Low inventory, a crucial part of just-in-time (JIT) manufacturing systems,
enjoys increasing application worldwide, vet the behavioral effects of such
systems remain largely unexplored. Operations research (OR) models of low-
inventory systems typically use a simplifying assumption that processing ti
mes of individual workers are independent random variables. This leads to p
redictions that low-inventory systems will exhibit production interruptions
leading to lower productivity. Yet empirical results suggest that low-inve
ntory systems do not exhibit the predicted productivity losses. This paper
develops a model integrating feedback, goal setting, group cohesiveness, ta
sk norms, and peer pressure to predict how individual behavior may adjust t
o 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 throu
ghput by decreasing variance and increasing the speed of the slowest worker
s, even if accompanied by decreases in speed of the fastest workers. Findin
gs suggest that low-inventory systems induce individual and group responses
that cause behavioral changes that mitigate production interruptions.