The Defense Logistics Agency (DLA) is seeking proposals for an SBIR opportunity titled "AI-Powered Obsolescence for Product Prediction," aimed at leveraging AI and machine learning to predict product obsolescence within the DLA supply network. The objective is to develop innovative solutions that proactively identify equipment and support parts at risk of obsolescence, thereby enhancing mission readiness and optimizing resource allocation for the Department of Defense (DoD). This initiative is critical for addressing challenges posed by technological advancements and component shortages that affect military capabilities. Proposals for Phase I are due by June 12, 2024, with a budget not exceeding $100,000 for a 12-month duration, while Phase II can extend up to $1 million over 24 months. Interested parties can find more information and submit proposals through the DLA SBIR website.

## Description

The successful proposal should include, best practices, as well as innovation and the use of AI/ML to predict obsolescence of products within the DLA network. DLA J68 R&amp;D will provide the Platform used to develop the prototype (ARTET) Develop your phase I proposal with an end-goal in mind. A Phase III transition is the goal. TRL 3. (Analytical and Experimental Critical Function and/or Characteristic Proof of Concept)TRL 6. (System/Subsystem Model or Prototype Demonstration in a Relevant Environment)PROJECT DURATION and COST: Proposals exceeding these limits will not be evaluated.PHASE I: Not to exceed a duration of 12 months and cost of $100,000. (Firm Fixed Price)PHASE II: Not to exceed a duration of 24 months and cost of $1,000,000. (Firm Fixed Price/Level Of Effort)

## Objective

As part of DLA&rsquo;s strategic plan, one primary effort is to ensure mission readiness with equipment vital to the warfighter. DLA and the DoD face significant challenges in managing its vast and diverse equipment inventory. Obsolescence, driven by technological advancements, component shortages, and evolving geopolitical landscapes, can greatly impact operational effectives and readiness. Traditional methods of identifying obsolescence are quite often reactive and rely on manual analysis, leading to delays in product procurement and inefficient resource allocation. Obsolescence refers to the gradual loss of usefulness or value of a product or system due to advancements in technology, changes in needs, or deterioration of material. In the context of national defense, it can have significant implications for various aspects of military capabilities. Obsolescence not only applies to equipment and physical items, but also in outdated Commercial-off-the-shelf (COTS) software and operating systems that are now susceptible to cyberattacks. The Defense Logistics Agency (DLA) is seeking proposals regarding the use of AI/ML powered systems to predict obsolescence of DoD products within the DLA supply network. These predictions should plan to impact the overall DoD supply chain. By leveraging machine learning algorithms to analyze diverse data sources such as, including, but not limited to technical specifications, maintenance records, market trends, and geopolitical factors be able to identify equipment and support parts at risk from becoming obsolete. This proactive approach empowers DLA and the DoD to make informed decisions about sustainment, modernization, and lifecycle management, optimizing resource allocation and ensuring mission readiness. DLA&rsquo;s goal is to use AI/ML to address and predict a multitude of issues presented by obsolescence.

## Phase I

Proof of Concept, (TRL 3) This Phase of the project should include plans to:<ol><li>&emsp;Identify all Cyber and physical security requirements and develop a plan to meet these requirements prior to commencing a Phase II effort.</li><li>&emsp;Identify the J6 Sponsor the champion the Phase II and III efforts. </li><li>&emsp;Identify the required data from various sources, including technical manuals, maintenance logs, procurement records, market research reports, and any material related to product development and lifespan. </li><li>&emsp;Develop machine learning algorithms to analyze the collected data and identify patterns and trends that indicate potential impacts and suggesting mitigation strategies. </li><li>&emsp;Develop a plan that will enable DLA to simulate different scenarios and assess the impact of obsolescence on specific equipment and product categories or operational capabilities. </li><li>&emsp;Identify the paths required to make the system continuously learn and improve its predictive accuracy over time, adapting to changing market conditions and technological advancements. </li><li>&emsp;Establish the framework to build a collaborative library (database) of parts at risk for obsolescence and suitable replacement parts or companies that could assist in reengineering the part. </li><li>&emsp;Use AI/ML to implement strategies to extend the life of existing systems through reverse engineering and alternative sourcing to create for the development of a comprehensive obsolescence management program.</li></ol>

## Phase II

This Phase of the project should include a prototype that:1.&emsp;Confirm the J6 Sponsor the champion the Phase II and III efforts. 2.&emsp;Develop the prototype on the DLA J68 Platform3.&emsp;Integrate all required Cyber and Physical security requirements.4.&emsp;Integrate data from various sources, including technical manuals, maintenance logs, procurement records, market research reports, and any material related to product development and lifespan. 5.&emsp;Employ machine learning algorithms to analyze the collected data and identify patterns and trends that indicate potential impacts and suggesting mitigation strategies. 6.&emsp;Enable DLA to simulate different scenarios and assess the impact of obsolescence on specific equipment and product categories or operational capabilities. 7.&emsp;Have the system continuously learn and improve its predictive accuracy over time, adapting to changing market conditions and technological advancements. 8.&emsp;Build a collaborative library (database) of parts at risk for obsolescence and suitable replacement parts or companies that could assist in reengineering the part. 9.&emsp;Use AI/ML to implement strategies to extend the life of existing systems through reverse engineering and alternative sourcing to create for the development of a comprehensive obsolescence management program.

## Phase III: Dual Use Applications

Phase III is any proposal that derives from, extends or completes a transition from a Phase I or II project. Phase III proposals will be accepted after the completion of Phase I and or Phase II projects. There is no specific funding associated with Phase III, except Phase III is not allowed to use SBIR/STTR coded funding. Any other type of funding is allowed.Phase III proposal Submission. Phase III proposals are emailed directly to DLA SBIR2@dla.mil. The PMO team will set up evaluations and coordinate the funding and contracting actions depending on the outcome of the evaluations. A Phase III proposal should follow the same format as Phase II for the content and format. There are, however, no limitations to the amount of funding requested, or the period of performance. All other guidelines apply. Transition Plan1.&emsp;Period of Performance: TBD2.&emsp;Budget: $ TBDThis Phase of the project should include:1.&emsp;Delivery of a production level product to J68 ready for integration into the overall DLA Enterprise system.2.&emsp;Develop a sustainment plan to support the delivered system for the lifetime of the program.

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