Knowledge that Transforms

We study how the seat value perceived by consumers attending an event in a theater/stadium depends on the location of their seat relative to the stage/field. We develop a measure of seat value, called the Seat Value Index, and relate it to seat location and consumer characteristics. We implement our analysis on a proprietary data set that a professional baseball franchise in Japan collected from its customers, and provide recommendations. For instance, we find that customers seated in symmetric seats on left and right fields might derive very different valuations from the seats. We also find that the more frequent visitors to the stadium report extreme seat value less often when compared with first‐time visitors. Our findings and insights remain robust to the effects of price and game‐related factors. Thus, our research quantifies the significant influence of seat location on the ex‐post seat value perceived by customers. Utilizing the heterogeneity in seat values at different seat locations, we provide segment‐specific pricing recommendations based on a service‐level objective that would limit the fraction of customers experiencing low seat value to a desired threshold.

We develop an optimal control model to maximize the net value provided by a software system over its useful life. The model determines the initial number of features in the system, the level of dynamic enhancement effort, and the lifetime of the system. The various factors affecting these optimal choices are systems characteristics (e.g., complexity, age, quality), user learning, and process maturity. We also consider that there is a time lag between the addition of a feature and the realization of its benefit to users. The basic model is extended to consider the decision of replacing the existing system by a new one.

We analyze the value of and interaction between production postponement and information sharing, which are two distinct strategies to reduce manufacturers’ uncertainty about demand. In both single‐level and two‐level supply chains, from the manufacturer's perspective, while information sharing is always valuable, production postponement can sometimes be detrimental. Furthermore, the value of production postponement is not merely driven by savings in inventory holding cost as postponement enables the manufacturer to avoid both excess and shortfall in production. We find that production postponement and information sharing strategies may substitute, complement, or conflict with each other, depending on the extent of the increase in the unit production cost when production is postponed. In a two‐level supply chain, from the retailer's perspective, information sharing and production postponement can be beneficial or detrimental. When information sharing is beneficial to the retailer, the retailer always shares her demand information with the manufacturer voluntarily. In addition, this voluntary information sharing is truthful because inflated or deflated demand information hurts the retailer through a higher wholesale price or a stock‐out. However, the retailer never shares her demand information voluntarily if the manufacturer has already adopted production postponement because production postponement and information sharing strategies always conflict with each other. Even when the retailer does not benefit from information sharing, we show that the manufacturer can always design an incentive mechanism to induce the retailer to share the demand information, irrespective of whether the manufacturer has already implemented production postponement or not. The above findings underscore the need for a careful assessment of demand uncertainty‐reduction strategies before the supply chain players embark upon them.

The globalization of markets and geographic dispersion of production facilities, combined with a heavy outsourcing of supply chain processes, have substantially increased the exposure of supply chains to supply lead‐times of long and uncertain nature. In this paper, we study the potential use of two contingency strategies on top of the conventionally used time buffer—statically planned safety lead‐time (SL)—approach to deal with the lead‐time uncertainty. These are (1) the ex‐ante planning for disruption safety stock (DSS) to be released when a “disruption” (in this case, late delivery of the order) occurs; and (2) the ex‐post dynamic emergency response (DER), which dynamically decides on the timing and size of an emergency order to be placed. Our work elaborates on the optimal parameter setting for these strategies, compares their added values when used to complement the traditional SL approach, and examines how the use of the contingency strategies affects the SL and corresponding cycle length of a periodic review system. Our research finds that: (1) the above contingency strategies reduce the reliance on the SL and are cost effective when the coefficient of variation (CV) of the uncertain lead‐time is high; (2) it is important to re‐optimize the SL to account for the contingency plans; and (3) re‐optimization of the cycle length to account for the presence of the contingency responses, as opposed to using an EOQ‐determined cycle length, does not significantly improve the cost performance. However, such re‐optimization does well in the SL approach when the CV of the uncertain lead‐time is high.

E‐Services, or the company's portfolio of service offerings available to its customers through the Internet, are an emerging area of interest to operations management. Yet little is known about the operations and capabilities needed for provision of business‐to‐business (B2B) e‐services. This paper aims to make a contribution toward closing this gap. First, we develop a new construct of B2B e‐service capability , a term that captures a generic set of five interrelated and complementary dimensions: (1) e‐service recovery, (2) e‐customization, (3) ease of navigation, (4) service portfolio comprehensiveness, and (5) information richness. These combined operational abilities are associated with B2B service delivery, including its portal design, technology architecture, and mix of product and service offerings. They are posited to be necessary for delivering effective B2B e‐services. We also argue that, both service orientation (SO) and customer receptivity to technology, influence B2B e‐service capability. We empirically test a path model using structural equation modeling on a sample of 181 businesses that have deployed B2B e‐services. We find that the influence of SO on performance is not direct but rather mediated by the e‐service capability, a finding that holds for both goods producers and service providers. We suggest that a firm's SO may mitigate industrial customers' resistance toward conducting business online.

In this study, we propose a methodological framework to provide a road map to clinicians and system planners in developing chronic disease management strategies, and designing community‐based care. We extend the analytical epidemiologic model by utilizing a patient flow approach, in order to model the multiple care‐provider visit patterns of patients with a specific chronic illness. The patterns of care received by a group of patients are represented in compact form by means of a Markov model that is based on a disease‐specific state space. Our framework also reflects the case‐mix biases as well as the care‐provider level clustering of the patients. By using this approach, we identify the patterns of care, determine the care provider and patient characteristics associated with optimal management of care, and estimate the potential influence of various interventions. The framework is applied to the data of 4000+ stroke patients discharged from the acute care hospitals of Quebec to their homes. Our findings provide a basis for designing community‐based care initiatives for stroke survivors in the province.

Much attention has been paid to lengthy wait times in emergency departments (EDs) and much research has sought to improve ED performance. However, ED congestion is often caused by the inability to move patients into the wards while the wards in turn are often congested primarily due to patients waiting for a bed in a long‐term care (LTC) facility. The scheduling of clients to LTC is a complex problem that is compounded by the variety of LTC beds (different facilities and room accommodations), the presence of client choice and the competing demands of the hospital and community populations. We present a Markov decision process (MDP) model that determines the required access in order for the census of patients waiting for LTC in the hospitals to remain below a given threshold. We further present a simulation model that incorporates both hospital and community demand for LTC in order to predict the impact of implementing the policy derived from the MDP on the community client wait times and to aid in capacity planning for the future. We test the MDP policy vs. current practice as well as against a number of other proposed policy changes.

When operating room schedules in hospitals are produced, the constraints and preferences of surgeons and hospital workers are a primary consideration. The downstream impact on post‐operative bed availability is often ignored. This can lead to the boarding of patients overnight in the post‐anesthesia care unit (PACU) because intensive care unit beds are unavailable. In this paper, we apply integer programming and simulation to develop improved surgical scheduling assignments. We want to balance new surgeries with hospital discharges in order to reduce the variability of occupied beds from one day to the next and, as a result, to reduce boarding in the PACU.

In this paper, we consider data‐driven approaches to the problem of inventory control. We first consider the approach of operational statistics and review related results which enable us to maximize a priori expected profit uniformly over all parameter values, when the demand distribution is known up to the location and scale parameters. For the case of the unknown shape parameter, we first suggest a heuristic approach based on operational statistics to obtain improved ordering policies and illustrate the same for the case of a Pareto demand distribution. In more general cases where the heuristic is not applicable, we suggest linear correction and support vector regression approaches to better estimate ordering policies, and illustrate these using a Gamma demand distribution. In certain cases, our proposed approaches are found to yield significant improvements.

Installed base management is the policy in which the manufacturer leases the product to consumers, and bundles repair and maintenance services along with the product. In this article, we investigate for the optimal leasing price and leasing duration decisions by a monopolist when the production and servicing capacity are constrained. The effect of diffusion of consumers in the installed base is considered, with the ownership of the product resting with the monopolist during the product lifecycle. The monopolist operating the installed base jointly optimizes the profits from leasing the product/service bundle along with maintenance revenues and remanufacturing savings. We formulate the manufacturer's problem as an optimal control problem and show that the optimal pricing strategy of the firm should be a skimming strategy. We also find that the effect of remanufacturing savings on the pricing decision and the length of the leasing duration changes significantly depending on the duration of the product's lifecycle. If the product lifecycle is long and remanufacturing savings are low, the firm should offer a shorter leasing duration, whereas if the remanufacturing savings are high, the firm should optimally offer a higher leasing duration. In contrast, if the time duration of the product lifecycle is low and remanufacturing savings are low, the firm prefers to offer a shorter leasing duration, whereas if the remanufacturing savings are high, the firm should optimally have a longer leasing duration. The article also shows that if the production capacity is small, the manufacturer increases the leasing duration. If the production capacity is very small, the manufacturer sets the leasing duration to be equal to the product lifecycle and does not use remanufacturing.