A Learning Real Options Framework with Application to Process Design and Capacity Planning

Luke T. Miller; Chan S. Park

doi:10.1111/j.1937-5956.2005.tb00006.x

← Search

A Learning Real Options Framework with Application to Process Design and Capacity Planning

Luke T. Miller¹; Chan S. Park²

¹ University of San Diego · ² Auburn University

Production and Operations Management 2005

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.

DOI: 10.1111/j.1937-5956.2005.tb00006.x
Export: BibTeX
Sources: openalex