Discussion
The results of this study are significant in a number of respects. Foremost among these was the use of theoretical frameworks proposed by Baines et al., 2005 and Blumberg and Pringle, 1982 and Jones (1993) to identify the most important human factors that affect the workforce planning process. In contrast to prior research that has relied exclusively on ignoring workers’ differences, this approach allowed us to address incorporating personality factor to decide what is the best scenario for hiring, firing and training workers to satisfy a company’s goals and without changing their rules. Second, the results indicate that worker differences should be considered in workforce planning to generate realistic plans with minimum costs. Thus, we have shown that incorporating worker differences in the planning process reduces the total costs. Third, unlike most prior studies of workforce planning, the current study suggests ways to quantify the intangible human factors that are difficult to measure. Finally, this paper investigates the effects of human performance and motivation on the workforce scheduling process. Human performance is a critical factor in hiring, layoff and training decisions. This model helps to find ways for keeping the workers based on their motivation and performance. Despite these strengths, a number of features of the current work also limit the conclusions that can be drawn from these results. First, although the model seems to provide reasonable results, the input data are assumed and generated based on the authors’ experience and opinions. A second limitation of the current study is all the human factors parameters and total demand are assumed to be certain and known, which may generate unrealistic results. A third limitation is that this model ignores many human factors that can affect the planning process. Some of these factors are communication, experience, learning and forgetting process. Although we did not study the relationships between human factors, we provided them as an aggregate number representing a group of workers having similar characteristics. Until further research clarifies the direction of these relationships and effects, causal statements can only be made with caution.
It is clear that research on workforce planning has not come to an end, and the path is still open to make the proposed model more comprehensive and more realistic. It may consider other human factors such as learning curves and experience which can be a promising area of work for future research. However, learning curve effects should be considered in formulating the model. In assembly activities that require more manual work, it has been observed that production time decreases as workers learn more about their work and how to perform it, and their experience increases. Also, refining the proposed model to consider worker experience would be another approach to integrate human related aspects into production planning. For example, labor wages can be a function of time and experience which reflects the current systems that management uses in different companies. Finally, future research might consider the development of an interactive DSS that will help managers to solve the model in the context of uncertainty of demand and costs parameters.