Areas of Expertise

Operations Management, Supply Chain Management, Managing Operations in the Presence of Uncertainty, Managing Product End of Life, Mass Customization

Academic Degrees

• PhD, Stanford University, 1997
• MBA, University of California at Berkeley, 1986
• BS, Brigham Young University, 1984

Professional Experience

• Indiana University, 2005-present
• Journal of Operations Management, 2017 to present, Associate Editor
• Production and Operations Management Society, 2004-present, Senior Editor
• University of North Carolina at Chapel Hill, 1997-2005
• Hewlett Packard Company, 1986-1992

Awards, Honors & Certificates

• Trustees Teaching Award (2010, 2012, 2018)

Selected Publications

• Cho, D., and Cattani, K. (2019). The Patient Patient. Decision Sciences, 50(4), 756-785.
• Cho, D., Bretthauer, K., Cattani, K., and Mills, A. (2019). Behavior-Aware Service Staffing. Production and Operations Management, 28(5), 1285-1304.
• DeYong, G., and Cattani, K. (2018). The Unlimited Newsvendor: A General Solution to a Class of Two-Period Newsvendor Problems. International Journal of Production Economics Research, 201, 173-192.
• De Treville, S., Cattani, K., and Saarinen, L. (2017). Technical note: Options-Based Costing and the Volatility Portfolio. Journal of Operations Management, 49-51, 77-81.
• DeYong, G., and Cattani, K. (2016). Fenced In? Stochastic and Deterministic Planning Models in a Time-Fenced, Rolling-Horizon Scheduling System. European Journal of Operational Research, 251(1), 85-95.
• Cattani, K., Souza, G. C., and Ye, S. (2014). Shelf Loathing: Cross Docking at an Online Retailer. Production and Operations Management, 23(5), 893-906.

  Abstract

  Abstract

  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.

• DeYong, G., and Cattani, K. (2012). Well Adjusted: Using expediting and cancelation to manage store replenishment for a seasonal good. European Journal of Operational Research, 20(1), 93-105.

  Abstract

  Abstract

  We consider an inventory problem that can be translated into a two-period newsvendor setting where the day prior to sales, the newsvendor places an initial preliminary order—a semi-binding forecast—with the publisher. At the beginning of the actual day of sales, the newsvendor has a better forecast for the day’s demand: based on knowing the actual content of the paper, he knows whether it will be a high-demand day due to breaking news or a low-demand day due to slow news. He then can revise the preliminary order quantity by expediting additional papers or canceling all or part of the order, but each of these activities has an associated cost. We find closed-form solutions for the optimal preliminary and revised orders in this two-period newsvendor problem where demand is characterized as a binary random variable in the first period and one of two general distributions in the second. At the beginning of the second period, the binary random variable has been realized and the general distribution is known. At this point, the preliminary order may be adjusted upward or downward, with these changes incurring expediting or cancelation costs, respectively.

• Cattani, K., Jacobs, F. R., and Schoenfelder, J. (2011). Common Inventory Modeling Assumptions that Fall Short: Arborescent Networks, Poisson Demand, and Single-Echelon Approximations. Journal of Operations Management, 29(5), 488-499. 

  Abstract

  Abstract

  Traditional multi-echelon inventory theory focuses on arborescent supply chains that use a central warehouse which replenishes remote warehouses. The remote warehouses serve customers in their respective regions. Common assumptions in the academic literature include use of the Poisson demand process and instantaneous unit-by-unit replenishment. In the practitioner literature, single-echelon approximations are advised for setting safety stock to deal with lead time, demand, and supply variations in these settings. Using data from a U.S. supplier of home improvement products, we find that neither the assumptions from the academic literature nor the approximations from the practitioner literature necessarily work well in practice.   In a variation of the strictly arborescent supply chain, the central warehouse at our real company not only replenishes other warehouses but also meets demand from customers in the region near the central warehouse. In this paper, we study this dual-role central warehouse structure, which we believe is common in practice. Using high and low volume product demand data from this company, we use Monte Carlo simulations to study the impact of (1) the use of a dual-role centralized warehouse, (2) common demand assumptions made in multi-echelon research, and (3) single-echelon approximations for managing a multi-echelon supply chain. We explore each of these under both centralized and decentralized control logic. We find that the common assumptions of theoretical models impede their usefulness and that heuristics that ignore the actual supply chain structure fail to account for additional opportunities to utilize safety stock more effectively. Researchers should be aware of the gap between standard assumptions in traditional literature and actual practice, and critically evaluate their assumptions to find a reasonable balance between tractability and relevance.

• Cattani, K., Dahan, E., and Schmidt, G. (2010). Lowest Cost May Not Lower Total Cost: Using “Spackling” to Smooth Mass-Customized Production. Production and Operations Management, 19(5), 531-545.

  Abstract

  Abstract

  Consider a manufacturer who mass customizes variants of a product in make-to-order fashion, and also produces standard variants as make-to-stock. A traditional manufacturing strategy would be to employ two separate manufacturing facilities: a flexible plant for mass-customized items and an efficient plant for standard items. We contrast this traditional focus strategy with an alternative that better utilizes capacity by combining production of mass-customized and standard items in one of two alternate spackling strategies: (1) a pure-spackling strategy, where the manufacturer produces everything in a (single) flexible plant, first manufacturing custom products as demanded each period, and then filling in the production schedule with make-to-stock output of standard products; or (2) a layered-spackling strategy, which uses an efficient plant to make a portion of its standard items and a separate flexible plant where it spackles. We identify the optimal production strategy considering the tradeoff between the cost premium for flexible (versus efficient) production capacity and the opportunity costs of idle capacity. Spackling amortizes fixed costs of capacity more effectively and thus can increase profits from mass customization vis-à-vis a focus strategy, even with higher cost production for the standard goods. We illustrate our framework with data from a messenger bag manufacturer.

• Cattani, K., and Heese, S.(2009). Seeking Closure: Competition in Complementary Markets. Decision Sciences, 40(4), 817-843.

  Abstract

  Abstract

  When offering a product that has a complementary product in a different market, a firm must consider the interdependence between the complementary products as well as the competition within markets. If the firm participates in both markets, the balancing act becomes even more challenging. This article provides insights about strategies in this latter setting: when should the firm seek to keep its products closed to competing complementary products, and when would the firm be better off by accepting a common standard? To address these questions, we employ standard game theoretic analysis to a simple spatial model that captures aspects of both intermarket externalities and intramarket competition. We find that if a firm participates in both markets and chooses a closed standard, it achieves lower profits compared to an open standard, but gains greater market share. Surprisingly, we find that customers are better off when standards are kept closed.

• Cattani, K., Marucheck, A., and Perdikaki, O. (2007). The Perishability of Online Grocers. Decision Sciences, 38(2), 329-355.

  Abstract

  Abstract

  In this article we explore the profitability of different operations models used by online grocers and develop a linear demand model in a competitive setting to better understand the trade-offs made by two competing online grocers in choices for distribution strategy (leverage or direct) and product focus (perishable or nonperishable). We find that the results derived in the duopoly setting are different from those in a monopolistic setting. Specifically, we determine that there is a threshold value for the secondary competitive effects in the demand function that determines how the prices and profitability of an online grocer will be affected by the supply chain length of its competitor. There is also a threshold value for the ratio of supply chain lengths of the two competitors that determines whether product perishability increases or decreases profits. We demonstrate that the existence of this threshold is robust when considering capacity constraints. Further, we show, assuming that supply chain length can be optimized, how the relative size of the infrastructure change cost (when compared with that of the competitor) coupled with the perishability of the product determines the profitability of an investment leading to a shorter supply chain.

• Cattani, K., Gilland, W., Heese S., and Swaminathan, J. (2006). Boiling Frogs: Pricing Strategies for a Manufacturer Adding a Direct Channel that Competes with the Traditional Channel. Production and Operations Management, 15(1), 40-56.
• Cattani, K., Ferrer, G., and Gilland, W. (2003). Simultaneous Production of Market-specific and Global Products: A Two-stage Stochastic Program with Additional Demand after Recourse. Naval Research Logistics, 50(5), 438-461.

  Abstract

  Abstract

  This paper analyzes the simultaneous production of market-specific products tailored to the needs of individual regions and a global product that could be sold in many regions. We assume that the global product costs more to manufacture, but allows the decision concerning the allocation of products to regions to be delayed until after the manufacturing process has been completed. We further assume that there is additional demand after the region allocation but prior to delivery, extending the two-stage stochastic program with recourse to include additional stochastic demand after the recourse. This scenario arises, for example, when there is additional uncertainty during a delivery delay which might occur with transoceanic shipments. We develop conditions for optimality assuming a single build-allocate-deliver cycle and stochastic demand during both the build and deliver periods. The optimal policy calls for the simultaneous production of market-specific and global products, even when the global product is substantially more costly than the market-specific product. In addition, we develop bounds on the performance of the optimal policy for the multicycle problem.

• Cattani, K., and Souza, G. C. (2003). Good Buy? Delaying End–of–Life Purchases. European Journal of Operational Research, 146(1), 216-228.

  Abstract

  Abstract

  We study the effects of delaying an end-of-life buy. Manufacturers sometimes are required to place a final, end-of-life buy for a component that the supplier will no longer provide. The manufacturer performs a newsvendor analysis with the possible result of significant expected overage and underage costs. If the decision can be delayed, the expected overage and underage costs can be reduced. We model the effects of a delay of the final purchase under various scenarios of remaining demand. We contrast the manufacturing benefits with the costs incurred by the supplier and show that the supplier, who benefits greatly from the end-of-life buy, likely will require an incentive to enact a delay. Our results provide an insight to the observation of increasing numbers of end-of-life buys and provides a framework for analysis as manufacturers strive to cope with the issue.

• Cattani, K., and Souza, G. C. (2002). Inventory Rationing and Shipment Flexibility Alternatives for Direct Market Firms. Production and Operations Management, 11(4), 441-457.

  Abstract

  Abstract

  This paper investigates inventory-rationing policies of interest to firms operating in a direct market channel. We model a single product with two demand classes, where one class requests a lower order fulfillment lead time but pays a higher price. Demand for each class follows a Poisson process. Inventory is fed by a production system with exponentially distributed build times. We study rationing policies in which the firm either blocks or backlogs orders for the lower priority customers when inventory drops below a certain level. We compare the performance of these rationing policies with a pure first-come, first-serve policy under various scenarios for customer response to delay: lost sales, backlog, and a combination of lost sales and backlog.

• Cattani, K. and Hausman, W. (2000). Why are Forecast Updates Often Disappointing? Manufacturing & Service Operations Management, 2(2) 107-219.

  Abstract

  Abstract

  Demand forecasts do not become consistently more accurate as they are updated. We present examples demonstrating this counterintuitive phenomenon and some theoretical results to explain its occurrence. Specifically, we analyze the effect of demand randomness on forecast-update performance. A surprising result is that under various theoretical models involving demand randomness alone, updated forecasts will be less accurate between 30% and 50% of the time.

Edited on September 21, 2022