Systems and methods for automated prediction of vendor product roadmap insights

Abstract

To provide a system and a method for automatically predicting insight of vendor product roadmap to improve strategic decision making.SOLUTION: The record keeping system 280 leverages a real-time data mesh (RTDM) module 310 to aggregate and standardize data from a variety of sources, including market trends, customer feedback, and technological advances. The analytics and machine-learning (AAML) module 315 analyzes this to generate predictive insights and facilitates tuning of the existing product roadmap. Dynamic adjustments are made using the roadmap optimization module, and communication is facilitated through a single pane of glass (SpPG) user interface (UI) 305.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, a particular problem 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] An automated predictive insights (PIPR) process for vendor product roadmaps is designed to improve product development and market strategy optimization in the technology sector by leveraging advanced integration of systems and activities through a unified interface. The system facilitates the generation of actionable insights for product roadmap development, ensuring alignment with market demand and improving competitive positioning. In some embodiments, a real-time data mesh (RTDM) aggregates and normalizes high-velocity, real-time data from diverse sources. This data infrastructure supports dynamic, data-driven decision-making and incorporates advanced security measures to protect sensitive information.

[0008] In some embodiments, a single pane of glass user interface (SPoG UI) serves as the primary interaction layer, providing users with comprehensive tools for data analysis and visualization. This interface simplifies the extraction and interpretation of actionable insights, enabling agile responses to market changes. The Advanced Analytics and Machine Learning (AAML) module incorporates specialized algorithms to analyze data and perform sentiment analysis, trend forecasting, and customer behavior analysis. It employs machine learning algorithms that are continuously refined based on a feedback loop to improve the accuracy of predictive insights for roadmap adjustments. The module's capabilities extend to extracting insights from unstructured text and incorporating explainable AI to provide transparency into the predictive modeling process.

[0009] In some embodiments, the Product Roadmap Insights (PRI) module performs analytics processed by the AAML module to generate targeted insights and recommend strategic adjustments to the product roadmap. It leverages predictive analytics to ensure products meet customer demand and employs dynamic catalog management and predictive maintenance capabilities to improve product reliability. The Market Analytics submodule can be configured to further extend these capabilities by analyzing market demand, competitor benchmarking, and industry trends to identify opportunities for innovation and differentiation.

[0010] In some embodiments, the innovation opportunity engine dynamically identifies product development opportunities and suggests innovation areas aligned with market needs by analyzing market data and customer insights. In a non-limiting example, the strategy adjustment engine is configured to enable vendors to adjust their product roadmaps in real time based on insights derived from the system, ensuring flexibility in response to market trends. In a non-limiting example, the roadmap optimization generator and competitive positioning calculator are configured to optimize product strategies and recommend market positioning strategies, respectively, to improve the vendor's competitive position.

[0011] In a non-limiting example, the consistency validator employs algorithms to ensure the alignment of roadmap strategies with market data and vendor goals, preventing errors and supporting strategic planning. System 700 integrates these components to automate the generation of predictive insights and streamline the process of developing and adjusting vendor product roadmaps. This comprehensive approach not only improves strategic decision-making, but also positions vendors for success in a rapidly evolving technology environment.

[0012] 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.

[0013] 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, streamlining the subscription management process.

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

[0015] 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.

[0016] 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 guides the creation of subscription packages that meet specific user requirements in areas such as software applications, cloud computing, and hardware needs.

[0017] 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.

[0018] 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 vendor product roadmaps. Automated vendor product roadmaps integrate products and services from different categories, ensuring each package meets the user's technical and service needs. Automated PIPR generation can include combinations of hardware, compatible software solutions, and cloud services, designed to improve user productivity and efficiency.

[0019] (Single pane of glass) Single Pane of Glass (SPoG) can provide a comprehensive solution designed to address these multifaceted challenges. It can be configured to provide a holistic, user-friendly, and efficient platform that streamlines the distribution process.

[0020] 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.

[0021] According to some embodiments, SPoG can consolidate multiple touchpoints into a single platform and emulate a direct consumer channel into a distribution platform. This consolidation provides a unified direct channel for consumers to interact with distributors, significantly reducing supply chain complexity and improving the overall customer experience.

[0022] 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.

[0023] 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.

[0024] According to some embodiments, SPoG consolidates data from various OEMs into a single platform to streamline SKU management and product localization. This not only ensures data consistency but also significantly reduces the chance for errors. Furthermore, it provides the ability to efficiently manage and distribute localized SKUs, thereby meeting the needs and requirements of specific markets.

[0025] 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.

[0026] 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.

[0027] 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.

[0028] SPoG's innovative approach to solving distribution industry challenges is an invaluable tool. By increasing supply chain visibility, streamlining inventory management, ensuring compliance, simplifying SKU management, and delivering a superior customer experience, it offers a comprehensive solution to the complex problems that have long plagued the distribution sector. 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.

[0029] (Real-time Data Mesh (RTDM)) According to some embodiments, the platform may include an implementation of Real-Time Data Mesh (RTDM). RTDM provides an innovative solution to address these challenges. RTDM 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.

[0030] RTDM's predictive analytics capabilities provide a solution for efficient inventory control, helping companies manage their inventory by providing insight into demand trends and reducing the risk of overstocking or out-of-stock.

[0031] 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.

[0032] RTDM also simplifies SKU management and localization by consolidating data from various OEMs, ensuring data consistency and reducing the chance of errors. Its ability to manage and distribute localized SKUs efficiently aligns with specific market needs.

[0033] 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.

[0034] (SPoG and RTDM Integration Advantages) 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, streamline inventory management, ensure compliance, simplify SKU management, and deliver a superior customer experience.

[0035] 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.

[0036] Integrating SPoG with RTDM also ensures data consistency and reduces errors in SKU management, and by providing a centralized platform for managing data from various OEMs, it simplifies product localization and helps meet market needs.

[0037] 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.

[0038] 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]

[0039] [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 PIPR and product roadmap generation 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 an automated PIPR and product roadmap generation process, according to one embodiment. [Figure 8] FIG. 1 is a flow diagram of a method for automated predictive analytics and insight generation in a PIPR system, according to some embodiments of the present disclosure. [Figure 9] FIG. 1 is a flow diagram of automated roadmap adjustment and optimization in a PIPR system, according to some embodiments of the present disclosure. [Figure 10] FIG. 1 is a flow diagram illustrating automated real-time data integration and analysis flow within a PIPR 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

[0040] 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.

[0041] 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.

[0042] 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.

[0043] 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. Through system 110, customers can also access real-time data and analytics to make informed decisions and optimize their IT infrastructure.

[0044] 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.

[0045] 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 leverage system 110 to streamline supply chain operations, manage pricing and promotions, and gain insights 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.

[0046] 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.

[0047] 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.

[0048] 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.

[0049] 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 within the IT distribution ecosystem, improve customer experience, and drive business success.

[0050] Figure 2 illustrates the PIPR platform's operating environment 200, expanding on the elements introduced in Figure 1. The environment features integration points 210, enabling data flow and connectivity between various systems, such as customer systems 220, vendor systems 240, reseller systems 260, and other entities important to the predictive insights process. Figure 2 illustrates the network interconnectivity and mechanisms that facilitate collaborative, data-driven decision-making to optimize vendor product roadmaps. Operating environment 200 is configured to leverage AI and ML technologies to automate the process of generating predictive insights and analyze comprehensive datasets for strategic product development.

[0051] Some embodiments of the PIPR process involve a structured approach to analyzing vast amounts of data, including market trends, consumer feedback, and product performance metrics, and transforming this data into actionable insights for vendor product roadmaps. This process utilizes key technology components: data collected from CRM systems, web analytics tools, and direct customer interactions is fed into a real-time data mesh (RTDM). RTDM processes and normalizes this data, serving as a centralized hub for real-time insights. An advanced analytics and machine learning (AAML) module then analyzes this aggregated data to identify trends, preferences, and opportunities for demand forecasting and market adaptation. The PIPR management module, informed by the AAML module's insights, applies predictive models to adapt product development strategies and roadmap adjustments to anticipated market demand. Users interact with these insights through the SPoG UI, enabling a dynamic and responsive approach to product roadmap planning. The system includes a feedback mechanism to capture market responses and refine predictive models for continuous improvement.

[0052] AI algorithms within the PIPR process are adapted to optimize product roadmaps through advanced data analysis. Machine learning models such as deep learning networks and decision trees are employed to process and interpret complex data sets. These models enable the system to predict market trends and consumer needs with high accuracy. The integration of AI and ML technologies in the operating environment 200 utilizes both supervised and unsupervised learning algorithms for comprehensive pattern recognition and predictive analysis, ensuring product roadmaps remain agile and adapted to changing market conditions.

[0053] The operating environment 200 positions the system 110 as a central platform for managing the PIPR process, bridging customer systems 220, vendor systems 240, reseller systems 260, and other related entities. Communication, data exchange, and collaborative decision-making are facilitated, providing a unified interface for all parties. The integration point 210 employs a hybrid architecture combining RESTful APIs and WebSockets to ensure efficient, real-time data exchange and synchronization across networks secured with SSL / TLS protocols to protect data integrity.

[0054] 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 allows customers to participate in the PIPR process. This integration provides an automated, real-time solution for automating the PIPR process, improving operational efficiency for customers.

[0055] Data exchange between customer systems 220, vendor systems 240, and reseller systems 260 is enabled by robust ETL processes, ensuring data consistency and reliability. This interaction can be governed by predefined business rules and logic that dictate data flow and processing methodology. Advanced mapping and transformation tools are employed to harmonize disparate data formats and enable the integration and utilization of data across systems. Orchestrated data exchange supports synchronized operations, enabling efficient, informed decision-making across the distribution network.

[0056] 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 PIPR processing by providing relevant market and product data.

[0057] Vendor System Integration: Integration point 210 facilitates connectivity between system 110 and vendor systems 240. Vendor systems 240 may include entities such as vendor system 241, vendor system 242, and vendor system 243, which represent inventory management, pricing systems, and product catalogs. Integration with vendor systems 240 ensures that vendors can efficiently update their product offerings and receive real-time notifications, facilitating the PIPR process.

[0058] 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, and represents the reseller's operations such as sales, customer management, and order processing. The integration allows resellers to access up-to-date product information and effectively manage customer relationships.

[0059] 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 automated PIPR processing and decision-making in the distribution ecosystem.

[0060] The system 110 configuration includes advanced AI and ML capabilities to automate PIPR processing according to individual preferences and ensure relevance and optimization in the distribution process.

[0061] 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 operational and 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 and 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.

[0062] 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.

[0063] 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.

[0064] 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.

[0065] In some embodiments, system 110 leverages advanced technologies such as Typescript, NodeJS, ReactJS, .NET Core, C#, and other suitable technologies to support integration points 210 and enable communication within operating environment 200. These technologies provide a 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.

[0066] 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.

[0067] 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.

[0068] The customer system and its components may be operator-configurable using applications, including a web browser, running on a central processing unit, such as an Intel Pentium processor or similar processor. Similarly, the distribution platform (system 110) and its components may be operator-configurable using applications running on a central processing unit, such as an Intel Pentium processor or similar processor, and / or a processor system that may include multiple processing units.

[0069] An embodiment of a computer program product includes a machine-readable storage medium containing instructions for programming a computer to perform the processes described herein. The computer code for operating and configuring the distribution platform and customer systems, vendor systems, reseller systems, and systems of other entities to 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.

[0070] 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.

[0071] It will be appreciated that the present embodiment can be implemented in a variety of programming languages ​​executed on the client system, server, or server system, and the choice of language may depend on the particular requirements and environment of the distribution platform.

[0072] This 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.

[0073] 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.

[0074] 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 allows users to access relevant information and tools, enabling data-driven decision-making and efficient supply chain and distribution activity management.

[0075] 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.

[0076] SPoG UI 305 integrates with other modules in the system 300 to facilitate real-time data exchange, synchronized operations, and streamlined 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.

[0077] 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.

[0078] 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.

[0079] 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.

[0080] 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.

[0081] 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.

[0082] 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.

[0083] 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.

[0084] 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.

[0085] In addition to forecasting demand, the AAML module can provide insights into customer behavior, enabling targeted marketing campaigns and personalized customer experiences. For example, by analyzing customer data, the module can identify cross-selling or up-selling opportunities and recommend relevant products to individual customers.

[0086] 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 guide product development decisions, identify emerging market trends, and adapt business strategies to meet evolving consumer expectations.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 4 illustrates one embodiment of an automated predictive insights for vendor product roadmaps (PIPR) system 400 designed to generate actionable insights for optimizing product development and market strategies. The system comprises key components: a single pane of glass user interface (SPoG UI) 405, a data mesh 410, an AI module 460, a customer insight collection engine 420, a product development insights engine 450, and a market trend analysis engine 440. The architecture is configured to support vendors in roadmap forecasting and strategic market positioning.

[0091] The SPoG UI 405 serves as an interactive interface for users, allowing input of data and parameters for analysis, as well as displaying insights on market trends, customer behavior, and possible roadmap adjustments. It directly connects to the AI ​​module 460 for processing and visualizing predictive analytics, facilitating an intuitive and responsive user experience for strategic decision-making.

[0092] SPoG UI405 serves as a central hub for user interaction, providing tools and functionality for data analysis, visualization, and simulation of market and customer insights. The interface provides access to real-time and historical data analysis, market trend observation, and product roadmap proposals, and integrates with broader system components to present a unified analytical dashboard. SPoG UI405 was developed using advanced web technologies to ensure accessibility from a wide range of devices and improve operational flexibility and responsiveness.

[0093] Data Mesh 410 serves as an advanced data integration and management framework, aggregating and harmonizing data from diverse sources, including internal customer databases, external market research platforms, and third-party data services, ensuring a unified data repository for all operating modules within System 400 and facilitating access to consistent, up-to-date information essential for informed decision-making on product development and market strategy.

[0094] Within Data Mesh 410, real-time data processing capabilities are emphasized to support dynamic updates of insights and predictions. The module employs advanced techniques such as change data capture (CDC) to track updates to data sources, ensuring the system's predictive models and analytical outputs reflect the latest market conditions and customer feedback.

[0095] The AI ​​module 460 utilizes advanced machine learning algorithms and data analytics to process inputs from the data mesh 410 and the customer insights collection engine 420 to generate predictive insights into market trends, customer needs, and roadmap opportunities. This analysis informs strategic decision-making regarding product features, development priorities, and go-to-market timing.

[0096] The AI ​​module 460 incorporates a wide variety of analytical techniques, including deep learning for pattern recognition of market trends, decision trees for strategic decision support, and clustering for market segmentation. These methodologies enable the module to provide sophisticated insights into customer preferences and behaviors, supporting targeted product roadmap forecasting and differentiation strategies.

[0097] The module also uses reinforcement learning to refine product strategy based on ongoing market feedback, ensuring product development efforts continuously align with market demand and customer expectations. It employs natural language processing (NLP) techniques to analyze customer feedback and market comments to enrich product development insights with qualitative data.

[0098] The real-time processing power of the AI ​​module 460, supported by the data mesh 410, enables dynamic adjustments to product strategy based on the latest market trends and customer interactions. Predictive analytics tools facilitate forecasting of future market demand, while Monte Carlo simulation and scenario analysis assist in the evaluation of strategic options and their possible market impacts.

[0099] The Customer Insights Collection Engine 420 (CICE420) is designed to autonomously aggregate and analyze customer data across multiple touchpoints, including direct feedback, social media interactions, purchase history, and engagement analysis. Utilizing one or more capabilities such as web scraping, API integration, and direct data feeds, CICE420 compiles comprehensive data sets reflecting customer behavior, preferences, and sentiment. The engine employs natural language processing (NLP) to interpret open-ended feedback, sentiment analysis algorithms to measure customer satisfaction, and machine learning models to identify patterns in purchasing behavior. Collected data is sanitized and structured for compatibility with the broader System 400 ecosystem, particularly for analysis by the AI ​​module 460.

[0100] CICE 420 acts as a centralized repository that feeds structured customer insights into data mesh 410, ensuring uniformity and accessibility of data. This integration enables AI module 460 to utilize up-to-date customer insights to generate predictive analytics, ensuring recommendations are closely aligned with customer needs and expectations. In a non-limiting example, CICE 420 may collect and analyze customer reviews from online retail sites via data mesh 410 to gauge sentiment about new software updates and generate recommendations to vendors to address common issues in the next update cycle.

[0101] The Product Development Insights Engine 450 (PDIE450) applies advanced analytics to translate aggregated market data and customer insights into actionable product development strategies. Leveraging advanced data analytics, including regression analysis to predict future market needs and conjoint analysis to evaluate product feature tradeoffs, PDIE450 identifies areas where product innovation or improvement could provide a competitive advantage. The engine is tightly integrated with the AI ​​module 460, utilizing its predictive modeling capabilities to anticipate market reactions to potential product changes and enable data-driven decision-making. In a non-limiting example, PDIE450 may work with one or more AI / ML models and statistical algorithms provided by the AI ​​module 460 to analyze historical sales data and external market conditions to forecast demand for a new smartphone model. This analysis enables a mobile phone vendor to adjust production schedules and marketing strategies to align with anticipated peaks and valleys in demand. In another example, PDIE450 may identify gaps in a vendor's market for environmentally friendly packaging based on competitor analysis and consumer trend data and provide insights to incorporate these and improve biodegradable packaging solutions.

[0102] PDIE 450 recommendations are incorporated into the data mesh 410, facilitating a dynamic feedback loop with the AI ​​module 460, which refines these insights through predictive modeling. This integration ensures that product development strategies are not only informed by historical data, but are also forward-looking, taking into account estimated market trends and customer preferences.

[0103] The Market Trend Analysis Engine 440 (MTAE440) is tasked with continuously monitoring and analyzing global market trends, competitor strategies, and the regulatory environment. Utilizing a combination of data mining techniques to gather relevant market intelligence and complex event processing for real-time trend analysis, MTAE440 provides a comprehensive overview of external factors influencing market dynamics. The engine employs predictive analytics to anticipate future market shifts and anticipate competitor moves, providing a strategic advantage to align product roadmaps with future market needs. In a non-limiting example, MTAE440 monitors global trends indicating growing interest in home automation, encouraging home appliance manufacturers to invest in smart home devices, driving conversions and market share growth earlier in the trend curve.

[0104] The insights generated by MTAE 440 are fed into Data Mesh 410, which enriches the dataset with an external market perspective. This comprehensive market understanding supports AI Module 460 in developing sophisticated predictive models that consider both internal customer insights and external market factors, enabling the creation of robust, market-aligned product strategies.

[0105] Integration between CICE 420, PDIE 450, and MTAE 440 is facilitated through integration with Data Mesh 410 and AI Module 460, enabling system 400 to provide an efficient approach to product roadmap planning. This integrated system architecture ensures all components contribute to a unified analysis, leveraging diverse data sources to inform strategic product development decisions. By automating the collection, analysis, and application of market and customer insights, system 400 streamlines the product roadmap forecasting process, improving its efficiency and effectiveness.

[0106] Vendors interact with the system 400 through the SPoG UI 405, inputting product parameters and market queries to initiate the analysis process. Powered by the AI ​​module 460, the system 400 processes these inputs in collaboration with the insight engine to deliver strategic recommendations and predictive insights. This process involves complex data transformation and analysis, made accessible and actionable through the intuitive interface of the SPoG UI 405.

[0107] This enables system 400 to automate the generation of predictive insights for vendor product roadmaps, facilitating advanced market analysis, mining deep customer insights, and strategic product roadmap recommendations. This automation helps vendors make informed decisions, optimize their product offerings, and stay competitive in a dynamic marketplace.

[0108] 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 insight 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.

[0109] 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.

[0110] 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. It utilizes front-end technologies such as ReactJS, TypeScript, and Node.js to create an interactive and responsive user interface. These technologies enable the SPoG UI 505 to deliver a user-friendly experience, allowing users to access relevant information, navigate through different modules, and perform tasks efficiently.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] The Performance and Insights Marker Display 545 collects, analyzes, and visualizes real-time performance metrics and insights related to supply chain operations. Utilizing tools like 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.

[0119] 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.

[0120] 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.

[0121] 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 delivery of relevant information.

[0122] 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.

[0123] 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.

[0124] 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 insight 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.

[0125] (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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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 ERPs (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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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 real-time insights into the distribution ecosystem.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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, insight 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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 to make informed decisions and optimize supply chain operations. Thus, the RTDM module 600 facilitates supply chain and distribution management by providing a scalable, real-time data management solution. Its 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.

[0145] (Automated Predictive Insights (PIPR) System for Vendor Product Roadmaps) In one embodiment, FIG. 7 illustrates a system 700 configured to generate predictive insights for vendor product roadmaps. The system 700 includes a real-time data mesh (RTDM) 710, a single pane of glass user interface (SPoG UI) 705, an advanced analytics and machine learning (AAML) module 715, and a product roadmap insights (PRI) module 720. The system 700 can integrate streaming data processing capabilities within the RTDM 710, enabling it to handle high-velocity real-time data, which is essential for rapid market responsiveness. The RTDM 710 aggregates and normalizes data from diverse sources and processes this information in real time, providing the foundation for dynamic, data-driven decision-making. The RTDM 710 also integrates advanced data security measures, including encryption and granular access control, to protect sensitive information and ensure compliance with strict data protection regulations.

[0146] The Single Pane of Glass User Interface (SPoG UI) 705 serves as the primary interface for the system 700, providing users with access to a wide array of data analysis tools and visualizations. The SPoG UI 705 allows users to interact with complex data sets, perform market trend analysis, and receive predictive recommendations for roadmap adjustments. This interface streamlines the process of extracting and interpreting actionable insights from data, enabling agile responses to evolving market conditions.

[0147] The Real-Time Data Mesh (RTDM) 710 serves as the central repository for data aggregation and standardization within the system 700. RTDM 710 collects data from a variety of sources, including customer feedback, product performance metrics, competitive intelligence, and market trends. It employs data warehouses and data lakes to efficiently manage both structured and unstructured data. Through the use of extract, transform, and load (ETL) processes and data normalization techniques, RTDM 710 ensures data consistency and accessibility across systems, facilitating real-time analysis and decision-making.

[0148] The Advanced Analytics and Machine Learning (AAML) module 715 serves as the analytics engine of the system 700, leveraging specialized algorithms to analyze data aggregated by the RTDM 710. The AAML module 715 utilizes big data analytics tools and deep learning capabilities to perform sentiment analysis, trend forecasting, and customer behavior analysis. It integrates machine learning algorithms trained on historical and real-time data to identify patterns and generate predictive insights for product roadmap adjustments. The module's architecture supports continuous learning, allowing algorithms to be refined based on feedback loops to improve the precision and accuracy of insights. The Advanced Analytics and Machine Learning (AAML) module 715 can employ advanced NLP techniques to perform deeper analysis of customer feedback and market trends. For example, the AAML module 715 can be configured to extract insights from unstructured text to better anticipate customer needs and identify emerging market opportunities. The AAML module 715 can also be configured to incorporate explainable AI (XAI) capabilities, providing transparency regarding the predictive modeling process and increasing user trust by making it clear how insights and recommendations are derived.

[0149] The Product Roadmap Insights (PRI) module 720 can be configured to utilize data processed by the AAML module 715 to generate targeted insights for product roadmap development. The PRI module 720 can also be configured to evaluate alignment between product offerings and customer demand and utilize predictive analytics to recommend strategic adjustments and improvements to product roadmaps. This module applies advanced methodologies to interpret market signals, align product strategy with market demand, and foster a proactive approach to product development. The Product Roadmap Insights (PRI) module 720 can leverage data insights to provide targeted recommendations for product roadmap development and incorporates dynamic catalog management for automatically updating product offerings based on the results of predictive analytics. The PRI module 720 includes predictive maintenance capabilities for hardware vendors, using IoT data to anticipate maintenance needs and improve product reliability.

[0150] The Market Analysis submodule 725 extends the analytical capabilities of the system 700, processing data from the RTDM 710 for comprehensive market demand analysis, competitor benchmarking, and industry trend assessment. This submodule employs analytical tools to assess product market fit and identify opportunities for improvement and differentiation within the product roadmap. The Market Analysis submodule 725 integrates market trend estimation tools to analyze long-term market trends and assists in strategic planning, including monitoring regulatory compliance, to alert vendors to changes that may impact product development and strategy.

[0151] The innovation opportunity engine 730 identifies and prioritizes product development opportunities by analyzing market data and customer insights from the RTDM 710. By integrating the predictive models of the AAML module 715, the engine dynamically suggests areas for innovation and improvement to ensure product offerings align with market needs and customer expectations. The innovation opportunity engine 730 can include enhanced capabilities to dynamically identify product development opportunities and analyze market data and customer insights to assess sustainability impacts and guide vendors toward environmentally conscious product strategies. The innovation opportunity engine 730 can perform analysis that enables the identification of untapped markets for specific mobile apps using office productivity trends and recommends to vendors that apps be developed to meet the needs of this demographic.

[0152] The Strategy Adjustment Engine 735 provides vendors with the tools to adjust their product roadmaps based on real-time insights from the RTDM 710. Integrated with the SPoG UI 705, the engine supports strategic selection and customization of roadmap strategies, providing the flexibility needed to adapt to market changes and customer feedback. The Strategy Adjustment Engine 735 provides an array of tools for real-time roadmap strategy adjustment and supports multi-tenancy, allowing multiple departments or organizations to use the system 700 while ensuring data isolation and security.

[0153] The roadmap optimization generator 740 performs real-time data synchronization with the RTDM 710 through RESTful APIs and data connectors to optimize product roadmap strategies to capitalize on market opportunities and align with vendor capabilities. The roadmap optimization generator 740 introduces workflow automation to orchestrate complex data processing and analysis tasks across modules to efficiently optimize product roadmap strategies to align with market opportunities and vendor capabilities. For example, after a competitor releases a groundbreaking product, the roadmap optimization generator 740 may perform an evaluation of the vendor's roadmap to accelerate the development of competing technologies and ensure market competitiveness.

[0154] The Competitive Positioning Model 745 uses data from the RTDM 710 to analyze the competitive landscape and recommend optimal market positioning strategies to improve a vendor's market differentiation and competitive advantage. For example, using competitive intelligence captured via the RTDM 710, the Competitive Positioning Model 745 can generate insights to advise a hardware vendor on positioning new products in higher segments based on feature uniqueness and market readiness, and optimize pricing strategies.

[0155] The validator 750 verifies that roadmap strategies and product development plans are aligned with market data and vendor goals, employing algorithms for consistency checking and error prevention to support strategic roadmap planning. The competitive positioning calculator 745 and validator 750 are configured with federated learning capabilities to perform analysis across distributed datasets while maintaining data privacy. They utilize predictive analytics and machine learning algorithms to improve competitive positioning and ensure roadmap consistency.

[0156] System 700 integrates multi-layer management modules for handling interactions across the product development, market analysis, and strategy formulation layers. It includes API integration and lifecycle management tools for executing roadmap strategies. System 700 integrates data from multiple sources into a unified interface via SPoG UI 705, automates the generation of predictive insights for product roadmaps via AAML 715, and maintains a real-time, standardized data repository via RTDM 710. This architecture enables an efficient and accurate process for developing and aligning vendor product roadmaps, improving strategic decision-making in the technology product industry.

[0157] 8 illustrates a flow diagram of a method 800 for predictive analytics and insight generation in a PIPR system, tailored to optimizing product roadmap strategy through data-driven insights. The method outlines a series of operations that leverage the capabilities of the Advanced Analytics and Machine Learning (AAML) module 715 and focuses on extracting, analyzing, and applying predictive insights derived from comprehensive data sets.

[0158] At operation 810, the system begins the predictive analytics process. The AAML module 715 begins by aggregating data collected by the RTDM 710, which includes a wide range of inputs such as customer feedback, market trends, competitive intelligence, product performance metrics, etc. This aggregation process is fundamental and ensures that the analysis is based on a comprehensive view of the data environment relevant to product development and market positioning.

[0159] At operation 820, the AAML module 715 performs advanced natural language processing (NLP) analysis. This operation involves parsing and interpreting unstructured text data from various sources, using sentiment analysis to measure customer sentiment and employing techniques such as thematic analysis to identify prevailing topics and trends within customer feedback and market commentary. The ability to extract meaningful insights from unstructured data is essential to understanding complex market demands and customer expectations.

[0160] At operation 830, the AAML module 715 can perform trend forecasting and customer behavior analysis. Using statistical models, machine learning algorithms, and / or a combination thereof, the AAML module 715 analyzes historical and real-time data to predict future market trends and customer behavior. This predictive modeling considers factors such as seasonal fluctuations, market dynamics, and emerging trends to forecast future conditions and identify potential opportunities or challenges in product strategy.

[0161] At operation 840, the AAML module 715 integrates AI and / or ML algorithms to improve the transparency and reliability of the predictive modeling process. Operation 840 may include generating insights into how the module's algorithms arrived at their predictions and recommendations, providing the user with a clear understanding of the analytical foundation behind proposed roadmap adjustments. In some embodiments, operation 840 may include incorporating explainable AI (XAI) capabilities into the roadmap generation analysis. Incorporating XAI can improve the transparency of the system's output, enabling more informed decision-making.

[0162] At operation 850, the system synthesizes insights generated from the predictive analytics process and formulates recommendations for product roadmap development. The PRI module 720 leverages these insights to assess alignment between existing product offerings and identified market needs and propose strategic adjustments or improvements to the product roadmap. This operation is essential for turning data-driven insights into tangible roadmap strategies that align with market demands and customer expectations.

[0163] At operation 860, the system communicates the predictive insights and roadmap recommendations to the user. Through the SPoG UI 705, the user is presented with a comprehensive overview of the insights derived from the analysis process, along with specific recommendations for adjusting the product roadmap. In some embodiments, operation 860 can include automated implementation of one or more roadmap recommendations. Operation 860 enables the user to benefit from pattern recognition and analytical decision-making to improve the competitiveness and market fit of their product.

[0164] As shown in Figure 8, method 800 represents a systematic approach for leveraging advanced analytics and machine learning for the generation of predictive insights within a PIPR system. By following this method, vendors can harness the power of data to guide and optimize their product roadmap strategy, ensuring their offerings remain responsive to market trends and customer needs.

[0165] 9 illustrates a flow diagram of a method 900 for roadmap adjustment and optimization in a PIPR system, designed to dynamically align product roadmaps with evolving market trends and customer demands. The method utilizes the Predictive Insights and Roadmap Integration (PRI) module 720 and its integration with other system components to ensure product strategies are forward-looking and based on data-driven insights.

[0166] At operation 910, the system initiates the roadmap adjustment process. The PRI module 720 employs insights generated from advanced analytics processes to perform a comprehensive assessment of the current product roadmap. This involves comparing the existing roadmap trajectory with the latest predictive insights into market trends, customer demand, and the competitive environment to identify areas requiring adjustment or improvement.

[0167] In operation 920, based on the evaluation, the system formulates strategic adjustments or improvements to the product roadmap. This step leverages data-driven insights for decision-making and ensures that proposed changes are not only responsive to current market conditions but proactive in anticipating future shifts. Strategies may include introducing new features, reprioritizing development efforts, or adjusting product launch timelines.

[0168] At operation 930, the system implements dynamic catalog management to reflect strategic adjustments guided by predictive insights. This operation involves automatically updating product catalogs and offerings in real time to ensure that market-facing aspects of product strategy are aligned with internal roadmap adjustments. This dynamic catalog management is facilitated through integration with a dynamic catalog management system (DCMS) to enable instant updates across digital and physical sales channels.

[0169] At operation 940, the method incorporates hardware product performance monitoring and maintenance prediction. Utilizing IoT data, the system implements a predictive maintenance strategy that anticipates potential service needs before they become critical issues. This preventative maintenance approach not only improves product reliability, but also optimizes resource allocation to maintenance activities and ensures that service interventions are timely and efficient.

[0170] In operation 950, the system establishes a feedback loop for continuous learning and adaptation, which involves continually refining predictive models and strategies based on real-time market response and product performance data. By incorporating feedback mechanisms, the system ensures that the predictive analysis and roadmap optimization process remains dynamic and responsive to changing market conditions and customer feedback.

[0171] At operation 960, the method concludes with the implementation of the adjusted roadmaps and ongoing monitoring of their performance. Stakeholders are provided with detailed reports on the success of the roadmap adjustments, including metrics related to market response, product performance, and customer satisfaction. This final step ensures that strategic decisions guided by predictive insights are effectively executed and the impact of these decisions is closely monitored for continuous improvement.

[0172] As shown in Figure 9, method 900 outlines a comprehensive approach to ensuring that product roadmaps are continuously optimized to align with market demands and customer expectations. By integrating predictive analytics, dynamic catalog management, and continuous model training mechanisms, the PIPR system enables vendors to maintain a competitive advantage through a data-driven roadmap strategy.

[0173] 10 illustrates a flow diagram of a method 1000 for real-time data integration and analysis flow within a predictive insights and product roadmap (PIPR) system. The method facilitates the integration, processing, and analysis of real-time data to drive predictive insights and strategic decision-making.

[0174] In operation 1010, the PIPR system begins the data collection process, which involves aggregating real-time data from a myriad of sources, such as customer feedback platforms, market trend analysis, competitive intelligence databases, and product performance metrics. This comprehensive data collection is essential to understanding the multifaceted aspects of market demand, customer expectations, and the competitive environment.

[0175] In operation 1020, the collected data is processed by the Real-Time Decision Manager (RTDM) 710. This step encompasses the Extract, Transform, Load (ETL) process, where data is carefully extracted from its sources, transformed to ensure consistency and relevance, and finally loaded into a centralized data warehouse or data lake for further analysis.

[0176] In operation 1030, data standardization occurs within the RTDM 710. This operation applies advanced data normalization techniques to ensure uniformity across diverse data sets. The standardization process is essential to facilitate accurate and meaningful analysis by removing inconsistencies and preparing the data for a comprehensive analysis process.

[0177] In operation 1040, the PIPR system implements security measures to protect the integrity and confidentiality of collected data, including enforcing data encryption standards and strict access control policies. Such security implementations are critical to maintaining customer and partner trust while ensuring compliance with regulatory requirements.

[0178] In act 1050, the normalized and secured data is subjected to analysis by the AAML module 715. Act 1050 may include performing analytics to process the data in real time, allowing for the rapid identification of trends, insights, and potential opportunities for the product roadmap. Through this dynamic analysis, the system can generate actionable intelligence to guide decision-making and strategy development.

[0179] In act 1060, insights derived from real-time data analysis are integrated into the decision-making process. This involves using the insights to guide and adjust product roadmaps, marketing strategies, and customer engagement plans. The ability to act on real-time insights ensures that organizations remain agile and responsive to market trends and customer needs.

[0180] In operation 1070, the PIPR system establishes a continuous feedback loop. This operation involves monitoring the success of the implemented strategies and the performance of the product in the market. Feedback collected through this loop is fed back into the system, allowing for continuous learning and adaptation of the predictive models and strategies based on real-world performance data and market reactions.

[0181] Through method 1000, the PIPR system leverages real-time data integration and analysis to foster a proactive and informed approach to product development, marketing, and customer engagement. The method emphasizes the importance of agile decision-making supported by data-driven insights, ensuring organizations can quickly adapt to market changes and customer demands.

[0182] 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.

[0183] 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 .

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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 computer-implemented method for generating predictive insights regarding a vendor product roadmap, comprising: Automatically collecting data from two or more predefined sources, including market trends, customer feedback, and technology advancements, which data is collected and standardized by a data layer within a real-time data mesh (RTDM); analyzing the collected data using machine learning models in an Analysis and Machine Learning (AAML) module to identify one or more patterns, the models including at least one of a convolutional neural network for image data patterns and a recurrent neural network for temporal data patterns; generating predictive insights based on the patterns for a vendor product roadmap based on the analysis, the insights suggesting one or more potential product modifications; and automatically updating a vendor product roadmap in a roadmap management module to incorporate the generated predictive insights.

2. 10. The method of claim 1, further comprising validating the predictive insights against historical data to ensure accuracy and relevance, wherein the validating is performed using statistical analysis algorithms to compare the predictive insights against a historical data repository of the RTDM and validate the relevance of the predictive insights based on a predefined accuracy threshold.

3. The method of claim 1 , wherein the machine learning model is dynamically updated to incorporate new data sources and types.

4. 10. The method of claim 1, further comprising visualizing the predictive insights on a user interface for interpretation, and wherein generating the predictive insights comprises identifying the one or more potential product modifications via a decision tree analysis algorithm.

5. The method of claim 1 , further comprising notifying stakeholders of key insights or roadmap changes through automated communication channels.

6. The method of claim 1 , wherein the collected data includes competitive analysis to gauge market positioning and differentiation opportunities.

7. The method of claim 1 , further comprising integrating feedback from the updated product roadmap to refine future predictions.

8. 1. A computer-implemented method for optimizing product roadmap development based on predictive insights, comprising: receiving predictive insights generated from an analysis of market trends, customer feedback, and / or technology advancements collected and normalized by a data layer within a real-time data mesh (RTDM) via an analytics and machine learning (AAML) module; Dynamically adjusting existing product roadmaps to align with the latest predictive insights, including modifying development timelines and product priorities, using a roadmap optimization module; and communicating the adjusted product roadmap to a user through a unified communication module of a single pane of glass (SPoG) user interface (UI) to facilitate action.

9. 10. The method of claim 8, further comprising applying scenario analysis to evaluate possible outcomes of roadmap adjustments based on real-time data captured and processed by the RTDM.

10. 10. The method of claim 8, comprising utilizing a decision support system to prioritize roadmap adjustments based on strategic importance and resource availability based on insights derived via the data engine layer of the RTDM.

11. The method of claim 8 , further comprising aggregating insights from multiple data sources for product improvement.

12. 10. The method of claim 8, wherein adjustments to the product roadmap are dynamically reflected in project management tools and resource allocations through real-time data synchronization with the RTDM.

13. 10. The method of claim 8, further comprising continuously monitoring market reaction to implemented roadmap changes via the data layer of the RTDM to inform future adjustments.

14. 10. The method of claim 8, further comprising leveraging a collaborative platform for user feedback regarding adjustments to the roadmap to ensure alignment with market needs and / or organizational goals.

15. 1. A system for generating and optimizing a vendor product roadmap based on predictive insights, comprising: a real-time data mesh (RTDM) configured to automatically aggregate data from predefined sources relevant to product development; an analytics and machine learning (AAML) module comprising a machine learning engine designed to analyze the aggregated data and generate predictive insights regarding market trends, customer preferences, and technological advancements; a roadmap optimization module for dynamically adjusting a product roadmap based on the generated insights to ensure the roadmap is aligned with current market demands and opportunities; and a communications module for distributing updated roadmaps and insights to stakeholders to enable informed decision-making and strategic planning.

16. 16. The system of claim 15, wherein the data collection module integrates real-time market data to ensure up-to-date analysis via a data layer within the RTDM.

17. The system of claim 15 , wherein the AAML module performs predictive analysis and / or scenario modeling for decision support.

18. 16. The system of claim 15, comprising an automated notification system for alerting users of important insights and roadmap updates.

19. 16. The system of claim 15, comprising a feedback mechanism for incorporating user input into the roadmap development process via a single pane of glass (SPoG) user interface (UI).

20. 20. The system of claim 19, wherein the SPoG UI is configured to support cross-functional accessibility and enables collaborative review and adjustment of product roadmaps.

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