The Effects of Low Inventory on the Development of Productivity Norms

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

Clinical validation of cutoff target ranges in newborn screening of metabolic disorders by tandem mass spectrometry: A worldwide collaborative project

PURPOSE:: To achieve clinical validation of cutoff values for newborn screening by tandem mass spectrometry through a worldwide collaborative effort. METHODS:: Cumulative percentiles of amino acids and acylcarnitines in dried blood spots of approximately 25-30 million normal newborns and 10,742 deidentified true positive cases are compared to assign clinical significance, which is achieved when the median of a disorder range is, and usually markedly outside, either the 99th or the 1st percentile of the normal population. The cutoff target ranges of analytes and ratios are then defined as the interval between selected percentiles of the two populations. When overlaps occur, adjustments are made to maximize sensitivity and specificity taking all available factors into consideration. RESULTS:: As of December 1, 2010, 130 sites in 45 countries have uploaded a total of 25,114 percentile data points, 565,232 analyte results of true positive cases with 64 conditions, and 5,341 cutoff values. The average rate of submission of true positive cases between December 1, 2008, and December 1, 2010, was 5.1 cases/day. This cumulative evidence generated 91 high and 23 low cutoff target ranges. The overall proportion of cutoff values within the respective target range was 42% (2,269/5,341). CONCLUSION:: An unprecedented level of cooperation and collaboration has allowed the objective definition of cutoff target ranges for 114 markers to be applied to newborn screening of rare metabolic disorders. © 2011 Lippincott Williams & Wilkins