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 previouis 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.