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An Entropy-Based Methodology for Valuation of Demand Uncertainty Reduction

We propose a distribution-free entropy-based methodology to calculate the expected value of an uncertainty reduction effort and present our results within the context of reducing demand uncertainty. In contrast to existing techniques, the methodology does not require a priori assumptions regarding the underlying demand distribution, does not require sampled observations to be the mechanism by which uncertainty is reduced, and provides an expectation of information value as opposed to an upper bound. In our methodology, a decision maker uses his existing knowledge combined with the maximum entropy principle to model both his present and potential future states of uncertainty as probability densities over all possible demand distributions. Modeling uncertainty in this way provides for a theoretically justified and intuitively satisfying method of valuing an uncertainty reduction effort without knowing the information to be revealed. We demonstrate the methodology’s use in three different settings: 1) a newsvendor valuing knowledge of expected demand, 2) a short-lifecycle product supply manager considering the adoption of a quick response strategy, and 3) a revenue manager making a pricing decision with limited knowledge of the market potential for his product.