Knowledge that Transforms

The continued success of Manufacturing & Service Operations Management (M&SOM) depends on the volunteer work of many professionals who take their precious time to provide careful and constructive reviews of the manuscripts submitted to the journal in a timely manner. On behalf of M&SOM, Editor-in-Chief Christopher Tang would like to express his deepest gratitude to all those who served as reviewers for the journal in 2018. Among all reviewers, some individuals have distinguished themselves by reviewing several manuscripts, and with each manuscript by writing a fair, critical, and constructive review in a timely fashion. In recognition of the outstanding service they provided in supporting the journal’s scholarly mission, M&SOM grants the 2018 Meritorious Service Award to…

I was honored when Chris Tang requested my recollections about the launch of Manufacturing & Service Operations Management (M&SOM) 20+ years ago. Yet I have scant memories of those seven years, although I remember the challenging ones very well. Also, I have no records. So, many thanks to Steve Graves, whose memory and record-keeping are better than mine. Special thanks to Mathew Walls, publications director of INFORMS, who provided copies of INFORMS documents. I quote some below. Regardless, I take full responsibility regarding the “truth” of what follows. In other words, it’s my story, and I'm stickin’ to it.

In a globalized economy, the competitive advantages of modern companies are defined by the number of knowledge assets and organizational relationships which determines the uniqueness of the company in the market and its key capabilities. The article clarifies the meaning of the key capabilities of the company as a unique set of business processes and attributes of the company, which, combined with resources and technologies, are embodied into new products, processes and services with new consumer properties and provide competitive advantages. The authors conceptualized the process of identification and measurement of key capabilities of the company and emphasized that its main goal is to ensure the company's competitive advantages. The authors synthesized tools and methods for measuring the key capabilities of the company and showed the processes that require such measurement: business process modeling, competitive positioning of the company based on the analysis of capabilities, formation of a competitive strategy of the company, ensuring its competitive advantages.

Problem definition: How can one adapt multiechelon inventory models to capture the cash flows generated by various payment-timing contracts? What competitive inventory policy behavior arises under these various payment-timing arrangements? Academic/practical relevance: Technology advancements are increasing the variety of payment triggers between supply chain stages. However, traditional multiechelon inventory models, which were originally developed for vertically integrated systems, do not explicitly account for payments flowing up the supply chain nor issues of payment timing between stages. Methodology: We introduce an analytical modeling methodology to incorporate financial inventory costs generated by payment-timing arrangements between stages in multiechelon inventory models. We also combine these methods with established inventory theory to study competitive inventory policies in a two-echelon system. Results: Under a class of payment-timing contracts, we show how to express payment flows at each stage in terms of standard physical inventory metrics and the demand-arrival process. We also show how to calculate the average inventory costs for each stage under given inventory policies and payment-timing contracts. In the two-echelon base-stock model, we first show that the cost of decentralization under standard wholesale price contracts is significantly driven by too much inventory at the supplier; it is not exclusively driven by too little inventory at the retailer as in the selling-to-the-newsvendor literature. We show that wholesale price contracts with a type of popular consignment payment timing still leads to too little inventory at the retailer. However, we prove there exists a wholesale price contract with partial consignment timing that can achieve the centralized inventory levels at both the supplier and the retailer. Managerial implications: Researchers can leverage our methodology to incorporate the price transfers and timing aspects of contracts in multiechelon inventory models. Our insights also help managers better understand the impact of prices and payment timing on decentralized chain behavior and performance.

Problem definition: Standard choice models used to optimize product line prices are effective at capturing the effects of prices on the affordability of products but do not incorporate intraline price spillovers, or the effects of prices on consumer preferences for branded product lines. To date, no approach exists to measure such spillovers, and thus their existence and characteristics are subject to controversy. Academic/practical relevance: Different authors have proposed that different product line items, here referred to as known value items (KVIs), generate price spillovers. Yet product line prices and quality levels are typically highly correlated, and thus it is not clear which spillovers should be modeled. No empirical approach exists to test for multiple price spillovers simultaneously while controlling for quality spillovers. Methodology: This study proposes an empirical approach that includes a choice model to abstract rich demand patterns and an estimation algorithm that addresses the endogeneity and collinearity of KVI prices. An empirical application based on vehicle choice data illustrates how the approach can be implemented in practice and demonstrates the importance of modeling spillovers while yielding interesting managerial implications. Results: The results establish that different KVIs generate separate spillovers and illustrate their distinct roles. KVI price spillovers can be economically important, inducing large cross-price elasticities that range from −1.31 to 0.72. Furthermore, neglecting spillovers can bias own-price elasticity estimates by up to 15% and cross-price elasticities by more than 100%. Managerial implications: The empirical results generate KVI-specific insights that managers in the automobile industry can use to improve their pricing strategies. For other industries, the paper proposes an approach and an implementation algorithm that managers can use to estimate spillovers and optimize product line prices. The empirical application illustrates the implementation of the approach and shows how managers can model price spillovers to increase profits by almost 3%.

Problem definition: Inspired by new developments in dynamic spectrum access, we study the dynamic pricing of wireless Internet access when demand and capacity (bandwidth) are stochastic. Academic/practical relevance: The demand for wireless Internet access has increased enormously. However, the spectrum available to wireless service providers is limited. The industry has, thus, altered conventional license-based spectrum access policies through unlicensed spectrum operations. The additional spectrum obtained through these operations has stochastic capacity. Thus, the pricing of this service by the service provider has novel challenges. The problem considered in this paper is, therefore, of high practical relevance and new to the academic literature. Methodology: We study this pricing problem using a Markov decision process model in which customers are posted dynamic prices based on their bandwidth requirement and the available capacity. Results: We characterize the structure of the optimal pricing policy as a function of the system state and of the input parameters. Because it is impossible to solve this problem for practically large state spaces, we propose a heuristic dynamic pricing policy that performs very well, particularly when the ratio of capacity to demand rate is low. Managerial implications: We demonstrate the value of using a dynamic heuristic pricing policy compared with the myopic and optimal static policies. The previous literature has studied similar systems with fixed capacity and has characterized conditions under which myopic policies perform well. In contrast, our setting has dynamic (stochastic) capacity, and we find that identifying good state-dependent heuristic pricing policies is of greater importance. Our heuristic policy is computationally more tractable and easier to implement than the optimal dynamic and static pricing policies. It also provides a significant performance improvement relative to the myopic and optimal static policies when capacity is scarce, a condition that holds for the practical setting that motivated this research.

Problem definition: This study examines the relationship between organizational focus, diversification of services, and consumer choice in the market for healthcare services. Academic/practical relevance: While previous studies have examined the roles of focus and diversification in efficiency and quality of care, their role in patient choice has received less attention. Methods: We use hospital inpatient data from the state of Florida for the years 2006–2010 to examine this problem in the context of obstetrics services. Results: Patients are more likely to choose hospitals that specialize in obstetrics services. They also show preferences for diversification across hospital units but not diversification within the focal unit. Specifically, they prefer hospitals offering wider ranges of services for newborns, which often involve different departments in the hospital but do not prefer hospitals offering wider ranges of obstetrics services. Managerial implications: Hospitals can gain an advantage by focusing on a small set of services. However, focus has important limits, as patients may have needs that cut across medical specialties and prefer facilities that can serve these sometimes complex needs. We discuss potential implications for hospital performance.

Problem definition: We analyze a catalyst-activated batch-production process with uncertainty in production times, learning about catalyst-productivity characteristics and decay of catalyst performance across batches. The goal is to determine the quality level of batches and to decide when to replenish a catalyst so as to minimize average costs, consisting of inventory-holding, backlogging, and catalyst-switching costs. Academic/practical relevance: This is an important problem in a variety of process-industry sectors, such as food processing, pharmaceuticals, and specialty chemicals, but has not been adequately studied in the academic literature. This paper also contributes to the stochastic economic lot-sizing literature. Methodology: We formulate this problem as a semi-Markov decision process (SMDP) and develop a two-level heuristic to solve this problem. The heuristic consists of a lower-level problem that plans the duration of batches within the current campaign to maximize the efficiency of the catalyst while ensuring that the target attribute level for each batch is set to meet a quality specification represented by an average attribute level across all the batches in a campaign. The higher-level problem determines when to replace the costly catalyst as its productivity decays. To evaluate our heuristic, we present a lower bound on the optimal value of the SMDP. This bound accounts for all costs, as well as the randomness and discreteness in the process. We then extend our methods to multiple-product settings, which results in an advanced stochastic economic lot-sizing problem. Results: We test our proposed solution methodology with data from a leading food-processing company and show that our methods outperform current practice with an average improvement of around 22% in costs. In addition, compared with the stochastic lower bounds, our results show that the two-level heuristic attains near-optimal performance for the intractable multidimensional SMDP. Managerial implications: Our results generate three important managerial insights. First, our simulation-based lower bound provides a close approximation to the optimal cost of the SMDP, and it is nearly attainable by using a relatively simple two-level heuristic. Second, the reoptimization policy used in the lower-level problem adequately captures the value of information and Bayesian learning. Third, in the higher-level problem of choosing when to replace a catalyst, the intractable multidimensional state of the system is efficiently summarized by a single statistic: the probability of inventory falling below a specific threshold.