DOD SBIR 24.2 Annual

Active
No
Status
Open
Release Date
April 17th, 2024
Open Date
May 15th, 2024
Due Date(s)
June 12th, 2024
Close Date
June 12th, 2024
Topic No.
DLA242-005

Topic

AI-Powered Obsolescence for Product Prediction

Agency

Department of DefenseN/A

Program

Type: SBIRPhase: BOTHYear: 2024

Summary

The Department of Defense (DOD) is seeking proposals for the topic of "AI-Powered Obsolescence for Product Prediction" as part of their Small Business Innovation Research (SBIR) program. The Defense Logistics Agency (DLA) is specifically interested in the use of AI/ML powered systems to predict obsolescence of DoD products within the DLA supply network. The goal is to leverage machine learning algorithms to analyze diverse data sources and identify equipment and support parts at risk of becoming obsolete. This proactive approach will enable informed decisions about sustainment, modernization, and lifecycle management, optimizing resource allocation and ensuring mission readiness. The project duration for Phase I is 12 months with a cost of $100,000, while Phase II has a duration of 24 months and a cost of $1,000,000. The successful proposal should include best practices, innovation, and the use of AI/ML to predict obsolescence. The project should also include plans for cyber and physical security requirements, data collection and analysis, simulation of different scenarios, and the establishment of a collaborative library of parts at risk for obsolescence. The ultimate goal is to develop a comprehensive obsolescence management program. Phase III proposals will be accepted after the completion of Phase I and II projects, with no specific funding associated. The proposal should include the delivery of a production-level product and a sustainment plan. Relevant keywords for this topic include obsolescence, artificial intelligence (AI), machine learning (ML), and commercial-off-the-shelf (COTS).

Description

OUSD (R&E) CRITICAL TECHNOLOGY AREA(S): Trusted AI and Autonomy

 

The technology within this topic is restricted under the International Traffic in Arms Regulation (ITAR), 22 CFR Parts 120-130, which controls the export and import of defense-related material and services, including export of sensitive technical data, or the Export Administration Regulation (EAR), 15 CFR Parts 730-774, which controls dual use items. Offerors must disclose any proposed use of foreign nationals (FNs), their country(ies) of origin, the type of visa or work permit possessed, and the statement of work (SOW) tasks intended for accomplishment by the FN(s) in accordance with the Announcement. Offerors are advised foreign nationals proposed to perform on this topic may be restricted due to the technical data under US Export Control Laws.

 

OBJECTIVE: As part of DLA’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’s goal is to use AI/ML to address and predict a multitude of issues presented by obsolescence.

 

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&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)

 

PHASE I: Proof of Concept, (TRL 3)

 

This Phase of the project should include plans to:

Identify all Cyber and physical security requirements and develop a plan to meet these requirements prior to commencing a Phase II effort.



Identify the J6 Sponsor the champion the Phase II and III efforts. 
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. 
Develop machine learning algorithms to analyze the collected data and identify patterns and trends that indicate potential impacts and suggesting mitigation strategies. 
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. 
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. 
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. 
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 II: This Phase of the project should include a prototype that:

Confirm the J6 Sponsor the champion the Phase II and III efforts. 
Develop the prototype on the DLA J68 Platform
Integrate all required Cyber and Physical security requirements.
Integrate data from various sources, including technical manuals, maintenance logs, procurement records, market research reports, and any material related to product development and lifespan. 
Employ machine learning algorithms to analyze the collected data and identify patterns and trends that indicate potential impacts and suggesting mitigation strategies. 
Enable DLA to simulate different scenarios and assess the impact of obsolescence on specific equipment and product categories or operational capabilities. 
Have the system continuously learn and improve its predictive accuracy over time, adapting to changing market conditions and technological advancements. 
Build a collaborative library (database) of parts at risk for obsolescence and suitable replacement parts or companies that could assist in reengineering the part. 
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 Plan

  1. Period of Performance:  TBD

  2. Budget: $ TBD

 

This Phase of the project should include:

  1. Delivery of a production level product to J68 ready for integration into the overall DLA Enterprise system.

  2. Develop a sustainment plan to support the delivered system for the lifetime of the program.

 

REFERENCES:

A.K. Dass and S.D. Lokhande, “Machine Learning Based Prediction of Obsolescence Risk”, International Journal of Intelligent Systems and Applications in Engineering, 11(4), pp. 293-301, 2023.

 

KEYWORDS: Obsolescence, Artificial Intelligence (AI), Machine Learning (ML), Commercial-Off-The-Shelf (COTS)

Similar Opportunities

DOD SBIR 24.4 Annual - AI-Enhanced TPS Development and Sustainment
Department of Defense
The Department of Defense (DOD) is seeking proposals for the topic of "AI-Enhanced TPS Development and Sustainment" as part of their SBIR program. The objective of this project is to develop an organic capability for field-level maintenance and repair of weapon system electronics, reducing supply chain latency and screening electronic components for No Evidence of Failure (NEOF) at the source. The goal is to achieve faster weapon system repairs, shorter component Turn-Around-Times (TAT), high equipment Operational Availability (Ao), and high unit readiness at lower life-cycle costs. The project will apply Artificial Intelligence (AI) and Model-Based Systems Engineering (MBSE) to improve the development, operation, and sustainment of Test Program Sets (TPS) for electronic components of weapon systems. Phase I proposals are accepted with a budget of up to $250,000 for a 6-month period of performance. Phase II will involve TPS hardware and software prototype development, critical design, test, integration, verification, validation, and acceptance. Phase III will focus on dual-use applications such as sensor integration in mobile platforms with AI-assisted guided diagnostics. The project has potential applications across all Army commodities, ground, air, missile, and C5ISR. The solicitation is open until March 31, 2025. For more information, visit the [SBIR topic link](https://www.sbir.gov/node/2651293) or the [solicitation agency website](https://www.defensesbirsttr.mil/SBIR-STTR/Opportunities/).
DOD SBIR 24.4 Annual - Artificial Intelligence/ Machine Learning (AI/ML) Focused Open Topic
Department of Defense
The Department of Defense (DOD) is seeking proposals for an Artificial Intelligence/Machine Learning (AI/ML) Focused Open Topic. The purpose of this open topic is to bring potentially valuable small business innovations to the Army and expand the relevance of the Army SBIR program to firms who do not normally compete for SBIR awards. The topic accepts both Phase I and Direct to Phase II submissions. Phase I proposals are accepted for a cost up to $250,000 for a 6-month period of performance, while Direct to Phase II proposals are accepted for a cost up to $2,000,000 for a 24-month period of performance. The research areas of focus include synthetic data generation, data validation and verification, AI risk mitigation, large language models, retrieval augmented generation, and collaborative AI technologies. Phase I submissions require a 5-page technical volume, an 8-slide commercialization plan, and a statement of work outlining deliverables. Phase II submissions require a 10-page technical volume, a 5-page feasibility documentation, an 8-slide commercialization plan, and a statement of work. During Phase II, firms must produce prototype solutions that are practical and feasible to operate in edge and austere environments. Phase III focuses on the maturation of the technology to TRL 6/7 and the production of prototypes to support further development and commercialization. The Army will evaluate each product in a realistic field environment and provide solutions to stakeholders for further evaluation. The submission deadline is March 31, 2025. For more information, visit the [solicitation link](https://www.sbir.gov/node/2651299).
DOD SBIR 24.4 Annual - A/I Enabled ARP, Select, and Monitor
Department of Defense
The Department of Defense (DOD) is seeking proposals for the topic "A/I Enabled ARP, Select, and Monitor" as part of its SBIR program. The objective of this solicitation is to develop and deliver an Artificial Intelligence (AI)-enabled system to modernize and automate the Army's acquisition process, starting with the development of an Acquisition Requirements Package (ARP). The proposed system aims to alleviate current problems in the Army acquisition community, such as inconsistent results, lengthy and inefficient ARP development, and limited tools available to the workforce. The technology will use AI/ML to assist program managers in building ARP, conducting source selection activities, and monitoring contracts post-award. The Phase I of this project is only accepting Direct to Phase II (DP2) proposals with a cost of up to $2,000,000 for a 24-month period of performance. The Phase II effort is expected to deliver a basic AI capability to guide ARP development, with potential for expansion to other types of acquisitions. The Phase III dual-use applications include the use of Contract Management Software (CMS) technologies in various sectors such as healthcare, manufacturing, retail, IT and telecom, transportation and logistics, government, and financial services. The deadline for proposal submission is March 31, 2025. For more information, visit the solicitation agency's website [here](https://www.defensesbirsttr.mil/SBIR-STTR/Opportunities/).
DOD SBIR 24.4 Annual - xTechScalable AI
Department of Defense
The Department of Defense (DOD) is seeking proposals for the topic "xTechScalable AI" as part of the SBIR program. The Army branch is specifically interested in novel and disruptive concepts and technology solutions that can address the vulnerabilities of current machine learning pipelines and models. The goal is to develop comprehensive security models capable of defending against universal AI threat vectors. The Army is prioritizing proposals that focus on systematic testing and evaluation methods, trusted and secure validation and verification strategies, continuous monitoring capabilities, improved transparency and assurance of code and data, and improved telemetry capabilities. The Army will use the xTechScalable AI prize competition to identify small businesses that meet the criteria for award, and only winners of the competition will be eligible to submit a proposal under this topic. The project will have three phases: Phase I involves submitting a Direct to Phase II (DP2) proposal, Phase II involves producing prototype solutions for evaluation by soldiers, and Phase III involves completing the maturation of the technology and producing prototypes for further development and commercialization. The deadline for proposal submission is March 31, 2025. For more information and to submit a proposal, visit the solicitation agency's website at [solicitation_agency_url].