Adam J. Mersereau's research while affiliated with University of North Carolina at Chapel Hill and other places
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Publications (23)
Problem definition: We compare several approaches for generating a prioritized list of items to be counted in a retail store, with the objective of detecting inventory record inaccuracy and unknown out of stocks. Academic/practical relevance: We consider both “rule-based” approaches, which sort items based on heuristic indices, and “model-based” ap...
Problem definition: We examine a brick-and-mortar retailer’s choice of which product to include in a promotional display (e.g., an “endcap” display). The display provides a visibility advantage to both the featured product and its category, but it also has consequences for customer traffic and substitution. Academic/practical relevance: Although th...
Problem definition: We study the practice-motivated problem of dynamically procuring a new, short-life-cycle product under demand uncertainty. The firm does not know the demand for the new product but has data on similar products sold in the past, including demand histories and covariate information such as product characteristics. Academic/practic...
We consider the allocation of inventory to stores in a “merchandise test,” whereby a fashion retailer deploys a new product to stores in limited quantities in order to learn about demand prior to the main selling season. Our problem formulation includes practical considerations like fixed costs and multiperiod inventory considerations but is challe...
We consider the inventory management problem of a firm reacting to potential change-points in demand, which we define as known epochs at which the demand distribution may (or may not) abruptly change. Motivating examples include global news events (e.g., the 9/11 terrorist attacks), local events (e.g., the opening of a nearby attraction), or intern...
Aretailer cannot sell more than it has in stock; therefore, its sales observations are a censored representation of the underlying demand process. When a retailer forecasts demand based on past sales observations, it requires an estimation approach that accounts for this censoring. Several authors have analyzed inventory management with demand lear...
Armed with a number of modern and emerging visibility technologies and facing increased competition from the internet channel, retail managers are seeking ever deeper visibility into store operations. We review two established streams of operations management research that try to overcome shortcomings of common retail data sources. The first is dem...
Inspired by recent empirical work on inventory record inaccuracy, we consider a periodic review inventory system with imperfect inventory records and unobserved lost sales. Record inaccuracies are assumed to arrive via an error process that perturbs physical inventory but is unobserved by the inventory manager. The inventory manager maintains a pro...
We study the problem faced by a supplier deciding how to dynamically allocate limited capacity among a portfolio of customers who remember the fill rates provided to them in the past. A customer's order quantity is positively correlated with past fill rates. Customers differ from one another in their contribution margins, their sensitivities to the...
Given uncertain popularity of new products by location, fast fashion retailer Zara faces a tradeoff. Large initial shipments to stores reduce lost sales in the critical first days of the product life cycle, but maintaining stock at the warehouse allows restocking flexibility once initial sales are observed. In collaboration with Zara, we develop an...
A growing segment of the revenue management and pricing literature assumes “strategic” customers who are forward-looking in their pursuit of utility. Recognizing that such behavior may not be directly observable by a seller, we examine the implications of seller uncertainty over strategic customer behavior in a markdown pricing setting. We assume t...
We consider a multiarmed bandit problem where the expected reward of each arm is a linear function of an unknown scalar with a prior distribution. The objective is to choose a sequence of arms that maximizes the expected total (or discounted total) reward. We demonstrate the effectiveness of a greedy policy that takes advantage of the known statist...
A growing segment of the revenue management and pricing literature assumes "strategic" customers who are forward-looking in their pursuit of utility. Recognizing that such behavior may not be directly observable by a seller, we examine the implications of seller ignorance of strategic customer behavior in a two-period markdown pricing setting. We a...
A growing segment of the revenue management and pricing literature assumes "strategic" customers who are forward-looking in their pursuit of utility. Recognizing that such behavior may not be directly observable by a seller, we examine the implications of seller ignorance of strategic customer behavior in a two-period markdown pricing setting. We a...
We consider a broad class of stochastic dynamic programming problems that are amenable to relaxation via decomposition. These problems comprise multiple subproblems that are independent of each other except for a collection of coupling constraints on the action space. We fit an additively separable value function approximation using two techniques,...
Inventory record inaccuracy is a significant problem for retailers using automated inventory management systems. While investments in preventative and corrective measures can be effective remedies, gains can also be achieved through inventory management tools that account for record errors. In this paper, we consider intelligent inventory managemen...
We consider a multiarmed bandit problem where the expected reward of each arm is a linear function of an unknown scalar with a prior distribution. The objective is to choose a sequence of arms that maximizes the expected total (or discounted total) reward. We demonstrate the effectiveness of a greedy policy that takes advantage of the known statist...
When a marketer in an interactive environment decides which messages to send to her customers, she may send messages currently thought to be most promising (exploitation) or use poorly understood messages for the purpose of information gathering (exploration). We assume that customers are already clustered into homogeneous segments, and we consider...
We explore methods for dynamic classification of visitors to an e-commerce web site based on visit sequences of page accesses. The time aspect is important in the processing of such data, and we require techniques that yield information before a customer's full sequence is realized. Further, we recognize that the timing of classification decisions...
Citations
... This assumption is in line with the current literature (DeHoratius and Raman 2008). In cases where the out-of-stock rate shows relevant values, the daily demand distribution can be approximated by the demand values of days that have a positive stock level at the end of each day (Chuang et al. 2016;DeHoratius et al. 2023). ...
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... To deal with this critical problem, a PrefixSpan algorithm [32][33][34] has been implemented. Prior to this algorithm, authors [35][36][37][38] paid attention to the management of the location of each specific shelf in a supermarket. Although this method is able to increase sales, it does not consider possible correlations between products on different shelves by analyzing customers' shopping lists, a gap that could lead to crucial sales losses. ...
... A prevalent approach that integrates machine learning and mathematical programming in prescriptive settings is "predict-then-optimize", which involves first making accurate predictions from data, using machine learning tools, and then solving optimization problems taking such predictions as input [2,3]. Noting that the criteria for improving predictions and improving decisions are often not aligned, a growing stream of recent work looks into more integrated, end-to-end approaches to guide decisions directly from historical data that leverage contextual information [4,5,6]. To address the potential misalignment between the loss function for a predictive model and the objective function in the downstream optimization model, [1] define suitable loss functions that take the downstream optimization problem into account when measuring errors in predictions. ...
... In order for production processes to continue without an interruption, requirements must be supplied in a timely manner. Order fulfillment [25], situations where the distribution of demand suddenly changes [26], dynamic inventory management and control [27] and inventory control [28] are studied in the literature. ...
... Several papers (e.g., Bensoussan et al. 2007;2011;Atali et al. 2009, Mersereau 2013 consider inventory replenishment decisions but not counting decisions in settings with inventory inaccuracy. Chen and Mersereau (2015) provide a review of DeHoratius et al.: Count Prioritization Procedures to Improve Inventory Accuracy this work. Other papers focus on the value of tracking technologies, like radio frequency identification (Gaukler et al. 2007, Lee andÖzer 2007, Camdereli and Swaminathan 2010, Hardgrave et al. 2013. ...
... As shown in Braden and Freimer (1991), this parametric distribution approach allows for a neat updating of the prior distribution, which can lead to a tractable and, in some cases, closed-form characterization of the optimal policy. This approach is followed in several papers under different variations of the lost sales case, including product substitution, inventory perishability, and recording inaccuracy (Chen and Plambeck, 2008;Lu et al., 2008;Chen, 2010;Bisi et al., 2011;Mersereau, 2015;Besbes et al., 2022). However, none of these works model uncertain supply capacity, as we do, and so our work fills this gap. ...
... et al., 2020). The remaining ten studies investigate how various factors affect performance outcome (sales and revenue) in the context of offline retailers (Gaur and Fisher, 2005;Abbey et al., 2015b;Gallien et al., 2015;Chuang et al., 2016;Ding et al., 2021;Buell and Kalkanci, 2021) and online retail management (Gallino and Moreno, 2018;Zhang et al., 2019;Cui et al., 2019a;Feldman et al., 2022). In service operations, researchers have utilized field experiments to causally evaluate the effect of policy changes on performance outcomes, such as consumer outcomes (Retana et al., 2016;Buell et al., 2017;Zhang et al., 2017;Jung et al., 2021;Lu et al., 2021), auction outcomes (Abhishek and Hosanagar, 2013;Haruvy et al., 2014), scheduling efficiency (Bray et al., 2016;Bichler and Merting, 2021), ride-sharing (Mejia and Parker, 2021;Cohen et al., 2021), and fairness (Cui et al., 2020). ...
Reference: Field experiments in operations management
... Furthermore, POMDPs have been frequently used in the literature on inventory management where learning is somehow involved. In particular to learn unknown demand distributions from (censored) observations in a single location (e.g., Azoury 1985, Chen and Plambeck 2008, Chen 2010) and across multiple locations (e.g., Chen et al. 2017), as well as to learn inventory levels when records are inaccurate (e.g., DeHoratius et al. 2008, Mersereau 2013 or when there is unobserved inventory shrinkage (e.g., Chen 2021, Li et al. 2022). ...
... With the development of virtual reality technology, showrooms have made the transition from physical to virtual. Shoppers can try new products online through virtual showrooms [7] . On the website of glasses retailer BonLook, consumers can know how well the glasses fit them through the lens. ...
... We refer the reader to Araman and Caldentey (2010) for a recent review paper on the topic. The issue of model misspecification, and its potential negative implications has surfaced in several recent papers: see, for example, Cooper et al. (2006), Cachon and Kök (2007) in a newsvendor context, and Mersereau and Zhang (2010) and Cooper et al. (2009) in pricing settings. ...
