TECHNOLOGY AREAS: Trusted AI and Autonomy; Advanced Computing and Software; Biotechnology; Hypersonics; Integrated Network System-of-Systems OBJECTIVE: The objective of this topic is to enhance the efficiency, resilience, and responsiveness of the DAF supply chain through cutting-edge predictive analytics. By leveraging advanced machine learning models, particularly those utilizing the DAF's existing knowledge graphs and graph databases, the program aims to develop innovative solutions for supply chain analysis. It seeks proposals that explore novel ML techniques, drawing inspiration from breakthroughs like AlphaFold's deep learning approach to predict and mitigate potential disruptions. Additionally, the program invites optional exploration into quantum machine learning to enhance these predictive capabilities further. The overarching goal is to address critical supply chain questions, such as anticipating demand fluctuations, predicting disruptions from various factors, identifying delivery bottlenecks, detecting counterfeiting, and optimizing resource allocation and cost-effectiveness, thereby ensuring mission readiness and national security. The end state for this project would be a highly efficient, resilient, and responsive DAF supply chain system powered by advanced predictive analytics. Specifically, this would include: 1. Implementation of cutting-edge machine learning models that leverage DAF's existing knowledge graphs and graph databases to provide accurate supply chain predictions. 2. Development of innovative solutions for supply chain analysis, inspired by breakthroughs like AlphaFold's deep learning approach, to predict and mitigate potential disruptions. 3. A robust system capable of addressing critical supply chain questions, including: Anticipating demand fluctuations with high accuracy Predicting disruptions from various factors (e.g., geopolitical events, natural disasters) Identifying and resolving delivery bottlenecks proactively Detecting counterfeit parts or materials in the supply chain Optimizing resource allocation and cost-effectiveness across the entire supply network 4. Improved mission readiness and enhanced national security through a more agile and resilient supply chain. 5. Significant reduction in supply chain-related issues, leading to improved operational efficiency and reduced downtime for DAF assets. 6. Enhanced collaboration and information sharing across different branches of the DAF and with key suppliers, facilitated by the new predictive analytics platform. DESCRIPTION: To meet the objective of establishing a highly efficient, resilient, and responsive DAF supply chain system powered by advanced predictive analytics and machine learning technologies, the following comprehensive work plan is proposed. This plan encompasses research, development, integration, testing, training, and support activities required to achieve the end state described above. 1. Needs Assessment and Requirement Analysis Conduct a detailed assessment of the current supply chain operations, identifying specific needs and pain points. Collaborate with DAF stakeholders to gather requirements, ensuring the solution aligns with operational goals and security protocols. Develop a comprehensive requirements document outlining technical, functional, and performance criteria. 2. Data Collection and Integration Inventory and catalog existing data sources, including the knowledge graph and graph database, ERP systems, logistics databases, and external data sources (e.g., weather data, geopolitical data). Design and implement data integration frameworks to consolidate and harmonize data from diverse sources into a centralized data repository. Ensure data quality, consistency, and security through robust data governance practices. 3. Predictive Analytics Platform Development Design and develop a scalable predictive analytics platform tailored to the DAF supply chain needs. Implement advanced machine learning models and algorithms capable of performing real-time data analysis and forecasting. Explore and integrate quantum machine learning techniques to enhance predictive capabilities for complex supply chain scenarios. Develop a user-friendly interface with advanced visualization tools and dashboards to present actionable insights. 4. Algorithm Development and Testing Develop and fine-tune predictive models for various supply chain scenarios, including demand forecasting, disruption prediction, resource optimization, and counterfeit detection. Conduct rigorous testing and validation of models using historical data and simulated scenarios to ensure accuracy and reliability. Implement continuous improvement mechanisms to refine models based on real-time feedback and performance metrics. 5. Blockchain Integration for Supply Chain Security Collaborate with DIB partners to design and implement a blockchain-based system for supply chain security and transparency. Develop and integrate blockchain protocols to detect and prevent counterfeiting, ensuring the integrity of parts and supplies. Conduct security assessments and penetration testing to ensure the blockchain solution meets DAF security standards. 6. System Integration and Interoperability Integrate the predictive analytics platform with existing DAF systems, ensuring seamless data flow and interoperability. Develop APIs and middleware solutions to facilitate integration with external systems and data sources. Conduct end-to-end testing to validate the integrated system's functionality, performance, and reliability. 7. User Training and Support Develop comprehensive training programs to equip supply chain managers and decision-makers with the skills to effectively use the predictive analytics platform. Provide hands-on training sessions, workshops, and e-learning modules to ensure widespread adoption and proficiency. Establish a support center to offer ongoing technical assistance, troubleshooting, and system updates. 8. Pilot Testing and Evaluation Implement a pilot program to test the predictive analytics platform in a controlled environment, involving selected DAF units. Collect feedback and performance data during the pilot phase to identify areas for improvement. Evaluate the pilot results against predefined success criteria, making necessary adjustments before full-scale deployment. 9. Full-Scale Deployment and Implementation Develop a phased deployment plan to roll out the predictive analytics platform across all relevant DAF units. Ensure a smooth transition from pilot to full-scale deployment, providing continuous support and training. Monitor and evaluate the system's performance post-deployment, making iterative improvements as needed. The Air Force Research Laboratory (AFRL) Information Directorate has been identified as the primary partner organization for transition to STRATFI. AFRL will work closely with the successful Phase II awardee to integrate the developed predictive analytics platform into broader DAF supply chain management systems. 10. Ongoing Research and Development Establish a dedicated R&D team to continuously explore new technologies and methodologies to enhance the predictive analytics platform. Foster collaboration with academia, industry partners, and other stakeholders to stay at the forefront of technological advancements. Conduct periodic reviews and updates to the platform to ensure it remains state-of-the-art and aligned with evolving operational needs. PHASE I: The DAF require a robust, advanced predictive analytics platform to enhance their supply chain operations. While the ultimate goal is to develop and implement this platform, it is crucial to begin with a Phase I feasibility study. This Phase I program will lay the groundwork by thoroughly assessing current systems, identifying pain points, evaluating technological potentials, and designing preliminary models. Specifically, the program should evaluate how predictive analytics can be applied to answer critical supply chain questions, such as anticipating demand fluctuations, predicting disruptions, and optimizing resource allocation. Additionally, proposers should identify the types of data required to train these models and ensure their accuracy and reliability in operational scenarios. A comprehensive understanding of the existing supply chain operations, systems, and data sources is essential. This analysis will identify inefficiencies, bottlenecks, and areas for improvement. Engaging with stakeholders to gather specific requirements and align the program's objectives with operational goals is critical. Without this foundational work, the program risks developing solutions that do not meet end-user needs. Assessing the potential applications of predictive analytics for supply chain optimization is necessary. This step ensures that the most effective and appropriate technologies are chosen. Evaluating the potential of knowledge graphs and graph databases is crucial to understand integration potentials and challenges. Conducting small-scale tests during Phase I will help in understanding the feasibility and scalability of the proposed solutions. It is vital to identify and address technological and operational challenges early. Initial security assessments are needed to develop strategies for data protection and compliance, ensuring the solution meets stringent security requirements. A preliminary cost-benefit analysis will provide insights into the potential efficiency gains, cost savings, and return on investment. This analysis will inform decision-making and justify further investment. Developing a detailed plan for the Phase II research and development effort, including identifying key milestones, deliverables, and resource requirements, will ensure a smooth transition from Phase I to Phase II. The objectives of Phase I include gaining a comprehensive understanding of the current supply chain operations and pain points, gathering detailed requirements from stakeholders, and ensuring alignment with operational goals. Evaluating the potential and applicability of advanced technologies such as predictive analytics and AI will identify integration challenges and propose viable solutions. Developing initial designs and models that will form the basis for the predictive analytics platform, and ensuring these designs meet the identified requirements, will establish a solid foundation for further development. Conducting feasibility testing will identify potential risks and address them early in the program lifecycle. Developing strategies for data security and compliance will mitigate associated risks. Providing a clear understanding of the potential benefits and costs associated with the proposed solution through a preliminary cost-benefit analysis will justify the need for further investment in a Phase II program. Developing a detailed plan for the Phase II research and development effort, including key milestones, deliverables, and resource requirements, will ensure a smooth transition from Phase I to Phase II. The objective of Phase I is to conduct a comprehensive feasibility study and design analysis for developing an advanced predictive analytics platform for the DAF supply chain. This phase will focus on: 1. Assessing the current state of DAF and/or Space Force supply chain operations. 2. Evaluating the potential of advanced technologies like machine learning, quantum computing, and blockchain for supply chain optimization. 3. Designing preliminary models and architectures for the predictive analytics platform. 4. Conducting initial testing and validation of key concepts. 5. Developing a detailed plan for the Phase II research and development effort. Work to be Accomplished 1. Needs Assessment and Requirements Analysis Conduct a detailed assessment of current DAF supply chain operations Identify specific pain points and areas for improvement Collaborate with stakeholders to gather requirements and align with operational goals 2. Technology Evaluation Assess the potential of machine learning, quantum computing, and blockchain for supply chain optimization Evaluate existing DAF knowledge graphs and graph databases for integration potential Identify key technological challenges and potential solutions 3. Preliminary Design and Architecture Develop conceptual designs for the predictive analytics platform Create initial data integration frameworks Design preliminary predictive model PHASE II: The objective of Phase II is to build upon the foundational work conducted in Phase I, advancing the development of a predictive analytics platform for the DAF supply chain. This phase will focus on research, development, testing, and refinement to produce a well-defined deliverable prototype that meets the specified requirements and expectations. The period of performance for Phase II is 18-20 months, during which time the project will achieve key milestones and deliver tangible results. Objectives 1. Advanced Predictive Analytics Platform Development Develop and refine the predictive analytics platform, ensuring it leverages machine learning and quantum machine learning techniques. Integrate the platform with the existing knowledge graph and graph database, ensuring seamless data flow and interoperability. Implement robust data governance practices to ensure data quality, consistency, and security. 2. Algorithm Development and Optimization Design and optimize predictive models for various supply chain scenarios, including demand forecasting, disruption prediction, resource optimization, and counterfeit detection. Conduct iterative testing and validation of models to ensure accuracy and reliability. Explore and integrate quantum machine learning algorithms to enhance predictive capabilities for complex supply chain scenarios. 3. Blockchain Integration for Supply Chain Security Collaborate with DIB partners to design and implement a blockchain-based system for supply chain security and transparency. Integrate blockchain protocols to detect and prevent counterfeiting, ensuring the integrity of parts and supplies. Conduct security assessments and penetration testing to ensure the blockchain solution meets DAF security standards. 4. User Interface and Visualization Tools Develop a user-friendly interface with advanced visualization tools and dashboards to present actionable insights. Ensure the interface is intuitive and accessible, facilitating quick and informed decision-making for supply chain managers and decision-makers. 5. System Integration and Interoperability Integrate the predictive analytics platform with existing DAF systems, ensuring seamless data flow and interoperability. Develop APIs and middleware solutions to facilitate integration with external systems and data sources. Conduct end-to-end testing to validate the integrated system's functionality, performance, and reliability. 6. User Training and Support Develop comprehensive training programs to equip supply chain managers and decision-makers with the skills to effectively use the predictive analytics platform. Provide hands-on training sessions, workshops, and e-learning modules to ensure widespread adoption and proficiency. Establish a support center to offer ongoing technical assistance, troubleshooting, and system updates. 7. Pilot Testing and Evaluation Implement a pilot program to test the predictive analytics platform in a controlled environment, involving selected DAF units. Collect feedback and performance data during the pilot phase to identify areas for improvement. Evaluate the pilot results against predefined success criteria, making necessary adjustments before full-scale deployment. 8. Full-Scale Deployment and Implementation Develop a phased deployment plan to roll out the predictive analytics platform across all relevant DAF units. Ensure a smooth transition from pilot to full-scale deployment, providing continuous support and training. Monitor and evaluate the system's performance post-deployment, making iterative improvements as needed. 9. Ongoing Research and Development Establish a dedicated R&D team to continuously explore new technologies and methodologies to enhance the predictive analytics platform. Foster collaboration with academia, industry partners, and other stakeholders to stay at the forefront of technological advancements. Conduct periodic reviews and updates to the platform to ensure it remains state-of-the-art and aligned with evolving operational needs. 10. Performance Metrics and Continuous Improvement Define key performance indicators (KPIs) to measure the effectiveness and impact of the predictive analytics platform. Implement a continuous improvement process to regularly assess performance metrics and identify opportunities for enhancement. Solicit feedback from end-users to ensure the platform meets their needs and expectations, making adjustments as necessary. Prototyping Expectations 1. Operating Parameters The prototype platform should operate seamlessly with existing DAF systems, ensuring high compatibility and integration. The platform should process real-time data efficiently, providing timely and accurate predictive insights. Ensure the platform can handle large volumes of data, maintaining performance and responsiveness. 2. Testing Requirements Conduct comprehensive functional testing to ensure all features and capabilities of PHASE III DUAL USE APPLICATIONS: PHASE III DUAL USE APPLICATIONS: The contractor will pursue commercialization of the various technologies developed in Phase II for transitioning expanded mission capability to a broad range of potential government and civilian users and alternate mission applications. Direct access with end users and government customers will be provided with opportunities to receive Phase III awards for providing the government additional research & development, or direct procurement of products and services developed in coordination with the program. "Phase III efforts will focus on: Full-scale deployment of the predictive analytics platform across DAF supply chains Continued refinement and optimization of predictive analytics models and algorithms Expansion of blockchain integration for comprehensive supply chain security Development of commercial applications for the technology in non-defense sectors Ongoing support, maintenance, and updates to ensure long-term effectiveness Potential integration with other DoD branches and government agencies Collaboration with industry partners for wider adoption and further innovation REFERENCES: 1. https://doi.org/10.1038/s41586-019-1923-7; 2. https://doi.org/10.1016/j.cbpa.2021.04.005; 3. https://doi.org/10.3390/su152015088 KEYWORDS: Predictive analytics; Supply chain management; DAF; Space Force; Machine learning; Quantum machine learning; Blockchain; Supply chain security; Demand forecasting; Risk management; Resource optimization; Counterfeit detection; Data integration; Operational readiness; Logistics; AI
