Abstract
In the retail industry, demand seasonality, uncertainty, and unscheduled personnel absenteeism create an unbalance between staffing and staff demand, which can significantly increases over/understaffing costs. The use of a multiskilled workforce is an attractive flexibility source to minimize such mismatch. This paper presents a mixed integer linear programming model for structuring the multiskilling skills of a set of employees. To explicitly incorporate demand uncertainty, we develop a two-stage stochastic optimization approach. Then, using a Monte Carlo simulation we analyze the performance of the solutions for different levels of demand variability. To test the performance of the proposed stochastic approach, it is compared with the solutions of two myopic approaches: zero multiskilling and total multiskilling. The proposed methodology is applied to a case study in a Chilean retail store, using real and simulated data. Our results showed that the proposed approach reported the maximum possible benefit for all levels of variability. Finally, we provide information to decision makers to address a key aspect of multiskilling: how much to add.
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This research was supported by "Fundación para la Promoción de la Investigación y la Tecnología (FPIT)" under Grant 4.523.
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Mercado, Y.A., Henao, C.A. (2021). Benefits of Multiskilling in the Retail Industry: k-Chaining Approach with Uncertain Demand. In: Rossit, D.A., Tohmé, F., Mejía Delgadillo, G. (eds) Production Research. ICPR-Americas 2020. Communications in Computer and Information Science, vol 1407. Springer, Cham. https://doi.org/10.1007/978-3-030-76307-7_10
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