The Effects of Low Inventory on the Development of Productivity Norms

Schultz, Kenneth L.; Juran, David; Boudreau, John W.

doi:10.1287/mnsc.45.12.1664

Cited by 103 publications

(105 citation statements)

References 33 publications

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“…The authors also show that such temporary service rate increases are not sustainable (i.e., workers become fatigued) and can have potentially serious quality implications. The results obtained by Kc and Terwiesch complement a set of prior lab experiments conducted by Schultz et al (1998Schultz et al ( , 1999) that establish that workers in an assembly line adjust their service rates in response to the amount of work in process inventory between the workers. Powell et al (2004) …nd that such worker-level state-dependent behavior has implications for the entire process ‡ow.…”

Section: Literature Reviewsupporting

confidence: 62%

An Econometric Analysis of Patient Flows in the Cardiac Intensive Care Unit

Terwiesch

2012

M&SOM

210

135

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This paper explores the rationing of bed capacity in a cardiac intensive care unit (ICU). We find that the length of stay for patients admitted to the ICU is influenced by the occupancy level of the ICU. In particular, a patient is likely to be discharged early when the occupancy in the ICU is high. This in turn leads to an increased likelihood of the patient having to be readmitted to the ICU at a later time. Such "bounce-backs" have implications for the overall ICU effective capacity-an early discharge immediately frees up capacity, but at the risk of a (potentially much higher) capacity requirement when the patient needs to be readmitted. We analyze these capacity implications, shedding light on the question of whether an ICU should apply an aggressive discharge strategy or if it should follow the old quality slogan and "do it right the first time. " By comparing the total capacity usage for patients who were discharged early versus those who were not, we show that an aggressive discharge policy applied to patients with lower clinical severity levels frees up capacity in the ICU. However, we find that an increased number of readmissions of patients with high clinical severity levels occur when the ICU is capacity constrained, thereby effectively reducing peak bed capacity. Christian TerwieschThe Wharton School, University of Pennsylvania terwiesch@wharton.upenn.eduThis paper explores the rationing of bed capacity in a cardiac intensive care unit (ICU). We …nd that the length of stay for patients admitted to the ICU is in ‡uenced by the occupancy level of the ICU. In particular, a patient is likely to be discharged early when the occupancy in the ICU is high. This in turn leads to an increased likelihood of the patient having to be readmitted to the ICU at a later time. Such "bounce-backs" have implications for the overall ICU e¤ective capacity -an early discharge immediately frees up capacity, but at the risk of a (potentially much higher) capacity requirement when the patient needs to be readmitted. We analyze these capacity implications, shedding light on the question if an ICU should apply an aggressive discharge strategy or if it should follow the old quality slogan and "do it right the …rst time." By comparing the total capacity usage for patients who were discharged early versus those who were not, we show that an aggressive discharge policy applied to patients with lower clinical severity levels frees up capacity in the ICU. However, we …nd that an increased number of readmissions of patients with high clinical severity levels occur when the ICU is capacity constrained, thereby e¤ectively reducing peak bed capacity.

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Section: Literature Reviewsupporting

confidence: 62%

An Econometric Analysis of Patient Flows in the Cardiac Intensive Care Unit

Terwiesch

2012

M&SOM

210

135

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“…Since this is likely to be perceived as increased pressure to perform, the effect will be a positive motivational one (Kiggundu, 1983;Stewart & Barrick, 2000;Wong & Campion, 1991 Proposition 3 predicts a regression to mediocrity on static lines when there is heterogeneity: faster employees slow down, while slower employees speed up. This is consistent with the findings of Doerr et al (1996), Shultz et al (1998), andBoudreau (1999). The opposite effect is predicted for a dynamic line: faster employees, having autonomy and control over work pace and perceiving responsibility for others, speed up, while slower employees, experiencing negative feedback from constant interruption of their work flow and loss of autonomy over work pace, slow down.…”

Section: Motivation and Boundary Rulessupporting

confidence: 89%

Heterogeneity and Variability in the Context of Flow Lines

Doerr¹,

Mitchell²,

Schriesheim³

et al. 2002

The Academy of Management Review

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We propose a model in which between-individual differences in performance (heterogeneity) and within-individual differences in periormance over time (variability) affect flow line performance. The impact of heterogeneity and variability is contingent upon the flow line context, particularly the rules governing the way work moves between employees (work flow policy). We show how subtle changes in this policy can have a motivational effect on heterogeneity and variability and how these, in turn, can impact the relationship between work flow policy and flow line periormance.In this paper we develop a model of production line performance in a particular operational context, and we integrate elements of the operations management literature on flow lines and the organizational behavior literature on workgroups and motivation to develop a behavioral model of flow line performance. We show that these two bodies of literature interrelate in important ways that have implications for both organizational behavior and operations management theory and research. Given the potential difficulties of integrating these areas, we build our model by focusing narrowly on specific operating policies of production flow lines.A production flow line involves multiple employees completing tasks that are sequenced in a particular way. Flow lines are used to produce such goods as automobiles, jet aircraft, and perWe thank Chris Earley, Kevin Gue, Steven Powell, Ken Schultz, Ruth Wageman, and the anonymous reviewers for their helpful comments on an earlier draft of the manuscript. sonal computers; this is the recommended form of production for discrete-item mass production (Hayes & Wheelwright, 1984). The detailed study of these lines is important, because flow lines are used in situations of high-demand volume, and even small improvements in their per-unit operation can yield large gains in profitability (Wild, 1972). Because of the popularity and efficiency of this type of line, it has been the subject of considerable research in the field of operations management (Gagnon & Ghosh, 1991;Ghosh & Gagnon, 1989). Another reason for the attention given to the context of flow lines is that, to our knowledge, no production method has yet been developed that can rival its efficiency.However, historically, individual attributes have been virtually ignored in operations management flow line models, in spite of a wealth of evidence suggesting that significant individual differences exist, even for simple manual tasks (Hunter, Schmidt, & Judiesch, 1990;Schmidt & Hunter, 1983;Schmidt, Hunter, Outerbridge, & Goff, 1988), and that those differences are re- Mitchell, Schriesheim, Freed, and Zhou 595 lated to individual performance. Recently, however, researchers have proposed operating policies for flow lines that not only acknowledge individual differences but also rely on them (Doerr, Klastorin, & Magazine, 2000;Zavadlav, McClain, & Thomas, 1996). Unfortunately, the operations management models of individuals are rather like "stick figures": in the m...

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“…In a similar vein, Berk and Moinzadeh (1998) Our work also extends prior studies of the impact of production system design on the productivity of employees. For example, using lab-based experiments, Schultz et al (1998Schultz et al ( , 1999 consider serial production systems in which adjacent workers in a serial assembly line can observe each others'productivity, as measured by inventory levels between them. A key insight from this work is that workers tend to work faster or slower depending on the work in process inventory.…”

Section: Literature Reviewmentioning

confidence: 99%

Impact of Workload on Service Time and Patient Safety: An Econometric Analysis of Hospital Operations

2009

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Much of prior work in the area of service operations management has assumed service rates to be exogenous to the level of load on the system. Using operational data from patient transport services and cardiothoracic surgery-two vastly different health-care delivery services-we show that the processing speed of service workers is influenced by the system load. We find that workers accelerate the service rate as load increases. In particular, a 10% increase in load reduces length of stay by two days for cardiothoracic surgery patients, whereas a 20% increase in the load for patient transporters reduces the transport time by 30 seconds. Moreover, we show that such acceleration may not be sustainable. Long periods of increased load (overwork) have the effect of decreasing the service rate. In cardiothoracic surgery, an increase in overwork by 1% increases length of stay by six hours. Consistent with prior studies in the medical literature, we also find that overwork is associated with a reduction in quality of care in cardiothoracic surgery-an increase in overwork by 10% is associated with an increase in likelihood of mortality by 2%. We also find that load is associated with an early discharge of patients, which is in turn correlated with a small increase in mortality rate. Abstract Much of prior work in the area of service operations management has assumed service rates to be exogenous to the level of load on the system. Using operational data from patient transport services and cardiothoracic surgery -two vastly di¤erent healthcare delivery services -we show that the processing speed of service workers is in ‡uenced by the system load. We …nd that workers accelerate the service rate as load increases. In particular, a 10% increase in load reduces length of stay by 2 days for cardiothoracic surgery patients, while a 20% increase in the load for patient transporters reduces the transport time by half a minute. Moreover, we show that such acceleration may not be sustainable. Long periods of increased load (overwork) have the e¤ect of decreasing the service rate. In cardiothoracic surgery, an increase in overwork by 1% increases length of stay by 6 hours. Consistent with prior studies in the medical literature, we also …nd that overwork is associated with a reduction in quality of care in cardiothoracic surgery -an increase in overwork by 10% is associated with an increase in likelihood of mortality by 2%. We also …nd that load is associated with an early discharge of patients, which is in turn correlated with a small increase in mortality rate.We are grateful to the cardiac anesthesiologists and executives at the teaching hospital where this study was conducted. We also thank the Management Science review team as well as Stefanos Zenios, Chris Lee, and Marcelo Olivares for their insightful and constructive comments on an earlier version of this paper. The authors can be reached at dkc@emory.edu and terwiesch@wharton.upenn.edu.

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The Effects of Low Inventory on the Development of Productivity Norms

Cited by 103 publications

References 33 publications

An Econometric Analysis of Patient Flows in the Cardiac Intensive Care Unit

An Econometric Analysis of Patient Flows in the Cardiac Intensive Care Unit

Heterogeneity and Variability in the Context of Flow Lines

Impact of Workload on Service Time and Patient Safety: An Econometric Analysis of Hospital Operations

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