Knowledge that Transforms

Information delays exist when the most recent inventory information available to the Inventory Manager (IM) is dated. Such situations arise when it takes a while to process the demand data, count the inventory, and pass the results to the IM. We show that the optimal total inventory‐related cost decreases when the length of the information delay decreases. The amount of the decrease is an important datum for an IM interested in considering whether or not to invest in reducing the delay. The investment is required to finance design and acquisition of an information (collection and dissemination) system that can reduce the information delay. Such systems include phone calls, business meetings, and the use of information collection mechanisms such as radiofrequency identification tags.

Information delays exist when the most recent inventory information available to the Inventory Manager (IM) is dated. In other words, the IM observes only the inventory level that belongs to an earlier period. Such situations are not uncommon, and they arise when it takes a while to process the demand data and pass the results to the IM. We introduce dynamic information delays as a Markov process into the standard multiperiod stochastic inventory problem with backorders. We develop the concept of a reference inventory position . We show that this position along with the magnitude of the latest observed delay and the age of this observation are sufficient statistics for finding the optimal order quantities. Furthermore, we establish that the optimal ordering policy is of state‐dependent base‐stock type with respect to the reference inventory position (or state‐dependent ( s, S ) type if there is a fixed ordering cost). The optimal base stock and ( s, S ) levels depend on the magnitude of the latest observed delay and the age of this observation. Finally, we study the sensitivity of the optimal base stock and the optimal cost with respect to the sufficient statistics.

Martin K. Starr facilitated the creation of an identity for production and operations management (POM) as an academic discipline. This paper aims to summarize Starr's substantial contributions to scholarly inquiry on system integration and interfunctional coordination, modular production, and catastrophe avoidance. Even after four decades, we describe how his legacy in these areas continues to define several major drivers of operations and supply chain management research and practice. Starr has influenced several generations of students, professors, and executives with his writings, teaching, and leadership roles in the POM community that include 32 years on the faculty of the Columbia School of Business, 15 years as Editor‐in‐Chief of Management Science, and presidency of the Production and Operations Management Society.

We address the problem of an express package delivery company in structuring a long‐term customer contract whose terms may include prices that differ by day‐of‐week and by speed‐of‐service. The company traditionally offered speed‐of‐service pricing to its customers, but without day‐of‐week differentiation, resulting in customer demands with considerable day‐of‐week seasonality. The package delivery company hoped that using day‐of‐week and speed‐of‐service price differentiation for contract customers would induce these customers to adjust their demands to become counter‐cyclical to the non‐contract demand. Although this usually cannot be achieved by pricing alone, we devise an approach that utilizes day‐of‐week and speed‐of‐service pricing as an element of a Pareto‐improving contract. The contract provides the lowest‐cost arrangement for the package delivery company while ensuring that the customer is at least as well off as he would have been under the existing pricing structure. The contract pricing smoothes the package delivery company's demand and reduces peak requirements for transport capacity. The latter helps to decrease capital costs, which may allow a further price reduction for the customer. We formulate the pricing problem as a biconvex optimization model, and present a methodology for designing the contract and numerical examples that illustrate the achievable savings.

We study multiunit double auctions accepting bids with indivisibility constraints. Modeling the auction problem as a Multiple Choice Knapsack Problem and using dynamic programming, we show that incremental computations during bid processing can speed the handling of key auction operations such as clearing and quoting. We propose different price‐quote policies and study their influence on the efficiency of market‐based allocation. Using a reconfigurable manufacturing scenario where agents trade large quantities of multiple goods, we demonstrate potential benefits of supporting indivisibility constraints in bidding. These benefits are highly sensitive to the form of price quote provided, indicating interesting tradeoffs in communication and allocation efficiency.

John A. Buzacott is a pioneering and premier contributor to the field of production and operations management. He has been a principal architect of the development of a unified framework and a rigorous engineering foundation for many of the major approaches currently used in the design, planning, and control of manufacturing and service systems. His innovative use of stochastic models to explain many phenomena occurring in manufacturing and service organizations has distinguished him not only as a great researcher but also as a great teacher. His contributions have inspired scholars throughout the world. We provide an overview of John's research works and accomplishments.

Although the initial euphoria about Internet‐enabled reverse auctions has given way to a cautious but widespread use of reverse auctions in business‐to‐business (B2B) procurement, there is a limited understanding of the effect of auction design parameters on buyer surplus. In this paper, we study the effect of bidding competition, information asymmetry, reserve price, bid decrement, auction duration, and bidder type on buyer surplus. We collected field data on more than 700 online procurement auctions conducted by a leading auctioneer and involving procurement items worth millions of dollars. Consistent with the predictions of auction theory, the results indicate that bidding competition, reserve price, and information sharing affect buyer surplus. Unlike previous findings in the consumer‐to‐consumer context, we find that bid decrement and auction duration have no effect in B2B procurement auctions. Our results suggest that use of the rank‐bidding format increases buyer surplus when incumbent suppliers participate in the auction. We discuss the theoretical and managerial implications of these findings for future research and for optimal design of online procurement auctions.

We study the benefit obtained by exploiting modular product design in fulfilling exogenous demand for both a complete assembly and its components in a service parts inventory system. Our goal is to reduce overall service system costs by allowing assembly and/or disassembly (A/D) to occur at some unit cost per A/D action. In an extensive set of computational experiments, we compare a naïve stocking and operating policy that treats all items independently and ignores the modular product structure and related A/D capability to the optimal base stock policy, and to a policy that allows A/D from the naïve stocking levels. While extensive computational analysis shows that the optimal base stock policy improves the system cost between 3 to 26% over the naïve approach, simply allowing A/D from the naïve stocking levels captures a significant portion (an average of 67%) of the naïve–optimal gap. Our computational results demonstrate that the optimization shifts the component‐assembly mix from the naïve levels and that limiting A/D capacity affects this mix. Limiting A/D capacity can actually increase the expected number of A/D actions (versus the uncapacitated case), since the optimization shifts stocking levels to reduce the probability that “too many” actions will be required.

The enormous contributions of Bob Hayes to Operations Management (OM) are reviewed. His early work made innovative contributions to probability theory and utility estimation that enabled existing Operations Research theory to be applicable to real problems. Later, inspired by field trips to Japanese and German manufacturers, he joined Kim Clark to conduct an ambitious study of 12 plants in three companies, establishing the impact on productivity of factors such as reject rate, work‐in‐process, and production rate variation. In the 1980s, when adoption of advanced manufacturing automation was in vogue, Bob joined Jai Jaikumar to offer a caution, that new manufacturing technologies required new ways of managing and that advanced technology coupled with obsolete management would produce poorer, not better, results. Perhaps Bob's greatest contribution was to raise OM to a more strategic level. With numerous coauthors, notably Steve Wheelwright, he provided a framework for corporate and manufacturing strategy and showed how to achieve alignment between the two, particularly in the choice of production processes. Recent papers articulated a vision for OM in which a focus on the issues of operating managers provides a consistent framework, but enables our research agenda to evolve as the world's economy changes.

The purpose of this paper is to introduce supply chain management researchers to industry cluster theory within the context of supply chain management decisions. Industry cluster theory emphasizes the explicit and implicit benefits that accrue to various economic players due to geographic proximity. As such, it provides a contrasting view to the current pressure on supply chains to seek out the “best” partners, regardless of location. We review the theory behind industry clusters, and illustrate it using the example of the New England cotton textile industry. Incorporating these concepts into future research has the potential to improve our understanding of how decisions regarding supply chain location and sourcing decisions are currently made, and what role location‐based benefits should play in these decisions.