COMMENTARY
has made it easier to incorporate new AI/ ML models, but without strict criteria and a connection between operational results and model quality it was initially unclear what level of predictive accuracy the new models would require. Te ongo- ing research collaboration with faculty and cadets at West Point have helped start to address this gap through model-qual- ity simulations and operational impact assessment.
WEST POINT DEPARTMENT OF MATHEMATICAL SCIENCES Currently, cadets and faculty at West Point are researching ways to translate model quality to the Army’s vernacular through the perspective of unit predictive main- tenance. If an AI/ML model can predict when a part breaks with a sensitivity of 90%, then what does that mean from the perspective of a maintenance officer? It is tempting to assume that if this model is utilized, the unit’s operational readiness rates will rise to at least 90%, but this is not generally a true statement. Tere- fore, model sensitivity is a component that needs to be considered when translating an AI/ML model to operational readi- ness rates, but it is not the only metric. For example, if a model is 90% sensitive
OPERATIONAL READINESS RATE VS. SENSITIVITY AND TIME HORIZON
The output of the model that shows the predicted operational readiness rates given specific specificity and time horizons for AI/ML models. The model is using notional maintenance data. (Graphic by Lt. Col. Jonathan Paynter, Maj. Thomas Mussman, Capt. Dylan Hyde and Hannah Ball, United States Military Academy West Point)
but can only predict one operating hour in the future, then it provides almost no benefit to operational readiness, especially if parts are not on hand.
What is Sensitivity?
Sensitivity is a quality metric that measures how well a model is correctly making a relevant selection. In our predictive maintenance case, it is the number of times a model correctly predicts that a part will break, divided by the total number of times that the part actually breaks.
Te framework for mapping an AI/ML model’s quality to a unit’s operational read- iness rate has been developed by cadets and faculty at West Point. To create this model quality to readiness map, historical infor- mation is needed about the vehicle and its components, such as number of vehicles in the fleet and general information about how often components of each vehicle fail. Given this information, a simulation maps the model quality to expected aver- age operational readiness rates for a fleet of vehicles. Currently, model quality is repre- sented by the sensitivity and the amount of
time in the future the model can predict out to.
For example, in a fictional situation where a unit’s average operational readiness rate is 80%, historical data could be used to set the parameters of the simulation. Once these parameters are established, the simu- lation can identify the sensitivity and how far out an AI/ML model would need to predict in order to increase that unit’s aver- age operational readiness rate to any given rate, such as 90%. Tis information can then be used to set the requirements for procuring an AI/ML model.
Further work on this framework will need to implement metrics for inventory management. Use of AI/ML models have
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