High volume production processes and many processes using automated sampling technology yield process data which are autocorrelated. One technique proposed for monitoring autocorrelated data involves the application of the Individuals control chart to forecast residuals from an appropriate time series model of the process. This study examines the following issues concerning forecast-based monitoring schemes: (i) the affect of forecast recovery from step changes on the average run length (ARL) of control charts applied to forecast residuals (ii) the proposal of the cumulative distribution fbnction (CDF) of the run lengths as an appropriate criterion for chart comparisons and (iii) the relative performance of the Individuals control chart, the Cumulative Sum (CUSUM) control chart and the Exponentially Weighted Moving Average control chart applied to forecast residuals using both the ARL and CDF criteria. 645

There is consensus that money invested in quality programs provides a high rate of return, but disagreement on how the optimal level of quality investment can be modeled. It has been postulated that there is no correct cost of quality (COQ) model since quality costs are dynamic and firm specific. Firms tend to move to new quality levels over time as they increase their prevention activities aimed at detecting and eliminating cause of variation. Many firms tend to misallocate quality spending by investing the greatest percentage of quality costs to the lowest yielding categories and the lowest percentage of quality costs to the highest yielding categories. Examines the various COQ models that have been proposed and explains the misallocation of quality investment by firms. Describes why quality initiatives undertaken by a firm are generally consistent with corporate goals and strategy, the maturity level of a firm, and management commitment.

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