Service level improvement due to worker cross training with stochastic worker absence

Abstract

To react on increasing customer demand uncertainty, production systems have to be flexible concerning the provided capacity. With respect to labour, one opportunity to gain such flexibility is to assign workers to different work stations which often require different skills to be operated. Therefore, cross-trained workers are needed to enable this flexibility. Since the qualifying workforce implies costs, a relevant problem is how much skills and what mix of skills is optimal for a production system. In addition, the workforce may be on vacation or have a sick leave and hence is not always available. In this paper, we study the effect of different predefined workforce qualification profiles for a streamlined production system with simulation and compare the results with simulation-based optimisation using a genetic algorithm. Specifically the effect of stochastic worker absence, in comparison to workers being always available, is evaluated for different production system scenarios. The results show that cross-trained workers can significantly improve the service level achieved and that simulation-based optimisation can provide a much better worker specific mix of skills than predefined qualification profiles. Another managerial insight is that there is a trade-off between number of skills and number of workers needed to obtain the same service level.

Keywords:

• cross training
• worker skills
• stochastic worker absence
• worker assignment
• production planning
• simulation
• simulation optimisation

Acknowledgements

The work described in this paper was done within the project ‘Digitale Methoden für verbesserte Personalqualifizierungsstrategien’ (OPTIMAL WORKFORCE, #862008), funded by the Austrian Research Promotion Agency (FFG) and the Government of Upper Austria.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by Österreichische Forschungsförderungsgesellschaft [grant number 862008].