Bayesian real options control chart

Abstract

AbstractIn this paper, we present the concept of a novel control chart, which uses economic considerations within the real options–inspired framework together with the principles of Bayesian statistics to produce a continuously updated estimate of the parameters of the actual process, and thus to decide whether to continue running the process or to recalibrate it instead. Bayesian estimate allows the decision maker to combine prior information about the process with the continuously incoming data in a natural flexible manner. In the real options framework, at any given moment, we compare the cost of recalibrating the process to the cost of postponing the (optimal) decision for later. The decision is thus based on cost‐benefit analysis rather than statistically significant deviations from the in‐control process. To have a clear focus on the conceptual representation of the novel methodology, we consider a continuously sampled binary process. We derive the algorithm for the control chart, which can also, in this discrete setting, be represented as a table, a matrix, or a tree. We also investigate the performance of the method in different settings with particular attention being paid to the role of Bayesian prior. Being flexible in prior beliefs leads to better results anywhere outside of the in‐control process. Together, Bayesian paradigm and dynamic decision‐making approach create a realistic representation of a real‐life decision‐making process.