Military deployments to environments lacking basic infrastructure – whether humanitarian missions or combat operations – involve extensive logistical planning. As part of a research project for the U.S. Army, researchers at North Carolina State University designed a model to help military leaders better account for logistical risk and uncertainty during operational planning and execution.
The research, published in Journal of Defense Modeling & Simulation, uses an enterprise resource planning system that handles everything from requisitions to shipment of supplies to inventory tracking, to create computational models that can be used to identify the most efficient means of meeting the military’s logistical needs.
“This research lays the mathematical and operational foundation for construction of a network-based model that captures routing alternatives and characterizes solutions for capacity planning and resiliency analysis in near-real time,” said Dr. Joseph Myers, Army Research Office mathematical sciences division chief at the U.S. Army Combat Capabilities Development Command’s Army Research Laboratory. “This project will provide military logistics planners with capabilities that are currently lacking in prevalent logistics planning tools.”
“These models would be particularly valuable during expeditionary operations, in which the military is seeking to establish its presence – and its supply chain – in an environment that is subject to a fair amount of uncertainty,” said Professor Brandon McConnell, a research assistant professor in NC State’s Edward P. Fitts Department of Industrial and Systems Engineering and the paper’s coauthor. “The model that we’ve developed can not only facilitate the military’s ability to efficiently determine what will be needed where, but can also assess risk in near real time in order to account for uncertainty.”
The new model, called the Military Logistics Network Planning System, draws on three sources of information. First is logistical data from the enterprise resource planning system. Second is operational data, such as an operation’s mission, organization and timeline. Third is data on “mission specific demand,” meaning logistical requirements that are particular to a given mission and its environment. For example, a combat operation being conducted in a cold, damp environment would have different requirements than a humanitarian mission being conducted in a hot, dry environment.
The system also uses two factors to assess risk and determine how risk might affect military capacities. The first factor is the likelihood that an event will happen; the second factor is what the consequences of that event will be. For example, if the likelihood of factors is identical, the model would give more weight to the event that could have a greater adverse impact on military personnel and mission performance.
“The MLNPS uses all of the available data, accounts for risk, then forecasts what the logistical outcomes will look like in reality,” said McConnell, a former infantry captain in the U.S. Army who served two tours in Iraq. “The MLNPS can be used as a decision planning aid, allowing leaders to test-drive plans in order to identify courses of action that will best support carrying out an operation.”
The MLNPS could also be used while an operation is being executed, as part of contingent logistical planning efforts that take place as circumstances change on the ground.
“Right now, the MLNPS is a robust proof-of-concept prototype, designed to demonstrate the potential value of powerful computational tools that can make use of [enterprise resource planning] systems,” said McConnell. “Existing logistical tools are both valuable and powerful. However, I’m not aware of any other methods that make use of [enterprise resource planning] data and are also fast enough for operational use when time is of the essence.”