Modeling customer bounded rationality in operations management: A review and research opportunities

Many studies in operations management started to explicitly model customer behavior. However, it is typically assumed that customers are fully rational decision-makers and maximize their utility perfectly. Recently, modeling customer bounded rationality has been gaining increasing attention and interest. This paper summarizes various approaches of modeling customer bounded rationality, surveys how they are applied to relevant operations management settings, and presents the new insights obtained. We also suggest future research opportunities in this important area

Advance Selling and Advertising:A Newsvendor Framework

Many firms offer consumers the opportunity to place advance orders at a discount when introducing a new product to the market. Doing so has two main advantages. First, it can increase total expected sales by exploiting valuation uncertainty of the consumers at the advance ordering stage. Second, total sales can be estimated more accurately based on the observed advance orders, reducing the need for safety stock and thereby obsolescence cost. In this research, we derive new insights into trading off these benefits against the loss in revenue from selling at a discount at the advance stage. In particular, we are the first to explore whether firms should advertise the advance ordering opportunity. We obtain several structural insights into the optimal policy, which we show is driven by two dimensions: the fraction of consumers who potentially buy in advance (i.e., strategic consumers) and the size of the discount needed to make them buy in advance. If the discount is below some threshold, then firms should sell in advance and they should advertise that option if the fraction of strategic consumers is sufficiently large. If the discount is above the threshold, then firms should not advertise and only sell in advance if the fraction of strategic consumers is sufficiently small. Graphical displays based on the two dimensions provide further insights

Joint Pricing and Inventory Control under Reference Price Effects

In this work, we address the problem of simultaneously determining a pricing and inventory replenishment strategy under reference price effects. This reference price effect models the fact that consumers not only react sensitively to the current price, but also to deviations from a reference price formed on the basis of past purchases. Immediate effects of price reductions on profits have to be weighted against the resulting losses in future periods. By providing an analytical analysis and numerical simulations we study how the additional dynamics of the consumers’ willingness to pay affect an optimal pricing and inventory control model and whether a simple policy such as a base-stock-list-price policy holds in such a setting

A loss averse competitive newsvendor problem with anchoring

We study a loss averse competitive newsvendor problem with anchoring under prospect theory. We consider two demand-splitting rules for quantity competition, including proportional demand allocation and demand reallocation. We characterize the optimal order quantity decisions under both demand rules. We find that the newsvendor's order quantity is decreasing with the degree of loss aversion and the value of the anchor. Compared with an integrated risk-neutral supply chain, a positive anchor always leads to inventory understocking, whereas a negative anchor may result in a serious overstocking. Under competition with homogeneous newsvendors, competition always makes newsvendors order more, which does not necessarily lead to a loss of profit. For newsvendors with a high anchor, competition helps to prevent understocking caused by the anchoring effect, which leads to an increase in profit. For newsvendors with a low anchor, competition exacerbates overstocking, which results in a loss of profit. Under competition with heterogeneous newsvendors, a newsvendor with a higher degree of loss aversion or with a higher anchor adopts a more conservative strategy (i.e. choose a lower order quantity), which results in a smaller market share. (C) 2017 Elsevier Ltd. All rights reserved

Constructive solution methodologies to the capacitated newsvendor problem and surrogate extension

The newsvendor problem is a single-period stochastic model used to determine the order quantity of perishable product that maximizes/minimizes the profit/cost of the vendor under uncertain demand. The goal is to fmd an initial order quantity that can offset the impact of backlog or shortage caused by mismatch between the procurement amount and uncertain demand. If there are multiple products and substitution between them is feasible, overstocking and understocking can be further reduced and hence, the vendor\u27s overall profit is improved compared to the standard problem. When there are one or more resource constraints, such as budget, volume or weight, it becomes a constrained newsvendor problem. In the past few decades, many researchers have proposed solution methods to solve the newsvendor problem. The literature is first reviewed where the performance of each of existing model is examined and its contribution is reported. To add to these works, it is complemented through developing constructive solution methods and extending the existing published works by introducing the product substitution models which so far has not received sufficient attention despite its importance to supply chain management decisions. To illustrate this dissertation provides an easy-to-use approach that utilizes the known network flow problem or knapsack problem. Then, a polynomial in fashion algorithm is developed to solve it. Extensive numerical experiments are conducted to compare the performance of the proposed method and some existing ones. Results show that the proposed approach though approximates, yet, it simplifies the solution steps without sacrificing accuracy. Further, this dissertation addresses the important arena of product substitute models. These models deal with two perishable products, a primary product and a surrogate one. The primary product yields higher profit than the surrogate. If the demand of the primary exceeds the available quantity and there is excess amount of the surrogate, this excess quantity can be utilized to fulfill the shortage. The objective is to find the optimal lot sizes of both products, that minimize the total cost (alternatively, maximize the profit). Simulation is utilized to validate the developed model. Since the analytical solutions are difficult to obtain, Mathematical software is employed to find the optimal results. Numerical experiments are also conducted to analyze the behavior of the optimal results versus the governing parameters. The results show the contribution of surrogate approach to the overall performance of the policy. From a practical perspective, this dissertation introduces the applications of the proposed models and methods in different industries such as inventory management, grocery retailing, fashion sector and hotel reservation

Operational and Economical Perspectives on Consumer Returns

Customer return policies are one of common after sale services offered by a retailer in order to boost sales, improve customer satisfaction and diminish customer fit uncertainty. With such a service, the retailer accepts the return of a product after the sale has occurred, if it does not satisfy the customers expectations. This study investigates consumer returns from three different perspectives. First, we start with a single period inventory planning problem of multi-variants in which customers have a right to return products in case of dissatisfaction. We assume that returns can be as-good-as-new condition after a minor restocking process and they are resalable in the same selling period. At the time of purchasing, a customer may choose to substitute her choice with another variant of the item, if the former is sold out. This, so called substitution, and resalable returns are important parameters in assortment planning that might affect the total profit dramatically. Under this setting, we aim to illustrate the effect of return and substitution on the optimal order quantities of variants and the total expected profit. We show that the total profit decreases as the probability of returns or the probability of resalable returns increases. In addition, the probability of substitution, return, or resalable return has a negative effect on inventory level of variants. Second, we analyze a retailers return policy problem when the market consists of loss-averse customers who are more sensitive to losses than gains instead of being risk-neutral customers. We examine the situation in which a seller makes price and quantity decisions for a single item and also designs an appropriate returns policy in order to maximize his profit. We analyze the case where the seller offers either a full-refund or a partial-refund policy if he decides to accept returns or chooses not to accept any returns. With the full-refund policy, the seller reimburses the consumer the full price of the product if it does not fit the customers preferences. With a partial-refund policy, the seller offers a refund which is strictly less than the purchase price. We assume that customers are strategic customers aiming to maximize their utilities of the product. With this model, we aim to analyze the impact of loss aversion on the sellers price and order quantity decisions. We show that the seller keeps fewer inventories as customers get more loss-averse and loss-aversion has a negative effect on the expected unit profit. Thus, the total profit decreases as loss-aversion. Finally, we present a model that investigates the effects of return policies on each of the two sellers pricing decisions when these sellers engage in market size competition. Our model simultaneously addresses a consumers purchase decision and the competing sellers price decisions, along with their respective return policies. We assume that two competing sellers which do not have a capacity problem only decide their prices and return policies. The sellers may independently offer no-refund, full-refund of the price of the product, or a partial-refund. The market share of each party depends on his and the rivals price and return policy; customer valuations of the product and the degree of competition between the sellers. Before purchasing, a consumer cannot evaluate the products utility which is a decreasing function of the price set by the seller from which she chooses to purchase it and the disutility of purchase and/or return which is related to the physical distance between the consumer and the sellers location. The return policy of a seller may also affect a consumers decision via her expected utility. Thus, pricing and return policy decisions play an important role in the division of the total market. We show that the full-refund policy yields a higher purchasing price compare to the no-refund or partial-refund policy. However, it only performs better in terms of profit when the product has a small salvage value and a partial-refund policy is not an option for the retailer. When the retailer offers the partial-refund policy, it always dominates the other refund policies. In addition, we show that the full-refund policy provides the highest consumer surplus since customers do not face any risk of misfit. On the other hand, the full-refund policy is socially efficient only when the salvage value is high. For low salvage values, the partial-refund policy yields a higher level of social welfare.Ph.D., Decision Sciences -- Drexel University, 201

Selling to Conspicuous Consumers: Pricing, Production, and Sourcing Decisions

Consumers often purchase goods that are “hard to find” to conspicuously display their exclusivity and social status. Firms that produce such conspicuously consumed goods such as designer apparel, fashion goods, jewelry, etc., often face challenges in making optimal pricing and production decisions. Such firms are confronted with precipitous trade-off between high sales volume and high margins, because of the highly uncertain market demand, strategic consumer behavior, and the display of conspicuous consumption. In this paper, we propose a model that addresses pricing and production decisions for a firm, using the rational expectations framework. We show that, in equilibrium, firms may offer high availability of goods despite the presence of conspicuous consumption. We show that scarcity strategies are harder to adopt as demand variability increases, and we provide conditions under which scarcity strategies could be successfully adopted to improve profits. Finally, to credibly commit to scarcity strategy, we show that firms can adopt sourcing strategies, such as sourcing from an expensive production location/supplier or using expensive raw materials

The data-driven newsvendor problem:Achieving on-target service-levels using distributionally robust chance-constrained optimization

The classical approach to the newsvendor problem is to first estimate the demand distribution (or assume it to be given) and then determine the optimal inventory level. Data-driven optimization offers an alternative, where the inventory level is determined directly from the data. In this paper, we consider the data-driven newsvendor problem under a service-level constraint. We show that existing approaches to this problem suffer from overfitting, resulting in service-levels that are below the target service-level. We propose new data-driven approaches and corresponding mathematical optimization models based on methods of distributionally robust chance-constrained optimization—which have not yet been applied and empirically tested in the context of the data-driven newsvendor problem. We assess the effectiveness of our approaches by means of an extensive numerical study. To that end, we conduct structured experiments based on simulation as well as experiments based on a real-life bikesharing system where we consider the daily usage data along with information on weather and seasonal factors. The results demonstrate that our methods achieve on-target service-levels even in absence of large amounts of data. All in all, our study provides ample empirical evidence that distributionally robust chance-constrained optimization is a viable approach for addressing the data-driven newsvendor problem