The central objective includes more quickly and accurately forecasting and justifying manpower, personnel, and training requirements to ensure the training readiness of the Navy in the most efficient and effective manner. A three-phased strategy was developed to accomplish this effort.
Phase one involved the design, development, and delivery of a process model prototype spanning initial entry training through initial qualification training. This shows the capability of simulation tools and technology, as well as the application of specific steps in applying these tools. The Navy selected ProcessModel simulation modeling to help determine ways to save time and money pertaining to Naval student training.
They chose a particular scenario to help demonstrate the flow of activity. They showed how an Avionics Technician receives training for the first 12-18 months of his career. The model focused on the technician moving through specific training courses, and the many variables involved in the technician moving between training courses and the Navy’s operational forces (or Fleet). Composed of several organizations, the Integrated Product Team (IPT) provided the necessary information to create the simulation model of the Navy training production flow, which will eventually span the entire Navy.
This simulation model provides a number of performance measures to help the Chief of Naval Education and Training (CNET) evaluate production flow improvements. These performance measures fall into two major areas: the time a Navy student is at the schoolhouse, but Awaiting Instruction (AI) or Awaiting Transfer (AT), and the number of students enrolled in specific courses.
AI measures the time a student is waiting (beyond the standard accepted time) at the schoolhouse to begin formal instruction. AT measures the time a student is waiting (beyond the standard accepted time) at the schoolhouse to transfer to a new location.
Although the amount of waiting time for one student may be relatively small, the total numbers from all students can end up as a very large number. This creates a “domino” effect because it steals time from the mission at the Fleet, which—in turn— can impact readiness. The additional time spent in AI and AT also costs the Navy additional dollars in lodging, per diem, and administrative support.
The seat utilization of specific courses is a critical measurement of cost and effective use of available resources. It measures the number of filled seats in a particular course. If the seats of a course are only partially filled, then the per capita cost of training those students is higher than a class with all the seats filled. Regardless of how many seats are filled, the course still has many fixed costs of required instructors, facilities, and equipment, etc.
However, it is the combination of these two performance measures that must be balanced to ensure the best training service to the Navy, while keeping the training costs to a minimum. For example, sometimes a student gets classified as AI because there are no available sets in a course and they must wait for the next course to begin. If we only focused on decreasing the AI time, we might decide to hold classes more often so students would not have to wait so long. However, in implementing this solution, we may find the seat utilization of the course drops considerably, and the training costs increase proportionately. When both performance measures are considered together, the increase in training costs may substantially outweigh the decrease in AI cost, and the proposed solution would not reduce overall costs for the Navy.
The Navy chose ProcessModel simulation modeling to help evaluate these kinds of questions. In addition, the model is designed to incorporate cost factors, training time, facility and instructor constraints, and many other real-world variables.
The data provided from the simulation model revealed a wealth of information— not only on AI, AT, and seat utilization— but also on many other critical aspects, such as total time to train and cost to train.
The analysis supplied by the simulation model could not have been accurately captured in a non-animated (static) tool. For example, the schoolhouse can predict the influx of new recruits from Navy recruiting projections. However, the influx of Navy personnel from the Fleet is much more dynamic and harder to predict. The influx of students also varies greatly from month to month, and unforeseen budget constraints can quickly impact the flow of students into the schoolhouse. Through simulation, CNET can analyze the impact of these many variables and evaluate courses of action to mitigate the impact.
Using a simulation tool, CNET is able to capture the many dynamics impacting their day-to-day operations, and more accurately forecast the impact of changes to the training production flow. Through the use of IPTs, the simulation model provides a composite picture across the entire Navy and incorporates the right combination of performance measures to evaluate Navy benefit. And as the effort continues through the remaining phases, CNET will have a decision support system to evaluate all aspects of the Navy training continuum and any proposed changes to the current way of doing business.