Most Productive Scale Size versus Demand Fulfillment: A Solution to the Capacity Dilemma
26 Pages Posted: 18 Jan 2014
Date Written: January 16, 2014
The field of economics tends to associate capacity planning with economic scale size and find the characteristics of the production function whereas the field of operations research community tends to focus on demand fulfillment to reduce the loss of sales or inventory for profit maximization. However, it is troublesome capacity dilemmas for firms that need to achieve economic scale size and demand fulfillment simultaneously; in particular, firms operate in stochastic environments. This study fills a gap between these two. To improve capacity planning, this study proposes a multi-objective mathematical programming with data envelopment analysis (DEA) constraints. In particular, compromise programming sets a target which shows a tradeoff between the most-productive-scale-size (MPSS) benchmark and a potential demand fulfillment benchmark. In addition, the minimax regret (MMR) approach and the stochastic programming (SP) technique are used to address target variation caused by demand fluctuations. This study pushes the ex-post DEA analysis of efficiency estimation (i.e. position) towards the ex-ante DEA analysis of production planning (i.e. direction). The result shows that the proposed models provide managerial insights to address the capacity dilemma.
Keywords: data envelopment analysis, multi-objective decision analysis, demand uncertainty, most productive scale size, stochastic programming, capacity planning
JEL Classification: D24, C44, D61
Suggested Citation: Suggested Citation