Knowledge that Transforms

We study a joint implementation of price‐ and availability‐based product substitution to better match demand and constrained supply across vertically differentiated products. Our study is motivated by firms that utilize dynamic pricing as well as customer upgrades, as ex ante and ex post mechanisms, respectively, to mitigate inventory mismatches. To gain insight into how offering product upgrades impacts optimal price selection, we formulate a multiple period, nested two‐stage model where the firm first sets prices and replenishment levels for each product while the demand is still uncertain, and after observing the demand, decides how many (if any) of the customers to upgrade to a higher quality product. We characterize the structure of the optimal upgrade, pricing and replenishment policies and find that firms having greater flexibility to offer product upgrades can restrain their reliance on dynamic pricing, enabling them to better protect the price differentiation between the products. We also show how the quality differential between the products or changes in the replenishment cost structures influence the optimal policy. Using insights gained from the optimal policy structure, we construct a heuristic policy and find that it performs well across various parameter values. Finally, we consider an extension in which the firm dynamically sets upgrade fees in each period. Our results overall help further our understanding of the intricate relationship among a firm's decisions on pricing, replenishment, and product upgrades in an effort to better match demand and constrained supply.

Provision of safe, timely care to hospital patients requires services from multiple support departments, such as environmental services and pharmacy. However, few studies have examined the impact of the service quality of internal support departments on clinical performance. The lack of studies linking internal service quality (ISQ) to clinical performance creates a gap in healthcare operations management theory and—from a practice standpoint—might contribute to underinvestment in the quality of services delivered by internal support departments. To address these issues, we test whether higher ISQ is associated with a lower rate of adverse events. We leverage a unique dataset from a hospital that developed a measure of ISQ provided by support departments. Using over a year's worth of monthly data on the average ISQ delivered by 11 support departments to five nursing units, we test the impact of ISQ on two nursing‐sensitive adverse events: patient falls with injury and hospital‐acquired pressure ulcers. We find support for our hypothesis that higher levels of ISQ are associated with lower rates of adverse events, controlling for patient acuity and other confounding factors. Our results show that improving the overall average ISQ received by a nursing unit by 0.1 on a 5‐point scale has almost the same benefit for reducing adverse events as would increasing staffing on that unit by one full‐time equivalent nurse. Our study has important implications for theory and practice as it points to a fruitful, cost effective, and yet underutilized avenue for reducing adverse events experienced by hospital patients.

In scheduling medical residents, the objective is often to maximize resident satisfaction across the space of feasible schedules, relative to the many hard constraints that ensure appropriate patient coverage, adequate training opportunities, etc. A common metric of resident satisfaction is the number of time‐off requests that are granted. Simply maximizing this total, however, may lead to undesirable schedules since some requests have higher priority than others. For example, it might be better to grant one resident's request for a family member's wedding in place of two residents’ requests to attend a rugby game. Another approach is to assign a weight to each request and maximize the total weight of granted requests, but determining weights that accurately represent residents’ and schedulers’ preferences can be quite challenging. Instead, we propose to identify the exhaustive collection of maximally feasible and minimally infeasible sets of requests which can then be used by schedulers to select their preferred solution. Specifically, we have developed two algorithms, which we call Sequential Request Selection Via Cuts (Sequential RSVC) and Simultaneous Request Selection Via Cuts (Simultaneous RSVC), to identify these sets by solving two sequences of optimization problems. We present these algorithms along with computational results based on a real‐world problem of scheduling residents at the University of Michigan C.S. Mott Pediatric Emergency Department. Although we focus our exposition on the problem of resident scheduling, our approach is applicable to a broad class of problems with soft constraints.

In this study, we examine performance‐based payment contracts to promote the optimal use of an optional diagnostic test for newly diagnosed cancer patients. Our work is inspired by three trends: tremendous increases in the cost of new, advanced cancer drugs; development of new diagnostic tests to allow physicians to tailor treatment to patients; and changes in healthcare funding models that reward quality care. We model the interaction between two parties—a healthcare payer and an oncologist, in which the oncologist has private information about patients’ characteristics (adverse selection) and the payer does not know whether the oncologist takes the optimal course of action (moral hazard). We show that, in the presence of information asymmetry, a healthcare payer should never incentivize an oncologist to use a diagnostic test for all patients, even if the diagnostic test is available for free. Moreover, although the oncologist has additional information about a patient's risk, he cannot always benefit from this private information. We also find that social welfare may not increase as a result of a decrease in the oncologist's concerns regarding the health outcome of patients. Finally, we show that it is not always socially optimal to make a diagnostic test compulsory even if such a policy can be implemented for free.

The nature of operations executives’ strategic cognition, as the antecedent to their choices about operations strategy, remains underexplored in the literature. This mixed‐methods study examines executives’ thinking about supply chain strategy through the lens of managerial cognition. Our qualitative study at a pharmaceutical distributor, which examined 25 executives’ outlook on the future of the turbulent U.S. healthcare sector and their suggestions for adapting the company's supply chain strategy to that future, suggests that an executive's strategic cognition can be defined by its regulatory focus—whether the executive envisions the future environment in terms of opportunities or threats—and the level of optimism in regards to the envisioned future. We propose a typology that predicts the strategic choices of operations executives based on four types of cognition: pioneering, pushing, protective, and provocative. It describes whether an executive's strategic choices target traditional or novel sources of revenue, and if they seek to influence either the firm's structure and practices or its environment. Our empirical test of the typology using quantitative data collected in a survey of senior operations executives supports the study's propositions associating three of the four types of cognition with their respective preferred strategic choices.

The performance of a retail store depends on its ability to attract customer traffic, match labor with incoming traffic, and convert the incoming traffic into sales. Retailers make significant investments in marketing activities (such as advertising) to bring customers into their stores and in‐store labor to convert that traffic into sales. Thus, a common trade‐off that retail store managers face concerns the allocation of a store's limited budget between advertising and labor to enhance store‐level sales. To explore that trade‐off, we develop a centralized model to allocate limited store budget between store labor and advertising with the objective of maximizing store sales. We find that a store's inherent potential to drive traffic plays an important role, among other factors, in the relative allocation between advertising and store labor. We also find that as advertising instruments become more effective in bringing traffic to stores, managers should not always capitalize this effectiveness by increasing their existing allocations to advertising. In addition, we discuss a decentralized setting where budget allocation decisions cannot be enforced by a store manager and present a simple mechanism that can achieve the centralized solution. In an extension, we address the budget allocation problem in the presence of marketing efforts to shift store traffic from peak to off peak hours and show that our initial findings are robust. Further, we illustrate how the solution from the budget allocation model can be used to facilitate store level sales force planning/scheduling decisions. Based on the results of our model, we present several insights that can help managers in budget allocation and sales force planning.

It is widely accepted that the information technology (IT) industry has high clockspeed. This very phenomenon has led to IT OEMs finding themselves selling new generation models only to be left holding returned merchandise from older generations. Similarly, customers who migrate to newer generations of products experience uncertainty about how to dispose of older but functional IT equipment. Online liquidation markets have emerged to address these needs by finding ways to resell this equipment. On these liquidation markets, sellers of out‐of‐date or lightly used durable items like computers and tablets can transact with buyers interested in these products at discounted prices, without needing to alter the state/quality of the product. There is limited understanding of how these markets function and how they may be designed to increase their effectiveness. We report on a unique opportunity for a field experiment that was conducted through the co‐operation of a large liquidation company (wholesale liquidator) for IT equipment in the United States. With the specific intention of understanding the design of these liquidation auctions, the research site allowed us to conduct a field experiment on their auction platform for different categories of iPad tablets. By manipulating auction starting prices, we are able to provide insight into the effect of starting prices on the final auction prices of the returned IT products, and find evidence of cross‐product dependencies. To the extent that efficient and viable liquidation markets have ecological and market value, our work provides insights into how sellers may, through the adjustment of starting prices, increase their final prices from online auctions.

Inventory displayed on the retail sales floor not only performs the classical supply function but also plays a role in affecting consumers’ buying behavior and hence the total demand. Empirical evidence from the retail industry shows that for some types of products, higher levels of on‐shelf inventory have a demand‐increasing effect (“billboard effect”) while for some other types of products, higher levels of on‐shelf inventory have a demand‐decreasing effect (“scarcity effect”). This suggests that retailers may use the amount of shelf stock on display as a tool to influence demand and operate a store backroom to hold the inventory of items not displayed on the shelves, introducing the need for efficient management of the backroom and on‐shelf inventories. The purpose of this study is to address such an issue by considering a periodic‐review inventory system in which demand in each period is stochastic and depends on the amount of inventory displayed on the shelf. We first analyze the problem in a finite‐horizon setting and show under a general demand model that the system inventory is optimally replenished by a base‐stock policy and the shelf stock is controlled by two critical points representing the target levels to raise up/drop down the on‐shelf inventory level. In the infinite‐horizon setting, we find that the optimal policies simplify to stationary base‐stock type policies. Under the billboard effect, we further show that the optimal policy is monotone in the system states. Numerical experiments illustrate the value of smart backroom management strategy and show that significant profit gains can be obtained by jointly managing the backroom and on‐shelf inventories.

We study a single‐stage, continuous‐time inventory model where unit‐sized demands arrive according to a renewal process and show that an ( s, S) policy is optimal under minimal assumptions on the ordering/procurement and holding/backorder cost functions. To our knowledge, the derivation of almost all existing ( s, S)‐optimality results for stochastic inventory models assume that the ordering cost is composed of a fixed setup cost and a proportional variable cost; in contrast, our formulation allows virtually any reasonable ordering‐cost structure. Thus, our paper demonstrates that ( s, S)‐optimality actually holds in an important, primitive stochastic setting for all other practically interesting ordering cost structures such as well‐known quantity discount schemes (e.g., all‐units, incremental and truckload), multiple setup costs, supplier‐imposed size constraints (e.g., batch‐ordering and minimum‐order‐quantity), arbitrary increasing and concave cost, as well as any variants of these. It is noteworthy that our proof only relies on elementary arguments.

In practice, deterministic, multi‐period lot‐sizing models are implemented in rolling schedules since this allows the revision of decisions beyond the frozen horizon. Thus, rolling schedules are able to take realizations and updated forecasts of uncertain data (e.g., customer demands) into account. Furthermore, it is common to hold safety stocks to ensure given service levels (e.g., fill rate). As we will show, this approach, implemented in rolling schedules, often results in increased setup and holding costs while (over‐)accomplishing given fill rates. A well‐known alternative to deterministic planning models are stochastic, static, multi‐period planning models used in the static uncertainty strategy, which results in stable plans. However, these models have a lack of flexibility to react to the realization of uncertain data. As a result, actual costs may differ widely from planned costs, and downside deviations of actual fill rates from those given are very high. We propose a new strategy, namely the stabilized cycle. This combines and expands upon ideas from the literature for minimizing setup and holding costs in rolling schedules, while controlling actual product‐specific fill rates for a finite reporting period. A computational study with a multi‐item capacitated medium‐term production planning model has been executed in rolling schedules. On the one hand, it demonstrates that the stabilized‐cycle strategy yields a good compromise between costs and downside deviations. Furthermore, the stabilized‐cycle strategy weakly dominates the order‐based strategy for both constant and seasonal demands.