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Abstract
In this paper we present a framework that balances the significant tradeoffs and helps managers in crafting a strategy for the induction of contingent workers in a complex assembly environment. The key issues we have considered in this paper include distinct manufacturing sub-processes, hierarchical or nested workforce skills, regular and overtime capacity, and impact of learning. We report a real life case study pertaining to the Singapore operations of a global computer manufacturer that served as the backdrop of this research and provided us with several intuitions. A linear programming model is presented to help determine the optimal allocation of permanent and contingent workers to all sub-processes. Our numerical study comprising more than 165 distinct experiments indicates that both the firm's cost performance and the number of contingent workers inducted are significantly affected by key parameters such as cost of induction, overtime premium cost, and overtime capacity. We highlight the impact of demand variability, and emphasize the overall value of the model presented in this research through the managerial insights that can be drawn from it.
Acknowledgements
The first author would like to thank the Singapore-MIT Alliance Program for research grants that made this project possible. The authors also gratefully acknowledge the valuable comments of Prof. Stephen C. Graves, Sloan School of Management, MIT, on the earlier versions of this paper.
Notes
†Nahmias (Citation2001) defines the learning curve according to the relationship, Y(u) = au − b where Y(u) is the number of hours required to produce the uth unit, a is the time required for the first unit, and b is the learning rate. In this paper the learning rate is defined for successive days rather than successive units.
