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

Abstract

To provide a computerized system and method for converting traditional technology products into an "As a Service" (AaS) model, facilitating transition from capital expenses (CapEx) to operational expenses (OpEx).SOLUTION: A method includes receiving user inputs for technology product selections and accessing a real-time data mesh (RTDM) 310 to retrieve data. An advanced analytics and machine learning (AAML) module 315 analyzes the user inputs and market data, optimizing conversion into subscription-based services. Process results are displayed to a user through a single pane of glass user interface (SPoG UI) 305. An AaS conversion module performs transition of products into customizable subscription packages.SELECTED DRAWING: Figure 3

Description

[Technical Field]

[0001] (CROSS-REFERENCE TO RELATED APPLICATIONS) This application is a continuation-in-part (CIP) of U.S. Patent Application No. 18 / 341,714, filed June 26, 2023, and 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, and U.S. Provisional Application No. 63 / 515,076, filed July 21, 2023. Each of these applications is incorporated herein by reference in its entirety.

[0002] (background) Traditional ordering processes in distribution and supply chain platforms are plagued by inefficiencies, delays, and inaccuracies. In traditional environments, multiple systems and vendors typically perform each activity independently, from creating bills of materials to registering transactions, applying pricing, generating quotes, and issuing orders. This approach leads to operational inefficiencies and increased potential for errors.

[0003] Enterprise resource planning (ERP) systems have served as the workhorse in managing business processes, including distribution and supply chains. These systems act as a central repository where different departments, such as finance, human resources, and inventory management, can access and share real-time data. While ERP is comprehensive, it comes with several challenges in today's complex distribution and supply chain environment. One of the main challenges is data fragmentation. Data silos across different departments or separate ERP systems make it difficult to achieve real-time visibility. Users lack a comprehensive view of key distribution and supply chain metrics, which negatively impacts the decision-making process.

[0004] Furthermore, ERP systems often do not offer effective data integration capabilities. Traditional ERP systems are not designed for efficient integration with external systems or between different modules within the same ERP suite. This design results in cumbersome, error-prone manual processes for transferring data between systems, negatively impacting the flow of information throughout the supply chain. When information exists in different formats across systems, data inconsistencies arise, preventing accurate data analysis and leading to uninformed decision-making.

[0005] Data inconsistencies pose another challenge. When data exists in different formats or units across departments or ERPs, standardizing this data for meaningful analysis can be a tedious process. Businesses often rely on time-consuming manual processes to convert and validate data, further delaying decision-making. Additionally, legacy ERP systems often lack the ability to effectively handle large amounts of data. These systems struggle to provide timely insights for operational improvement, which is particularly problematic for businesses dealing with complex and expanding distribution and supply chain networks.

[0006] Data security is another concern, especially considering the sensitivity of supply chain data, which includes customer details, pricing, and contracts. Ensuring compliance with global regulations on data security and governance adds another layer of complexity. Traditional ERP systems often lack robust enough security features to adapt to the ever-evolving landscape of cybersecurity threats and compliance requirements. Summary of the Invention

[0007] The automated "as-a-service" (AaS) model conversion process is designed to address gaps in the technology distribution industry by integrating disparate systems and activities into a unified interface, enabling the conversion of technology products, such as hardware, software, and cloud services, to a subscription-based AaS model. This transformation from a capital expense to an operational expense eases 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 effectively integrates and accelerates the conversion process while ensuring data security and compliance.

[0008] 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 becoming increasingly inadequate, especially due to shifts in consumer expectations and regulations. 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.

[0009] According to some embodiments, the conversion 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 conversion module that is integrated with a real-time data mesh (RTDM) and a single pane of glass user interface (SPoG UI) to optimize the delivery of subscription-based services. Advanced algorithms are used to adapt the delivery based on real-time market data and customer usage patterns, improving the flexibility and scalability of service options.

[0010] In non-limiting examples, subscription recommendation engines employ advanced algorithms to provide users with dynamic, usage-based service options, and dynamic pricing engines use 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.

[0011] In one embodiment, a subscription management and real-time pricing module operatively connected with the RTDM and SPoG UI manages the lifecycle of subscriptions. The module optimizes service options based on real-time data and uses algorithms to dynamically adjust pricing and service configurations. The system includes a pricing engine for cost prediction and adapts to variables such as usage intensity and market trends.

[0012] In some embodiments, the system allows users to convert their selections to a subscription model with a single click via the SPoG UI and includes modules for checking user permissions and aggregating configuration options based on current offerings, facilitating the subscription management process.

[0013] 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 to ensure consistent and up-to-date information across subscription models.

[0014] The embodiments disclosed herein integrate multiple systems, automate processes, and validate and automate the conversion of technology products into subscription-based services. By implementing intelligent rules and validations, the system efficiently performs complex tasks, reducing time and errors. The adaptability of the system ensures it remains current and evolves to meet market and customer demands.

[0015] The system uses a data-driven methodology to automate the creation and management of subscription packages based on users' consumption patterns. This includes assembling various technology products and services into coherent subscriptions that align with individual usage patterns and preferences. The system generates user profiles based on comprehensive data analysis that encompasses 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 needs.

[0016] This process automatically generates personas based on comprehensive market research and real user data, encompassing a wide range of attributes including demographics, purchasing patterns, digital engagement, and preferences across various product categories. Identifying personas enables a granular understanding of customer needs in areas such as technology, software applications, cloud computing solutions, and hardware requirements.

[0017] The system incorporates advanced algorithms to analyze user data, including usage history and interaction patterns, to identify user preferences and predict needs. This facilitates the creation of relevant and compelling subscription packages. Automated subscription bundling integrates products and services from different categories, ensuring each package meets the user's technical and service needs. Automated AaS subscription generation can include combinations of hardware, compatible software solutions, and cloud services, designed to improve user productivity and efficiency.

[0018] Single Pane of Glass Single Pane of Glass (SPoG) can provide a comprehensive solution aimed at addressing these multifaceted challenges. It can be configured to provide a holistic, user-friendly, and efficient platform that expedites the distribution process.

[0019] 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 analytics, SPoG can provide valuable insight into inventory levels and product status, ensuring supply chain and distribution management processes are handled efficiently.

[0020] According to some embodiments, SPoG can consolidate multiple touchpoints into a single platform, emulating a direct consumer channel into a distribution platform. This consolidation provides a unified direct channel for consumers to interact with distributors, significantly reducing supply chain complexity and improving the overall customer experience.

[0021] SPoG provides innovative solutions for improved inventory management through advanced forecasting capabilities. These predictive analytics highlight demand trends and guide companies to manage inventory more efficiently, reducing the risk of stock-outs or overstocks.

[0022] According to some embodiments, the SPoG can include a global compliance database that is updated in real time, allowing distributors to stay abreast of the latest international regulations. This feature significantly reduces the burden of manual tracking and ensures smooth and compliant cross-border transactions.

[0023] According to some embodiments, to facilitate as-a-service (AaS) conversions, SPoG consolidates data from various OEMs into a single platform. This not only ensures data consistency but also significantly reduces the chance for errors. Furthermore, it provides the ability to efficiently manage AaS conversions, thereby providing capabilities aligned with specific market needs and requirements.

[0024] According to some embodiments, SPoG is a highly configurable and user-friendly platform whose intuitive interface allows users to easily access and purchase technology, thereby meeting the expectations of a new generation of technology buyers.

[0025] Additionally, SPoG's advanced analytical capabilities provide valuable insights that can drive strategy and decision-making. Trends can be tracked and analyzed in real time, enabling companies to stay ahead of the curve and adapt to changing market conditions.

[0026] SPoG's flexibility and scalability make it a future-proof solution, able to adapt to changing business needs and allowing companies to scale operations up or down as needed without making major changes to their infrastructure.

[0027] SPoG's innovative approach to solving distribution industry challenges is an invaluable tool. By increasing supply chain visibility, facilitating inventory management, ensuring compliance, simplifying AaS conversions, and delivering a superior customer experience, it offers a comprehensive solution to the complex problems that have long plagued the distribution industry. Through implementation, distributors can expect to see increased efficiency, reduced errors, and improved customer satisfaction, leading to sustainable growth in an ever-evolving global marketplace.

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

[0029] RTDM's predictive analytics capabilities provide a solution for efficient inventory control. By providing insight into demand trends, it helps companies manage their inventory and reduces the risk of overstocking or running out of stock.

[0030] RTDM's global compliance database is updated in real time, ensuring distributors are up to date with international regulations, significantly reducing the burden of manual tracking and enabling cross-border transactions.

[0031] RTDM also simplifies AaS conversion by consolidating data from various OEMs, ensuring data consistency and reducing the chance of errors. Its ability to manage product and market data efficiently aligns with specific market needs.

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

[0033] Benefits of SPoG and RTDM Integration Integrating the SPoG platform with RTDM offers numerous benefits. First, it provides a holistic solution to a long-standing problem in the distribution industry. RTDM's capabilities enable SPoG to improve supply chain visibility, facilitate AaS conversions, and deliver a superior customer experience.

[0034] The real-time tracking and analytics provided by RTDM improves SPoG's ability to effectively manage its supply chain and inventory, providing accurate, current information that enables distributors to make informed decisions quickly.

[0035] Integrating SPoG with RTDM also ensures data consistency and reduces errors in AaS conversions, and provides a centralized platform for managing data from various OEMs, simplifying product localization and helping to meet market needs.

[0036] RTDM's global compliance database is integrated with SPoG to facilitate compliant cross-border transactions and reduce the burden of manual tracking, saving significant time and resources.

[0037] In some embodiments, a distribution platform incorporates SPoG and RTDM to provide an improved, comprehensive distribution system that can leverage the advantages of the distribution model, address its existing challenges, and position it for sustained growth in a constantly evolving global marketplace. [Brief explanation of the drawings]

[0038] [Figure 1] 1 illustrates one embodiment of an operating environment for a distribution platform, referred to herein as a system. [Figure 2] 2 illustrates one embodiment of an operating environment for a distribution platform built with the elements introduced in FIG. 1. [Figure 3] 1 illustrates one embodiment of a system for distribution management. [Figure 4] 1 illustrates a system for an automated AaS conversion process according to one embodiment. [Figure 5] 1 illustrates an RTDM module according to one embodiment. [Figure 6] 1 illustrates a SPoG UI according to one embodiment. [Figure 7] 1 illustrates a system for automated AaS conversion according to one embodiment. [Figure 8] FIG. 1 is a flow diagram of a method for an automated AaS conversion process according to some embodiments of the present disclosure. [Figure 9] FIG. 1 is a flow diagram of automatic service configuration in an AaS conversion system, according to some embodiments of the present disclosure. [Figure 10] FIG. 1 is a flow diagram of an automated data management and analysis process in an AaS conversion system, according to some embodiments of the present disclosure. [Figure 11] FIG. 2 is a block diagram of exemplary components of a device according to some embodiments of the present disclosure. [Figures 12A-12Q] 1 illustrates various screens and functionality of the SPoG UI, according to some embodiments. DETAILED DESCRIPTION OF THE INVENTION

[0039] The present embodiments may be implemented in hardware, firmware, software, or any combination thereof. The present embodiments may also be implemented as instructions stored on a machine-readable medium, which may be read and executed by one or more processors. A machine-readable medium may include any mechanism for storing or transmitting information in a form readable by a machine (e.g., a computing device). For example, a machine-readable medium may include read-only memory (ROM), random-access memory (RAM), magnetic disk storage media, optical storage media, flash memory devices, and others. Furthermore, firmware, software, routines, and instructions may be described herein as performing particular actions. However, it should be understood that such description is merely for convenience and that such actions are actually the results obtained by a computing device, processor, controller, or other device executing the firmware, software, routines, instructions, etc.

[0040] It should be understood that the acts shown in the example methods are not exhaustive and that other acts may similarly occur before, after, or between any of the acts shown. In some embodiments of the present disclosure, acts may be performed in a different order and / or may differ.

[0041] 1 illustrates a distribution platform operating environment 100, referred to in this embodiment as system 110. System 110 operates within the context of an information technology (IT) distribution model and serves the needs of various users, such as customers 120, end customers 130, vendors 140, resellers 150, and other entities involved in the distribution process. This operating environment encompasses a wide range of characteristics and dynamics that contribute to the success and efficiency of the distribution platform.

[0042] 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 diverse range of IT products, such as hardware components, software applications, network equipment, or cloud-based services. System 110 provides customers with a user-friendly interface, allowing them to browse, search, and select the most suitable IT solution based on their requirements. Customers also have access to real-time data and analytics through system 110, enabling them to make informed decisions and optimize their IT infrastructure.

[0043] End customers 130 can be the ultimate beneficiaries of the IT solutions provided by system 110. End customers 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 on the market. System 110 allows end customers to track orders, receive delivery status updates, and access customer support services, thereby enhancing their overall experience.

[0044] Vendors 140 play a key role within the operating environment of system 110. These vendors include manufacturers, distributors, and suppliers that offer a diverse range of IT products and services. System 110 serves as a centralized platform for vendors to showcase their offerings, manage inventory, and facilitate transactions with customers and resellers. Vendors can leverage system 110 to facilitate supply chain operations, manage pricing and promotions, and gain insight into customer preferences and market trends. By integrating with system 110, vendors can expand their reach, access new markets, and improve their overall visibility and competitiveness.

[0045] Resellers 150 can be intermediaries in the distribution model that bridge the gap between vendors and customers. Resellers play a vital role in the IT distribution ecosystem by connecting customers to the right IT solutions from various vendors. Resellers may include retailers, value-added resellers (VARs), system integrators, or managed service providers. System 110 enables resellers to access a comprehensive catalog of IT solutions, manage their sales pipeline, and provide value-added services to their customers. By leveraging system 110, resellers can improve customer relationships, optimize their product offerings, and increase revenue streams.

[0046] Within the operating environment of the system 110, there may be various dynamics and characteristics that contribute to its effectiveness. These dynamics include real-time data exchange, integration with existing enterprise systems, scalability, and flexibility. The system 110 ensures that relevant data is exchangeable between users in real time, enabling accurate decision-making and timely action. Integration with existing enterprise systems, such as enterprise resource planning (ERP) systems, customer relationship management (CRM) systems, and warehouse management systems, enables communication and interoperability, eliminating data silos and enabling end-to-end visibility.

[0047] System 110 provides scalability and flexibility to accommodate the growing demands of IT distribution models, whether they involve a growing customer base, an increasing number of vendors, or a broader 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. Additionally, system 110 leverages a technology stack that includes .NET, Java, and other suitable technologies, providing a solid foundation for its operation.

[0048] In summary, the operating environment of system 110 within the IT distribution model encompasses customers 120, end customers 130, vendors 140, resellers 150, and other entities involved in the distribution process. 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 enables users to optimize operations, improve customer experience, and drive business success within the IT distribution ecosystem.

[0049] Figure 2 illustrates a distribution platform operating environment 200, expanding on the elements introduced in Figure 1. The environment features integration points 210, which enable data flow and connectivity between various systems, such as customer systems 220, vendor systems 240, reseller systems 260, and other entities in the AaS conversion process. Figure 2 illustrates the network interconnectivity and mechanisms that facilitate collaborative, data-driven decision-making for AaS conversion. Operating environment 200 is configured to automate the AaS conversion process using AI and ML techniques to process and analyze data for service transformation.

[0050] Some embodiments of the AaS conversion process involve a systematic approach to converting a legacy product or service to a subscription-based model with minimal manual intervention. This process encompasses the collection of diverse data, including several technical elements: product specifications, user service interactions, and usage patterns. This data is aggregated from sources such as CRM systems and web analytics tools and fed into a real-time data mesh (RTDM). RTDM processes and normalizes this data and serves as a central repository for real-time data updates and searches. The AAML module analyzes this aggregated data to identify optimal strategies for service conversion. Services are segmented based on data-driven prospects and predicted user preferences. The AaS conversion module, informed by the AAML module, configures subscription services for each user or market segment. Predictive models and heuristic algorithms are applied to determine service offerings that fit specific user requirements. Users interact with these services through the SPoG UI to customize and confirm their subscription selections. The system includes a feedback loop that collects and analyzes responses to subscription services to continuously refine the service offerings.

[0051] AI algorithms in the AaS conversion process address inventory management, service customization, and user selection optimization. Machine learning models, such as neural networks and decision trees, refine service offerings. The AaS process uses ML-based algorithms for real-time service composition. Advanced analytics, such as ensemble learning or reinforcement learning, continuously optimize the AaS process. The AI ​​and ML technologies within operating environment 200 employ 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, and optimize decision-making paths through reinforcement learning. The ML component leverages predictive analytics and continuously refines its output by learning new data, improving the accuracy and relevance of service conversions.

[0052] The operating environment 200 includes system 110 as a central hub for managing the AaS conversion process. System 110 serves as a bridge between customer system 220, vendor system 240, reseller system 260, and other entities. It integrates communication, data exchange, and transaction processes to provide a cohesive experience. Additionally, the environment 200 features integration point 210, which uses a hybrid architecture combining RESTful APIs and WebSockets to achieve real-time data exchange and synchronization. This architecture is secured by SSL / TLS protocols to protect data in transit.

[0053] Customer System Integration: Integration point 210 allows system 110 to connect with customer system 220, facilitating efficient data exchange and synchronization. Customer system 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 system 220 enables customers to access real-time information about their AaS subscriptions, including personalized bundles, pricing details, order tracking, and other related data, improving their decision-making capabilities. This integration provides an automated, real-time solution for creating and managing the AaS conversion process, improving operational efficiency for customers.

[0054] Data exchange between customer systems 220, vendor systems 240, and reseller systems 260 is enabled by robust ETL (extract, transform, load) as described below with reference to a real-time data mesh architecture, ensuring data consistency and reliability. This interaction can be governed by predefined business rules and logic that dictate data flow and processing methods. Advanced mapping and transformation tools are employed to harmonize disparate data formats and enable data integration and utilization across these systems. Orchestrated data exchange supports synchronized operations, enabling efficient, informed decision-making across the distribution network.

[0055] Trading Partner System Integration: Integration points 210 allow system 110 to connect to trading partner systems 230, facilitating efficient data exchange and synchronization. These systems contribute to the overall efficiency of the AaS conversion process by providing relevant market and product data.

[0056] Vendor System Integration: Integration point 210 facilitates connectivity between system 110 and vendor system 240. Vendor system 240 may include entities such as vendor system 241, vendor system 242, and vendor system 243, which represent inventory management, pricing systems, and product catalogs. Integration with vendor system 240 ensures that vendors can efficiently update their product offerings and receive real-time notifications, facilitating the AaS conversion process.

[0057] Reseller System Integration: Integration point 210 allows reseller system 260 to connect with system 110. Reseller system 260 encompasses entities such as reseller system 261, reseller system 262, and reseller system 263 that handle sales, customer management, and service delivery. The integration allows resellers to access up-to-date product information and effectively manage customer relationships.

[0058] Other Entity System Integration: Integration points 210 further connect with other entities involved in the distribution process to facilitate collaboration and efficient distribution. This integration ensures real-time data exchange for AaS conversion processing and decision-making in the distribution ecosystem.

[0059] The system 110 configuration includes advanced AI and ML capabilities to automate the AaS conversion process according to individual preferences and ensure relevance and optimization in the distribution process.

[0060] Integration point 210 also enables connectivity with systems of record 280 for additional data management and integration. Representing systems of record 280 can represent enterprise resource planning (ERP) systems or customer relationship management (CRM) systems, including both legacy ERP systems (e.g., SAP, Impulse, META, I-SCALA, etc.) as well as future systems. Systems of record can include one or more storage repositories of critical legacy business data. This facilitates integrated data exchange and synchronization between the distribution platform, system 110, and the ERP, enabling real-time updates and ensuring the availability of accurate, up-to-date information. Integration point 210 establishes connectivity between systems of record 280 and the distribution platform, enabling stakeholders to leverage the rich data stored in the ERP for efficient collaboration, data-driven decision-making, and streamlined distribution processes. These systems represent internal systems utilized by customers, vendors, and others.

[0061] Integration points 210 within 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. System 110 employs industry-standard protocols, such as RESTful APIs, SOAP, or GraphQL, to establish communication channels and enable data exchange.

[0062] In some embodiments, the system 110 may incorporate authentication and authorization mechanisms to ensure secure access and data integrity. Technologies such as OAuth or JSON Web Token (JWT) may be employed to authenticate users, authorize data access, and maintain the integrity and confidentiality of exchanged information.

[0063] In some embodiments, integration points 210 and data flow 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, shipping details, and sales analytics, flows between customer systems 220, vendor systems 240, reseller systems 260, and other entities. This data exchange facilitates real-time visibility, enables data-driven decision-making, and improves operational efficiency across the distribution platform.

[0064] In some embodiments, system 110 utilizes 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 solid foundation for system 110, ensuring scalability, flexibility, and efficient data processing capabilities. Furthermore, integration points 210 may also employ algorithms, data analytics, and machine learning techniques to derive valuable insights, optimize distribution processes, and personalize customer experiences. Integration points 210 and the data flow 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. The integrated data can be processed, harmonized, and made available to relevant users in real time through system 110. This real-time access to accurate and current information enables users to make informed decisions, optimize supply chain operations, and improve customer experiences.

[0065] 2 may include conventional, well-known elements that are only briefly described herein. For example, each of the customer systems, such as customer system 220, may include a desktop personal computer, a workstation, a laptop, a PDA, a mobile phone, or any Wireless Access Protocol (WAP)-enabled device, or any other computing device capable of interfacing directly or indirectly with the Internet or other network connection. Each of the customer systems is typically capable of running an HTTP client, such as Microsoft's Edge browser, Google's Chrome browser, Opera's browser, or a WAP-enabled browser for mobile devices, enabling the customer systems to access, process, and display information, pages, and applications available from the distribution platform over a network.

[0066] Additionally, each customer system may typically be equipped with a user interface device, such as a keyboard, mouse, trackball, touchpad, touchscreen, pen, or similar device for interacting with the graphical user interface (GUI) provided by the browser. These user interface devices enable users of the customer systems to navigate the GUI, interact with pages, forms, and applications, and access data and applications hosted by the distribution platform.

[0067] The customer system and its components may be operator-configurable using applications, including a web browser, running on a central processing unit, such as an Intel Pentium processor or similar processor. Similarly, the distribution platform (system 110) and its components may be operator-configurable using applications running on a central processing unit, such as an Intel Pentium processor or similar processor, and / or a processor system that may include multiple processing units.

[0068] An embodiment of a computer program product includes a machine-readable storage medium containing instructions for programming a computer to perform the processes described herein. The computer code for operating and configuring the distribution platform and customer systems, vendor systems, reseller systems, and systems of other entities to intercommunicate 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.

[0069] Furthermore, computer code for implementing the present embodiments can be transmitted and downloaded from a software source via the Internet or any other conventional network connection using communications media and protocols such as TCP / IP, HTTP, HTTPS, Ethernet, etc. The code can also be transmitted over an extranet, VPN, LAN, or other network and executed on a client system, server, or server system using programming languages ​​such as C, C++, HTML, Java, JavaScript, ActiveX, VBScript, or others.

[0070] It will be appreciated that the present embodiment can be implemented in a variety of programming languages ​​executed on the client system, server, or server system, and the choice of language may depend on the particular requirements and environment of the distribution platform.

[0071] This allows the operating environment 200 to couple the distribution platform with one or more integration points 210 and data flows to enable efficient collaboration and a streamlined distribution process.

[0072] Figure 3 illustrates a system 300 for supply chain and distribution management. System 300 (Figure 3) is a supply chain and distribution management solution configured to address the challenges faced by a fragmented distribution ecosystem in the global distribution industry. System 300 may include several interconnected components and modules that work in harmony to optimize supply chain and distribution operations, improve collaboration, and drive business efficiency.

[0073] The Single Pane of Glass (SPoG) UI 305 serves as a centralized user interface, providing users with a unified view of the entire supply chain. It aggregates information from various sources and presents real-time data, analytics, and functionality tailored to users' specific roles and responsibilities. By providing a customizable and intuitive dashboard-style layout, the SPoG UI enables users to access relevant information and tools to make data-driven decisions and efficiently manage supply chain and distribution activities.

[0074] For example, logistics managers can use the SPoG UI to monitor shipment status, track delivery routes, and view real-time inventory levels across multiple warehouses. They can visualize the data through interactive charts, such as a map showing the current location of each shipment, or a bar graph showing inventory levels by product category. Having a unified view of the supply chain allows logistics managers to identify bottlenecks, optimize routes, and ensure timely delivery of goods.

[0075] SPoG UI 305 integrates with other modules in the system 300 to facilitate real-time data exchange, synchronized operations, and workflows. Through API integration, data synchronization mechanisms, and an event-driven architecture, SPoG UI 305 ensures smooth information flow and enables collaborative decision-making across the distribution ecosystem. Designed with a user-centric approach, SPoG UI 305 features an intuitive and responsive layout. Utilizing front-end technology, it provides dynamic and interactive data visualizations. Customizable dashboards allow users to tailor views based on their 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 allow users to efficiently navigate and access relevant supply chain data and insights.

[0076] For example, when a purchase order is generated in the SPoG UI, the system automatically updates inventory levels, triggers notifications to the warehouse management system, and initiates the shipping process. This integration enables efficient order fulfillment, reduces manual errors, and improves overall supply chain visibility.

[0077] The Real-Time Data Mesh (RTDM) module 310 is another component of the system 300 that is responsible for ensuring the flow of data within the distribution ecosystem, collecting and harmonizing data from multiple sources and ensuring its availability in real time.

[0078] In 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 harmonizing formats, standardizing units of measure, and reconciling inconsistencies. The harmonized data can then be made available in real time, allowing users access to accurate, current information across the supply chain.

[0079] The RTDM module 310 can be configured to capture data changes across multiple transaction systems in real time. It employs an advanced change data capture (CDC) mechanism that constantly monitors transaction systems to detect updates or modifications. The CDC component can be specifically configured to work with a variety of transaction systems, including legacy ERP systems, customer relationship management (CRM) systems, and other enterprise-wide systems, ensuring compatibility and flexibility for business operations in diverse environments.

[0080] Having access to real-time data allows users to make timely decisions and respond quickly to changing market conditions. For example, if the RTDM module detects a sudden spike in demand for a particular product, it can trigger an alert to the production team so that they can adjust production schedules to prevent stockouts.

[0081] The RTDM module 310 facilitates data management within supply chain operations. It enables real-time reconciliation of data from multiple sources, freeing vendors, resellers, customers, and end customers from the constraints imposed by legacy ERP systems. This increased flexibility supports improved efficiency, customer service, and innovation.

[0082] Another component of the system 300 is the Advanced Analytics and Machine Learning (AAML) module 315. Leveraging powerful analytics tools and algorithms such as Apache Spark, TensorFlow, or scikit-learn, the AAML module extracts valuable insights from the collected data, enabling advanced analytics, predictive modeling, anomaly detection, and other machine learning capabilities.

[0083] 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 stock availability during busy periods, and minimize excess inventory costs. By leveraging machine learning algorithms, the AAML module automates repetitive tasks, predicts customer preferences, and optimizes supply chain processes.

[0084] In addition to forecasting demand, the AAML module can provide insight into customer behavior, enabling targeted marketing campaigns and personalized customer experiences. For example, by analyzing customer data, the module can identify cross-selling or up-selling opportunities and recommend relevant products to individual customers.

[0085] Additionally, the AAML module can analyze data from various sources, such as social media feeds, customer reviews, and market trends, to gain a deeper understanding of customer intentions and preferences. This information can be used to inform product development decisions, identify emerging market trends, and adapt business strategies to meet evolving consumer expectations.

[0086] 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 comprehensive and connected distribution ecosystem.

[0087] The implementation and deployment of system 300 can be tailored to meet specific business needs. It can be deployed as a cloud-native solution using containerization technologies such as Docker and orchestration frameworks such as Kubernetes. This approach ensures scalability, easy management, and efficient updates across different environments. The implementation process involves configuring the system to meet specific supply chain requirements, integrating with existing systems, and customizing modules and components based on business needs and preferences.

[0088] The system 300 for supply chain and distribution management is a comprehensive and innovative solution that addresses the challenges faced by a fragmented distribution ecosystem. It combines the power of the SPoG UI 305, RTDM module 310, and AAML module 315, along with integration with existing systems. By leveraging a diverse technology stack, scalable architecture, and robust integration capabilities, the system 300 provides end-to-end visibility, data-driven decision-making, and optimized supply chain operations. The examples and options provided herein are non-limiting and can be customized to meet specific industry requirements, driving efficiency and success in supply chain and distribution management.

[0089] 4 illustrates one embodiment of a system 400 for automated conversion of technology products to an "as a service" (AaS) model, incorporating SPoG UI, RTDM, and AI / ML technologies through interactions to achieve a comprehensive service conversion system. System 400 is configured for integration with existing reseller systems to ensure efficient data exchange and system synchronization.

[0090] The SPoG UI 405 serves as the primary user interface. Users can interact with this interface to perform various tasks, providing straightforward navigation and customization. It displays information and options relevant to the reseller's unique business model and customer demographics. It displays real-time data from the data mesh 410 and provides controls for initiating actions within the system 400. For example, users can interact directly with the SPoG UI 405 with dynamic displays of service options, interactive elements for subscription customization, and tools for real-time feedback on user selections. It integrates with other system components to reflect accurate service information and user customization options. The SPoG UI was developed using web-based technology, allowing it to be accessed from a variety of devices, including 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 and provides a streamlined experience for resellers. In some embodiments, the SPoG UI 405 includes a dynamic pricing tool that displays fluctuating costs based on individual user consumption patterns. The dynamic pricing tool allows users to consider how their usage impacts AaS subscription costs, improving pricing transparency and flexibility.

[0091] Data Mesh 410 is an advanced data management layer. It aggregates and harmonizes data from various sources, including ERPs, vendor platforms, third-party databases, etc. This component ensures that all operational modules within System 400 have access to consistent and up-to-date information. System 400 can synchronize with existing reseller systems, ensuring efficient data exchange and system functionality.

[0092] Data Mesh 410 aggregates and harmonizes data from various systems such as inventory management, point of sale, CRM, etc. to ensure real-time availability. It employs Change Data Capture (CDC) to track real-time changes in transactional systems. This module standardizes data formats and units, ensuring data consistency and accuracy for decision-making processes related to service delivery.

[0093] The AI ​​module 460 uses machine learning algorithms and predictive modeling to automate the conversion of products and services to subscription models. The AI ​​module 460 analyzes market trends, user preferences, and consumption data to dynamically adjust service offerings. The AI ​​module 460 is configured to dynamically adjust pricing and service options based on real-time usage data. This enables flexible subscription models that adapt to changing user needs and consumption habits.

[0094] The AI ​​module 460 includes a decision support system for tailoring subscriptions based on advanced data analysis. In some embodiments, the AI ​​module 460 employs deep learning neural networks, specifically 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, the AI ​​module 460 can use decision trees for classification and regression tasks. These decision trees analyze user data and market conditions and segment users into different categories based on service preferences. Random forest and gradient boosting algorithms, i.e., ensemble methods for decision trees, improve the accuracy and stability of predictions. In some embodiments, clustering, specifically K-means and hierarchical clustering, is employed to segment the market and user base into distinct groups. Market / user segmentation helps the AI ​​module 460 understand varying user preferences and customize AaS subscription models for different market segments.

[0095] In some embodiments, the AI ​​module 460 can use reinforcement learning (RL) to adapt service delivery based on user feedback. RL algorithms, particularly Q-learning and policy gradient methods, can tune models to maximize user satisfaction and learn from each interaction to improve recommendation accuracy. This module integrates reinforcement learning algorithms to continuously adapt service delivery based on user feedback, improving the accuracy and relevance of customized subscriptions over time. Additionally, NLP techniques can be employed to analyze user feedback and queries. Utilizing tokenization, sentiment analysis, and named entity extraction, the AI ​​module 460 interprets user feedback and improves the service customization process.

[0096] Real-time processing based on data mesh 410 allows AI module 460 to dynamically adjust service offerings based on current usage patterns and instant feedback from the market. Data mesh 410 also precisely tracks real-time usage data, enabling usage-based pricing strategies to be implemented. Data mesh 410 includes a collaborative filtering and content-based recommendation system that can analyze user behavior and preferences, compare them with similar user profiles or content characteristics, and suggest appropriate service adjustments.

[0097] In some embodiments, the AI ​​module 460 can integrate predictive analytics tools and employ time series forecasting methods (e.g., autoregressive integrated moving average, exponential smoothing, etc.) to forecast future service demand. Optimization algorithms such as linear programming and genetic algorithms can consider various factors such as cost, user preferences, and resource availability to recommend the most effective service bundles and facilitate optimal subscription configurations. The AI ​​module 460 can employ Monte Carlo simulation and scenario analysis for risk assessment and strategic planning, simulating various market scenarios and evaluating the potential impact of various subscription models under different conditions.

[0098] The service management module 430 automatically monitors the lifecycle of services, including subscription initiation, modification, and termination. It incorporates tools for service level management, compliance monitoring, customer request handling, and service modification processes. The service management module 430 can employ real-time data to ensure delivery of services that meet user expectations and contractual agreements. This module can also be configured to manage multi-tier subscription environments, facilitating efficient handling of complex contracts involving various technology products.

[0099] The subscription billing and analytics module 440 handles financial transactions and provides analytical insight into service usage. The billing and analytics module 440 includes functionality for bill generation, payment processing, and financial reporting. Its analytics component analyzes consumption patterns, service popularity, and revenue trends to provide strategic insight into business decisions. The subscription billing and analytics module 440 can be configured to accommodate variable charges based on actual service usage, providing deeper analytical insight into user consumption patterns and preferences.

[0100] 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, providing a tailored service experience. The service customization engine 450 can be configured to offer comprehensive personalization options, allowing users to personalize their AaS subscription packages with various components, such as hardware, software, and additional services.

[0101] The asset management module 470 automatically tracks and manages the allocation and utilization of physical and digital assets within a subscription model. The asset management module 470 includes an inventory management system, asset tracking functionality, and resource coordination tools. The asset management module 470 can explicitly represent ownership of physical assets by the service provider, aligning with the "as a service" model. This ensures users have access to the latest technology without the burden of ownership. It ensures that assets are optimally utilized and accurately accounted for in the service delivery process.

[0102] System400 transforms traditional product sales into a flexible, subscription-based service model. Leveraging real-time data processing, AI-driven analytics, and user customization capabilities, System400 delivers a seamless "as-a-service" experience.

[0103] FIG. 5 illustrates an embodiment of an advanced distribution platform including a system 500 for managing a complex distribution network, which may be an embodiment of system 300, providing a technology distribution platform for optimizing the management and operation of a distribution network. System 500 includes several interconnected modules, each performing a specific function and contributing to the overall efficiency of supply chain operations. In some embodiments, these modules may include a SPoG UI 505, a CIM 510, an 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 prospect marker display 545, a predictive analytics module 550, a recommendation system module 555, a notification module 560, a self-onboarding module 565, and a communications module 570.

[0104] System 500, as an embodiment of system 300, can enable supply chain and distribution management using a wide range of technologies and algorithms that facilitate efficient data processing, personalized interactions, real-time analytics, secure communications, and effective management of documents, catalogs, and performance standards.

[0105] The SPoG UI 505, in some embodiments, serves as a central interface within the system 500, providing users with a unified view of the entire distribution network. Front-end technologies such as ReactJS, TypeScript, and Node.js are utilized 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.

[0106] CIM510, the Customer Interaction Module, employs algorithms and technologies from Oracle Eloqua, Adobe Target, and Okta to manage customer relationships within the distribution network. These technologies enable the module to handle customer data securely, personalize the customer experience, and provide access control to users.

[0107] The RTDM module 515, or real-time data mesh module, is a component of the 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 volumes 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 transactional systems, such as legacy ERP and CRM systems. This capability allows users to access current, accurate information and make informed decisions.

[0108] The AI ​​module 520 in the system 500 can extract valuable insights from data using advanced analytics and machine learning algorithms, including Apache Spark, TensorFlow, and scikit-learn. These algorithms enable the module to automate repetitive tasks, forecast demand patterns, optimize inventory levels, and improve overall supply chain efficiency. For example, the AI ​​module 520 can utilize predictive models to forecast demand, allowing users to optimize inventory management and minimize out-of-stock or overstock situations.

[0109] The Interface Display module 525 focuses on presenting data and information in a clear, user-friendly manner. It utilizes technologies such as HTML, CSS, and JavaScript frameworks like 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, enabling efficient data understanding, comparison, and trend analysis.

[0110] The personalized interaction module 530 utilizes customer data, historical behavior, and machine learning algorithms to generate personalized recommendations for products or services. It employs technologies such as Adobe Target, Apache Spark, and TensorFlow for data analysis, modeling, and providing targeted recommendations. For example, the module can analyze customer preferences and purchase history to provide personalized product recommendations, improve customer satisfaction, and drive sales.

[0111] The document hub 535 serves as a central repository for storing and managing documents within the system 500. It utilizes technologies such as SeeBurger and Elastic Cloud for efficient document management, storage, and retrieval. For example, the document hub 535 employs SeeBurger's document management capabilities to categorize and organize documents based on type, such as contracts, invoices, product specifications, or compliance documents, allowing users to easily access and find relevant documents when needed.

[0112] The catalog management module 540 enables the creation, management, and distribution of current product catalogs. This ensures that users have access to current product information, including specifications, pricing, availability, and promotions. Technologies such as Kentico and Akamai can be employed to facilitate catalog updates, content distribution, 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 geographic location.

[0113] The Performance and Outlook Marker Display 545 collects, analyzes, and visualizes real-time performance metrics and insights related to supply chain operations. Utilizing tools such as Splunk and Datadog, it enables effective performance monitoring and provides actionable insights. For example, the module can leverage Splunk's log analysis capabilities to identify performance bottlenecks in the supply chain, allowing users to take proactive measures to optimize operations.

[0114] The predictive analytics module 550 employs machine learning algorithms and forecasting models to forecast demand patterns, optimize inventory levels, and improve overall supply chain efficiency. It utilizes technologies such as Apache Spark and TensorFlow for data analysis, modeling, and forecasting. For example, the module can use TensorFlow's deep learning capabilities to analyze historical sales data and predict future demand, allowing users to optimize inventory levels and minimize costs.

[0115] The recommender system module 555 focuses on providing intelligent recommendations to users within 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 employed for data analysis, modeling, and providing targeted recommendations. For example, the module can use Adobe Target's recommendation engine to analyze customer preferences and behaviors and deliver personalized product recommendations across various channels, improving customer engagement and driving sales.

[0116] The notification module 560 enables the delivery of real-time notifications to users regarding important events, updates, or alerts within the supply chain. It utilizes message queues, event-driven architectures, and technologies such as Apigee X and TIBCO for notification delivery. For example, the module can utilize TIBCO's messaging infrastructure to send notifications to users' devices in real time, ensuring timely distribution of relevant information.

[0117] 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 become familiar with the system and its functionality. Technologies such as Okta and Kentico can be employed 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 permissions, and guide them through the system's functionality.

[0118] The communications module 570 enables communication and collaboration within the system 500. It provides users with channels for interaction, messaging, document sharing, and project collaboration. Technologies such as Apigee Edge and Adobe Launch can be employed to facilitate secure and efficient communication, document sharing, and version control. For example, the module can leverage the API management capabilities of Apigee Edge to ensure secure and reliable communication between users, enabling effective collaboration.

[0119] This allows the system 500 to incorporate various modules that utilize a diverse range of technologies and algorithms to optimize supply chain and distribution management. These modules, including the 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 prospect marker display 545, predictive analytics module 550, recommendation system module 555, notification module 560, self-onboarding module 565, and communication module 570, work together to provide end-to-end visibility, data-driven decision-making, personalized interactions, real-time analytics, and streamlined communication within the distribution network. The incorporation of specific technologies and algorithms enables efficient data management, secure communication, personalized experiences, and effective performance monitoring, contributing to improved operational efficiency and success in supply chain and distribution management.

[0120] Real-time Data Mesh 6 illustrates an RTDM module 600 according to one embodiment. The RTDM module 600 may be an embodiment of the RTDM module 310 and may include interconnected components, processes, and subsystems configured to enable real-time data management and analysis.

[0121] The RTDM module 600 represents an effective data mesh and change capture component within the overall system architecture, as shown in Figure 5. The module can be configured to provide real-time data management and standardization capabilities, enabling efficient operations within the supply chain and distribution management domain.

[0122] The RTDM module 600 can include an integration layer 610 (also called a "system of record") that integrates with various enterprise systems. These enterprise systems can include, for example, ERPs such as SAP, Impulse, META, and I-SCALA, as well as other data sources. The integration layer 610 can handle data exchange and synchronization between the RTDM module 600 and these systems. Data feeds can be established to retrieve relevant information from the systems of record, such as sales orders, purchase orders, inventory data, and customer information. These feeds enable real-time data updates, ensuring the RTDM module operates with the most current and accurate data.

[0123] The RTDM module 600 can include a data layer 620 configured to process and translate data for search and analysis. The data layer 620 includes a data mesh, a cloud-based infrastructure configured to provide scalable, fault-tolerant data storage capabilities. Within the data mesh, multiple purpose-built 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. A PDS can be configured to store specific types of data, such as customer data, product data, financial data, etc. These PDSs act as repositories of normalized and / or standardized data, ensuring data consistency and integrity across systems.

[0124] In some embodiments, the RTDM module 600 implements a data replication mechanism to capture real-time changes from multiple data sources, including transactional systems like ERP (e.g., SAP, Impulse, META, I-SCALA). The captured data can then be processed and normalized on the fly and transformed into a standardized format suitable for analysis and integration. This process ensures that data is readily available and current within the data mesh, facilitating real-time insights and decision-making.

[0125] More specifically, the data layer 620 within the RTDM module 600 can be configured as a powerful, flexible foundation for managing and processing data within a distribution ecosystem. In some embodiments, the data layer 620 can encompass a highly scalable and robust data lake, which can be referred to as a data lake 622, along with 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.

[0126] The data layer 620 includes a 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 the Apache Hadoop Distributed File System (HDFS) or Amazon S3, the data lake provides a unified, scalable platform for storing both structured and unstructured data. Using the elasticity and fault-tolerance of cloud-based storage, the data lake 622 can accommodate an influx of data from diverse sources.

[0127] Accompanying the data lake 622 may be a population of purpose-built data stores PDS624.1-624.N. Each PDS624 may serve as a dedicated repository optimized for storing and retrieving a particular type of data related to a supply chain domain. In some non-limiting examples, PDS624.1 may be dedicated to customer data, storing information such as customer profiles, preferences, and transaction history. PDS624.2 may focus on product data, encompassing details regarding SKU codes, descriptions, pricing, and inventory levels. These purpose-built data stores enable efficient data retrieval, analysis, and processing to meet the diverse needs of supply chain users.

[0128] To ensure real-time data synchronization, 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 legacy ERPs like SAP, Impulse, META, and I-SCALA, as well as other enterprise-wide systems. CDC constantly monitors these systems for updates, modifications, or new transactions and captures them in real time. By capturing these changes, data layer 620 ensures that the data in data lake 622 and PDS 624 remains current, providing users with a real-time view into the distribution ecosystem.

[0129] In some embodiments, data layer 620 can be implemented using one or more frameworks, such as .NET or Java, to facilitate integration with existing enterprise systems, ensuring compatibility with a wide range of existing systems and providing flexibility for customization and extensibility. For example, data layer 620 can utilize a Java technology stack, including frameworks such as Spring and Hibernate, to facilitate integration with systems of record with a diverse population of ERP systems and other enterprise-wide solutions. This can facilitate smooth data exchange, process automation, and end-to-end visibility across the supply chain.

[0130] In terms of data processing and analytics, the data layer 620 can use the power of distributed computing frameworks, such as Apache Spark or Apache Flink, in some non-limiting examples. These frameworks can enable parallel processing and distributed computing across large datasets stored in the data lake and PDS. By using these frameworks, supply chain users can perform complex analytical tasks, apply machine learning algorithms, and derive valuable insights from the data. For example, the data layer 620 can use Apache Spark's machine learning libraries to develop predictive models for demand forecasting, optimizing inventory levels, and identifying potential supply chain risks.

[0131] In some embodiments, data layer 620 can incorporate robust data governance and security measures. Elaborate access control mechanisms and authentication protocols ensure that only authorized users can access and modify data in the data lake and PDS. Data encryption techniques protect sensitive supply chain information from unauthorized access, both at rest and in transit. Additionally, data layer 620 can implement data lineage and audit trail mechanisms to enable users to track data provenance and history, ensuring data integrity and compliance with regulatory requirements.

[0132] In some embodiments, data layer 620 can be deployed in a cloud-native environment using containerization technologies such as Docker and orchestration frameworks like Kubernetes. This approach ensures scalability, resilience, and efficient resource allocation. For example, data layer 620 can be deployed on cloud infrastructure offered by AWS, Azure, or Google Cloud to take advantage of their managed services and scalable storage options. This enables scaling of resources based on demand, minimizes operational overhead, and provides an elastic infrastructure for managing supply chain data.

[0133] The data layer 620 of the RTDM module 600 can incorporate a highly scalable data lake, Data Lake 622, along with purpose-built PDSs, PDSs 624.1-624.N. By employing a CDC mechanism, the data layer 620 ensures efficient data management, standardization, and real-time availability. In a non-limiting example, the data layer 620 can be implemented using appropriate technologies, such as .NET or Java, and / or distributed computing frameworks like Apache Spark, to enable powerful data processing, advanced analytics, and machine learning capabilities. With robust 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, optimizing operations and driving business success in dynamic, complex distribution environments.

[0134] 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, current insights. The AI ​​module 630 may generate predictions, recommendations, and alerts and publish such insights to a dedicated data feed.

[0135] The data engine layer 640 comprises a set of interconnected systems responsible for data ingestion, processing, transformation, and integration. The data engine layer 640 of the RTDM module 600 can include a collection of autonomously operating headless engines 640.1-640.N. These engines represent distinct functionality within the system and can include, for example, one or more recommendation engines, forecasting engines, and subscription management engines. The engines 640.1-640.N can provide specific business logic and services using standardized data stored in the data mesh. Each engine can be configured to be pluggable, allowing for flexibility and future expansion of the module's capabilities. Exemplary engines are shown in FIG. 5 and are not intended to be limiting. Any additional headless engines can be included in the data engine layer 640 or other exemplary layers of the disclosed system.

[0136] These systems can be configured to receive data from multiple sources, such as transactional systems, IoT devices, and external data providers. The data ingestion process involves extracting data from these sources and converting it into a standardized format. Data processing algorithms are applied to cleanse, aggregate, and enrich the data, making it ready for further analysis and integration.

[0137] Additionally, a data distribution mechanism can be employed to facilitate integration and access to the RTDM module 600. The data distribution mechanism 645 can include one or more APIs and be configured to facilitate data distribution from the data mesh and engine to various endpoints, including user interfaces, micro-frontends, and external systems.

[0138] The experience layer 650 focuses on providing an intuitive, user-friendly interface for interacting with supply chain data. It can include data visualization tools, interactive dashboards, and user-centric functionality. Through this layer, users can search and analyze real-time data related to various supply chain metrics, such as inventory levels, sales performance, and customer demand. The user experience layer supports personalized data feeds, allowing users to customize views and receive relevant updates based on their roles and responsibilities. Users can subscribe to specific data updates, such as inventory changes, pricing updates, or new SKU notifications, depending on their preferences and role.

[0139] Thus, in some embodiments, the RTDM module 600 for supply chain and distribution management may include integration with systems of record and may include one or more data layers with a data mesh and purpose-built data stores, an AI component, a data engine layer, and a user experience layer. These components work together to provide users with intuitive access to real-time supply chain data, efficient data processing and analysis, and integration with existing enterprise systems. Technical feeds and searches within the module ensure users can find relevant, current information and insights, 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 diverse ERPs while maintaining auditable, repeatable transactions offers a distinct advantage: enabling a unified view for vendors, resellers, customers, end customers, and other entities within the distribution system, including IT distribution systems.

[0140] Automated AaS Model Conversion 7 illustrates a system 700 for converting technology products to an AaS model. The system 700 includes a real-time data mesh 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.

[0141] In some embodiments, the SPoG UI 705 can be an embodiment of the SPoG UI described above and can be enhanced with a one-click conversion feature, allowing a user to instantly convert a shopping cart to a subscription-based service model, thereby simplifying the transition to a monthly payment plan and improving the user experience.

[0142] The RTDM 710 aggregates and normalizes real-time data from various sources that is essential for the efficient operation of the AaS conversion module 720. This includes data on product specifications, subscription usage patterns, and market trends. The RTDM 710 aggregates and normalizes data from multiple sources, such as ERP, CRM systems, and market intelligence, to establish a centralized, unified data hub. It utilizes a mix of data warehouses and data lakes to efficiently handle both structured and unstructured data. The RTDM 710 employs ETL processes and data normalization techniques to ensure data uniformity and accessibility. This normalized data is essential for the functioning of the AaS conversion module 720 and provides the input needed to accurately and effectively convert products to subscription-based services. The RTDM 710 maintains data integrity and relevance, which are essential for the automated AaS conversion process. In some embodiments, the RTDM 710 is configured to interface with asset management systems, supporting service provider ownership of physical assets while allowing users to access and utilize these assets under comprehensive service agreements.

[0143] The AAML module 715 serves as the central processing unit for the AaS conversion process. It contains dedicated rules and algorithms designed for key tasks in AaS conversion, such as analyzing product compatibility for service conversion, optimizing subscription models, and applying dynamic pricing strategies. The AAML module 715 employs analytical tools for big data processing and deep learning capabilities. It performs sentiment analysis, trend forecasting, and behavioral analysis to understand and anticipate market and user demand. The AAML module 715 integrates and trains machine learning algorithms based on historical datasets to identify usage patterns and create predictive recommendations for subscription models. It adapts its algorithms based on a continuous feedback loop, refining its accuracy over time. This module performs essential functions in the automated AaS conversion process, ensuring that services are aligned with individual user preferences and market conditions.

[0144] In one embodiment, the AaS conversion module 720 processes data to convert legacy technology products into subscription-based services. It integrates data from the RTDM 710 regarding product specifications, subscription usage patterns, and market trends. The module 720 analyzes product and service compatibility and utilizes information from the AAML 715 for dynamic pricing. It performs the product-to-service conversion, ensuring alignment with market data and user preferences. The AaS conversion module 720 incorporates a methodology for converting traditional one-time purchase technology items into subscription-based services. This mechanism can, for example, convert an entire shopping cart to a services model, facilitating the transition from capital expenditures (CapEx) to operational expenditures (OpEx). In some embodiments, the AaS conversion module 720 is configured to bundle various technology products, including hardware, software, cloud services, and warranties, into a single unified subscription package, improving the value proposition and simplifying the user experience.

[0145] Service Analysis Submodule 725: Submodule 725 evaluates products for suitability within a subscription model. It processes data from RTDM 710 and focuses on market demand analysis, user preference assessment, and technical feasibility studies. This module applies analytical tools to assess the suitability of products within a subscription model and evaluate service quality criteria.

[0146] The subscription pricing engine 730 employs algorithms to dynamically price the subscription service. It processes market data and user engagement metrics from the RTDM 710 and applies predictive models from the AAML 715. The engine adapts prices to diverse customer profiles and takes market conditions into account to maintain the financial viability of the subscription service. In some embodiments, the subscription pricing engine 730 can implement dynamic pricing algorithms that adjust rates based on actual service usage, such as reducing costs during periods of reduced usage (e.g., during holidays), thereby reflecting a true consumption-based pricing model.

[0147] The service configuration engine 735 facilitates the selection and customization of subscription services for users. It is integrated 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 can include a flexibility module configured to allow users to switch between different services and scales, for example, modifying subscription levels or migrating between various streaming services. This module ensures adaptability to changing user needs and markets.

[0148] The subscription package generator 740 compiles service packages by matching the inventory and service capabilities from the RTDM 710 with the user's requirements, and uses RESTful APIs and data connectors for real-time data synchronization to optimize subscription package configurations.

[0149] The Dynamic Pricing Calculator 745 calculates the cost of subscription packages. It retrieves pricing data and promotion details from the RTDM 710 and applies user-specific discounts. This calculator ensures competitive, personalized pricing for each subscription package.

[0150] The Error Check Integrator 780 validates the subscription configuration and selection against predefined criteria from the AAML 715. It employs accuracy verification algorithms to ensure error-free and consistent subscription packages before committing.

[0151] The system 700 can be configured to integrate multi-tier 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, the system 700 can include advanced tools for managing subscription technical and administrative issues, including billing management, service term adjustments, and dynamic adaptation to changes in subscription terms and conditions, via the SPoG 705.

[0152] As a result, system 700 is configured to integrate data from multiple sources into a unified interface via SPoG UI 705, automate various tasks in the AaS conversion process via AAML 715, and maintain a real-time, standardized data repository via RTDM 710. This architecture enables an efficient and accurate process for converting technology products to an AaS model, improving the overall customer experience in the IT distribution industry.

[0153] 8 illustrates a flow diagram of a method 800 for an AaS conversion process according to an embodiment of the present disclosure. The flowchart depicts operations from initiation to completion and highlights the AaS conversion module 720 of the system 700.

[0154] In operation 801, a user begins the conversion process by interacting with a Single Pane of Glass User Interface (SPoG UI) 705. Operation 801 involves collecting user inputs such as product selection and service requirements. The SPoG UI 705 then analyzes these inputs in conjunction with an Advanced Analytics and Machine Learning (AAML) module 715.

[0155] In operation 802, the AAML module 715 performs a preliminary analysis to identify the user's specific needs for AaS conversion. Algorithms within the AAML 715 evaluate the user's input, taking into account factors such as historical usage patterns and current market conditions, to develop an optimal service conversion strategy.

[0156] In operation 803, the process proceeds to the Real Time Data Mesh (RTDM) 710. The RTDM 710 uses a RESTful API for data retrieval to gather data relevant to the user's selections, including current service specifications and product availability.

[0157] At operation 804, the AaS conversion module 720 processes the user request. This module incorporates tools such as a service analysis submodule 725 and a subscription pricing engine 730 to build a tailored subscription package. The service analysis submodule 725 assesses product compatibility for the subscription model, while the subscription pricing engine 730 calculates pricing for the service package.

[0158] In operation 805, the error checking integrator 780 verifies the accuracy and feasibility of the proposed subscription package. This integrator utilizes algorithms from AAML 715 to ensure the integrity and coherence of the subscription service.

[0159] In operation 806, the system presents the proposed subscription package, including components and pricing, to the user on the SPoG UI 705 for review and approval.

[0160] In operation 807, the AaS conversion module 720 employs machine learning models to analyze the post-implementation conversion process. These models apply predictive analytics to refine conversion mechanisms and improve future conversions based on updated user data and market trends.

[0161] In operation 808, a logging mechanism within the AaS conversion module 720 records transaction details, including the user's selections and confirmed subscription package compositions, for continued improvement of the system.

[0162] In operation 809, the user reviews, adjusts as necessary, and confirms the subscription package on the SPoG UI 705. The user's confirmation indicates the completion of the AaS conversion process.

[0163] This operational flow integrates the SPoG UI 705, RTDM 710, AAML 715, and AaS conversion module 720. Each module plays a specific role and collectively automates and refines the AaS conversion process. Alternative embodiments may include variations in machine learning algorithms, data acquisition techniques, and user interface design to improve the adaptability and scalability of the system 700.

[0164] 9 illustrates a flow diagram of a method 900 for user interaction and service configuration according to an embodiment of the present disclosure. The flowchart outlines a user's journey from selecting to confirming a subscription service, focusing on interactions with the SPoG UI 705 and service configuration engine 735 in the system 700.

[0165] In operation 901, a user initiates the subscription selection process through the SPoG UI 705. This operation involves the user entering service preferences and requirements into the interface. The SPoG UI 705 is designed to capture these inputs and send them to the service configuration engine 735 for further processing.

[0166] At operation 902, the service configuration engine 735 receives user input from the SPoG UI 705. It analyzes the provided data and suggests a range of subscription services that match the user's specified preferences. The engine leverages real-time data from the RTDM 710 to ensure that service offerings are current and relevant.

[0167] 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 they align with their particular needs.

[0168] In operation 904, the user interacts with the SPoG UI 705 to adjust and refine their subscription selections, which may include selecting additional features, altering service levels, or modifying other aspects of the subscription package.

[0169] In operation 905, the user's refined selections are passed back to the service configuration engine 735 for final processing. The engine applies any additional user modifications and prepares the subscription package for final review.

[0170] In operation 906, the error checking integrator 780 evaluates the configured subscription package for accuracy and completeness. This integrator applies criteria from the AAML 715 to verify the user's selections and the coherence of the subscription package.

[0171] In operation 907, the SPoG UI 705 displays the final subscription package to the user, including a detailed breakdown of services, features, and pricing, allowing the user to make a final review before confirming.

[0172] In operation 908, the user confirms the subscription package via the SPoG UI 705 or returns to the previous operation for further adjustments. Upon confirmation, the subscription process proceeds to completion.

[0173] In operation 909, a logging mechanism within the service composition engine 735 records the final subscription details, including user input and package composition. This data contributes to continuous improvement in user interface design and the service composition process.

[0174] The method 900 integrates the SPoG UI 705, the service configuration engine 735, and the error checking integrator 780 into a structured flow. This integration facilitates user-centric subscription configuration and optimization, improving the overall user experience within the system 700. Alternate embodiments may involve different user interface layouts, configuration algorithms, and validation mechanisms to suit diverse user needs and preferences.

[0175] 10 shows a flow diagram of a method 1000 for data management and analytics workflow in accordance with an embodiment of the present disclosure. The flowchart highlights the process through which the RTDM 710 aggregates, normalizes, and manages real-time data, and the subsequent utilization of this data by the AaS conversion module 720 and AAML 715.

[0176] In operation 1001, the RTDM 710 initiates a data aggregation process, which involves collecting data from various sources, including ERP, CRM systems, and external market intelligence platforms. This operation focuses on gathering a variety of data relevant to the AaS conversion process, such as product specifications, user interaction data, and market trends.

[0177] In operation 1002, the RTDM 710 normalizes the aggregated data. It employs an ETL (extract, transform, load) process to uniformly format the data and ensure consistency across various data types and sources. This step is essential to maintain data integrity and facilitate effective data analysis.

[0178] In operation 1003, the 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 value ranges. This ensures that the data is ready for further analysis and processing by the AaS conversion module 720 and AAML 715.

[0179] At operation 1004, the processed data flows to the AaS conversion module 720, where the module analyzes the data to determine the feasibility of converting a particular product or service to an AaS model, assessing various factors such as market viability, user demand, and technical compatibility.

[0180] In operation 1005, the AAML 715 receives data from the RTDM 710 for advanced analysis. The 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.

[0181] In operation 1006, the AaS conversion module 720 utilizes the insights provided by the AAML 715 to refine the AaS conversion process, integrating the results of the analysis to optimize service packages and pricing models and align them with current market conditions and user expectations.

[0182] In operation 1007, the RTDM 710 updates its data repository with new insights and analytical findings from the AAML 715 and the AaS conversion module 720. This continuous feedback loop ensures that the RTDM 710 maintains an up-to-date and relevant data pool, which is essential to the ongoing effectiveness of the AaS conversion process.

[0183] Method 1000 presents a comprehensive workflow that integrates RTDM 710, AaS conversion module 720, and AAML 715. This workflow is critical to data management and analysis and directly impacts the effectiveness of the AaS conversion process within system 700. Alternate embodiments may incorporate varying data processing techniques, analysis algorithms, and data flow structures to meet evolving data management needs and conversion strategy expectations.

[0184] 11 is a block diagram of example components of a device 1100. One or more computer systems 1100 may be used, for example, to implement any of the embodiments described herein, as well as combinations and subcombinations thereof. The computer system 1100 may include one or more processors (also referred to as central processing units or CPUs), such as processor 1104. The processor 1104 may be connected to a communication infrastructure or bus 1106.

[0185] The computer system 1100 may also include user input / output devices 1103 , such as a monitor, keyboard, pointing device, etc., which may communicate with a communications infrastructure 1106 through a user input / output interface 1102 .

[0186] One or more of the 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 structure that can be efficient for parallel processing of large blocks of data, such as mathematically intensive data common in computer graphics applications, images, videos, etc.

[0187] The computer system 1100 may also include a main or primary memory 1108, such as random access memory (RAM). The main memory 1108 may include one or more levels of cache. The main memory 1108 may have control logic (i.e., computer software) and / or data stored therein.

[0188] The computer system 1100 may also include one or more secondary storage devices or memories 1110. The secondary memory 1110 may include, for example, a hard disk drive 1112 and / or a removable storage device or drive 1114.

[0189] The removable storage drive 1114 may interact with a removable storage unit 1118. The removable storage unit 1118 may include a computer-usable or readable storage device having computer software (control logic) and / or data stored thereon. The removable storage unit 1118 may be a program cartridge and cartridge interface (such as found in video game devices), a removable memory chip (such as an EPROM or PROM) and associated socket, a memory stick and USB port, a memory card and associated memory card slot, and / or other removable storage unit and associated interface. The removable storage drive 1114 may read from and / or write to the removable storage unit 1118.

[0190] Secondary memory 1110 may include other means, devices, components, intermediaries, or other approaches for allowing computer programs and / or other instructions and / or data to be accessed by computer system 1100. Such means, devices, components, intermediaries, or other approaches may include, for example, removable storage unit 1122 and interface 1120. Examples of removable storage unit 1122 and interface 1120 may include a program cartridge and cartridge interface (such as found in a video game device), a removable memory chip (such as an EPROM or PROM) and associated socket, a memory stick and USB port, a memory card and associated memory card slot, and / or other removable storage unit and associated interface.

[0191] Computer system 1100 may further include a communications or network interface 1124. Communications interface 1124 may enable computer system 1100 to communicate and interact with a combination of external devices, external networks, external entities, etc. (individually and collectively referred to by reference numeral 1128). For example, communications interface 1124 may enable computer system 1100 to communicate with external or remote devices 1128 via communications path 1126, which may be wired and / or wireless (or a combination thereof) and may include a combination of a LAN, a WAN, the Internet, etc. Control logic and / or data may be transmitted to and from computer system 1100 via communications path 1126.

[0192] Additionally, computer system 1100 may be a personal digital assistant (PDA), a desktop workstation, a laptop or notebook computer, a netbook, a tablet, a smartphone, a smartwatch or other wearable, an appliance, part of the Internet of Things, and / or an embedded system, or any combination thereof, to name a few non-limiting examples.

[0193] The computer system 1100 may be a client or server that accesses or hosts applications and / or data through a delivery model, including, but not limited to, remote or distributed cloud computing solutions, local or on-premise software ("on-premise" cloud-based solutions), "as a service" models (e.g., Content as a Service (CaaS), Digital Content as a Service (DCaaS), Software as a Service (SaaS), Managed Software as a Service (MSaaS), Platform as a Service (PaaS), Desktop as a Service (DaaS), Framework as a Service (FaaS), Backend as a Service (BaaS), Mobile Backend as a Service (MBaaS), Infrastructure as a Service (IaaS)), and / or hybrid models including combinations of the foregoing examples or other service or delivery models.

[0194] Applicable data structures, file formats, and schemas in computer system 1100 may be derived from standards including, but not limited to, JavaScript Object Notation (JSON), Extensible Markup Language (XML), Yet Another Markup Language (YAML), Extensible Hypertext Markup Language (XHTML), Wireless Markup Language (WML), MessagePack, XML User Interface Language (XUL), or other functionally similar representations, alone or in combination. Alternatively, proprietary data structures, formats, or schemas may be used, either exclusively or in combination with known or open standards.

[0195] In some embodiments, a tangible, non-transitory apparatus or article of manufacture comprising a tangible, non-transitory computer-usable or readable medium having control logic (software) stored thereon may also be referred to herein as a computer program product or program storage device. This includes, but is not limited to, computer system 1100, main memory 1108, secondary memory 1110, and removable storage units 1118 and 1122, as well as tangible articles of manufacture embodying combinations of the foregoing. Such control logic, when executed by one or more data processing devices (such as computer system 1100), may cause such data processing devices to operate as described herein.

[0196] Figures 12A-12Q show various screens and functionality of the SPoG UI related to vendor onboarding, partner dashboard, customer cart, order summary, SKU generation, order tracking, shipment tracking, subscription history, and subscription changes. A detailed description of each figure is provided below.

[0197] Figure 12A shows the Vendor Onboarding Start Screen, which represents the first step in the vendor onboarding process. It provides a form or interface where vendors can express their interest in joining the distribution ecosystem. Vendors can enter basic information such as company details, contact information, and product catalog.

[0198] Figure 12B shows a vendor onboarding guide that displays a step-by-step guide or checklist for vendors to follow during the onboarding process, outlining the necessary tasks and requirements to ensure the vendor has a clear understanding of the onboarding process and can proceed smoothly.

[0199] 12C illustrates a vendor onboarding call scheduler that facilitates scheduling a call or meeting between a vendor and a platform partner or representative responsible for guiding the vendor through the onboarding process. The vendor can select a preferred time slot or request a call, ensuring effective communication and assistance throughout the onboarding process.

[0200] FIG. 12D shows a vendor onboarding task list that presents a comprehensive task list or dashboard outlining the specific steps and actions required for successful vendor onboarding. An overview of pending tasks, completed tasks, and upcoming deadlines is provided to help vendors track progress and ensure timely completion of each onboarding task.

[0201] 12E shows a vendor onboarding completion screen confirming successful completion of the vendor onboarding process, which may display a congratulatory message or a summary of the tasks completed indicating that the vendor is now officially onboarded into the distribution ecosystem.

[0202] Figure 12F shows the Partner Dashboard, which provides partners or users with an aggregated view of relevant information and metrics regarding their partnerships with the distribution ecosystem, providing an overview of performance indicators, key data points, and actionable insights to facilitate effective collaboration and decision-making.

[0203] FIG. 12G shows a customer product cart, representing a customer's product cart, to which the customer can add items they wish to purchase. It displays a list of selected products, quantities, prices, and other relevant details. The customer can review and modify the contents of the cart before proceeding to the checkout process.

[0204] Figure 12H shows the customer subscription cart, which allows customers to manage their subscription-based purchases. It displays the selected subscription plan, pricing, and duration. Customers can review and modify subscription details before finalizing their selection.

[0205] FIG. 12I shows a customer order summary that provides a summary of the customer's order, including details such as the products or subscriptions purchased, quantity, pricing, and any discounts or promotions applied, allowing the customer to review their order before confirming their purchase.

[0206] 12J shows a vendor SKU generation screen for generating unique stock-keeping unit (SKU) codes for vendor products, which may include fields or options that allow vendors to specify product details, attributes, and pricing, and the system will auto-generate the corresponding SKU code.

[0207] Figures 12K and 12L show a dashboard order summary for displaying summary information about orders placed within a distribution ecosystem. These present key order details, such as order number, customer name, product or subscription information, quantity, and order status. The dashboard provides an overview of order activity and allows users to efficiently track and manage orders.

[0208] Figure 12M shows the customer subscription cart, which allows customers to add, modify, or delete subscription plans. A list of selected subscriptions, pricing, and renewal dates can be displayed. Customers can manage their subscriptions and make changes according to their preferences and requirements.

[0209] Figure 12N shows the Customer Order Tracking screen, which allows customers to track the status and progress of their orders within the supply chain. It displays real-time updates on order fulfillment, including processing, packing, and shipping. Customers can monitor the movement of their orders and estimate delivery times.

[0210] FIG. 12O shows customer shipment tracking, which provides customers with real-time tracking information about their shipments. Details such as the carrier, tracking number, current location, and estimated delivery date may be included. Customers can stay informed about the whereabouts of their shipments.

[0211] Figure 12P shows Customer Subscription History, which presents a historical record of a customer's subscription activity. It displays a list of previous subscriptions, including subscription plan, duration, and status. Customers can review their subscription history, track past payments, and view details of previous subscriptions.

[0212] Figure 12Q shows the Customer Subscription Modification dialog, which allows customers to modify their existing subscriptions. It provides options to upgrade or downgrade subscription plans, change billing details, or adjust other subscription-related preferences. Customers can manage their subscriptions according to their evolving needs or preferences.

[0213] The UI screens shown are not limiting. In some embodiments, the UI screens of Figures 12A-12Q collectively represent the diverse functionality and features provided by the SPoG UI, providing users with a comprehensive, user-friendly interface for vendor onboarding, partnership management, customer interaction, order management, subscription management, and tracking within the distribution ecosystem.

[0214] It is understood that the Detailed Description section, and not the Abstract section, is intended to be used to interpret the claims. The Abstract section may describe one or more, but not all, example embodiments of the invention as contemplated by the inventors, and thus is not intended to limit the invention and the appended claims in any way.

[0215] The present invention has been described above with the aid of functional building blocks illustrating the implementation of certain functions and relationships thereof. The boundaries of these functional building blocks have been arbitrarily defined herein for convenience of description. Alternative boundaries may be defined so long as the certain functions and relationships thereof are properly performed.

[0216] The foregoing description of specific embodiments fully discloses the general nature of the present invention, and by applying the knowledge of those skilled in the art, such specific embodiments can be readily modified and / or adapted for various uses without undue experimentation and without departing from the general concept of the present invention. Such adaptations and modifications are therefore intended to be within the meaning and range of equivalents of the disclosed embodiments, based on the teaching and guidance presented herein. It should be understood that the phraseology or terminology used herein is for the purpose of description and not of limitation, and should therefore be interpreted in light of the teaching and guidance provided by those skilled in the art.

[0217] The breadth and scope of the present invention should not be limited by any of the above-described exemplary embodiments, but should be defined only in accordance with the following claims and their equivalents.

Claims

1. 1. A computerized method for performing AaS model conversion, comprising: receiving user input specifying preferences for technology product conversions; accessing a real-time data mesh (RTDM) to retrieve data related to said user preferences and market conditions; utilizing an Advanced Analytics and Machine Learning (AAML) module to analyze the user input and market data for suitability in an AaS model; generating AaS conversion recommendations through an AaS conversion module; presenting the AaS options to the user via a Single Pane of Glass user interface (SPoG UI); Facilitating completion of the AaS conversion process and transferring the order to a vendor system; executing the AaS conversion order by integrating data from the SPoG UI, RTDM, and vendor systems; The method, wherein the method is performed by a computer system having a unified platform that integrates data from multiple sources for AaS conversion.

2. 10. The method of claim 1, further comprising validating the AaS conversions using rules and algorithms in the AAML module to ensure accuracy and relevance of the conversion recommendations.

3. The method of claim 1 , wherein the AAML module utilizes dynamic machine learning algorithms that adapt to changing user preferences and market conditions for effective AaS conversion.

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

5. The method of claim 1 , further comprising generating real-time reports related to the AaS conversion process, including user engagement metrics and conversion success rates.

6. The method of claim 1 , wherein the vendor system for completing the AaS conversion is selected based on criteria including service availability and capabilities.

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

8. 1. A computerized method for optimizing AaS conversion decisions, comprising: Initiating a subscription request via the SPoG UI; Retrieving user preference and historical data for AaS conversion; querying the RTDM to fetch real-time data related to the AaS conversion; applying predictive analytics by the AAML module to determine an optimal subscription model; configuring said subscription package based on user preferences and available data; validating the subscription configuration using the AAML module; presenting the final subscription package to the user via the SPoG UI; Logging details of the AaS conversion process for future analysis and system refinement; and and initiating a feedback loop within the system for continuous improvement of the AaS conversion.

9. 10. The method of claim 8, further comprising utilizing a machine learning algorithm in the feedback loop to analyze user feedback and system performance for continuous optimization of the AaS conversion process.

10. The method of claim 8 , wherein the RTDM fetches real-time data based on current market conditions and service availability.

11. The method of claim 8 , further comprising generating real-time reports related to the AaS conversion process, including metrics such as user satisfaction and service customization levels.

12. The method of claim 8 , wherein product selection for the AaS subscription is made based on predefined criteria including user preferences, market trends, and service compatibility.

13. The method of claim 8 , further comprising sending a notification to the user when the AaS subscription package is successfully created and available for use.

14. 10. The method of claim 8, wherein the feedback loop for AaS conversion decisions is performed within a defined timeframe based on user engagement and system analysis.

15. 1. A system for automating an AaS conversion process, comprising: a real-time data mesh configured to aggregate and distribute data including user preferences, market trends, and service information; a single pane of glass user interface that enables user interaction and displays subscription options; an advanced analytics and machine learning module that processes the data and generates intelligent AaS conversion recommendations; an AaS conversion module that interacts with the SPoG UI and RTDM to perform a conversion process including user preference analysis, service selection, and subscription package generation.

16. 16. The system of claim 15, wherein the AaS conversion module further comprises a logging mechanism for tracking user interactions and subscription selections for auditing and analysis purposes.

17. 16. The system of claim 15, wherein the AaS conversion module is integrated with the AAML module for validation and optimization purposes and refines subscription recommendations using algorithms stored in the AAML module.

18. 16. The system of claim 15, wherein the SPoG UI is designed to be accessible and responsive across a variety of devices and provides a unified user experience for subscription customization.

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

20. 16. The system of claim 15, further comprising a machine learning model in the AaS conversion module configured to continuously refine the conversion process based on user feedback and evolving market data.

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