Army Logistics Embraces Predictive Analytics


Pat Sullivan – VP Army Programs

“The purpose of predictive analysis is to determine the impact of resourcing decisions, alternatives, changes to strategy, and demand for forces, on Army readiness.  Impacts must be assessed over the near and mid-term … Unforeseen changes in funding, demand for forces, or other factors have varying degrees of impact on current projections.” – Army Regulation 525-30. Unfortunately, the complex mathematics and stringent analysis that are necessary for predictive analytics have been performed typically by folks from the Center for Army Analysis or by cells of operational research specialists spread across the force.  The challenge for all analysts, from an operational perspective, has been in compiling the data and creating a picture that is worthy of command-level decision making.

The Army Materiel Command (AMC) has pressed forward in applying predictive analytics to create greater efficiency in supporting the Army through the Logistics Readiness Centers (LRCs).  As part of the AMC mission to provide logistical services, the command assumed responsibility for 73 LRCs worldwide.  The purpose of a LRC is to provide installation support with a broad range of essential services that include maintenance, food service, ammunition, general supply, and laundry.  Since assuming this mission, AMC has been squarely focused on enhancing customer satisfaction and readiness while efficiently managing a dwindling budget—not an easy task.

In July 2014, ProModel initiated a proof of concept (POC) project in support of AMC for a decision-making capability that will accommodate both the overall enterprise level and the tactical, local level of the LRCs.  The project required ProModel to learn LRC processes and to evaluate and analyze existing LRC maintenance records in order to identify areas for potential improvement.  For the purposes of the POC project, AMC decided to focus the efforts on one LRC, therefore the process-education and data-collection efforts required for the creation, extraction, and compilation of data were focused on the Ft. Hood LRC.  During the POC effort, ProModel pinpointed the data necessary for analysis and identified several functional needs at the LRC level.  For example, Ft. Hood LRC management expressed a need for a labor-optimization software tool that can take into account labor requirements and overtime planning on a local, LRC-based level.

The software model of processes developed for Ft. Hood was proven to work, so a scaled enterprise solution is currently in development.  The model provided sufficient evidence that the trial scenarios created during the project can be expanded to a larger scale and adapted to incorporate additional requirements.  ProModel is now tasked with delivering a labor optimization capability and a workload-management software module to support the operations of the Army Field Support Brigades (AFSBs) that manage a number of LRCs.  This new capability will enable business-case analysis of the movement of future workloads from one location to another, and it will facilitate the consolidation of resources in order to support a requirement at a particular location.

The POC effort demonstrated that, by incorporating predictive analytic methods into a custom software application, the AMC, the U.S. Army Sustainment Command, the AFSBs, and the individual LRCs will have decision-support capabilities to accommodate trial “what-if” scenarios and experimental process simulations at both the enterprise and local levels.  AMC is proceeding to the next level of development of a software tool with enterprise-wide applicability.  Soon, AMC will experience a substantial positive effect on the command’s process efficiency and on the resulting cost-management controls.  ProModel is confident that this development will provide to the AMC and to the Army a great, leading-edge, predictive-analytic tool that will change the culture of Army logistics management.

Contact VP of Army Programs – Pat Sullivan  for more information. Or, visit our web site to learn more.






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