In-House Globalization: The Role of Globally Distributed Design and Product Architecture on Product Development Performance

Changes in the global economy and technological advances are stimulating an increasing geographic distribution of new product design and development efforts. For large organizations that design and develop complex products, this geographic distribution has added a new layer of complexity to product development operations. In this empirical study of a large auto manufacturer, we examine the operational performance implications of splitting the design of vehicle subsystems across multiple geographic locations. Our results indicate that global distribution diminishes the chance of completing tasks on time and degrades subsystem design quality. Finally, by examining the interplay between subsystem centrality and global distribution, we found that higher centrality in the product architecture amplifies the impact of global distribution on subsystem error rates.http://deepblue.lib.umich.edu/bitstream/2027.42/85793/1/1163_Hopp.pd

The Impact of Discussion, Awareness, and Collaboration Network Position on Research Performance of Engineering School Faculty

We use a social network analysis to examine the role of various types of interactions among the faculty of an American engineering school, ranging from mere awareness to full coauthorship, on academic research productivity (measured by weighted publication rates) and impact (measured by weighted citation rates). Our results suggest that central positions in the discussion network have the most significant impact on individual work performance. However, we observe that increasing centrality exhibits diminishing returns, presumably because of the overhead associated with sustaining too many research interactions. Our results also suggest that interdisciplinary research discussions promote both research productivity and impact.http://deepblue.lib.umich.edu/bitstream/2027.42/85794/1/1164_Hopp.pd

Risk-sensitive sizing of responsive facilities

We develop a risk-sensitive strategic facility sizing model that makes use of readily obtainable data and addresses both capacity and responsiveness considerations. We focus on facilities whose original size cannot be adjusted over time and limits the total production equipment they can hold, which is added sequentially during a finite planning horizon. The model is parsimonious by design for compatibility with the nature of available data during early planning stages. We model demand via a univariate random variable with arbitrary forecast profiles for equipment expansion, and assume the supporting equipment additions are continuous and decided ex-post. Under constant absolute risk aversion, operating profits are the closed-form solution to a nontrivial linear program, thus characterizing the sizing decision via a single first-order condition. This solution has several desired features, including the optimal facility size being eventually decreasing in forecast uncertainty and decreasing in risk aversion, as well as being generally robust to demand forecast uncertainty and cost errors. We provide structural results and show that ignoring risk considerations can lead to poor facility sizing decisions that deteriorate with increased forecast uncertainty. Existing models ignore risk considerations and assume the facility size can be adjusted over time, effectively shortening the planning horizon. Our main contribution is in addressing the problem that arises when that assumption is relaxed and, as a result, risk sensitivity and the challenges introduced by longer planning horizons and higher uncertainty must be considered. Finally, we derive accurate spreadsheet-implementable approximations to the optimal solution, which make this model a practical capacity planning tool.© 2008 Wiley Periodicals, Inc. Naval Research Logistics, 2008Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/58077/1/20278_ftp.pd

On the Interface Between Operations and Human Resources Management

Operations management (OM) and human resources management (HRM) have historically been very separate fields. In practice, operations managers and human resource managers interact primarily on administrative issues regarding payroll and other matters. In academia, the two subjects are studied by separate communities of scholars publishing in disjoint sets of journals, drawing on mostly separate disciplinary foundations. Yet, operations and human resources are intimately related at a fundamental level. Operations are the context that often explains or moderates the effects of human resource activities such as pay, training, communications and staffing. Human responses to operations management systems often explain variations or anomalies that would otherwise be treated as randomness or error variance in traditional operations research models. In this paper, we probe the interface between operations and human resources by examining how human considerations affect classical OM results and how operational considerations affect classical HRM results. We then propose a unifying framework for identifying new research opportunities at the intersection of the two fields

A Causal Tree Approach for Personalized Health Care Outcome Analysis

Using patient-level data from 35 hospitals for 6 cardiovascular surgeries in New York, we provide empirical evidence that outcome differences between health care providers are heterogeneous across different groups of patients. We then use a causal tree approach to identify patient groups that exhibit significant differences in outcome. By quantifying these differences, we demonstrate that a large majority of patients can achieve better expected outcomes by selecting providers based on patient-centric outcome information. We also show how patient-centric outcome information can help providers to improve their processes and payers to design effective pay-for-performance programs.http://deepblue.lib.umich.edu/bitstream/2027.42/136093/1/1336_Wang.pd

Big Data and the Precision Medicine Revolution

The big data revolution is making vast amounts of information available in all sectors of the economy including health care. One important type of data that is particularly relevant to medicine is observational data from actual practice. In comparison to experimental data from clinical studies, observational data offers much larger sample sizes and much broader coverage of patient variables. Properly combining observational data with experimental data can facilitate precision medicine by enabling detection of heterogeneity in patient responses to treatments and tailoring of health care to the specific needs of individuals. However, because it is high-dimensional and uncontrolled, observational data presents unique methodological challenges. The modeling and analysis tools of the production and operations management field are well-suited to these challenges and hence POM scholars are critical to the realization of precision medicine with its many benefits to society.https://deepblue.lib.umich.edu/bitstream/2027.42/145441/1/1386_Hopp.pd

Estimating the throughput of an exponential CONWIP assembly system

We consider a production system consisting of several fabrication lines feeding an assembly station where both fabrication and assembly lines consist of multiple machine exponential workstations and the CONWIP (CONstant Work-In-Process) mechanism is used to regulate work releases. We model this system as an assembly-like queue and develop approximations for the throughput and average number of jobs in queue. These approximations use an estimate of the time that jobs from each line spend waiting for jobs from other lines before being assembled. We use our approximations to gain insight into the related problems of capacity allocation, bottleneck placement and WIP setting.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/47599/1/11134_2005_Article_BF01153531.pd

Cost-Effectiveness of Referring Patients to Centers of Excellence for Mitral Valve Surgery

BACKGROUND The 2014 American Heart Association/American College of Cardiology Valvular Heart Disease Guidelines state that mitral valve diseases should be repaired at a Center of Excellence (CoE). We evaluate the cost-effectiveness of such referrals. METHODS We estimate patients’ life expectancy based on projected survival of patients after mitral valve surgery and develop a cost model to calculate short- and long-term benefits and costs to both patients and payers. Benefits include increased life expectancy and avoidance of medical complications for patients. Short-term costs include all upfront payments by patients and payers at the time of discharge. Long-term costs include all payments associated with the condition that prompted the surgical procedure incurred during the remainder of a patient’s life. We assess cost-effectiveness of treating patients with various ages and major comorbidities at CoEs vs non-CoEs. RESULTS Full implementation of the guidelines would result in an increase in the percentage of patients obtaining mitral valve repair instead of valve replacement from 58% to 72%. Depending on the patient’s age and comorbidities, it would also result in a 6.64% to 12.47% reduction in mortality, 7.85% to 9.97% reduction in reoperation, 9.97% to 17.16% reduction in stroke, and an average gain of 3.77 to 9.88 months of life expectancy. Finally, greater reliance on CoEs results in financial savings to payers, due to avoidance of the costs of future complications. CONCLUSION Patients benefit from mitral valve surgery at a CoE regardless of their age or comorbidities. Payers may incur additional short-term costs when patients are referred to a CoE, but these are fully offset by long-term savings at the current repair rate gap of 24% between CoEs and non-CoEs in New York State. Redesigning co-pay structures and/or refining the set of patients who are referred to CoEs could further align the incentives of patients and payers on a case-by-case basis and achieve an even more desirable social outcome.http://deepblue.lib.umich.edu/bitstream/2027.42/111881/1/1281_Wang.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/111881/4/1281_Wang_May2015.pdfDescription of 1281_Wang_May2015.pdf : May 2015 revisio

Racial and Ethnic Differences in End‐of‐Life Medicare Expenditures

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134292/1/jgs14263.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/134292/2/jgs14263_am.pd