No abstract

The authors investigate through several simulations how patterns of learning and forgetting affect the operating performance of an assembly line. A unique aspect of this study is that a distribution of learning/forgetting behavior based on an empirical population of workers is used rather than assuming the same learning pattern for all employees. The paper demonstrates that modeling only central tendency and not the variations across workers tends to systematically underestimate overall productivity. The data used to estimate the parameters for the distribution of learning curves were collected from an assembly line that produces car radios. Analysis of the models fit to a population of workers reveals that higher levels of previous experience are positively correlated with higher steady-state productivity levels and negatively correlated with the learning rate. To further motivate the study, a conceptual model with several factors hypothesized to influence assembly line productivity is presented. Among key factors included in the model are the rate of worker learning, the size of the worker pool, task tenure, and the magnitude of worker forgetting. In controlled computer simulation experiments, each of these factors was found to be statistically significant, as were a number of the two-way interaction terms.Learning, Forgetting, Worker Heterogeneity, Simulation

The purpose of this study is to investigate the impact of adopting Six Sigma on corporate performance. Although there is a fairly large and growing body of anecdotal evidence associated with the benefits of implementing Six Sigma, there is very little systematic and rigorous research investigating these benefits. This research extends previous research in several important ways including utilizing a sample of 84 Six Sigma firms that represent a wide variety of industries and firm characteristics, utilizing rigorously constructed control groups to ensure the validity of our comparisons and conclusions, and investigating the impact of adopting Six Sigma on corporate performance over a ten year period. To carry out this investigation, the event study methodology is employed. The ten year period consists of three years prior to Six Sigma implementation, the event year corresponding to the year Six Sigma is adopted, and six years post Six Sigma implementation. To assess the impact of adopting Six Sigma on corporate performance we utilize commonly used measures including Operating Income/Total Assets (OI/A), Operating Income/Sales (OI/S), Operating Income/Number of Employees (OI/E), Sales/Assets (S/A), and Sales/Number of Employees (S/E). The sample Six Sigma firms are compared to different benchmarks including the overall industry performance and to the performance of carefully selected portfolios of control firms. The results of the study indicate that adopting Six Sigma positively impacts organizational performance primarily through the efficiency with which employees are deployed. More specifically, enhanced employee productivity results were observed in both static analyses that assessed the performance of the sample Six Sigma firms relative to their control groups at discrete points in time and dynamic analyses of the Six Sigma firms’ rate of improvement relative to the rate of improvement of their control groups. Benefits in terms of improved asset efficiency were not observed. Finally, there was no evidence that Six Sigma negatively impacts corporate performance.

No abstract

This paper addresses the suitability of cellular manufacturing under a variety of operating conditions. Queueing theoretic and simulation models of cellular and functional layouts are developed for various shop operating environments to investigate several factors believed to influence the benefits associated with a cellular manufacturing layout. The queueing models show how operations overlapping, which is more practical with a cellular layout, becomes more beneficial as the lot size increases. The simulation models are developed to study the performance of cellular and functional layouts in a wide variety of operating environments by varying the levels of four factors: (1) the degree to which natural part families occur, (2) the number of operations required to process the parts, (3) the processing times of the parts at each machine, and (4) the lot size. Two response variables are used to measure shop performance: the average time spent by a batch in the system, and the average work-in-process level. Statistically significant reductions in the average time in the system and average work-in-process measures were detected for the cellular layouts in all the operating environments studied.