Determining the Required Training Capacity Within a Military Establishment

Abstract

We address the problem of deciding how many positions to set aside for military recruits undergoing training. Within a cap on the total number of military members, we vary the ratio between positions allocated to the training pipeline versus those required in the trained effective establishment. This is done with the goal of determining the extent to which given ratios are sustainable. We use a Markovian model of the training pipeline, with parameters derived from historical personnel data. Through Monte Carlo simulation, we predict how often a given ratio allows the required trained force to be fully generated, as well as the surplus of trained personnel, it is expected to generate. We extend our previous work in this area by considering an alternative Human Resources policy that uncaps the training pipeline. Our modelling results have informed ongoing initiatives to optimize the force mix and structure of the Canadian Armed Forces.

Keywords: Markov Manpower model; Military establishment; Monte Carlo simulation; Personnel operations research; Training pipeline; Workforce analytics.

Fig. 1

Histogram of the proportion of recruits who either do not require training or who complete their training within the year when they are hired, 2006–2021; a Gaussian fit is also shown as the dashed line

Fig. 2

Histogram of the proportion who graduates within the year, among those who had first appeared in the training pipeline in the previous year, 2006–2021; a Gaussian fit is also shown as the dashed line

Fig. 3

Illustration of our model of the Canadian Armed Forces training pipeline (modified from [1])

Fig. 4

Five hundred years of simulation result showing excess TES—trained effective personnel generated above the set requirement, when the number of training pipeline positions was set to 1600 and the TES to 10,000

Fig. 5

Fraction of simulated years when a TES shortage is observed, as a function of the number of training pipeline credits, when 10,000 trained effective positions require filling

Fig. 6

Mean TES above the requirement in those simulated years when the TES requirement is exceeded, as a function of the number of training pipeline credits, when 10,000 trained effective positions require filling

Fig. 7

Fraction of simulated years when a TES shortage is observed, as a function of the number of training pipeline credits, when 10,000 trained effective positions require filling, using the original model and the modified model with an uncapped training pipeline

Fig. 8

Mean TES above the requirement in those simulated years when the TES requirement is exceeded, as a function of the number of training pipeline credits, when 10,000 trained effective positions require filling, using the original model and the modified model with an uncapped training pipeline

Fig. 9

Range between the maximum and minimum TES observed over 200,000 modelled years, expressed as a percentage of the required TES, graphed as a function of the number of training pipeline credits, given a 10,000 TES requirement

Fig. 10

Fraction of simulated years when various magnitudes of TES shortages are observed, as a function of the number of training pipeline credits, when 10,000 trained effective positions require filling, with an uncapped training pipeline