Proactive data envelopment analysis: Effective production and capacity expansion in stochastic environments

• Proposes a short-run capacity planning method termed proactive DEA.
• Uses stochastic programming methods to account for demand.
• Accounts for decreasing marginal benefits of inputs in short-run capacity planning.
• Quantifies the effectiveness of a firm’s production system under demand uncertainty.
• Demonstrate the proposed model through an empirical study of Japanese convenience stores.

Demand fluctuations that cause variations in output levels will affect a firm’s technical inefficiency. To assess this demand effect, a demand-truncated production function is developed and an “effectiveness” measure is proposed. Often a firm can adjust some input resources influencing the output level in an attempt to match demand. We propose a short-run capacity planning method, termed proactive data envelopment analysis, which quantifies the effectiveness of a firm’s production system under demand uncertainty. Using a stochastic programming DEA approach, we improve upon short-run capacity expansion planning models by accounting for the decreasing marginal benefit of inputs and estimating the expected value of effectiveness, given demand. The law of diminishing marginal returns is an important property of production function; however, constant marginal productivity is usually assumed for capacity expansion problems resulting in biased capacity estimates. Applying the proposed model in an empirical study of convenience stores in Japan demonstrates the actionable advice the model provides about the levels of variable inputs in uncertain demand environments. We conclude that the method is most suitable for characterizing production systems with perishable goods or service systems that cannot store inventories.