We demonstrate the need to view in a dynamic context any decision based on limited information. We focus on the use of product costs in selecting the product portfolio. We show how ex post data regarding the actual costs from implementing the decision leads to updating of product cost estimates and potentially trigger a revision of the initial decision. We model this updating process as a discrete dynamical system (DDS). We define a decision as informationally consistent if it is a fixed-point solution to the DDS. We employ numerical analysis to characterize the existence and properties of such solutions. We find that fixed points are rare, but that simple heuristics find them often and quickly. We demonstrate the usefulness and robustness of our methodology by examining the interaction of limited information with multiple decision rules (heuristics) and problem features (size of product portfolio, profitability of product markets). We discuss implications for research on cost systems.
Anand, V., Balakrishnan, R. and Labro, E. (2017), Obtaining Informationally Consistent Decisions When Computing Costs with Limited Information. Production and Operations Management, 26: 211–230. doi:10.1111/poms.12631
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