Impact of Labor Productivity and Multiskilling on Staff Management: A Retail Industry Case

Abstract

This research seeks to evaluate the effect of the learning and forgetting phenomenon on multiskilling decisions. This phenomenon occurs when multiskilled employees lose productivity in those tasks they perform less frequently. The literature recommends considering this decrease in productivity in multiskilling decisions, in order to find personnel planning alternatives that improve workforce performance. The methodology proposes a mixed integer programming model that explicitly incorporates the learning and forgetting phenomenon and, also, the use of multiskilled workforce through a k-chaining approach. This formulation allows to determine how many employees should be multiskilled, in how many additional departments they will be trained, and what amount of productivity is expected in each trained department. The solution to the problem minimizes the costs of understaffing, training, and productivity loss. The case study is applied in the retail industry, in which two experiments were evaluated that faced three different levels of demand variability. The first experiment generated lower costs because it did not incorporate the productivity loss associated with the learning and forgetting phenomenon. The second experiment, which incorporated the learning and forgetting phenomenon, generated a higher total cost but its solution is better adjusted to the real operation of a retail store. Finally, given the incorporation of the learning and forgetting phenomenon, the second experiment required higher levels of multiskilling to minimize understaffing costs and, in turn, compensate for the productivity loss.