The US Army Materiel Systems Analysis Activity, known as AMSAA, conducts critical analyses to equip and sustain weapons and materiel for soldiers in the field and future forces. The Army is charged with determining the best possible choice among several acquisition options, taking care to examine alternatives in tradespace, sensitivity, and cost and schedule risk mitigation. AMSAA Mathematician and Statistician John Nierwinski decided to use @RISK to integrate schedule and cost consequences in order to provide a single risk rating that decision-makers can efficiently use to inform the overall decision. “This is cutting edge stuff,” says Nierwinski.
The first step in Nierwinski’s analysis requires a schedule network models and cost models, based on information from subject matter experts and available technology cost information.
With schedule and cost variables in place, Nierwinski then creates integrated outputs from the model, which will then, with several thousand iterations, create scenarios that involve an overrun or underrun, depending on the iteration. After running the integrated model, @RISK gives an output that tells the likelihood of not meeting the schedule, and the collection of maximum consequences from schedule overrun scenarios. Nierwinski then applies this distribution to a pre-established DOD risk reporting matrix to determine the transformed risk distribution, and to eventually come up with a specific risk rating.
“Once we’ve established a risk rating for a certain materiel option, let’s say it’s a particular alternative for a kind of tank, we can do all sorts of studies (i.e. tradespace, what-if scenarios, risk mitigations)—we can examine what would happen to the risk if we were to swap out its engine for a cheaper one,” says Nierwinski. “This can change a lot of things. We can study how the risk rating changes: for example, it may go from high risk to low risk.”
Nierwinski says that @RISK was instrumental in modeling the inherent risk and uncertainty with materiel acquisitions that AMSAA faces. “@RISK enables us to build various kinds of risk models quickly, with lots of flexibilities.”
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