Optimizing Demand Management in Stochastic Systems to Improve Flexibility and Performance
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In this thesis we analyze optimal demand management policies for stochastic systems. In the first system considered, a manufacturer decides how to manage demand from customers that differ in their priority level and willingness to pay. He has limited production capacity and predetermined prices throughout the horizon. We find an optimal production and inventory strategy that rations current and future limited capacity between customer classes through reserving inventory for the future and accepting orders now for future delivery. Next, we extend these results to the case when the customers have different tolerance to delayed fulfillment, namely, first-class customers never accept backlogging whereas second-class customers agree to wait one period for a discount. We find an optimal policy similar to the production and inventory strategy that is used for the first system based on threshold values. The third system considers a firm whose recent performance in meeting quoted leadtimes affects future demand arrivals. We assume that the probability of a customer placing an order depends on the quoted leadtime, and both customer arrivals and processing times are stochastic. When capacity of the firm is infinite, we find the optimal leadtime to quote, and when capacity is finite and leadtime is industry-dictated, we determine that the optimal demand acceptance policy does not necessarily have a nice structure. We comment on the structure of the optimal policy for a special case and develop several heuristics for the general case. The final system considered in this thesis is the Sports and Entertainment industry, where demand is managed for a season of several performances by selling season tickets initially and single events later in the selling horizon. We specifically study the optimal time to switch between these market segments dynamically as a function of the state of the system and show that the optimal switching time is a set of time thresholds that depend on the remaining inventory and time left in the horizon.