Optimizing Service Productivity With Substitutable and Limited Resources

Abstract

This article develops a decision model which enables service firms to optimize their productivity. Companies must efficiently determine the necessary resource input to increase service productivity to meet customer demand. In so doing, managers face service-specific challenges: They must select the appropriate type and quantity of limited resources to deliver services efficiently, consider the volatility of demand to provide services effectively, and integrate the interaction effects of resources in terms of substitution to utilize constraint resources optimally. In addressing these challenges, we develop an interdisciplinary approach by combining insights from service research and operations research to create a decision model that helps managers select the optimal type and quantity of resources available to overcome the abovementioned challenges. We validate our model in several case studies and further generalize our findings by applying it to different data settings. Ultimately, we prove that productivity can be increased significantly if firms optimize resource selection by considering stochastic demand, the effects of substitution among resources, and resource constraints.