Kenneth Schultz

Throughput in Serial Lines with State-Dependent Behavior

Experimental evidence suggests that production-line workers adjust their work rates in certain situations to prevent idle time. We refer to this asstate-dependent behavior, in contrast to thestate-independent behavior of machines. In this paper, we develop several models for the state-dependent behavior of production workers. We then use these models to analyze the relation of line length to throughput in these systems. We find that state-dependent behavior makes serial lines more efficient and reduces the detrimental effects that longer line lengths have on throughput. In some cases, line efficiency can actuallyincrease with length. This is a result of a higher percentage of workers having two buffers to provide feedback on the state of the line. Further, we show that workers who both speed up when they are likely to cause idle time for others and slow down when they are likely to become idle themselves improve the overall efficiency of the line.

An Example and a Proposal Concerning the Correlation of Worker Processing Times in Parallel Tasks

Models and understanding of line design depend on accurate assessments of the effects of design parameters on human actions. Although equity theory predicts that workers will react to the speed of people around them, experimental work has failed to find this effect in an industrial setting with parallel workstations or a change in coworkers. With the current research we contribute to the understanding of line design by using archival data from a manufacturing line. We show that workers do react to the speed of their coworkers, but that individual reactions vary widely. Because workers are different both in speed and reaction, managerial implications are not straightforward. We model an optimal and a heuristic rearrangement of workers and suggest a modified heuristic that performs well for increasing throughput. Our methodology combines empirical approaches, analytical modeling, and Monte Carlo simulation.

Load Effect on Service Times

In this paper, we develop a general framework to analyze the influence of system load on service times in queueing systems. Our framework unifies previous results and ties them to possible future studies to help empirical and analytical researchers to investigate and model the ways in which load impacts service times. We identify three load characteristics: changeover, instantaneous load, and extended load. The load characteristics induce behaviors, or mechanisms, in at least one of the system components: the server, the network, and the customer. A mechanism influences the service-time determinants: the work content or the service speed. We identify and define mechanisms that cause service times to change with load and use the framework to categorize them. We argue that an understanding of the relationship between load and service times can come about only by understanding the underlying mechanisms.