Secondary Titles (1)
- Rifkin Family Faculty Fellowship
Industry Expertise (2)
Areas of Expertise (6)
Supply Chain Management
Closed-Loop Supply Chains
Wickham Skinner Early Career Research Award (professional)
Krowe Teaching Award (professional)
2004 Awarded for excellence of the MBA program at the Smith School, University of Maryland
Research Award (professional)
2014 Kelley School of Business, Indiana University
University of North Carolina at Chapel Hill: Ph.D., Business Administration, Management and Operations 2000
Clemson University: MBA, Business Administration 1995
Instituto Tecnológico de Aeronáutica - ITA: B.E., Aerospace, Aeronautical and Astronautical Engineering 1990
The Role of Perceived Quality Risk in Pricing Remanufactured ProductsProduction and Operations Management
2016 Recent research indicates that consumers hold significant concerns about the quality of remanufactured products. To better understand this phenomenon, this manuscript combines surveys and experimental studies to identify the antecedents of perceived quality—in the form of perceived risk of functionality and cosmetic defects—and their significant impact on consumers' willingness to pay (wtp) for remanufactured electronics products. The study also controls for alternative explanations for wtp suggested in the literature, such as consumers' wtp for new products, environmental beliefs, disgust aversion toward used products, brand perceptions, risk aversion, and various demographic traits. Importantly, the study empirically estimates the magnitude and distribution of discount factors for remanufactured electronics products—the ratio between wtp for a remanufactured product and wtp for a corresponding new product—among consumers. Finally, the manuscript analytically compares a monopolist's decision to include remanufactured products in its portfolio under both the empirically derived discount factor distributions and the classical linear demand model, which assumes constant discount factors. Interestingly, the classical linear demand model remains reasonably robust for high-level insights, such as the presence of cannibalization and market expansion effects. However, the analytical model that uses the empirically-derived distributions of discount factors demonstrates significantly higher profitability than predicted by the classical linear model. This fundamental link between risk perceptions, wtp for remanufactured products, and profitability provides new insights on how to manage demand and product pricing in closed-loop supply chains.
Capacity Investment in Renewable Energy Technology with Supply Intermittency: Data Granularity Matters!Manufacturing & Service Operations Management
2015 We study an organization’s one-time capacity investment in a renewable energy-producing technology with supply intermittency and net metering compensation. The renewable technology can be coupled with conventional technologies to form a capacity portfolio that is used to meet stochastic demand for energy. The technologies have different initial investments and operating costs, and the operating costs follow different stochastic processes. We show how to reduce this problem to a single-period decision problem and how to estimate the joint distribution of the stochastic factors using historical data. Importantly, we show that data granularity for renewable yield and electricity demand at a fine level, such as hourly, matters: Without energy storage, coarse data that does not reflect the intermittency of renewable generation may lead to an overinvestment in renewable capacity. We obtain solutions that are simple to compute, intuitive, and provide managers with a framework for evaluating the trade-offs of investing in renewable and conventional technologies. We illustrate our model using two case studies: one for investing in a solar rooftop system for a bank branch and another for investing in a solar thermal system for water heating in a hotel, along with a conventional natural gas heating system.
Shelf Loathing: Cross Docking at an Online RetailerProduction and Operations Management
2014 Online customers expect to wait, sometimes for a delay of many days. At the fulfillment center, there might be an opportunity to fill customer orders earlier than the due date through a cross-docking transaction: rather than picking the item from inventory, the item moves directly from the receiving to the shipping dock, saving shelving and picking transactions. While cross docking reduces shelving and picking costs, it risks changing customer expectations for how soon a product will be delivered. Given customer order arrivals random in quantity and due dates, random replenishment arrivals, and costs (or benefits) for shipping a product early, we characterize the optimal decision as to whether to cross dock a replenishment item to fulfill demand that is not immediately due or to wait to (hopefully) cross dock in later periods. With multiple demands and due dates, the cross-docking decision depends on the number of unfulfilled demands in each period across the horizon, the number of units that have just arrived (available for cross docking), picking and shelving costs, and the delay cost (or benefit). We formulate the problem as a Markov decision process, determine the structure of the optimal policy, and propose a well-performing heuristic.
Dynamic Capacity Investment with Two Competing TechnologiesManufacture and Service Operations Management
2013 With the recent focus on sustainability, firms making adjustments to their production or distribution capacity levels often have the option of investing in newer technologies with lower carbon footprints and/or energy consumption. These more sustainable technologies typically require a higher up-front investment but have lower operating (fuel or energy) costs. What complicates this decision is the fact that the projected dollar savings from the more sustainable technologies fluctuate considerably due to uncertainty in fuel prices, and the total capacity may not be utilized at 100% because of fluctuations in the demand for the product. We consider the firm's capacity adjustments over time given a portfolio of technology options when the demand and the fuel costs are stochastic and possibly dependent. Our model also allows for usage-based capacity deterioration. We provide the analytical structure of the optimal policy, which assigns different control limits for investing, staying put, and disinvesting in the capacities of the competing technology choices for each realization of demand and fuel costs at each period. We also present an application of our model to the problem of designing a delivery truck fleet for a beverage distributor.
How Does Product Recovery Affect Quality Choice?Production and Operations Management
2013 We study the impact of product recovery on a firm's product quality choice, where quality is defined as an observable performance measure that increases a consumer's valuation for the product. We consider three general forms of product recovery: (i) when product recovery reuses (after reprocessing) quality inducing components or material (e.g., remanufacturing), (ii) when product recovery does not reuse quality inducing components or material but it is overall profitable (e.g., cell phone recycling), and (iii) when product recovery is costly (but mandated by legislation, e.g., recycling of small appliances in the European Union). Using a stylized economic model, we show that the form of product recovery, recovery cost structure, and the presence of product take-back legislation play an important role in quality choice. Generally speaking, product recovery increases the firm's quality choice, except for some instances of recovery form (ii). In addition, we find that product take-back legislation can lead to higher quality choice as opposed to voluntary take-back. We further demonstrate that both the firm and the consumers benefit from recovery form (ii), while both are worse off with recovery form (iii). However, environmental implications of the three recovery modes differ from their impact on consumer surplus and firm profit. While recovery forms (i) and (iii) reduce consumption and increase environmental benefits, the same is not true with recovery form (ii), which can increase consumption, potentially resulting in higher environmental impact.