Modelling heterogeneity in manpower planning: dividing the personnel system into more homogeneous subgroups

Abstract

Manpower planning is very useful for human resource management in large organizations. Most manpower models are concerned with the prediction of the future behaviour of the staff: they might leave the organization, get promoted or acquire more and new skills. This behaviour can vary a lot among different employees, what makes prediction difficult. It is common to tackle this problem by dividing the whole heterogeneous personnel system in several more homogeneous subgroups. This approach is often used to develop manpower planning models for prediction, control or optimization. Although the division in homogeneous subcategories is a fundamental and important step in the application of the models, up till now literature neglects to discuss a procedure to deal with this in practice. This paper suggests a general framework to find the distinguished homogeneous subcategories by determining and considering observable sources of personnel heterogeneity. obvious actions that could be taken. Several operational research methods are developed to support organizations in their manpower planning challenge. Because of several reasons [3][4][5], almost all academic attention in the past is given to the development of methods for manpower supply forecasting. Those models attempt to predict the future evolution of the current and future employees. Several transitions between states are possible. The two most interesting potential flows of an individual are 'leaving the organization' and 'develop a broader range of skills'. Those transitions influence, respectively, the quantitative and qualitative supply of manpower. This behaviour highly depends on the individual. Several factors cause differences among employees in, for example, motivation, performance or commitment. This personnel heterogeneity makes the manpower supply forecasting difficult. Ugwuowo and McClean [6] made an overview of techniques to handle this heterogeneity. Several manpower planners suggest tackling this problem by dividing the whole heterogeneous personnel system in several more homogeneous subgroups that form a partition of the total personnel system. This simplifies the manpower supply prediction because it becomes acceptable to presume that everyone in the same group evolves analogously. Consequently, the assumption can be made that every employee within a homogeneous group has the same probability to leave the organization or to move towards another homogeneous group. The use of transition probabilities implies that the techniques are only suitable for large organizations. The way that those transitions are estimated and modelled, depends on several different model assumptions. Manpower planners commonly use time-homogeneous Markov models [1, 7], semi-Markov models [8,9], non-time-homogeneous Markov models [10,11] or non-time-homogeneous semi-Markov models [12,13]. The validity of those models highly depends on the homogeneity of the subgroups. In one of the most important reference works on manpower planning [1] the problem of heterogeneity in the sy...

Profile‐based push models in manpower planning

“…, 5) of S(X 3 = gr2) in transition probabilities of evolving for X 3 after one period of time to respectively, the value gr2 and gr3 (conditional on not leaving the system) as given in Table I. Since there are no demotions, the transition probability Pr(X 3 = x 3 2 → X 3 = x 3 1 ) equals zero. Table I.…”

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A Markov Model for Human Resources Supply Forecast Dividing the HR System into Subgroups

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Modeling mixed push and pull promotion flows in Manpower Planning

“…In this way, every employee in a homogeneous group is assumed to have the same transition probability. Various personnel characteristics can be used to divide the total group of employees in different homogeneous subgroups, for example gender, job description, age and length of service (De Feyter 2006;Bartholomew et al 1991). In most cases, the different grades in the hierarchy of the enterprise are used in this disaggregation.…”