Markdowns in Seasonal Conspicuous Goods

In common parlance, luxury and markdowns are, in many respects, contradictory concepts. Markdowns decrease product exclusivity and hence consumers’ willingness to pay (i.e., snob effect) since most consumers purchasing luxury desire uniqueness. Markdowns also encourage strategic (forward-looking) consumers to wait for lower prices (i.e., strategic effect). Yet, luxury retailers frequently adopt markdowns in practice to stimulate the demand for their seasonal products (i.e., sales effect). To study the impact of these three countervailing effects on a luxury retailer’s markdown policy and rationing strategy, this paper develops a game-theoretic model with strategic and exclusivity-seeking consumers who have heterogeneous (high and low) valuations. We characterize a luxury retailer’s equilibrium markdown and rationing strategies, and find that the retailer induces a buying frenzy (i.e., selling deliberately less than the demand) to increase consumers’ willingness to pay when they are sufficiently exclusivity-seeking. We show that the retailer’s markdown policy depends on consumers’ desire for exclusivity when the proportion of consumers with high valuation is not too high or too low. Interestingly, we find that, in such cases, consumers’ higher desire for exclusivity does not motivate the retailer to increase exclusivity and to adopt uniform pricing. To the contrary, it motivates the retailer to decrease the exclusivity and to adopt markdowns. By doing so, we identify exclusivity-seeking consumer behavior as another rationale behind markdown pricing. Lastly, we find that, when selling to exclusivity-seeking consumers, the negative impact of strategic consumer behavior is lower; however, ignoring it can be more costly

Solving Practical Dynamic Pricing Problems with Limited Demand Information

Dynamic pricing problems have received considerable attention in the operations management literature in the last two decades. Most of the work has focused on structural results and managerial insights using stylized models without considering business rules and issues commonly encountered in practice. While these models do provide general, high-level guidelines for managers in practice, they may not be able to generate satisfactory solutions to practical problems in which business norms and constraints have to be incorporated. In addition, most of the existing models assume full knowledge about the underlying demand distribution. However, demand information can be very limited for many products in practice, particularly, for products with short life-cycles (e.g., fashion products). In this dissertation, we focus on dynamic pricing models that involve selling a fixed amount of initial inventory over a fixed time horizon without inventory replenishment. This class of dynamic pricing models have a wide application in a variety of industries. Within this class, we study two specific dynamic pricing problems with commonly-encountered business rules and issues where there is limited demand information. Our objective is to develop satisfactory solution approaches for solving practically sized problems and derive managerial insights. This dissertation consists of three parts. We first present a survey of existing pricing models that involve one or multiple sellers selling one or multiple products, each with a given initial inventory, over a fixed time horizon without inventory replenishment. This particular class of dynamic pricing problems have received substantial attention in the operations management literature in recent years. We classify existing models into several different classes, present a detailed review on the problems in each class, and identify possible directions for future research. We then study a markdown pricing problem that involves a single product and multiple stores. Joint inventory allocation and pricing decisions have to be made over time subject to a set of business rules. We discretize the demand distribution and employ a scenario tree to model demand correlation across time periods and among the stores. The problem is formulated as a MIP and a Lagrangian relaxation approach is proposed to solve it. Extensive numerical experiments demonstrate that the solution approach is capable of generating close-to-optimal solutions in a short computational time. Finally, we study a general dynamic pricing problem for a single store that involves two substitutable products. We consider both the price-driven substitution and inventory-driven substitution of the two products, and investigate their impacts on the optimal pricing decisions. We assume that little demand information is known and propose a robust optimization model to formulate the problem. We develop a dynamic programming solution approach. Due to the complexity of the DP formulation, a fully polynomial time approximation scheme is developed that guarantees a proven near optimal solution in a manageable computational time for practically sized problems. A variety of managerial insights are discussed

Markdown or Everyday Low Price? The Role of Behavioral Motives

We study a seller’s optimal pricing and inventory strategies when behavioral (nonpecuniary) motives affect consumers’ purchase decisions. In particular, the seller chooses between two pricing strategies, markdown or everyday low price, and determines the optimal prices and inventory level. Two salient behavioral motives that impact consumers’ purchase decisions and the seller’s optimal strategies are anticipated regret and misperception of product availability. Regret arises when a consumer initially chooses to wait but encounters stockout later, or when the consumer buys the product at the high price but realizes that the product is still available at the markdown price. In addition, consumers often perceive the product’s future availability to be different than its actual availability. We determine and quantify that both regret and availability misperception have significant operational and profit implications for the seller. For example, ignoring these behavioral factors can result in up to 10% profit losses. We contrast the roles of consumers’ strategic (pecuniary) motives with their behavioral (nonpecuniary) motives in affecting purchase, pricing, and inventory decisions. The presence of the behavioral motives reinstates the profitability of markdown over everyday low price, in sharp contrast to prior studies of only strategic motives that suggest the contrary. We characterize how and why strategic versus behavioral motives affect decisions in distinctive manners. In doing so, this paper also introduces and determines the behavioral benefits of pricing in leveraging consumers’ behavioral regularities. We advocate that tactics that may intensify consumers’ misperception of availability, such as disclosing low inventory levels, can have a far-reaching impact on improving the seller’s profit

Decision Making in Supply Chains with Waste Considerations

As global population and income levels have increased, so has the waste generated as a byproduct of our production and consumption processes. Approximately two billion tons of municipal solid waste are generated globally every year – that is, more than half a kilogram per person each day. This waste, which is generated at various stages of the supply chain, has negative environmental effects and often represents an inefficient use or allocation of limited resources. With the growing concern about waste, many governments are implementing regulations to reduce waste. Waste is a often consequence of the inventory decisions of different players in a supply chain. As such, these regulations aim to reduce waste by influencing inventory decisions. However, determining the inventory decisions of players in a supply chain is not trivial. Modern supply chains often consist of numerous players, who may each differ in their objectives and in the factors they consider when making decisions such as how much product to buy and when. While each player makes unilateral inventory decisions, these decisions may also affect the decisions of other players. This complexity makes it difficult to predict how a policy will affect profit and waste outcomes for individual players and the supply chain as a whole. This dissertation studies the inventory decisions of players in a supply chain when faced with policy interventions to reduce waste. In particular, the focus is on food supply chains, where food waste and packaging waste are the largest waste components. Chapter 2 studies a two-period inventory game between a seller (e.g., a wholesaler) and a buyer (e.g., a retailer) in a supply chain for a perishable food product with uncertain demand from a downstream market. The buyer can differ in whether he considers factors affecting future periods or the seller’s supply availability in his period purchase decisions – that is, in his degree of strategic behavior. The focus is on understanding how the buyer’s degree of strategic behavior affects inventory outcomes. Chapter 3 builds on this understanding by investigating waste outcomes and how policies that penalize waste affect individual and supply chain profits and waste. Chapter 4 studies the setting of a restaurant that uses reusable containers instead of single-use ones to serve its delivery and take-away orders. With policy-makers discouraging the use of single-use containers through surcharges or bans, reusable containers have emerged as an alternative. Managing inventories of reusable containers is challenging for a restaurant as both demand and returns of containers are uncertain and the restaurant faces various customers types. This chapter investigates how the proportion of each customer type affects the restaurant’s inventory decisions and costs

Revenue Management in the Sport Industry: an Examination of Forecasting Models and Advance Seat Section Inventory in Major League Baseball

Technological advances in data storage and processing have led to more sophisticated ticket pricing strategies in professional sport. Sport organizations are beginning to adopt a form of revenue management known as dynamic ticket pricing. Effective pricing strategies such as dynamic ticket pricing require an in-depth understanding of the nature of advance ticket inventory and accurate forecasting models to predict remaining inventory at various time horizons prior to game time. The purpose of this study was to gain an understanding of the nature of advance seat section ticket inventory. The study built on and contributed to work in sport revenue management. Although studies of sport revenue management have examined the applicability of revenue management in a sport context, there has not been a study of advance seat section ticket inventory despite the fact that sport organizations utilize price discrimination strategies at the seat section level. As such, this study provided additional insight into the applicability and potential effectiveness of a sport revenue management strategy. The methodological focus on forecasting models and accuracy enabled another contribution. A 3x3x6x7 full factorial research design examined the accuracy of various forecasting models under different data strategies, time horizons, model parameters, and levels of the values of T and K used in the moving average and exponential smoothing forecasting models. Statistically reliable differences existed between data strategies with the classical pickup data strategy providing the best forecasts of final game day inventory. Within the classical pickup strategy, no reliable differences in forecast models were detected nor were forecasts found to significantly differ when changing the value of T or K. Finally, forecast accuracy was shown to follow the theoretically predicted best to worst pattern as days out increased. A profile analysis of seat section ticket inventory showed seat sections exhibit different slopes and changes in slope over time. The general pattern of ticket inventory followed a linear trend but with varying slopes. Steeper slopes were found at 20, 10, and 5 days out followed by a leveling out between 5 and 3 days out which was then followed by steeper slopes from 3 days to game day. This finding suggested that optimizing a sport revenue management plan should include forecasting at the seat section level

Optimal Production Control under Uncertainty with Revenue Management Considerations.

This dissertation consists of three essays on supply chain and revenue management. Its main focus is the integration of dynamic production and demand management decisions for multiple products facing uncertain demand. The study considers three problem settings to inquire into flexibility's influence on a dynamic pricing strategy, managing exogenous demand for intermediary products, and allocation of shared resources among multiple products in a make-to-order environment. The first chapter studies a joint mechanism of dynamic pricing and capacity flexibility to mitigate demand and supply mismatches. We consider a firm producing two substitutable products with price-correlated demands utilizing capacitated product-dedicated and flexible resources. We characterize the structure and sensitivity of the optimal production and pricing decisions and find that the existence of a flexible resource in the firm's capacity portfolio helps maintain stable price differences across items over time. This result has favorable ramifications from a marketing standpoint as it suggests that even when a firm applies a dynamic pricing strategy, it may still establish consistent price positioning among multiple products if it can employ a flexible replenishment resource. We also investigate the economic benefits of a joint strategy versus applying each tool individually. The second chapter considers a setting where several intermediate products are assembled into an end product with external demand for both the end and intermediary products. We address the questions on how to decide on the production quantities for each component, when to initiate an assembly operation and how to set admission rules for demands targeted at various products. We characterize the structure and sensitivity of the optimal policy and extend the model to multiple customer classes and partial revenue collecting schemes. We propose a novel heuristic policy that is efficient, easily implementable and robust with respect to the number of intermediate products. Finally, the third chapter studies a basic mass customization setting by focusing on a manufacturing firm that assembles products to customer orders. For a two-stage production-assembly operation, we partially characterize the optimal production and demand admission control decisions. Based on the insights gained, we propose a heuristic algorithm and provide computational results on its performance.Ph.D.Mechanical Engineering and Industrial and Operations EnginUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/75871/1/oceryan_1.pd

Three Essays on Managing Customer-Based Strategies: A Pricing and Revenue Management Approach

Many firms and organizations with already-optimized business functions are under market pressure to protect their narrow profit margins. Their need for supplemental and reliable revenues calls for performance optimization beyond the core business functions. Motivated by applications from online social media and the airline industry, in my dissertation, I focus on the revenue management and pricing decisions of customer-based plans and programs. More formally, the research question addressed in this study is: How can firms effectively use customer-based pricing strategies to boost revenues? My dissertation consists of three essays. In the first essay, I analyze the ongoing competition among online social media (OSMs) to attract users. Concentrating on the importance of community retention and expansion to OSMs in preserving financially sustainable business models, I investigate whether OSMs should develop revenue sharing programs and reward their contributing users from their limited revenue streams. I present a duopoly OSM game (with a less favourable and a more favourable OSM) in which heterogeneous users choose their levels of contribution with respect to each OSM based on their preferences. In this chapter, I explore how online users’ actions and perspectives impact the outcome of the competition among OSMs. Furthermore, I investigate how small social media firms can compete with a dominant firm in the market. In the second essay, I study the role of ancillary revenue and its significance for industries such as airlines. These firms can barely survive without ancillary fees, even when their capacities are almost fully utilized. I consider the case in which customers-changing rates between flights are stochastic but decreasing with reference to the change fees. In this essay, I examine how firms should design change fees to manage customers’ switching behaviour. Specifically, I incorporate change fee revenues as a portion of total revenue structure and investigate how firms should update their markdown pricing strategies when they face price-tracking customers. In the third essay, I focus on the dynamics between a firm and customers who are uncertain about their future travel plans. While the firm maximizes its revenue by imposing optimal change fees, customers consider their travel plan uncertainties and maximize their utilities by responding strategically to these fares. In this study, I seek to answer two important policy questions: Although imposing a change fee could increase total revenue, does it burden the firm with a lower customer demand? How should the optimal monopolistic price be set with the presence of a change fee? Without imposing any distributional assumptions, I analytically derive each market player’s best reaction to the other to prescribe the characteristics of the firm/customer interaction equilibrium

AN EMPIRICAL EXAMINATION OF NEW INNOVATIVE PROCESSES IN RETAIL

Retailers constantly innovate to improve their operations to maintain a competitive advantage, which has become even more apparent following the challenges from the COVID-19 pandemic. One challenge with innovating, however, is that limited information is available to evaluate the effectiveness of the operations. Fortunately empirical methodologies of structural estimation and field experimentation can be used to help determine if innovative processes at retail chains are fruitful when implemented. Field experiments provide direct causal evidence on whether the innovations will work while structural estimation allows for examining counterfactual scenarios to evaluate outcomes from such process innovations. In this dissertation, we leverage structural estimation and field experimentation to study three topics on the frontier of innovations in retail operations: a) dynamic pricing of product drops in the presence of resellers, b) localization of inventory for e-commerce retailers, and c) increasing customer recycling through operational incentives. The key results are as follows. In Chapter 2, through structural estimation we show that incorporating resellers into pricing improves retailer profit by 7% on average, and the impacts of the resale market to firm profit are heterogeneous across products based on the initial inventory relative to the initial demand. In Chapter 3, through structural estimation we find that distribution centers closer to the customer (front DCs) allow the e-commerce retailer to capture an average 10.7% benefit to profit by improving average promised delivery time by 28.3%. Front DCs allow to capture sales from high-margin SKUs with high demand where backup fulfillment results in much longer promised delivery time. In Chapter 4, through field experiments we find that the chosen value-based incentives and convenience-based incentives are ineffective at inducing customers to engage in recycling behavior, despite importance of these incentives toward recycling intentions reported in the literature. Our results suggest that offering programs to encourage e-waste recycling behavior can be a costly endeavor.Doctor of Philosoph