A central feature of most organizations is the use of measures to represent key elements of performance across multiple strategic objectives. Prior research demonstrates a tendency for individuals to treat these measures as though they are the higher order strategic objectives the measures were intended to represent as opposed to imperfect representations of those strategic objectives—a phenomenon labeled “surrogation.” We employ an experiment to further understand this phenomenon. In this study, we capture neural activation when processing measures and when processing strategic objectives. We find that, in regions of the brain with greater (less) activation for concrete versus abstract words, brain activation is greater (less) when processing measure phrases versus when processing strategy phrases. We further find evidence that greater brain activity and longer response times are associated with less surrogation. This affirms the notion that increased cognitive involvement can reduce surrogation.

In many production environments, a fixed network of capacity is shared flexibly between multiple products with random demands. What is the best way to configure the capacity of the production network and to allocate the available capacity to meet predetermined fill rate requirements? We develop a new approach for network capacity configuration and allocation and characterize the relationship between the capacity of the network and the attainable fill rate levels for the products, taking into account the flexibility structure of the network. This builds on a new randomized allocation mechanism to deliver the desired services. We use this theory to investigate the connection between the flexibility structure and capacity configuration. We provide a new perspective to the well-known phenomenon that “long chain is almost as good as the fully flexible network”: for given target fill rates, the required capacity level in a long-chain network is close to that in a fully flexible network and is much lower than a dedicated system. We apply these insights and techniques on problems arising in the design of last-mile delivery operations and in semiconductor production planning, using real data from two companies.

Clinical pathways outline standardized processes in the delivery of care for a specific disease. Patient journeys through the healthcare system, however, can deviate substantially from these pathways. Given the positive benefits of clinical pathways, it is important to measure the concordance of patient pathways so that variations in health system performance or bottlenecks in the delivery of care can be detected, monitored, and acted upon. This paper proposes the first data-driven inverse optimization approach to measuring pathway concordance in any problem context. Our specific application considers clinical pathway concordance for stage III colon cancer. We develop a novel concordance metric and demonstrate using real patient data from Ontario, Canada that it has a statistically significant association with survival. Our methodological approach considers a patient’s journey as a walk in a directed graph, where the costs on the arcs are derived by solving an inverse shortest path problem. The inverse optimization model uses two sources of information to find the arc costs: reference pathways developed by a provincial cancer agency (primary) and data from real-world patient-related activity from patients with both positive and negative clinical outcomes (secondary). Thus, our inverse optimization framework extends existing models by including data points of both varying “primacy” and “alignment.” Data primacy is addressed through a two-stage approach to imputing the cost vector, whereas data alignment is addressed by a hybrid objective function that aims to minimize and maximize suboptimality error for different subsets of input data.

With soaring labor and logistics costs in developing countries, supply chain disruptions during the COVID-19 pandemic triggered Western firms to “reshore” some of their offshore operations (performed in-house or outsourced) for certain strategically important products or production processes from foreign countries to their home countries. Although reshoring can create more domestic jobs and reduce supply chain risks, the impact of various external and internal risks associated with reshoring on market reaction remains unclear. This observation motivates us first to conduct a text mining analysis, revealing four important types of reshoring risks inherent to (1) foreign currency fluctuation, (2) intellectual property (IP) protection, (3) reshoring types (in-house, insourced, or outsourcing-to-outsourcing (OTO)), and (4) reshoring location choice (Republican- versus Democrat-led states). We then examine how these risk factors help explain the variations in reshoring’s market valuation based on 281 reshoring initiatives of 132 publicly traded firms in the United States announced between 2009 and 2022. Our empirical analysis reveals that the market reacts more positively to a firm’s reshoring announcement when the firm reshores under a high-currency-fluctuation environment or from countries with weak IP protection. However, the market’s reaction is more negative when the firm’s reshoring announcement entails insourced reshoring operations or when the reshored location is a Democrat- rather than Republican-led state. We do not find a significant market reaction to OTO reshoring.

The COVID-19 pandemic has seen dramatic demand surges for hospital care that have placed a severe strain on health systems worldwide. As a result, policy makers are faced with the challenge of managing scarce hospital capacity to reduce the backlog of non-COVID patients while maintaining the ability to respond to any potential future increases in demand for COVID care. In this paper, we propose a nationwide prioritization scheme that models each individual patient as a dynamic program whose states encode the patient’s health and treatment condition, whose actions describe the available treatment options, whose transition probabilities characterize the stochastic evolution of the patient’s health, and whose rewards encode the contribution to the overall objectives of the health system. The individual patients’ dynamic programs are coupled through constraints on the available resources, such as hospital beds, doctors, and nurses. We show that the overall problem can be modeled as a grouped weakly coupled dynamic program for which we determine near-optimal solutions through a fluid approximation. Our case study for the National Health Service in England shows how years of life can be gained by prioritizing specific disease types over COVID patients, such as injury and poisoning, diseases of the respiratory system, diseases of the circulatory system, diseases of the digestive system, and cancer. This paper was accepted by Chung-Piaw Teo, optimization. Funding: G. Forchini acknowledges funding from Jan Wallanders and Tom Hedelius Foundation and the Tore Browaldh Foundation, funding from MRC Centre for Global Infectious Disease Analysis [Reference MR/R015600/1], jointly funded by the UK Medical Research Council (MRC) and the UK Foreign, Commonwealth and Development Office (FCDO), under the MRC/FCDO Concordat agreement, part of the EDCTP2 program supported by the European Union; and acknowledges funding by Community Jameel. D. Rizmie acknowledges partial funding from the MRC Centre for Global Infectious Disease Analysis [Reference MR/R015600/1]. J. C. D’Aeth acknowledges funding from the Wellcome Trust [Reference 102169/Z/13/Z]. S. Moret acknowledges partial support from the Swiss National Science Foundation (SNSF) under [Grant P2ELP2_188028]. S. Ghosal was funded by the Imperial College President’s PhD Scholarship. F. Grimm was funded by the Health Foundation as part of core staff member activity. This research was funded in whole, or in part, by the Wellcome Trust [Grant 102169/Z/13/Z].

The evaluation and selection of novel projects lies at the heart of scientific and technological innovation, and yet there are persistent concerns about bias, such as conservatism. This paper investigates the role that the format of evaluation, specifically information sharing among expert evaluators, plays in generating conservative decisions. We executed two field experiments in two separate grant-funding opportunities at a leading research university, mobilizing 369 evaluators from seven universities to evaluate 97 projects, resulting in 761 proposal-evaluation pairs and more than $250,000 in awards. We exogenously varied the relative valence (positive and negative) of others’ scores and measured how exposures to higher and lower scores affect the focal evaluator’s propensity to change their initial score. We found causal evidence of a negativity bias, where evaluators lower their scores by more points after seeing scores more critical than their own rather than raise them after seeing more favorable scores. Qualitative coding of the evaluators’ justifications for score changes reveals that exposures to lower scores were associated with greater attention to uncovering weaknesses, whereas exposures to neutral or higher scores were associated with increased emphasis on nonevaluation criteria, such as confidence in one’s judgment. The greater power of negative information suggests that information sharing among expert evaluators can lead to more conservative allocation decisions that favor protecting against failure rather than maximizing success.

Hospitalists are medical doctors that specialize in the care of hospitalized patients, a role that until recently belonged to primary care physicians. We develop an operational model of hospitalist-patient interactions with rounding and responding service modes, optimizing hospitalist caseload and case-mix to achieve the maximal reduction in patient length of stay (LOS). We show that hospitalists are effective at reducing LOS for patients with complex conditions, corroborating intuitive reasoning. However, the optimal hospitalist case-mix also includes “simple” patients with few interventions and short LOS, as they can effectively reduce discharge delays. This actionable insight is particularly salient for small community hospitals with simple, short-stay patients, where hospitalists may be undervalued due to the prevailing belief that they are primarily effective for complex patients. We conduct a comparative case study of a small community hospital and a large academic hospital, drawing a stark contrast between the two in terms of ideal caseload and patient coverage. Despite the fact that the academic hospital treats higher complexity patients, hospitalists at the community hospital should actually have a lower caseload than hospitalists at the academic hospital due to shorter stays in the community hospital. We find that both hospitals are understaffed but for different reasons: the academic hospital needs to staff more hospitalists to reduce the current caseload of its hospitalists, whereas the community hospital needs to staff more hospitalists to expand its hospitalist coverage to more patients. We estimate that these hospitals can save on average $1.5 million annually by implementing the optimal staffing policies.