Pricing Strategy and Quick Response Adoption System with Strategic Customers

This study determined the competitive advantage of a quick response (QR) system when a firm faces forward-looking customers with heterogeneous and uncertain valuations for a product, uncertain demand, and two selling periods. We identify two classes of pricing strategies, namely, no-price commitment strategy and price commitment strategy. Interestingly, the unique equilibrium is proven to exist if and only if most customers have high tastes on a product’s value. We also prove that when customers possess beliefs about the markdown in the second period being smaller enough, a firm obtains a high profit with price commitment; otherwise he obtains a high profit without price commitment. Moreover, we distinguish the competitive advantage of a QR system from two strategies. When a firm uses no-price commitment strategy, the value of QR system in the first period decreases and in the second period increases with customer’s strategic behavior. When a firm provides price commitment, the value of QR system in the first period may increase, decrease, or decrease first and then increase with customer’s strategic behavior. And the value of QR in the second period under price commitment strategy decreases or rises first and then decreases with customer’s strategic behavior

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

Optimal pricing strategy:How to sell to strategic consumers?

Technological advances are preparing consumers to plan their purchases strategically. Selling to strategic consumers at a fixed price forgoes the profit from salvaging inventory, whereas high-low pricing, as a ubiquitous pricing strategy, is costly due to the offered markdown discount. This research explores the overall impact of consumer's strategic buying behaviour on a pricing strategy, and identifies conditions where fixed pricing, strategic high pricing, or high-low pricing is the best approach by analytically comparing the profits of the three pricing strategies. Our results show that high-low pricing is appropriate only if the offered markdown discount is relatively small. If strategic consumers have a small population and the needed markdown discount is relatively large, retailers can ignore strategic buying behaviour and sell products at a fixed price. Our results emphasize that the markdown discount for clearance sales and the market structure of heterogeneous consumers play vital roles in determining the optimal pricing strategy

From Stream to Pool: Dynamic Pricing Beyond i.i.d. Arrivals

The dynamic pricing problem has been extensively studied under the \textbf{stream} model: A stream of customers arrives sequentially, each with an independently and identically distributed valuation. However, this formulation is not entirely reflective of the real world. In many scenarios, high-valuation customers tend to make purchases earlier and leave the market, leading to a \emph{shift} in the valuation distribution. Thus motivated, we consider a model where a \textbf{pool} of $n$ non-strategic unit-demand customers interact repeatedly with the seller. Each customer monitors the price intermittently according to an independent Poisson process and makes a purchase if the observed price is lower than her \emph{private} valuation, whereupon she leaves the market permanently. We present a minimax \emph{optimal} algorithm that efficiently computes a non-adaptive policy which guarantees a $1/k$ fraction of the optimal revenue, given any set of $k$ prices. Moreover, we present an adaptive \emph{learn-then-earn} policy based on a novel \emph{debiasing} approach, and prove an $\tilde O(kn^{3/4})$ regret bound. We further improve the bound to $\tilde O(k^{3/4} n^{3/4})$ using martingale concentration inequalities

Consumer Returns Policies and Supply Chain Performance

This paper develops a model of consumer returns policies. In our model, consumers face valuation uncertainty and realize their valuations only after purchase. There is also aggregate demand uncertainty, captured using the conventional newsvendor model. In this environment, consumers decide whether to purchase and then whether to return the product, whereas the seller sets the price, quantity, and refund amount. Using our model, we study the impact of full returns policies (e.g., using 100% money-back guarantees) and partial returns policies (e.g., when restocking fees are charged) on supply chain performance. Next, we demonstrate that consumer returns policies may distort incentives under common supply contracts (such as manufacturer buy-backs), and we propose strategies to coordinate the supply chain in the presence of consumer returns. Finally, we explore several extensions and demonstrate the robustness of our findings

Identifying and Mitigating Fiscal Risks from State-Owned Enterprises (SOEs).

This paper focuses on the fiscal risks created by state-owned enterprises (SOEs). It analyzes the main sources of such risks, in particular flaws in their fiscal, including quasi-fiscal operations; excessive extraction of SOEs resources by their owner governments; preferential access of SOEs to financing; and information asymmetries between the SOEs and their owners. These are illustrated with reference to selected country experiences, mainly in Latin America. Based on this analysis, the paper outlines a number of policy recommendations to identify and mitigate such risks.Este artículo se centra en los riesgos fiscales creados por las empresas estatales. Analiza las principales fuentes de esos riesgos, en particular las fallas en sus operaciones fiscales, incluidas las cuasifiscales; la extracción excesiva de los recursos de las empresas estatales por parte de los gobiernos; el acceso preferencial de estas empresas a la financiación y las asimetrías de información entre ellas y sus propietarios. Todo esto se ejemplifica mediante las experiencias de países seleccionados, principalmente en América Latina. Basado en este análisis, el trabajo describe una serie de recomendaciones de políticas para identificar y mitigar dichos riesgos

The Value of Observing the Buyer Arrival Time in Dynamic Pricing

We consider a dynamic pricing problem where a firm sells one item to a single buyer in order to maximize expected revenues. The firm commits to a price function over an infinite horizon. The buyer arrives at some random time with a private value for the item. He is more impatient than the seller and strategizes the time of his purchase in order to maximize his expected utility, which implies either buying immediately or waiting to benefit from a lower price. We study how important is to observe the buyer arrival time in terms of the seller's expected revenue. When the seller can observe the arrival of the buyer, she can make the price function contingent on his arrival time. On the contrary, when the seller cannot observe the arrival, her price function is fixed at time zero for the whole horizon. The value of observability (VO) is defined as the worst case ratio between the expected revenue of the seller when she observes the buyer's arrival and that when she does not. First, we show that for the particular case where the buyer's valuation follows a monotone hazard rate distribution, the upper bound is e, and it is tight. Next, we show our main result: In a very general setting about valuation and arrival time distributions, the value of observability is at most 4.911. To obtain this bound we fully characterize the observable arrival setting and use this solution to construct a random and periodic price function for the unobservable case. Finally, we show by solving a particular example to optimality that VO has a lower bound of 1.017.Este documento es una versión del artículo publicado en Management Science (ahead of print