Dynamic competitive newsvendors with service-sensitive demands

When two firms compete for service-sensitive demands based on their product availability, their actions will affect the future market share reallocation. This problem was first studied by Hall and Porteus (2000) using a dynamic game model. We extend their work by incorporating a general demand model, which enables us to obtain properties that reveal the dynamics of the game and the behavior of the players. In particular, we provide conditions under which the market share of a firm has a positive value and give it an upper bound. We further extend the game competition model to an infinite-horizon setting. We prove that there exists a stationary equilibrium policy and that the dynamic equilibrium policy always converges to a stationary equilibrium policy. We demonstrate that demand patterns will dictate how firms compete rationally and show the likely outcomes of the competition

Emerging Operational Contracts in Competitive Markets.

This dissertation consists of three essays, each dealing with an emerging type of operational contracts. The first essay considers a resource exchange model where the effects of collaboration and competition are intertwined. Exchanging resources often improves utilization and is intended to increase profitability of involved firms. However, it does not guarantee success in competitive settings. More efficient use of resources might actually leads to increased competition. We explore how resource exchange contracts impact the firms and consumers. The results indicate that the resource exchange tends to benefit both firms and the consumers in most situations, except for the extreme situations where simultaneously competition is strong and the purchasing cost is either very low or very high. The second essay focuses on vertical pricing control contracts that manufacturers use to coordinate online and offline retailers. Resale Price Maintenance (RPM) policy requires all retailers to sell at the price suggested by manufacturers. Minimum Advertised Price (MAP) policy is less strict, as it allows retailers to sell at lower prices than the manufacturer suggested, as long as these lower prices are not advertised. This essay studies which of these two policies is more beneficial to each member of the supply chain. We show that manufacturers prefer MAP policy when the customers' valuations vary significantly and the information search requires significant effort. The MAP policy is also favorable to retailers and consumers under similar market conditions. The third essay concerns the contractual issues when energy service companies (ESCOs) provide energy efficiency projects to residential clients. While performance based contracts have been proven successful in public, commercial, and industrial sectors, ESCOs face challenges in the residential sector. Residential clients often change consumption behavior after the project, which makes the real energy savings difficult to measure. Additionally, residential clients are much more risk averse and vulnerable to uncertain outcomes of projects. We show that piecewise linear contracts perform reasonably well. To further improve profitability, ESCOs can either reduce uncertainty of technology involved or develop the ability to verify post-project energy efficiency. We also make recommendations in monetary incentives and regulations from policy makers' perspective.PhDBusiness AdministrationUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/133457/1/lgding_1.pd

Dynamic Pricing and Inventory Management: Theory and Applications

We develop the models and methods to study the impact of some emerging trends in technology, marketplace, and society upon the pricing and inventory policy of a firm. We focus on the situation where the firm is in a dynamic, uncertain, and (possibly) competitive market environment. The market trends of particular interest to us are: (a) social networks, (b) sustainability concerns, and (c) customer behaviors. The two main running questions this dissertation aims to address are: (a) How these emerging market trends would influence the operations decisions and profitability of a firm; and (b) What pricing and inventory strategies a firm could use to leverage these trends. We also develop an effective comparative statics analysis method to address these two questions under different market trends. Overall, our results suggest that the current market trends of social networks, sustainability concerns, and customer behaviors have significant and interesting impact upon the operations policy of a firm, and that the firm could adopt some innovative pricing and inventory strategies to exploit these trends and substantially improve its profit. Our main findings are: (a) Network externalities (the monopoly setting). We find that network externalities prompt a firm to face the tradeoff between generating current profits and inducing future demands when making the price and inventory decisions, so that it should increase the base-stock level, and to decrease [increase] the sales price when the network size is small [large]. Our extensive numerical experiments also demonstrate the effectiveness of the heuristic policies that leverage network externalities by balancing generating current profits and inducing demands in the near future. (Chapter 2.) (b) Network externalities (the dynamic competition setting). In a competitive market with network externalities, the competing firms face the tradeoff between generating current profits and winning future market shares (i.e., the exploitation-induction tradeoff). We characterize the pure strategy Markov perfect equilibrium in both the simultaneous competition and the promotion-first competition. We show that, to balance the exploitation-induction tradeoff, the competing firms should increase promotional efforts, offer price discounts, and improve service levels. The exploitation-induction tradeoff could be a new driving force for the fat-cat effect (i.e., the equilibrium promotional efforts are higher under the promotion-first competition than those under the simultaneous competition). (Chapter 3.) (d) Trade-in remanufacturing. We show that, with the adoption of the very commonly used trade-in remanufacturing program, the firm may enjoy a higher profit with strategic customers than with myopic customers. Moreover, trade-in remanufacturing creates a tension between firm profitability and environmental sustainability with strategic customers, but benefits both the firm and the environment with myopic customers. We also find that, with either strategic or myopic customers, the socially optimal outcome can be achieved by using a simple linear subsidy and tax scheme. The commonly used government policy to subsidize for remanufacturing alone, however, does not induce the social optimum in general. (Chapter 4.) (d) Scarcity effect of inventory. We show that the scarcity effect drives both optimal prices and order-up-to levels down, whereas increased operational flexibilities (e.g., the inventory disposal and inventory withholding opportunities) mitigate the demand loss caused by high excess inventory and increase the optimal order-up-to levels and sales prices. Our extensive numerical studies also demonstrate that dynamic pricing leads to a much more significant profit improvement with the scarcity effect of inventory than without. (Chapter 5.) (e) Comparative statics analysis method. We develop a comparative statics method to study a general joint pricing and inventory management model with multiple demand segments, multiple suppliers, and stochastically evolving market conditions. Our new method makes componentwise comparisons between the focal decision variables under different parameter values, so it is capable of performing comparative statics analysis in a model where part of the decision variables are non-monotone, and it is well scalable. Hence, our new method is promising for comparative statics analysis in other operations management models. (Chapter 6.

Coordinated planning in revenue management

Revenue management has been applied in service industries for more than thirty years. Since then, revenue management has been transferred to other industries like manufacturing or e- fulfillment. Short-term revenue management decisions are taken based on other, longer-term decisions such as decisions about actual capacity, segment-based prices or the price fences in place. While optimization approaches have been developed for each of these planning tasks in isolation, existing approaches typically do not consider interactions between planning tasks. This thesis considers coordinated planning in revenue management, that is the interaction of revenue management decisions with other planning tasks. First, we provide an overview of both the literature on coordinated decision making in the context of revenue management in different industries, and the literature on existing frameworks, which aim to structure the planning tasks around revenue management. We find that the planning tasks relevant to revenue management differ across the industries considered. Moreover, planning tasks are relevant on different hierarchical levels in different industries. We discuss an approach for an industry-independent framework. Based on the relevant planning tasks identified, we investigate the long-term performance of revenue management and therefore the integration of revenue management and customer relationship management. We present a stochastic dynamic programming approach, where the firm’s allocation decision impacts future customer demands by influencing the repurchase probabilities of customers, depending on whether their request has been accepted or rejected. We show that a protection level policy is not necessarily optimal in a two-period setting. In a numerical study, we find that the value of looking ahead in time is low on average but may be substantial in some scenarios. However, the benefit from regular demand updates is considerably higher than the additional value of looking ahead in time on average. Lastly, we investigate the interaction of revenue management and fencing. We account for the trade-off between price-driven demand leakage on the one hand and costs for fencing on the other hand. We show that fencing decisions have an impact on the optimal capacity allocation, but that this is not the case vice versa as the fencing decision does not depend on the allocation decision. Taking both decisions sequentially is therefore optimal. We extend our approach in order to account for additional stock-out-based demand substitution. Then, both decisions depend on each other and firms should take both decisions simultaneously