Revenue Management and Demand Fulfillment: Matching Applications, Models, and Software

Recent years have seen great successes of revenue management, notably in the airline, hotel, and car rental business. Currently, an increasing number of industries, including manufacturers and retailers, are exploring ways to adopt similar concepts. Software companies are taking an active role in promoting the broadening range of applications. Also technological advances, including smart shelves and radio frequency identification (RFID), are removing many of the barriers to extended revenue management. The rapid developments in Supply Chain Planning and Revenue Management software solutions, scientific models, and industry applications have created a complex picture, which appears not yet to be well understood. It is not evident which scientific models fit which industry applications and which aspects are still missing. The relation between available software solutions and applications as well as scientific models appears equally unclear. The goal of this paper is to help overcome this confusion. To this end, we structure and review three dimensions, namely applications, models, and software. Subsequently, we relate these dimensions to each other and highlight commonalities and discrepancies. This comparison also provides a basis for identifying future research needs.Manufacturing;Revenue Management;Software;Advanced Planning Systems;Demand Fulfillment

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

Quantum prices

Many retailers practice an extreme form of discrete pricing defined as quantum prices: differentiated products are priced using few and sparse price buckets. To show this, online data was collected for 350,000 products from over 65 fashion retailers in the U.S. and the U.K. This pricing strategy is observed within categories and across categories (i.e., similar or even disparately distinct products like jeans and bags have an identical price), as well as in product introductions, where new products come in at previous price buckets. Normalized indices indicate substantial price clustering after controlling for popular prices, convenient prices, assortment size, or digit endings. Quantum prices have implications for price adjustments through product shares, markdown prices, and for the law-of-one-price. A behavioral model of price salience and recall is discussed

Revenue Management and Demand Fulfillment: Matching Applications, Models, and Software

Recent years have seen great successes of revenue management, notably in the airline, hotel, and car rental business. Currently, an increasing number of industries, including manufacturers and retailers, are exploring ways to adopt similar concepts. Software companies are taking an active role in promoting the broadening range of applications. Also technological advances, including smart shelves and radio frequency identification (RFID), are removing many of the barriers to extended revenue management. The rapid developments in Supply Chain Planning and Revenue Management software solutions, scientific models, and industry applications have created a complex picture, which appears not yet to be well understood. It is not evident which scientific models fit which industry applications and which aspects are still missing. The relation between available software solutions and applications as well as scientific models appears equally unclear. The goal of this paper is to help overcome this confusion. To this end, we structure and review three dimensions, namely applications, models, and software. Subsequently, we relate these dimensions to each other and highlight commonalities and discrepancies. This comparison also provides a basis for identifying future research needs

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

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

General equilibrium oligopoly and ownership structure

We develop a tractable general equilibrium framework in which firms are large and have market power with respect to both products and labor, and in which a firm's decisions are affected by its ownership structure. We characterize the Cournot–Walras equilibrium of an economy where each firm maximizes a share‐weighted average of shareholder utilities—rendering the equilibrium independent of price normalization. In a one‐sector economy, if returns to scale are non‐increasing, then an increase in “effective” market concentration (which accounts for common ownership) leads to declines in employment, real wages, and the labor share. Yet when there are multiple sectors, due to an intersectoral pecuniary externality, an increase in common ownership could stimulate the economy when the elasticity of labor supply is high relative to the elasticity of substitution in product markets. We characterize for which ownership structures the monopolistically competitive limit or an oligopolistic one is attained as the number of sectors in the economy increases. When firms have heterogeneous constant returns to scale technologies, we find that an increase in common ownership leads to markets that are more concentrated

Mitigating demand risk of durable goods in online retailing

Purpose An uncertain product demand in online retailing leads to loss of opportunity cost and customer dissatisfaction due to instances of product unavailability. On the other hand, when e-retailers store excessive inventory of durable goods to fulfill uncertain demand, it results in significant inventory holding and obsolescence cost. In view of such overstocking/understocking situations, this study attempts to mitigate online demand risk by exploring novel e-retailing approaches considering the trade-offs between opportunity cost/customer dissatisfaction and inventory holding/obsolescence cost. Design/methodology/approach Four e-retailing approaches are introduced to mitigate uncertain demand and minimize the economic losses to e-retailer. Using three months of purchased history data of online consumers for durable goods, four proposed approaches are tested by developing product attribute based algorithm to calculate the economic loss to the e-retailer. Findings Mixed e-retailing method of selling unavailable products from collaborative e-retail partner and alternative product's suggestion from own e-retailing method is found to be best for mitigating uncertain demand as well as limiting customer dissatisfaction. Research limitations/implications Limited numbers of risk factor have been considered in this study. In the future, others risk factors like fraudulent order of high demand products, long delivery time window risk, damage and return risk of popular products can be incorporated and handled to reduce the economic loss. Practical implications The analysis can minimize the economic losses to an e-retailer and also can maximize the profit of collaborative e-retailing partner. Originality/value The study proposes a retailer to retailer collaboration approach without sharing the forecasted products' demand information