The Marine Corps wants to understand how changing the volume and frequency of resupply may increase operational reach. However, fuel distribution data is still a mystery, as the system is unable to track consumption, and human error reduces data quality.
The Marine Corps Expeditionary Energy Office manages the development of the Expeditionary Energy Command and Control System (E2C2S), which seeks to provide commanders with the ability to maximize operational reach. Understanding synergies gained from feedback loops in the system is important to maturing technology for effective use.
The problem is that dynamic demands create high stock requirements, which encumber supply and distribution capacity in the Marine Air Ground Task Force (MAGTF). This is a problem because the operational reach of the task force is constrained to the flow of fuel from external sources. Resupply missions create vulnerability to asymmetric threats on ground lines of communication. Marines risk casualties and material losses as they attempt to mitigate energy-based risks to front line missions.
With a lack of understanding of causal factors in fuel consumption, the exploratory phase of the study asked questions that would guide field observations and interviews. Qualitative in nature, the initial research question was open-ended. The sub-questions were more direct to fill gaps in the emergent themes in demand processing within the inductive, grounded approach.
The findings of the first phase were the input for data collection in the second phase. The quantitative analysis assumed that telematics would eliminate or reduce delays and distortion in processing demand information throughout the supply chain. The qualitative findings suggested delays are a cause of inefficiency and reduced operational reach. As such, the hypothesis states a directional relationship between telematics and operational reach.
The mixed methods study employed an exploratory sequential design. The output of the qualitative first phase was the input for the quantitative second phase. The qualitative phase collected data from participant observations, interviews, and extant literature on energy performance studies. The analysis led to an As-Is/To-Be conceptual model of the tactical fuel supply chain. The To-Be model applied system dynamics principles to reengineer the demand information processing based on a hypothetical system that would acquire data via telematics, which would make this data available to various levels of the system in near-real-time. Classic system dynamics supply chain models informed an experimental design for the quantitative phase.
Findings from the first phase attributed delays and distortion of supply and demand for dysfunction in the system. The quantitative methods applied a system dynamics modeling software, Stella, to create an experiment that would simulate the As-Is/To-Be supply chains in a controlled environment. The simulation captured the fuel stocks within a Marine Expeditionary Brigade that flowed to an artillery battery over a 30-day period.
Fully integrated telematics may provide total demand visibility, which is the ability of a supply chain activity to adjust supply line responses with near-real-time data of retail consumption. Whereas supply trains currently distribute demands registered up to 48 hours before, telematics may allow distributions to reduce the delay by a factor of two at each level of the system.
In the experimental design, an artillery battery plays the retail unit. The battery consumed fuel at the assault rate of 9,600 gallons per day. The MEB provided fuel to the battalion, and the battalion provided fuel to the battery. Supply trains that delivered fuel 24-hours after receiving orders connected the fuel stocks. On the fifth day of the simulation, the demand of the battery fell to and remained at the sustained rate, 9,000 gallons.
For the As-Is system, the change in demand threw the battery stock into oscillation indefinitely. The oscillations amplified by the same ratio in both systems. The difference is that the To-Be design is able to get ahead of dysfunction caused by the delays. When the change in demand occurs, each supply node changes its orders by the same fractional rate the orders changed at the retail level. The To-Be system absorbed the perturbation over a week and reached 95% equilibrium. Observations of the MEB inventory showed that operational reach between the two systems oscillated, but at 30 days, the As-Is MEB stock was 7% greater than the To-Be system. The results did not support the hypothesis as formulated.
This work was done by Jeremy F. Thomas for the Naval Postgraduate School. For more information, download the Technical Support Package (free white paper) below. NPS-0014
This Brief includes a Technical Support Package (TSP).
Exploring the Impact of Fuel Data Acquisition Technology on the USMC Expeditionary Energy Command and Control System
(reference NPS-0014) is currently available for download from the TSP library.
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