100-Metodos-de-Qualidade-Total

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DATA COLLECTION, ANALYSIS AND DISPLAY 175 Method 77 Geometric moving average Purpose To identify trends in small changes in the process mean. The geometric moving average is sometimes called the exponentially weighted moving average (EWMA). When to use When monitoring a process to detect changes in the process mean, or when a change has been made to process inputs to find out whether the process mean changes. Geometric moving average charts can be used to monitor both variables and attributes data and are particularly useful for detecting long-term trends in data. One advantage is that the procedure provides a forecast of where the process mean will be at the next time period. How to use There are four simple steps involved: 1 Collect the data. The type of data could be sales figures, factory volumes, error rate or exchange rate. 2 Calculate the subgroup averages by adding together the data in each subgroup and dividing by the subgroup size. 3 Calculate the overall mean of the data by adding together the subgroup averages and dividing by the number of subgroups. This statistic is designated WI' 4 For each successive subgroup, calculate the statistic WI as follows: where r is a constant between 0 and 1. The choice is governed by the trade-off between the need to detect an important change without false alarms. Control limits can also be calculated. Benefits Geometric moving average charts are relatively insensitive to short-term changes. This means that their main use is when you are trying to mask short-term variation in the process to highlight longer-term variations. The
176 100 METHODS FOR TOTAL QUALITY MANAGEMENT Table 1 Difference between sales targets and achievements Subgroup Subgroup no. average w 1 0.41 0.1025 2 1.11 0.3544 3 -0.04 0.2558 4 -1.04 -0.0682 5 -0.04 -0.0612 6 -0.68 -0.2159 7 -0.85 -0.3744 8 0.81 -0.0783 9 0.62 -0.0963 10 -0.38 -0.0228 11 0.54 0.1 179 12 0.18 0.1 134 13 0.33 0.1826 14 0.32 0.2170 15 0.44 0.2728 16 0.22 0.2596 17 0.88 0.4147 18 0.16 0.3510 19 0.95 0.5008 20 1.09 0.6481 technique can also be used to predict where the next data point will be, using past data. Example Table 1 shows the difference between sales targets and achievements for a sales team. The data are recorded daily, based upon a sample size of five. The subgroup average is based upon these five readings. The value of r chosen was 0.25. Reference Thomas P. Ryan (1989) Statistical Methods for Quality Improvement. New York: Wiley Interscience.
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100 METHODS FOR TOTAL QUALITY MANAG
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PREFACE An essential part of the de
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THE ROLE OF TQM METHODS Total quali
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LIST OF METHODS (BY CATEGORY) Manag
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MANAGEMENT METHODS Method 1 Accepta
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REFERENCES Akao, Y. (1991) Hoshin K
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INDEX acceptable quality level (AQL
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