Heterogeneous worker multi-functionality and efficiency in dual resource constrained manufacturing lines: an assessment by simulation

Abstract

Flexibility is a main mean to create resilient supply chains. The most flexible resources are often human resources but creating high, homogenous skill levels is not cost efficient. Heterogenous labour provides an alternative. The literature on Dual Resource Constrained (DRC) shops modelled heterogeneous labour with multi-functionality and efficiency matrices that indicate if a worker can perform at a station and according to which level of efficiency. However, this literature typically considered these matrices as a given environmental factor rather than a factor under firm control. Consequently, it typically neglected literature that can be used to guide matrix design. In response, this study draws on the literature on unbalanced lines to test eight different matrices to guide worker training. Discrete event simulation is then used to evaluate their performance in a DRC pure flow shop with high variability in demand and processing times. Results demonstrate that the matrix design impacts performance and that an unbalanced design has the potential to improve performance compared to a balanced design and full multi-functionality. Specifically, the bowl configuration shows much promise, which further confirms the “bowl phenomenon”. However, performance gains are dependent on the combination of When, Where and Who rules used to guide the worker assignment decision. So, the decision on worker training is also contingent on the decision which worker assignment rule to use. Findings have important implications for research and practice, providing guidance on how to design more resilient shops and supply chains.

A benchmark dataset for the retail multiskilled personnel planning under uncertain demand

“…Therefore, as multiskilled employees can be transferred from tasks with staffing surplus to those facing a staffing shortage, solving the MPAP enables the design of a workforce that can flexibly adapt to fluctuating demand patterns (e.g., [3,4,6,31,45,46]). In turn, an optimal training plan not only improves demand coverage but also minimizes labor costs arising from mismatches between staffing levels and staff demand ( [5,8,10,12,13,18,26,38]).…”