Knowledge that Transforms

We analyze production and capacity utilization strategies in a supply chain where individual components can be made either at a shared in‐house manufacturing facility or at dedicated facilities of external subcontractors. The manufacturer and the subcontractor differ in terms of costs, production capacities, rates, and service level capabilities. Using Markov decision process models, we determine the optimal policy and characterize its structure. We derive the set of conditions that partitions the state space into regions and characterize optimal policies in each region. We derive optimal policies for manufacturer and subcontractors under different settings and show that the optimal policy has a multi‐index structure in some settings.

Russell Ackoff's iconoclastic journey through the scholarly world illustrates just how far ahead of his time he was, and how much we all still owe him. His influence was so fundamental that today we accept his once‐controversial ideas as taken for granted realities. Every time you try to grasp the complexities of an interdependent system, try to understand the root causes of problems that appear on the surface or try to “connect the dots,” you are drawing on the groundwork laid by Russ. It would be difficult to overstate his impact.

We study the widespread practice of assigning a single surgeon to two operating rooms (ORs), which is known as overlapping surgery. We first describe the fundamental trade‐offs that motivate hospitals to employ the practice. Then, we conduct a mathematical analysis for the completion of a set of elective operations under an overlapping surgery policy and a more traditional, serial scheduling policy. We use this analysis, along with a cost function that considers surgeon idle time and total OR time, to derive two models for predicting environments where overlapping surgery is preferable. We use insights from our modeling efforts to provide objective evidence that supports or opposes claims made in the medical trade literature regarding the use of overlapping surgery. In particular, we show that contrary to claims in the medical trade literature, overlapping surgery does not allow a hospital to increase total surgical volume. Finally, we show that hospitals interested in employing overlapping surgery are best served by focusing efforts aimed at reducing time requirement uncertainty on turnover times instead of procedure times.

The study “Mitigating the U.S. Drug Shortages Through Pareto‐Improving Contracts” was selected as a runner‐up for the Ralph Gomory Best Industry Studies Paper Award by the Industry Studies Association in 2017. The award aims at “explicit recognition of the researchers’ engagement with industry practitioners and demonstrate the benefits of such interactions for scholarly research.” The awarded study addresses the drug shortages problem, a major challenge facing the U.S. pharmaceutical industry and government in recent years. This reflection article provides the “back story” of the study—the motivation, the challenges, the important steps, and the interactions with government agencies and industry practitioners.

Sabotage activities often raise controversies and regulatory concerns due to the potential negative effects on competition and social welfare. These concerns are amplified when firms serving complementary markets integrate due to the integrated firm’s capability to engage in multi‐market sabotage. We note that the integrated firm’s incentive to engage in sabotage activities and the potential impact on social welfare has not been examined, and this drives the primary focus of our study. Interestingly, we find that the integrated firm may not have the incentive to engage in sabotage at all. We show that the integrated firm prefers to engage in sabotage (single‐market or multi‐market) only when it has cost advantages over its rivals in at least one of the markets. At the extreme, a certain level of sabotage actions could even force some product combinations out of the market and thus, there might be a need for regulatory intervention. A counter‐intuitive result is that under certain market conditions, the integrated firm’s sabotage activities correspond to those that would optimize social welfare.

Most pricing and revenue management models have at their core an optimization problem; one needs to determine the optimal price or quantity to maximize a profit or revenue function. To ensure tractability, conditions that assure the objective function has a unique solution are enormously helpful. So far, several technical assumptions have been proposed for the continuous case, but comparatively little attention has been given to the discrete counterpart despite its prevalence in practice. Thus, this study aims to develop new technical assumptions, built upon relevant economic concepts, to guarantee the tractability of revenue management models in discrete settings. In particular, we present two sufficient conditions for the revenue function to be concave, in terms of quantity or price and propose a condition for the revenue function to be unimodal, called discrete increasing generalized failure rate (IGFR). Our definition has an appropriate economic interpretation and offers comparable properties to those of the continuous version. Finally, we show the discrete IGFR property holds for several discrete distributions.

Stem‐cell transplantation is the last chance for patients of various blood‐related diseases. Stem‐cell donation centers admit patients in need of a stem‐cell transplant and search for a perfect match between the patients and donors. The search process is time‐consuming and requires expensive advanced equipments, in particular for DNA typing. In this study, we are concerned with a capacity planning problem in a network of stem‐cell donation centers. The underlying optimization model integrates the operations for a donor search and aims to maximize the number of transplantations. A scenario‐based stochastic programming approach is introduced to investigate the effect of the demand and service time variabilities into the capacity planning problem. We consider the maximum possible waiting time during the search process to obtain robust solutions against uncertainties. For this purpose, we approximate the maximum waiting time in the advanced blood testing with a robust queuing approach. The computational experiments are designed to illustrate the performance of the capacity planning model.

Health care organizations have substantially invested in Health Information Technology (HIT) as part of an effort to improve quality. However, many hospitals fail to generate positive returns on this significant investment, based on reimbursements for quality measures through the Affordable Care Act (ACA). Given the high cost of lawsuits, we investigate if HIT adoption reduces lawsuits, and their attendant costs, as another consideration in HIT payoffs. We use operational transparency theory to develop hypotheses on the individual and joint impact of HIT and communication quality in influencing patients’ likelihood to file a lawsuit. We combine data on 168 hospitals in the state of Florida from 2007 to 2011 in order to investigate these relationships. Analysis using a fractional response model indicates that HIT has a direct impact in reducing the number of lawsuits, this effect being higher for hospitals with higher communication quality scores. These results remain consistent irrespective of the type of caregiver (physician vs nurse) communicating with the patient or the severity of injury resulting in the lawsuit. Our results also remain robust under different operationalization of key independent variables and alternate model specifications. These results provide a better understanding of the mechanisms that reduce lawsuits.

Use a unique dataset collected from a large classified ads website, we empirically examine the effect of the offline call intensity on the online consumer purchase probability of digital services and the carryover effect of the call intensity. We find that the online consumer purchase probability is increasing in the call intensity but at a decreasing rate. We further demonstrate that the decreasing rate is sizable enough that the relationship between the online consumer purchase probability and the call intensity is an inverted U‐shaped curve. In addition, there exists a strong carryover effect where the online call intensity in the past 4 weeks does not fade away but has a positive effect on recent consumer purchases. Our estimations show that both too much and too little call intensity will result in considerably worse outcomes. As compared with the call intensity at the optimal level, too much call intensity potentially reduces the online consumer purchase probability by 29.11%, and too little call intensity potentially reduces the online consumer purchase probability by 54.90%. Furthermore, making calls every week for 4 weeks can increase consumer purchase probability by 10.69 times as compared with just initiating calls with the consumers.

Manufacturers of durable goods often buy back older versions of their products from customers to encourage them to switch to improved versions and to create control over product return streams in their closed‐loop systems. Classical models and conventional wisdom have long ignored that the framing of these buyback schemes, whether through trade‐ins or upgrades, can matter for theory. Using the reference‐point shift mechanism, we provide experimental evidence that the alternative frames are not equivalent and that the framing effect induces customers to change which prices they anchor to as their reference points for the price for their current version. We then use the experimental findings to extend a reference‐dependence version of the classical model of trade‐ins and upgrades and show how the behavioral extension modifies key predictions of the classical model and provides predictions more in line with today's durable goods markets.