Aircraft and weapons systems depend on millions of lines of code for essential functions, and the percentage of a system that depends on software has grown rapidly. At the same time, the number of weapon system platforms is diminishing, and their projected service lifetimes are expanding. For these reasons, software sustainment has become an issue of significant concern to the Department of Defense (DoD). The issue is complex because funding decisions include input from many stakeholders and involve the tensions among the warfighters' operational need, the materiel of the portfolio, and the capability of the sustaining organization. To optimize long-term value to the services, DoD leaders must decide how to allocate resources between efforts that support the warfighter immediately and efforts that improve the performance of the organization.
The SEI is developing an economic model to help decision makers determine where and how much to invest in organizational capability and capacity. Traditional economic models are insufficient for software sustainment, where many factors can change at once and result in sudden, dramatic changes in outcome. We are building and calibrating a system dynamics model of investment in sustainment of software-intensive systems. The model demonstrates how varying the timing and amount of funding in response to changes in technology or threats affects the organizational performance of both sustainer and fleet. The final output will be an interactive, dynamic model that program managers can calibrate and then use to test the viability of several decision scenarios to inform funding discussions.
For example, an enemy has figured out how to counter one of our sensor or weapon technologies, and a more advanced technology must be rolled out to the fleet. We ran a baseline simulation to show steady-state values prior to introducing a disturbing stimulus—an increased threat. We simulated a scenario in which we increased the amount of technology change while allowing varying amounts of staff training to take place. We then modeled the threat change as a technology pulse at Month 6 during the simulation.
The results, illustrated in the figure below, show that decreasing the level of staff training and tool support to get more sustainment hours in the short term sacrifices organizational performance later. An improved outcome could result from increasing hiring, training, and tooling when there is a threat change.
Ferguson, Robert; Phillips, Mike; & Sheard, Sarah. "Modeling Software Sustainment." CrossTalk 27, 1 (Jan./Feb. 2014): 19–22.
Sheard, Sarah; Ferguson, Robert; Phillips, David Michael; & Moore, Andrew. "Dynamics of Software Sustainment." Journal of Aerospace Information Systems 11 (2014): forthcoming.
Sheard, Sarah; Moore, Andrew; & Ferguson, Robert. "Modeling Sustainment Dynamics." Presented at the 12th Annual Conference on Systems Engineering Research
(CSER 2014), Redondo Beach, CA, March 2014.