STRYKER READY
T
he Army’s current model to determine future equipment readiness levels falls short of enabling command decision-making in large-scale combat operations. Te current model uses the bank time
system, which calculates the sum of available equipment hours per fleet over a 30-day reporting window. But it does not consider external factors such as training conditions, personnel strength or parts availability, and only projects the current 30-day report- ing period (from the 15th of the month to the 14th of the next month). With the availability of other analytical tools, the sustainment community should explore alternatives.
During a rotational deployment to Korea, leaders from 2nd Stryker Brigade Combat Team, 4th Infantry Division pursued an improved model to forecast equipment readiness using machine learning tools, while considering the influence of exogenous data. Tis research focused on forecasting equipment readiness for the Stryker fleet within one Stryker infantry battalion, specifi- cally 1st Battalion, 41st Infantry Regiment (1-41). Training and maintenance data were included to establish impacts on current and future readiness. Machine learning then enabled the design of models based upon time-series data to assess their accuracy, with powerful results.
Using data available at the battalion level through U.S. Army systems of record, Soldiers from 2nd Stryker Brigade Combat Team developed a model to accurately predict one month of equipment readiness.
THE APPROACH Te team gathered two years of daily maintenance and training data for one Stryker infantry battalion and analyzed the data using a linear regression. While the linear regression fell short of a sufficiently accurate predictive model, it helped identify statisti- cally significant variables for determining maintenance readiness. With the regression analysis as a baseline, the team transitioned to more robust machine learning tools to find a best-fit predic- tive model. To assess accuracy, the team compared forecasted data with real data on a weekly basis over a 30-day period. To assess variation in forecasts and understand how each model learned, the team replaced forecasted data with current data on a weekly basis over the same 30-day period. At the conclusion of the study, each model’s 30-day forecasting performance was compared against the others to find which model provided the greatest accuracy over the longest period.
In developing the model, the team tested three time-series forecasting tools using machine learning and catalogued their
56 Army AL&T Magazine Summer 2024
accuracy. Among the models used for predicting Stryker oper- WHAT IS LINEAR REGRESSION?
A linear regression is a statistical model that esti- mates the relationship between a dependent variable and independent variables. A dependent variable relies on independent variables to determine its value; in the study, the dependent variable was Stryker read- iness. An independent variable is arbitrary and not reliant on outside values—essentially these were used to predict the dependent variable. Linear regres- sion is used to determine if the dependent variable can be explained or predicted by the independent variables.
ational readiness rate, Prophet proved most accurate. Prophet is an open-source machine learning model developed by Meta Platforms designed for producing forecasts from time-series data. Other models tested that showed promise include random- search/random-forest regression and a Bayesian gradient booster regression, and all produced better results than legacy tools and methods. Te random-search/random-forest is an ensemble machine learning model that creates nodes for testing and train- ing data for evaluation; the gradient boosting regressor model is an ensemble machine learning model.
THE DATA AND METHODS OF COLLECTION Initial attempts focused on data collection for an entire Stryker brigade but found too much variation in how training data was captured between battalions. To ensure data accuracy of training inputs, the team scaled down the sample to one Stryker infan- try battalion. Tis allowed control for variations in training data when assessing the impact on Stryker maintenance readiness, which served as the dependent variable. Te models used two datasets comprised of independent training and maintenance variables.
Training data was classified in a binary fashion to distinguish the days when equipment was operated from days when equipment sat idle. Te data also included days when the battalion had no scheduled activity (a day of no scheduled activities, or DONSA) and days when the battalion was moving to and from training. Tis categorization method effectively weighted training volume for each day over time. Te two datasets differed in the DONSA variable—one with this variable and one without.
Maintenance data was assembled from Global Combat Support System – Army, the U.S. Army’s system of record and a highly
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