Thus, the system 700 is configured to integrate data from multiple sources into a unified interface via the SPoG UI 705, automate various tasks in the AaS transformation process via AAML 715, and maintain a real-time, standardized data repository via RTDM 710. This architecture enables an efficient and accurate process for transforming any technology product into an AaS model, thereby enhancing the overall customer experience in the IT distribution industry.

[0170] Figure 8 A flowchart of a method 800 for an AaS conversion process according to an embodiment of the present disclosure is shown. The flowchart describes operations from initiation to completion, highlighting the AaS conversion module 720 in the system 700.

[0171] In operation 801, the user begins the conversion process by interacting with the Single Pane of Glass User Interface (SPoG UI) 705. Operation 801 includes collecting user inputs, such as product selections and service requirements. The SPoG UI 705 then communicates with the Advanced Analytics and Machine Learning (AAML) module 715 to analyze these inputs.

[0172] In operation 802, the AAML module 715 performs a preliminary analysis to determine the user's specific needs for AaS transition. Algorithms within the AAML 715 evaluate the user's input, taking into account factors such as historical usage patterns and prevailing market conditions, to develop an optimal service transition strategy.

[0173] In operation 803, the process proceeds to the real-time data grid (RTDM) 710. The RTDM 710 uses a RESTful API to retrieve data and collects data related to the user's selection, including current service specifications and product availability.

[0174] In operation 804, the AaS conversion module 720 processes the user's request. This module develops a customized subscription package in conjunction with tools such as the service analysis submodule 725 and the subscription pricing engine 730. The service analysis submodule 725 evaluates product compatibility for the subscription model, while the subscription pricing engine 730 calculates the pricing for the service package.

[0175] In operation 805, the error checking integrator 780 verifies the accuracy and feasibility of the proposed subscription package. The integrator utilizes algorithms from AAML 715 to ensure the integrity and consistency of the subscription service.

[0176] In operation 806 , the system presents the proposed subscription package, including its components and pricing, back to the user on the SPoG UI 705 for review and approval.

[0177] In operation 807, the AaS conversion module 720 employs machine learning models to analyze the conversion process after implementation. These models apply predictive analytics to refine the conversion mechanism to improve future conversions based on updated user data and market trends.

[0178] In operation 808, a logging mechanism within the AaS conversion module 720 records the transaction details, including the user selection and finalized subscription package composition, to facilitate ongoing enhancements to the system.

[0179] In operation 809, the user reviews, adjusts (if necessary), and confirms the subscription package on the SPoG UI 705. User confirmation marks the completion of the AaS conversion process.

[0180] This operational flow integrates SPoG UI 705, RTDM 710, AAML 715, and AaS transformation module 720. Each module fulfills a specific role, thereby collectively automating and refining the AaS transformation process. Alternative embodiments may include variations in machine learning algorithms, data collection techniques, and user interface design to enhance the adaptability and scalability of system 700.

[0181] Figure 9 A flowchart of a method 900 for user interaction and service configuration according to an embodiment of the present disclosure is shown. The flowchart outlines the user's journey from selecting their subscription service to finalizing their subscription service, focusing on their interaction with the SPoG UI 705 and the service configuration engine 735 in the system 700.

[0182] In operation 901, the user initiates the subscription selection process through the SPoG UI 705. This operation involves the user inputting their service preferences and requirements into the interface. The SPoG UI 705 is designed to capture these inputs and transmit them to the service configuration engine 735 for further processing.

[0183] In operation 902, the service configuration engine 735 receives user input from the SPoG UI 705. It analyzes the provided data to propose a series of subscription services that are consistent with the user's stated preferences. The engine utilizes real-time data from the RTDM 710 to ensure that service offerings are current and relevant.

[0184] In operation 903, the service configuration engine 735 presents the user with a selection of customizable subscription options. This step allows the user to review and modify the proposed services to ensure that they are consistent with their specific needs.

[0185] In operation 904, the user interacts with the SPoG UI 705 to adjust and refine their subscription selections. This operation may involve selecting additional features, changing the service level, or modifying other aspects of the subscription package.

[0186] The user's refined selections are returned to the service configuration engine 735 for final processing in operation 905. The engine applies any additional user modifications and prepares the subscription package for final review.

[0187] In operation 906, the error checking integrator 780 evaluates the accuracy and completeness of the configured subscription package. The integrator applies standards from AAML 715 to verify the consistency of the user's selection and the subscription package.

[0188] In operation 907, the SPoG UI 705 displays the finalized subscription package to the user. This includes a detailed breakdown of services, features, and pricing, allowing the user to take a final look before confirming.

[0189] In operation 908, the user confirms the subscription package via SPoG UI 705, or returns to the previous operation to make further adjustments. After confirmation, the subscription process will proceed to completion.

[0190] In operation 909, a recording mechanism within the service configuration engine 735 records the finalized subscription details, including user input and subscription package composition. This data helps in continuous improvement in user interface design and service configuration process.

[0191] Method 900 integrates SPoG UI 705, service configuration engine 735, and error checking integrator 780 in structured streaming. This integration facilitates user-centric subscription configuration and optimization, thereby enhancing the overall user experience within system 700. Alternative embodiments may involve different user interface layouts, configuration algorithms, and validation mechanisms to accommodate different user needs and preferences.

[0192] Figure 10 A flowchart of a method 1000 for data management and analysis workflow according to an embodiment of the present disclosure is depicted. The flowchart explains the process of aggregating, normalizing, and managing real-time data by RTDM 710, and the subsequent utilization of the data by AaS transformation module 720 and AAML 715.

[0193] In operation 1001, the RTDM 710 initiates the data aggregation process. This involves collecting data from various sources, including ERP, CRM systems, and external market intelligence platforms. This operation focuses on collecting a range of data related to the AaS transformation process, such as product specifications, user interaction data, and market trends.

[0194] In operation 1002, RTDM 710 standardizes the aggregated data. It uses an ETL (extract, transform, load) process to uniformly format the data, ensuring consistency across various data types and sources. This step is crucial for maintaining data integrity and facilitating effective data analysis.

[0195] In operation 1003, RTDM 710 processes the standardized data using data normalization techniques. This process involves adjusting the data to fit a common scale without distorting differences in the range of values. This ensures that the data is ready for detailed analysis and processing by AaS transformation module 720 and AAML 715.

[0196] In operation 1004, the processed data flows to the AaS conversion module 720. Here, the module analyzes the data to determine the feasibility of converting a particular product or service into an AaS model. It evaluates various factors such as market feasibility, user demand, and technical compatibility.

[0197] In operation 1005, AAML 715 receives data from RTDM 710 for advanced analysis. AAML 715 applies machine learning algorithms and predictive analytics to the data to extract insights related to dynamic pricing strategies, user behavior patterns, and market trends.

[0198] In operation 1006, the AaS transformation module 720 refines the AaS transformation process using the insights provided by the AAML 715. It combines the analytical results to optimize the service packages and pricing models so that they are consistent with current market conditions and user expectations.

[0199] In operation 1007, RTDM 710 updates its data repository with new insights and analysis results from AAML 715 and AaS transformation module 720. This continuous feedback loop ensures that RTDM 710 maintains an up-to-date and relevant data pool, which is critical to the continued effectiveness of the AaS transformation process.

[0200] Method 1000 presents a comprehensive workflow that integrates RTDM 710, AaS transformation module 720, and AAML 715. This workflow is critical in managing and analyzing data, directly impacting the efficiency of the AaS transformation process in system 700. Alternative embodiments may incorporate different data processing techniques, analysis algorithms, and data flow structures to meet evolving data management needs and transformation strategies.

[0201] Figure 11 A block diagram of example components of a device 1100 is depicted. One or more computer systems 1100 can be used, for example, to implement any of the embodiments discussed herein, as well as any of their combinations and subcombinations. The computer system 1100 can include one or more processors (also referred to as central processing units or CPUs), such as a processor 1104. The processor 1104 can be connected to a communication infrastructure or bus 1106.

[0202] The computer system 1100 may also include user input / output device(s) 1103 , such as a monitor, keyboard, pointing device, etc., which may communicate with the communication infrastructure 1106 through the user input / output interface(s) 1102 .

[0203] One or more processors 1104 may be a graphics processing unit (GPU). In one embodiment, a GPU may be a processor that can be a specialized electronic circuit configured to process mathematically intensive applications. A GPU may have a parallel architecture that can efficiently process large blocks of data (common mathematically intensive data such as computer graphics applications, images, and videos).

[0204] The computer system 1100 may also include a main memory or main storage 1108, such as random access memory (RAM). The main storage 1108 may include one or more levels of cache. The main storage 1108 may store control logic (ie, computer software) and / or data.

[0205] Computer system 1100 may also include one or more secondary storage devices or memories 1110. Secondary storage 1110 may include, for example, a hard drive 1112 and / or a removable storage device or drive 1114.

[0206] Removable storage drive 1114 can interact with removable storage unit 1118. Removable storage unit 1118 can include a computer-usable or readable storage device having computer software (control logic) and / or data stored thereon. Removable storage unit 1118 can be a program cartridge and cartridge interface (such as that found in video game devices), a removable memory chip (such as an EPROM or PROM) and associated sockets, a memory stick and USB port, a memory card and associated memory card slot, and / or any other removable storage unit and associated interface. Removable storage drive 1114 can read from and / or write to removable storage unit 1118.

[0207] Secondary storage 1110 may include other devices, equipment, components, tools, or other pathways for allowing computer system 1100 to access computer programs and / or other instructions and / or data. Such devices, equipment, components, tools, or other pathways may include, for example, a removable storage unit 1122 and an interface 1120. Examples of removable storage unit 1122 and interface 1120 may include a program cartridge and cartridge interface (such as that found in video game devices), a removable memory chip (such as an EPROM or PROM) and an associated socket, a memory stick and a USB port, a memory card and an associated memory card slot, and / or any other removable storage unit and associated interface.

[0208] The computer system 1100 may also include a communication or network interface 1124. The communication interface 1124 may enable the computer system 1100 to communicate and interact with any combination of external devices, external networks, external entities, and the like (individually and collectively referred to as 1128). For example, the communication interface 1124 may allow the computer system 1100 to communicate with an external or remote device 1128 via a communication path 1126, which may be wired and / or wireless (or a combination thereof) and may include any combination of a LAN, a WAN, the Internet, and the like. Control logic and / or data may be transmitted to and from the computer system 1100 via the communication path 1126.

[0209] The computer system 1100 may also be any one of a personal digital assistant (PDA), a desktop workstation, a laptop or notebook computer, a netbook, a tablet computer, a smart phone, a smart watch or other wearable device, an appliance, part of the Internet of Things, and / or an embedded system, to name a few non-limiting examples, or any combination thereof.

[0210] The computer system 1100 can be a client or server accessing or hosting any application and / or data through any delivery paradigm, including but not limited to remote or distributed cloud computing solutions; local or on-premises software ("local" cloud-based solutions); "as a service" models (e.g., Content as a Service (CaaS), Digital Content as a Service (DCaaS), Software as a Service (SaaS), Managed Software as a Service (MSaaS), Platform as a Service (PaaS), Desktop as a Service (DaaS), Framework as a Service (FaaS), Backend as a Service (BaaS), Mobile Backend as a Service (MBaaS), Infrastructure as a Service (IaaS), etc.); and / or a hybrid model including any combination of the foregoing examples or other services or delivery paradigms.

[0211] Any available data structures, file formats, and schemas in the computer system 1100 may be derived from standards including, but not limited to, JavaScript Object Notation (JSON), Extensible Markup Language (XML), another markup language (YAML), Extensible Hypertext Markup Language (XHTML), Wireless Markup Language (WML), message bundles, XML User Interface Language (XUL), or any other functionally similar representation (alone or in combination). Alternatively, proprietary data structures, formats, or schemas may be used exclusively or in combination with known or open standards.

[0212] In some embodiments, a tangible, non-transitory device 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 1100, main memory 1108, secondary memory 1110, and removable storage units 1118 and 1122, as well as tangible articles of manufacture implementing any combination of the foregoing. When executed by one or more data processing devices (such as computer system 1100), such control logic may cause such data processing devices to operate as described herein.

[0213] Figures 12A to 12Q Depicts the various screens and features of the SPoG UI related to seller login, partner summary form, customer shopping cart, order summary, SKU generation, order tracking, shipment tracking, subscription history, and subscription modification. A detailed description of each diagram is provided below:

[0214] Figure 12A Depicted is a merchant login splash screen, which represents the initial step of the merchant login process. It provides a form or interface in which merchants can express their interest in joining the distribution ecosystem, and merchants can enter their basic information such as company details, contact information, and product catalog.

[0215] Figure 12B Depicts a vendor onboarding guide that shows a step-by-step guide or checklist for vendors to follow during the onboarding process. It outlines the necessary tasks and requirements, ensuring that the vendor clearly understands the onboarding process and can proceed smoothly.

[0216] Figure 12C Depicts the seller onboarding call scheduler, which helps schedule calls or meetings between sellers and platform employees or representatives who will guide them through the onboarding process. Sellers can select a suitable time slot or request a call, ensuring effective communication and assistance throughout the onboarding journey.

[0217] Figure 12D Depicted is a vendor login task list that presents a comprehensive task list or summary table outlining the specific steps and actions required for a successful vendor login. It provides an overview of pending tasks, completed tasks, and upcoming deadlines, helping vendors track their progress and ensure timely completion of each login task.

[0218] Figure 12E Depicts the seller login completion screen confirming the successful completion of the seller login process. It can display a congratulatory message or a summary of the completed tasks, indicating that the seller is now officially logged into the distribution ecosystem.

[0219] Figure 12F Depicts a partner summary table that provides partners or users with a centralized view of relevant information and metrics related to their partnership with the distribution ecosystem. It provides an overview of performance indicators, key data points, and actionable insights to facilitate effective collaboration and decision-making.

[0220] Figure 12G Depicts a customer's product shopping cart representing a customer's product shopping cart, where they can add items they want to purchase. It displays a list of selected products, quantities, prices, and other relevant details. Customers can review and modify their shopping cart contents before proceeding to the checkout process.

[0221] Figure 12HDepicts a customer subscription shopping cart that allows customers to manage their subscription-based purchases. It displays the selected subscription plan, pricing, and duration. Customers can review and modify their subscription details before finalizing their selection.

[0222] Figure 12I Depicts a customer order summary that provides a summary of the customer's order, including details such as the product or subscription purchased, quantity, pricing, and any applied discounts or promotions. It allows customers to review their order before confirming their purchase.

[0223] Figure 12J A vendor SKU generation screen for generating unique stock keeping unit (SKU) codes for vendor products is depicted. It may include fields or options where the vendor can specify product details, attributes, and pricing, and the system automatically generates the corresponding SKU code.

[0224] Figure 12K and Figure 12L Summary tables, Order Summaries, are depicted to display summary information about orders placed within the distribution ecosystem. They present key order details such as order number, customer name, product or subscription information, quantity, and order status. Summary tables provide an overview of order activity, enabling users to efficiently track and manage orders.

[0225] Figure 12M Depicts a customer subscription cart that allows customers to add, modify, or remove subscription plans. It displays a list of selected subscriptions, pricing, and renewal dates. Customers can manage their subscriptions and make changes based on their preferences and requirements.

[0226] Figure 12N Depicts the customer order tracking screen that enables customers to track the status and progress of their orders through the supply chain. It displays real-time updates on order fulfillment, including processing, packing, and shipping. Customers can monitor the progress of their orders and anticipate delivery times.

[0227] Figure 12O Depicts customer shipment tracking that provides customers with real-time tracking information about their shipments. It can include details such as the carrier, tracking number, current location, and estimated delivery date. Customers can stay informed about the whereabouts of their shipments.

[0228] Figure 12P Depicts a customer subscription history that presents a history 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 reference previous subscription details.

[0229] Figure 12Q Depicts the customer subscription modification dialog that 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 based on their evolving needs or preferences.

[0230] The UI screens depicted are not limiting. In some embodiments, Figures 12A to 12Q The UI screens collectively present the various functions and features available through the SPoG UI, providing users with a comprehensive and user-friendly interface for seller onboarding, partnership management, customer interaction, order management, subscription management and tracking within the distribution ecosystem.

[0231] It should be understood that the Detailed Description section, and not the Summary and Abstract sections, is intended to be used to interpret the claims. The Summary and Abstract sections may set forth one or more, but not all, exemplary embodiments of the present invention as contemplated by the inventor(s), and thus, are not intended to limit the present invention and the appended claims in any way.

[0232] The present invention has been described above by means of functional building blocks illustrating embodiments of specified functions and relationships thereof. For ease of description, the boundaries of these functional building blocks have been arbitrarily defined herein. Alternative boundaries may be defined as long as the specified functions and relationships thereof are appropriately performed.

[0233] The foregoing description of specific embodiments will fully reveal the general nature of the invention so that others can easily make modifications and / or adjustments for various applications (such as specific embodiments) by applying the knowledge of those skilled in the art without excessive experimentation without departing from the overall concept of the invention. Therefore, based on the teachings and guidance presented herein, such adjustments and modifications are intended to be within the meaning and range of equivalents of the disclosed embodiments. It should be understood that the wording or terminology herein is for descriptive and not limiting purposes, so that the terms or wording of this specification are interpreted by the skilled person in accordance with the teachings and guidance.

[0234] The breadth and scope of the present invention should not be limited by any of the above-described exemplary embodiments, but should be defined only in accordance with the following claims and their equivalents.

Claims

1. A computerized method for performing an AaS model transformation, wherein: The computerized method comprises: receiving user input specifying preferences for technology product conversions; accessing a real-time data grid (RTDM) to retrieve data related to the user's preferences and market conditions; Utilizing advanced analytics and machine learning (AAML) modules to analyze the user input and market data for suitability in an AaS model; Generate AaS conversion recommendations through the AaS conversion module; Displaying AaS options to the user via a single pane of glass user interface (SPoG UI); Facilitate the completion of the AaS conversion process and transfer the order to the seller's system; and Execute AaS conversion orders by integrating data from the SPoG UI, RTDM and vendor systems, The method is performed by a computer system having a unified platform that integrates data from multiple sources for AaS transformation.

2. The method according to claim 1, wherein The method also includes validating the AaS transformation using rules and algorithms within the AAML module to ensure the accuracy and relevance of the transformation recommendation.

3. The method according to claim 1, wherein The AAML module utilizes a dynamic machine learning algorithm that adapts to changing user preferences and market conditions for efficient AaS conversion.

4. The method according to claim 1, wherein The RTDM is continuously updated with real-time inventory, user behavior data, and market trends to inform the AaS transformation process.

5. The method according to claim 1, wherein The method also includes generating real-time reports related to the AaS conversion process, including user engagement metrics and conversion success rates.

6. The method according to claim 1, wherein The vendor system used to implement the AaS transition is selected based on criteria including service availability and capabilities.

7. The method according to claim 1, wherein The method further includes sending a notification to the user upon successful completion and confirmation of the AaS conversion order.

8. A computerized method for optimizing AaS conversion decisions, wherein: The computerized method comprises: Initiate a subscription request via the SPoG UI; Retrieve user preferences and historical data for AaS conversion; Querying RTDM to obtain real-time data related to the AaS transformation; Apply predictive analytics through the AAML module to determine the optimal subscription model; Configure subscription packages based on user preferences and available data; Validate subscription configuration using the AAML module; Presenting the final subscription package to the user via the SPoG UI; Record the details of the AaS transformation process for future analysis and system refinement; and A feedback loop is initiated within the system to continuously improve the AaS transformation.

9. The method according to claim 8, wherein The method also includes utilizing a machine learning algorithm in the feedback loop to analyze user feedback and system performance to continuously optimize the AaS conversion process.

10. The method according to claim 8, wherein The RTDM acquires real-time data based on current market conditions and service availability.

11. The method according to claim 8, wherein The method also includes generating real-time reports related to the AaS conversion process, including metrics such as user satisfaction and service subscription levels.

12. The method according to claim 8, wherein Product selection for AaS subscriptions is performed based on predefined criteria including user preferences, market trends, and service compatibility.

13. The method according to claim 8, wherein The method further includes sending a notification to the user after the AaS subscription package is successfully generated and available.

14. The method according to claim 8, wherein Said feedback loop for AaS conversion decisions is conducted within a defined timeframe based on user engagement and system analysis.

15. A system for automating an AaS transition process, wherein: The system comprises: A real-time data grid configured to aggregate and disseminate data including user preferences, market trends, and service information; A single pane of glass user interface that enables user interaction and displays subscription options; Advanced analytics and machine learning modules that process data and generate intelligent AaS conversion recommendations; and AaS conversion module, which interacts with SPoG UI and RTDM to perform the conversion process including user preference analysis, service selection and subscription package generation.

16. The system according to claim 15, wherein: The AaS conversion module also includes a logging mechanism for tracking user interactions and subscription selections for auditing and analytical purposes.

17. The system according to claim 15, wherein: The AaS transformation module is integrated with the AAML module for validation and optimization purposes, thereby refining subscription recommendations using algorithms stored in the AAMI module.

18. The system according to claim 15, wherein: The SPoG UI is designed to be accessible and responsive across a variety of devices, providing an integrated user experience for subscription customization.

19. The system of claim 15, wherein: The RTDM is configured to normalize and harmonize data from different sources, making it suitable for consumption and analysis by the SPoG UI and other system modules.

20. The system of claim 15, wherein: The system also includes a machine learning model within the AaS conversion module that is configured to continuously refine the conversion process based on user feedback and evolving market data.

Images