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.

In this thesis, we analyze the impact of the decentralization of price and leadtime decisions made by the marketing and production departments, respectively, in a make-to-order firm. We first study a monopoly environment, and find that in the decentralized setting, the total demand generated is larger, leadtimes are longer, quoted prices are lower, and the firm profits are lower as compared to the centralized setting. We show that coordination can be achieved using a transfer price contract with bonus payments, where both departments receive a fraction of the total revenues generated as a bonus payment. In the second study, we extend this work to a duopoly environment, where two firms compete on the basis of their price and leadtime quotes in a common market. We find that under intense price competition, firms may suffer from a decentralized structure, particularly under high flexibility induced by high capacity, where revenue based sales incentives motivate sales/marketing for more aggressive price cuts resulting in eroding margins. We take the parameters of the demand models in the first two studies as constant, while estimating those parameters based on historical data is a very important problem in practice. In the last study of this thesis, we address the challenges encountered in estimating the price sensitivity of customers shifting focus to the passenger travel industry. We explore how to obtain better price elasticity estimates through an empirical study with an emphasis on the endogeneity problem, which arises as a result of the simultaneous determination of supply and demand. We show that if one does not account for endogeneity, price elasticities may induce an upward-sloping demand curve suggesting that high price produces high demand, or may be biased downward to the extent that elastic demand curves are incorrectly classified as inelastic. We show the improvement in price elasticities through an instrumental variable approach.