Manpower planning and labor are two large and important fields of research. In each of these connected fields researchers care about maximization of productivity and minimization of cost while hitting a threshold of need of personnel. In the literature, manpower planning models take into account availability of labor and their requirements for optimization (Abernathy et al., 1973; Akerlof, 19178; Driscoll et al., 2007; Grinold and Marshall, 1975; Jailett et al., 2019; Rao and Rao, 2018; Ding et al., 2015). These models take on multiple forms including linear regression, Poisson models, Markov decision processes and Markov chains, and flow networks. These types of models are based on simulation of manpower and optimization of costs and productivity, and often lead to gains for the firm optimizing (Abernathy et al., 1973; Akerlof, 19178; Driscoll et al., 2007; Grinold and Marshall, 1975; Jailett et al., 2019; Rao and Rao, 2018; Ding et al., 2015). These models have been used in general research and in military research, which is our focus for the summer.
Manpower planning is an important task for the Army. It allows them to measure their need for each position and provide adequate support for each individual. In the Military they have access to many resources and individuals and it matters which jobs are filled and what that cost is. In the Navy, they have begun to use simulations to measure if their positions are being filled using re-enlistment bonuses as an incentive. In this model they measure when they are running out of bonus funds and turn off that tool and if their positions can be filled by re-enlistees (Nanyar et al, 2021). In the Army they have begun to use the Total Army Personnel Life Cycle Model (TAPLIM) model. This model takes into account the starting inventories of enlisted soldiers, by grade, for different groups. They indexed new parameters by level of active-duty experience to divide soldiers into groups. They constructed mass balance flows for each time period to control the flow of qualified and unqualified soldiers from active to reserve component. They used historical transfer rates as a basis for their analysis. This use of modeling has saved the Army upwards of 1.6 billion dollars (Durso and Donahue, 1995). Manpower planning is therefore incredibly salient tool for minimizing costs and maximizing productivity in the Army. This also relates to person-job fit, which aims to minimizing costs and maximize individual productivity in a firm.
Person-job fit is a measure of the goodness of fit between individual and firm. In the manpower planning process we care not only that needs of the firm are filled, but that the individuals in those positions are doing their job effectively. Person-job fit is often measured using survey data (Boon and Biron, 2016). Individuals are asked about their position, if they like it, and how good they are at their job (Boon and Biron, 2016). After this, a coefficient of person job fit, commonly referred to as alpha, is created to measure their fit. Higher valued of alpha mean that an individual fits better into their job. Research has uncovered that higher values of person-job fit lead to more productive individuals who are happier in their position. There is also less turnover and therefore decreases costs of transition and training for the firm (Boon and Biron, 2016). To measure person-job fit without survey data, we collected skill data from Burning Glass and connected it to MOS codes using an O*NET crosswalk to see which skills were necessary for which job. In the future, we plan to measure person-job fit using matching techniques between officer skills and test scores and the skills needed for their Army jobs.