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We present a two-phase approach to design and analysis of manufacturing cells based on simulated experimentation and response surface methodology using a general manufacturing-cell simulation model. The first phase involves factor-screening simulation experiments to identity design and operational factors that have a significant effect on cell performance as measured by a comprehensive annual cost function. In the second phase of experimentation, we construct simulation (response surface) metamodels to describe the relationship between the significant cell design and operational factors ( the controllable input parameters) and the resulting simulation-based estimate of expected annual cell cost (the output response). We use canonical and ridge analyses of the estimated response surface to estimate the levels of the quantitative input factors that minimize the cell's expected annual cost. We apply this methodology to an assembly cell for printed circuit hoards. Compared to be current cell operating policy. the simulation metamodel-based estimate of the optimum operating policy is predicted to yield average annual savings of approximately $425 000, which is a 20% reduction in annual cost. In a companion paper, we detail the structure and operation of the manufacturing-cell simulation model. [ABSTRACT FROM AUTHOR]

We present a general manufacturing-cell simulation model for evaluating the effects of world-class manufacturing practices on expected cell performance. The modular structure of the simulation provides the flexibility to analyze a wide variety of manufacturing cells. We formulate a comprehensive annualized cost function for evaluation and comparison of alternative cell configurations. A case study involving assembly of printed circuit hoards illustrates the potential benefits of using this tool for cell design and analysis. The simulation model is intended for use in a two-phase approach to cell design that is based on simulated experimentation and response surface analysis as detailed in a companion paper. [ABSTRACT FROM AUTHOR]

In the last decade, over two dozen simulation studies have focused on comparing cellular and functional layouts. The results reported by these studies vary widely, however. This remains true even when the key performance measure is flow time. These variations reflect the disparate manufacturing and operating environments, as well as differences in parts demands, set-up economies, overall loads and other factors. This work attempts to reduce the sources of variation due to different operating assumptions while retaining the variability associated with differences in part mix and demand characteristics. Instead of focusing on a single data source, this study uses a test bed of six problems extracted from the literature and ensures they share the same operational rules. The simulation results show that conversion to CMS can reduce flow times (relative to the job shop configuration) consistently across all data sets, provided the same operating rules and ranges for key parameter are used. We investigate the reduction in flow time while controlling for the key factors of set-up reduction, overall load on the system and batch size. We also assess the benefits of using transfer batches as a further factor in reducing flow time. Our overall conclusion is that set-up reductions in cells can overcome pooling losses, even under the conservative assumptions where batch size remains unchanged and the material transport times in the job shop are assumed to be negligible. [ABSTRACT FROM AUTHOR]

An important element in the design and management of cellular manufacturing systems is the ability to determine capabilities. This paper describes a generic methodology of Capability Analysis (CA) which can be used for the integrated assessment of various capability factors. The main aspect of CA is the ability to compare different factors alongside one-another. This is done by determining priority confidence scores (PCS values) which are capability scores (measures of performance) represented as proportions of required capability scores adjusted to take into account improvement potential, factor importance and financial considerations. CA is used to form a recovery schedule of targets for improvement ranked in order of priority and is produced by ranking the PCS values for each capability level (cell, product, part, workstation and operation). Targets at a higher level can be broken down into more detailed targets at a lower level using the concept of transparency. The workings of CA are demonstrated using an example. [ABSTRACT FROM AUTHOR]