The problem
Where the time was going
The Navy wanted to forecast and justify its manpower, personnel, and training needs more quickly and accurately, so that training readiness could be maintained in the most efficient way. A three-phase strategy was set out, beginning with a working model that spanned initial entry training through initial qualification training.
Two measures sit at the heart of the problem and pull against each other. The first is the time a student spends at the schoolhouse but idle, either Awaiting Instruction before a course begins or Awaiting Transfer to the next location. Any single student's wait may be small, but summed across everyone it becomes large, and it has a domino effect: it steals time from the mission at the Fleet, adds lodging, per-diem, and administrative cost, and can ripple into readiness.
The second measure is seat utilization, the share of a course's seats that are actually filled. A course carries fixed costs for instructors, facilities, and equipment no matter what, so a half-full class costs far more per student. Holding classes more often would cut waiting, but it can leave seats empty and push total training cost up, so the two have to be balanced together rather than chased one at a time.
What we modeled
Mapping the process, then testing the fix
The model followed a concrete case to make the flow tangible: how an avionics technician is trained over the first 12 to 18 months of a career, moving through specific courses and between the schoolhouse and the operational Fleet. An Integrated Product Team drawn from several organizations supplied the data behind it.
ProcessModel was built to carry the real-world variables that drive the trade-off, including cost factors, training time, and facility and instructor constraints. It produces a range of performance measures, from Awaiting Instruction and Awaiting Transfer to seat utilization, total time to train, and cost to train, so the Chief of Naval Education and Training can weigh production-flow changes against all of them at once.
The result
The proof, and the payoff
The model gave the Navy a far richer picture than a static tool could. The influx of students is volatile, with recruits arriving on one schedule, Fleet personnel on another much harder to predict, and budget shifts changing the flow at short notice, all of which a non-animated calculation would miss.
By capturing those dynamics together, it let CNET test courses of action and see how each one moved waiting time and cost in tandem rather than in isolation. As the remaining phases come online, it becomes a decision-support system spanning the whole training continuum, giving a composite picture across the Navy instead of a fragmented one.
Part of our work in military and defense.