Tingliang Huang

Optimal Control of a Production-Inventory System with Both Backorders and Lost Sales

We consider the optimal control of a production inventory-system with a single product and two customer classes where items are produced one unit at a time. Upon arrival, customer orders can be fulfilled from existing inventory, if there is any, backordered, or rejected. The two classes are differentiated by their backorder and lost sales costs. At each decision epoch, we must determine whether or not to produce an item and if so, whether to use this item to increase inventory or to reduce backlog. At each decision epoch, we must also determine whether or not to satisfy demand from a particular class (should one arise), backorder it, or reject it. In doing so, we must balance inventory holding costs against the costs of backordering and lost sales. We formulate the problem as a Markov decision process and use it to characterize the structure of the optimal policy. We show that the optimal policy can be described by three state-dependent thresholds: a production base-stock level and two order-admission levels, one for each class. The production base-stock level determines when production takes place and how to allocate items that are produced. This base-stock level also determines when orders from the class with the lower shortage costs (class 2) are backordered and not fulfilled from inventory. The order-admission levels determine when orders should be rejected. We show that the threshold levels are monotonic (either non-increasing or non-decreasing) in the backorder level of class 2. We also characterize analytically the sensitivity of these thresholds to the various cost parameters. Using numerical results, we compare the performance of the optimal policy against several heuristics and show that those that do not allow for the possibility of both backordering and rejecting orders can perform poorly.

Bounded Rationality in Service Systems

The traditional operations management and queueing literature typically assumes that customers are fully rational. In contrast, in this paper we study canonical service models with boundedly rational customers. We capture bounded rationality using a model in which customers are incapable of accurately estimating their expected waiting time. We investigate the impact of bounded rationality from both a profit-maximizing firms perspective and a social planners perspective. For visible queues with the optimal price, bounded rationality results in revenue and welfare loss; with a fixed price, bounded rationality can lead to strict social welfare improvement. For invisible queues, bounded rationality benefits the firm when its level is sufficiently high. Ignoring bounded rationality, when present yet small, can result in significant revenue and welfare loss.

Sell Probabilistic Goods? A Behavioral Explanation for Opaque Selling

Probabilistic or opaque selling, whereby a seller hides the exact identity of a product until after the buyer makes a payment, has been used in practice and received considerable attention in the literature. Under what conditions, and why, is probabilistic selling attractive to firms? The extant literature has offered the following explanations: to price discriminate heterogeneous consumers, to reduce supply-demand mismatches, and to soften price competition. In this paper, we provide a new explanation: to exploit consumer bounded rationality in the sense of anecdotal reasoning. We build a simple model where the firm is a monopoly, consumers are homogeneous, and there is no demand uncertainty or capacity constraint. This model allows us to isolate the impact of consumer bounded rationality on the adoption of opaque selling. We find that although it is never optimal to use opaque selling when consumers have rational expectations, it can be optimal when consumers are boundedly rational. We show that opaque selling may soften price competition and increase the industry profits as a result of consumer bounded rationality. Our findings underscore the importance of consumer bounded rationality and show that opaque selling might be even more attractive than previously thought.

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

Abstract 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.

Managing Posterior Price Matching: The Role of Customer Boundedly Rational Expectations

The posterior price-matching policy, whereby a firm promises to reimburse the price difference to a customer who purchases a product before the firm marks it down, has been used in practice. The extensive literature has offered the following explanations for why posterior price matching is adopted: to reduce inventory, to soften competition, to price discriminate consumers, and to eliminate consumer strategic waiting incentives. In this paper, we provide a novel explanation and investigate the role of consumer bounded rationality in the sense of anecdotal reasoning. We adopt a simple model that allows us to isolate the role of customer bounded rationality on using posterior price matching. We demonstrate that while it is never optimal to adopt posterior price matching when consumers have rational expectations, it can be optimal when they have boundedly rational expectations. We show when and how a seller can intentionally mark down with some probability and adopt price matching to make a profit. Ignoring customer bounded rationality can result in a significant profit loss. Then, we build a dynamic programming model to investigate how the firm should dynamically manage its markdowns over the long run. We show that a cyclic policy switching between a high and low markdown probability is typically optimal for exploiting customer bounded rationality. We characterize the nature of the cyclic policy and the range in which it is optimal. Our findings underscore the importance of consumer bounded rationality and provide managerial and practical guidelines on how to manage price matching when customers are boundedly rational. The online supplement is available at https://doi.org/10.1287/msom.2016.0612 .

The Value of 'Bespoke': Demand Learning, Preference Learning, and Customer Behavior

“Bespoke,” or mass customization strategy, combines demand learning and preference learning. We develop an analytical framework to study the economic value of bespoke systems and investigate the interaction between demand learning and preference learning. We find that it is possible for demand learning and preference learning to be either complements or substitutes, depending on the customization cost and the demand uncertainty profile. They are generally complements when the personalization cost is low and the probability of having high demand is large. Contrary to usual belief, we show that higher demand uncertainty does not necessarily yield more complementarity benefits. Our numerical study shows that the complementarity benefit becomes weaker when customers are more strategic. Interestingly, the substitute loss can occur when the personalization cost is small and the probability of having high demand is large, when customers are strategic. The online supplement is available at https://doi.org/10.1287...

Managing Service Systems with Unknown Quality and Customer Anecdotal Reasoning

We consider service systems where customers do not know the distribution of uncertain service quality and cannot estimate it fully rationally. Instead, they form their beliefs by taking the average of several anecdotes, the size of which measures their level of bounded rationality. We characterize the customers’ joining behavior and the service provider’s pricing, quality control, and information disclosure decisions. Bounded rationality induces customers to form different estimates of the service quality and leads the service provider to use pricing as a market segmentation tool, which is radically different from the full rationality setting. As customers gather more anecdotes, the service provider may first decrease and then increase price, and the revenue is U-shaped. Interestingly, a larger sample size may harm consumer surplus, although it always benefits social welfare. When the service provider also has control over quality, we find that it may reduce both quality and price as customers gather more anecdotes. In addition, a high-quality service provider may not disclose quality information if the sample size is small, while a low-quality service provider may disclose if the sample size is large. Furthermore, as the expected waiting cost increases, information non-disclosure is more attractive, thereby highlighting the importance of incorporating customer bounded rationality in congested settings.

A Theoretical Analysis of the Lean Startup's Product Development Process

Lean startup paradigm is emerging as a best practice for early product development for entrepreneurs and is widely adopted by entrepreneurship curricula in business schools. Despite its influence, there is no theoretical underpinning, leading to lack of clarity in its generalizability and explanation of implementation challenges in practice. We present a stylized model of the lean startup implementation problem. We show how the (vertical) quality of minimum viable product impacts learning about consumer’s (horizontal) taste, and also how product-market characteristics impact its effectiveness.