This paper concerns the modeling of low inventory lines. Currently, most models assume that processing times are independent. We consider the differences in behavior of workers in low- and high-inventory production lines. Using a laboratory experiment we show that workers speed up when they are the cause of idle time on the line. This means that processing time distributions are not independent of the size of the buffer, of the processing speed of co-workers, or of the amount of inventory in the system. We show that the direction of these effects is predictable and that the magnitude is significant. In particular, there is less idle time and higher output than would be predicted using assumptions of independence. In this experiment the effect completely canceled productivity loss due to blocking and starving. This work is important in understanding both the motivation of workers in low-inventory systems and the implications of models of manufacturing flow lines.JIT, Serial Production Lines, Independence Assumption, Job Design

Low inventory, a crucial part of just-in-time (JIT) manufacturing systems, enjoys increasing application worldwide, yet the behavioral effects of such systems remain largely unexplored. Operations research (OR) models of low-inventory systems typically use a simplifying assumption that processing times of individual workers are independent random variables. This leads to predictions that low-inventory systems will exhibit production interruptions leading to lower productivity. Yet empirical results suggest that low-inventory systems do not exhibit the predicted productivity losses. This paper develops a model integrating feedback, goal setting, group cohesiveness, task norms, and peer pressure to predict how individual behavior may adjust to alleviate production interruptions in low-inventory systems. In doing so we integrate previous research on the development of task norms. Operations research models are used to show how norms can significantly improve throughput by decreasing variance and increasing the speed of the slowest workers, even if accompanied by decreases in speed of the fastest workers. Findings suggest that low-inventory systems induce individual and group responses that cause behavioral changes that mitigate production interruptions.group norms, work teams, job design, JIT, cohesiveness, feedback, peer pressure

This paper presents an approach to modeling workers where human performance has a significant impact on system productivity. Highly technical industries such as semiconductor manufacturing and service industries like banking are relying on fewer but more skilled workers. In these systems, productivity depends on worker availability and organization; therefore, modeling system performance may require accurate representations of individual worker behavior. This paper examines the tradeoffs in including information about the demographics and personalities of workers in system performance simulation models. A series of actual and simulated experiments in which personality and demographic data are used in different ways to model the performance of a team of workers is reported. Significant differences are found in predicted system performance demonstrating that model validity depends on the methodology used for modeling workers. These results have practical implication for the managerial processes of recruiting and selecting individual workers, as well as organizing teams of workers and assigning them to tasks.