How the Marine Corps Uses Predictive Analysis and Modeling
Q: How does the Marine Corps use predictive analysis and modeling to identify affordable technical alternatives throughout a program's life cycle?
A: The Marine Corps is using a model-based systems approach and a variety of technologies to improve the entire acquisition process.
Defense Department weapon systems typically have a large number of requirements with different and competing relationships. These relationships are often difficult to identify and may remain unknown before system development and integration. Modeling tools provide the department with the capability to better understand the relationships between cost, schedule, risk and performance before requirements are finalized and major costs are incurred. Marine Corps Systems Command (MCSC), as the Department of the Navy’s systems command for Marine Corps ground weapon and information technology system programs, is transitioning from a document-based, sequential systems-engineering approach to a model-based engineering approach to better understand and inform requirements prior to major milestones and contract awards. The foundation for this transformation has been the implementation of model-based systems engineering based on the application of the Systems Modeling Language (SysML). MCSC developed a unique SysML-based tool, the Framework for Assessing Cost and Technology (FACT), which allows concurrent tradespace analysis and incorporates the technological advantages of a model-based engineering approach to acquisition.
FACT is a government-owned, Web-based tool that provides the framework to integrate disparate data and models into a single-decision support environment that permits concurrent engineering and cost analysis. It allows for near real-time first- and second-order assessments of the impacts on cost of requirements, design and performance changes.
FACT supports systems of systems engineering and applies SysML to identify dependency and interactions of requirements and system parameters. It then assesses the impact on life-cycle costs resulting from changes to these requirements and parameters to assist the decision maker in managing risk while optimizing performance. FACT allows for bottom-up, detailed system designs to be modeled, explored concurrently in a top-down and cross-domain fashion, filtered and scored against a set of dynamically assigned requirements.
MCSC uses FACT, among other predictive modeling capabilities, to support weapon system programmatic decisions. These tools provide MCSC with the ability to examine multiple risks as well as the potential cost, performance and logistics impacts of executing particular courses of action throughout the weapon system life cycle. Conducting predictive analysis during early stages of acquisition assists decision makers in developing sound requirements with known impacts. This analysis is also critical for legacy equipment during the sustainment phase, which embodies the greatest life-cycle costs of a weapon system. Conducting predictive analysis throughout the life cycle allows program managers to plan and execute a course of action with a defined degree of confidence in the resultant life-cycle cost and technical performance.
While the capability is maturing in knowledge and application—to include organic capability—the Marine Corps is applying this predictive analysis strategy to support continuous process improvement across the full range of actions required to maintain and sustain ground equipment. As MCSC has only recently begun this transition to a model-based engineering and sustainment approach, actual returns on investment (ROIs) have not yet been formally realized and documented. However, recent analyses indicate the potential for significant ROIs, including the following examples involving Program Executive Officer Land Systems Marine Corps programs:
• Assault Amphibious Vehicle (AAV) Reliability-Centered Maintenance Analysis—Currently demonstrating the benefits of implementing changes to preventive maintenance checks and services strategy for the AAV to include cost, availability and man-hours. This analysis validated the Reliability-Centered Maintenance recommendations and identified the value of conducting these analyses.
• High Mobility Multi-Wheeled Vehicle Depot Study—Potential $430 million cost avoidance through 2030 by ceasing a schedule-based depot maintenance strategy and commencing with a demand-based strategy. Analysis showed great enterprise saving with a nominal impact to unit readiness.
• Logistics Vehicle System Replacement Sustainment Strategy—Identified strategy to achieve long-term objective of 90 percent material availability at lowest cost.
The use of a model-based systems approach coupled with set-based design principles allow for a rapid, comprehensive evaluation of a wide range of alternative system designs during the early analysis phases of an acquisition program. A comprehensive Analysis of Alternatives that integrates cost, schedule and performance trade-offs in a single data environment provides decision makers with a clearer understanding of risks, requirements feasibility and affordability before major decisions that impact program life-cycle costs are made. Predictive analysis, to include FACT, is instrumental in MCSC’s plan to efficiently and effectively execute acquisition programs according to Better Buying Power initiatives.
James Smerchansky is deputy commander for Systems Engineering, Interoperability, Architectures and Technology at Marine Corps Systems Command. He is a member of the Senior Executive Service and chief engineer for the Marine Corps.