Hedging Predictions for Operational Decision Making

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Over the past several years, with the growing availability of data, there has been an explosion in research on using machine learning (and statistical) techniques to make predictions. However, less attention has been paid to how to effectively use these predictions to make effective operational decisions, where the predictions are related to demand (processing times, demand quantities). In particular, the stochastic modelling literature is almost exclusively concerned with scenarios where predictions are error-free or where no predictions are available (decisions are based only on distributional information). This proposal addresses this gap in the literature in two directions. The first major focus is on quantifying the "price of misprediction", the cost of naively using predicted values in place of true values, measured against the cost of using only distributional information (no predictions). When the price of misprediction is high, it then becomes of interest to design operational decision policies that extract useful information from the noisy predictions while hedging against the potential negative impacts of prediction errors. The design of such policies is the second major focus of this proposal. To gain insight into these issues, two of the most popular stochastic models will be considered. The first, queueing models, will be studied when predictions of processing times are available. Of particular interest will be the effect of scaling up the size of the system (number of servers) on the price of misprediction and the design of effective server scheduling and job routing strategies. We believe that larger systems are less sensitive to prediction errors. The second, inventory models, will be studied when demand predictions are available. We will study the impact of the prediction accuracy and sensitivity of the impact of prediction error to the cost function being considered, in particular suggesting approaches to determine safety stocks to hedge against prediction errors. For both of these models, implementability will be a key concern. Optimal policies may become quite complicated, so the identification of simple, near optimal policies will be an important consideration that will allow this work to be translated to practice. Beyond addressing gaps in the academic literature, this work is expected to have practical impact. We are already involved in projects for inventory management for blood products, and have seen that demand for some products can be more accurately predicted than others, so our results should inform effective inventory management practices in this domain. Healthcare, retail sales, manufacturing and information systems are other examples of areas that are data-rich and predictions of underlying quantities related to demand are possible (or are already being performed). As such, they are all potential beneficiaries of the proposed research.