STRYKER READY
APPLICATION Te Prophet model can aid commanders in understanding where maintenance will impact future operations and where future oper- ations will impact readiness and operational endurance. Staffs can leverage this information for more precise assessments and improved planning. Picture the ability to understand the impact that changes to a training calendar have on Stryker readiness. For instance, when planned mechanic hours are diverted by unpre- dicted medical or other readiness requirements, a leader could communicate the tangible risk associated with those changes with precision. Using these models, commanders can test these unpre- dicted events to understand the impact on readiness.
A second application would be predicting Stryker readiness over a deployment period, such as a Korean Response Force rotation, a European Defender rotation or a combat training center rotation. Commanders would be able to identify periods where training will require a complimentary intense maintenance focus and plan accordingly or adjust their plan to meet Armywide training gate targets. Commanders also would be able to model the impact of changes in training plans or service schedules on equipment readiness during a rotation, informing decisions to maximize training and equipment readiness and identifying areas of risk.
A third application can be seen in an adaptation for large- scale combat operations. Instead of using a training calendar, a commander can use the operational synchronization matrix along with the maintenance plan to assemble the exogenous data and better understand which unit is best postured to be the main effort or when to commit a reserve for exploitation or rein- forcement. Tis enables improved decision-making and informs recommendations from staff to commanders at echelon.
Battalions can replicate these models by exploiting untapped data that is readily available and generated as a byproduct of daily activities. Te maintenance data used is common to all battalion maintenance technicians and the brigade support oper- ations staff. Te training data used is available and common to all battalion operations staff. Using a template and running the provided code into a Jupyter code environment—an online environment for writing and running programs, accessible on government computers through
https://jupyter.org—any battal- ion can produce these results.
Tese models were narrow in scope, and forecasted data was only observed for one month. Continued testing of these models will occur over a three-month period with 2nd Battalion, 23 Infan- try Regiment, 1st Stryker Brigade Combat Team, 4th Infantry
58 Army AL&T Magazine Summer 2024
Division to determine how each model performs over a longer period with data structured at the outset. Te team also will expand these models to the Stryker brigade and include all vehicle types to build a model capable of predicting maintenance read- iness at the brigade level.
RECOMMENDATIONS: WHERE THE ARMY NEEDS TO IMPROVE Data: Te foundation of every model is data. Te success of these models is reliant on the research team exploiting untapped data, transforming that data to an interoperable format and then load- ing it into the models for training. Tis process revealed gaps in the availability of data at the tactical level and interoperability of that data throughout Army systems. Both gaps are areas that the Army has published strategic intent on improving through the Federal Data Strategy 2020 Action Plan and theDepartment of Defense Data, Analytics and Artificial Intelligence Adoption Strategy. For the Army to achieve the goals outlined in these strategic documents, it must increase the availability of data and ensure the interoperability of that data once available.
Training: Te Army must lower the cost of entry for data and analytics. Specifically, the Army needs to continue to teach data familiarity in its professional military education at all levels and expect a baseline competency across the force. Data literacy is becoming as essential as the current requirement of a basic abil- ity to read and write. As data literacy increases, the force will become more comfortable interacting with and capturing data for informed decisions, improving the cognizance and utility of data collection and management. Additionally, leaders must improve their understanding of what data and models tell us. Looking at a chart predicting a future state is simple, but asking if the vari- ables were statistically significant and understanding the adjusted significance of a model should be second nature to decision- makers for the Army to become a data-centric force. Education and training are the bridge between leaders and analysts, help- ing organizations better employ effectively.
Software: Te Army cannot be datacentric without access to the tools to transform data and load it into models for analy- sis. Some of these tools are standard in the computers available for daily operations, but most are restricted by local or global network regulations. Te Army needs to prioritize accessibil- ity to such tools to enable future research on employing data for knowledge and understanding at echelon. Additionally, the Army should add data interoperability standards and practices that better align with machine learning requirements for all future software adoption contracts.
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