A learning real options framework with application to process design and capacity planning

This paper studies the impact of learning on a multi-staged investment scenario. In contrast to other models in the real options literature in which learning is viewed as a passive consequence of the delay period, this paper quantifies information acquisition by merging statistical decision theory with the real options framework. In this context, real option attributes are discussed from a Bayesian perspective, thresholds are identified for improved decision-making, and information's impact on downstream decision-making is discussed. Using real data provided by a firm in the aerospace maintenance, repair, and overhaul industry, the methodology is used to guide a multi-phased irreversible investment decision involving process design and capacity planning.