Secondary Titles (2)
- Chair of the Full-Time MBA Program
- E-II Faculty Fellow
Industry Expertise (8)
Areas of Expertise (5)
Trustees Teaching Award (professional)
Awarded by Indiana University
Trustees Teaching Award (professional)
Awarded by Indiana University
Stanford University: Ph.D., Industrial Engineering and Engineering Management 1997
University of California at Berkeley: M.B.A. 1986
Brigham Young University: B.S. 1984
It has been clearly established that a cost premium for responsiveness may be justified for profitable time-sensitive products, and that this cost premium may suffice to render production in a high-cost environment competitive. Time-insensitive products considered in isolation seldom justify a cost premium, leading many decision makers to conclude that their production does not belong in a high-cost environment. This leads to a manufacturing-location decision in which profitable and time-sensitive products are produced in a high-cost environment and time-insensitive products are transferred to a low-cost environment. Responsiveness, however, requires a capacity buffer that provides the option to meet a demand peak for a profitable, time-sensitive product. Leftover capacity can then be ideally deployed to manufacture time-insensitive products to stock. We propose that the cost of the capacity buffer be considered as an option cost and assigned to the time-sensitive product: “option-based costing”. We then demonstrate use of demand volatility to create a portfolio of products that are time sensitive and insensitive to generate profit and increase competitiveness. Option-based costing combined with a volatility portfolio reveals opportunities to produce competitively in high-cost environments that have typically been considered unfeasible.
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.
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.
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.
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.
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.