Improving Marine Corps Logistics with Model-driven Big Data

NPS NRP Executive SummaryReport Type: Final ReportProject Summary: The Marine Corps Logistics Command (MARCORLOGCOM) uses complex computer-based models to help manage the Corps’ materiel. These models help MARCORLOGCOM better understand the potential impacts and risks that operations and changes in policy may have on various units’ sustainability and readiness. This research improves upon the ability of MARCORLOGCOM analysts to quickly and efficiently obtain experimental information from their Repair Optimization Materiel Evaluator (ROME) model using data farming. ROME is used annually to assist in planning depot-level maintenance for ground support equipment given constrained resources. The Marine Corps depot level maintenance budget for fiscal year 2019 is projected to be nearly $350 million, which meets only 80% of the operating force’s requirements. Thus, maintenance choices must be made that will impact on unit readiness. In this research project, the Simulation Experiments and Efficient Designs (SEED) Center has built, tested, and documented software that enables data farming with ROME. That is, ROME has been embedded in an environment that facilitates massive experimentation using cutting-edge experimental designs. This new capability has been tested with some initial experimentation. A PowerPoint presentation serves as an initial documentation of the results of those experiments. Many results seemed intuitive, well-explained, and generally support model verification and validation. However, there are also a number of findings that we found counter-intuitive. Further investigation, in collaboration with ROME with model users, is ongoing. This investigation has also advanced our ability to data farm by improving the quality and speed with which we can glean insights through experimentation with ROME or other computational models.Marine Corps Logistics Command (MARCORLOGCOM)NPS-18-M319-AApproved for public release; distribution is unlimited