WASHINGTON -- NAVSEA Commander Vice Adm. Tom Moore looks to use modeling to improve the command’s analytical and technological processes to predict workforce capability and capacity needs.
In an environment where the work required by NAVSEA increases and declines over time, the ability to predict the number of employees, their particular skillsets and the length of service required for the acquisition and lifecycle maintenance of a new class of ship, can play a significant role in optimizing NAVSEA resources.
Charles “Tony” Kimberlin and his staff from the Corporate Operations’ Analytics and Modeling office, lead the effort to provide the NAVSEA Commander needed workforce data and modeling.
Models are complex mathematical algorithms that depend on data to reach a range of results. Before modeling, analysts undergo a process of “mining” reliable data to ensure more accurate outcomes.
“Within a year we’re looking to get to a certain level of comparative analysis and have a Workload Demand Forecast Model that will perform some of those analyses and begin to use it for modeling,” said Kimberlin.
To predict NAVSEA workforce needs, many different types of workforce data are used: employee training, how long a person has been in a position, employment grade and future workforce needs. Whether it is a numerical value or a yes/no, each piece of data has a role in predicting workforce capability and capacity needs. In addition to the different types of data, it comes from many different sources, such as the Defense Civilian Personnel Data System and the Navy Enterprise Resource Planning program.
This analysis, according to Kimberlin, is possible through machine learning, a form of artificial intelligence that provides computers the ability to learn without being explicitly programmed.
CAREER MANAGEMENT
Allocating people efficiently also requires leadership to understand the risk associated with any workforce-related decision. “When and whom to train, promoting people in the right ways and recruiting people for the right positions all come into play,” said Kimberlin.
Kimberlin explained, for example, that it is possible to pull statistics together to identify which employees have been at NAVSEA “X” amount of time and understand the likelihood of them advancing or moving to another position is “X” percent. By understanding the risk, leadership is better situated to plan for likely outcomes. “Statistically, the data is there. To date, it hasn’t been compiled into something that can be used in a model -- that can also help the employee,” he said.
From the perspective of an employee starting a NAVSEA career, Kimberlin foresees the potential for personnel to see their career from a holistic view.
“This is how my career could shape, based on the identified needs and the project outlook across our programs,” suggested Kimberlin. “Here’s my series . . . what steps do I need to take to advance my career while also supporting the command’s needs? I hope that we can put those two worlds together so that both ends [leadership and employees] can understand.”
Bringing those two ends together will take time.
“In three or four years hopefully we can get to a true end state for predictive analysis,” said Kimberlin. “But it may take longer. We need time to improve the fidelities of our core systems and to collect the right data to provide us with the capability to identify our workforce and capacity needs.”