The U.S. Army Reserve (USAR) is seeking proposals for the Small Business Innovation Research (SBIR) program focused on Large Scale Mobilization Operations Analysis. The objective is to identify challenges within the mobilization process and enhance efficiencies to better support combatant commanders during Large-Scale Combat Operations (LSCO). This initiative is critical for improving the readiness and timely deployment of soldiers, ensuring effective support for military operations. Proposals are due by March 20, 2024, and interested parties can find more information and submit their applications through the official SBIR website at https://www.dodsbirsttr.mil/topics-app/.

## Description

Lengthened Soldier mobilization timelines from the reserve component into active-duty roles affect the readiness of Army units to deploy. This ramp-up period impacts the timeliness of the support needed for combatant commanders to conduct operations. USAR leadership are exploring opportunities to improve the processes and create efficiency within LSMO by evaluating the outcomes from past mobilization training exercises and receiving insight from subject matter experts on how each process operates. Using this research, the USAR seeks to enhance the mobilization process, increasing the overall readiness and support for combatant commanders for LSCO. The program will share its findings with Army National Guard partners to support sister service processes.  

## Objective

The U.S. Army Reserve (USAR) recognizes challenges throughout the mobilization process and looks to clearly identify those challenges and create efficiencies in the process to better support the needs of combatant commanders. The USAR must mobilize and equip Soldiers quickly to support combatant commanders worldwide in the event of Large-Scale Combat Operations (LSCO) through Large Scale Mobilization Operations (LSMO).

## Phase I

This topic is accepting Direct to Phase II (DP2) proposals. Proposers interested in submitting a DP2 proposal must provide documentation to substantiate that the scientific and technical merit and feasibility equivalent to a Phase I project has been met. Documentation can include data, reports, specific measurements, success criteria of a prototype, etc.

## Phase II

In a direct-to-Phase 2 (DP2) transition context, a mathematical framework establishes the technical feasibility and proof of concept work typically associated with a Phase 1 effort. During Phase 1, companies rigorously assess their proposed solutions' viability and technical feasibility. Validation of deterministic and stochastic modeling techniques, coupled with deploying widely used tools like Python and RStudio, has gained significant recognition and support from numerous academic institutions. These techniques have been rigorously studied and tested and shown their effectiveness through real-world implementation across various academic and industrial settings. One of the key strengths lies in their practical application, as evidenced by the successful creation of deterministic activity networks that comprehensively capture the essential structural elements of complex processes such as mobilization. Stochasticity has also strengthened the models, making them more adaptable and resilient in the face of uncertainty. This widespread validation and practical demonstration affirm the robustness and versatility of these techniques, making them valuable assets in addressing complex challenges like mobilization processes in both academic and real-world contexts. Furthermore, the US Army Reserve (USAR) actively adopting the DOD product ADVANA and the existing installation of RStudio on USAR computers underscore the practicality and readiness of this approach. This demonstrates the ease of implementation and compatibility with the organization's operational environment. In summary, our DP2 transition approach is firmly grounded in technical feasibility and a proven concept, with practical solutions already in place and validated through an equivalent Phase 1 effort.

## Phase III: Dual Use Applications

<ul><li>&emsp;Primary commercial dual use potential for LSMOA technology is supply chain forecasting.</li><li>&emsp;The MIT CTL roundtable emphasizes the importance of predictive modeling in supply chains for its strategic role in enhancing operational efficiency and risk management by accurately forecasting demand, event timings, and potential disruptions.</li><li>&emsp;Top potential dual-use market applications for predictive data modeling technologies include:</li></ul>o&emsp;Supply chain forecasting: Predicting everything from equipment maintenance and mobilization to traffic control and demand forecasting.o&emsp;Weather risk intelligence: Enabling everything from crop intelligence to meteorological and natural disaster risk prevention.o&emsp;Banking and Financing: Crucial for significantly enhancing decision-making and financial performance, and necessitating investments.

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