Markov Analysis

Workforce planning or human resource planning is the process of forecasting the workforce requirement needs of an organization (Lewis & Heckman, 2016). Organizations may be constantly changing and/or growing, and the employees in the organization may change and grow as well. For example, employees in an organization may get promoted, transfer to other positions that are lateral in the hierarchy, or they may leave the organization. The organization would need to fill the empty positions, however, recruitment, hiring, and training take time and resources (Jin et al., 2020). As such, it may be beneficial for an organization to be in the recruitment process while workforce changes are being made. Alternately, an organization may need to grow to accommodate business trends or the company’s success (Bányai, Landschützer et al. 2018). For instance, if an organization’s goal was to increase in size by 10% each year for 5 years, the organization would need to target applicants and recruit for the positions it would intend to create and fill for each year. These are two examples of workforce planning that demonstrate the importance of being able to forecast a future state of an organization, based on the current state of an organization.

A Markov chain is a probability model of systems that are randomly changing. Markov models have been used in economics, financial planning, and workforce planning (Kemeny & Snell, 1976). Workforce planning and human resource management can benefit from the use Markov chains to visualize the flow of employees through an organization as well as to forecast turnover and recruitment needs for an organization (Jin et al., 2020). As such, Markov chains and Markov analysis are a valuable tool in the field of Industrial and Organizational (IO) Psychology. The purpose of this paper is to give an overview of workforce planning, Markov chains, and how Markov chains can be used in workforce planning with an applied example.