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First ask, ‘Why am I collecting data on this part?’
Steve Wise Published: 03/13/2012
Selecting the right control chart starts by knowing something about what you want the chart to say about the process—what questions do you want the chart to answer? Another way to look at this is to ask yourself, “Why am I collecting data on this part?” The answers to these questions will provide necessary information to determine the sampling strategy, sample size, and any special needs that would require implementing special processing options that extend the function of traditional charts. This column will address sample size, target charting, and multiple process streams with variables data. There are other considerations and data types that will be discussed in future columns.
The sample size is the number of measurement values, for a given test feature, to be gathered to represent a single “snapshot of time.” For example, if weights are taken from three consecutive filled bottles every 30 minutes, the sample size is three and the sampling interval is 30 minutes. The sample size does not represent the number of plot points on a chart. The common symbol used for sample size is n. There are three sample size considerations:
• Sample sizes of 1 (n = 1)
• Sample sizes between 2 and 9 (2 ≤ n ≤ 9)
• Sample sizes of 10 or larger (n ≥ 10)
When sample sizes are 1, the IX-MR (individual X and moving range) chart is used. For sample sizes of 2 through 9, the Xbar-range chart is used. For sample sizes of 10 or greater, the Xbar-Sigma chart is used. These are the three core variables control charts. Most all other variables charting techniques are rooted in one of these three core charts.
A process stream is characterized, using our InfinityQS software terminology, with Part, Process and Test. A single process stream generally represents a series of plot points from one part, one process and one test. For example, 50 ml bottle weight from fill nozzle A would be one process stream; 50 ml bottle weights from fill nozzle B would be another process stream. Because fill nozzle A could have a unique statistical personality than fill nozzle B, one would be ill advised to combine (confound) the data from both nozzles in a single subgroup. The better sampling strategy would be to treat the data from each fill nozzle as separate streams of data. When challenged with a process that generates multiple process streams one has the option of using one control chart for each process stream or use a specialized chart that allows all process streams to co-exist on the same chart. Charts for multiple process streams are called Group charts.
Processes are commonly used to produce different products. In many cases a product change-over means changing process set points in order to produce the different product. Continuing with the fill nozzle, when the line changes from a 50 ml bottle to a 100 ml bottle, the same nozzles are used, but are programmed to fill to 100 ml. When one wants to monitor a process’ ability to hold a set point, regardless of the product, the data can be combined across multiple set points by simply subtracting the set point from the actual output result. A fill of 100.3 would be represented on the chart as 0.3. By taking out the target values, a single chart can be used to monitor, in time-order, a process’ ability to hold a set point regardless of the spec of the product being produced at the time. These types of charts are called Target charts. As long as the combined products share similar variation, multiple parts can be represented on the same chart. Target charts are especially useful in short production run environments.
Control charts are designed for specific purposes. They are real-time graphical tools meant to identify when a process has changed or something unusual has occurred. There are hundreds of different types of charts, but they are all rooted in the original control charts developed by Walter A. Shewhart in the 1920s. Items to consider to help select the right chart for the job are sample size, the need to combine more than one process stream on the same chart, and whether there will be tests with different target values on the same chart. The three chart families we addressed were traditional charts, group charts, and target charts. Additional chart types will be discussed in future columns.
Steve Wise is vice president of statistical methods for InfinityQS, helping companies from all industries implement real-time production for statistical process control and advanced statistical tools. He co-authored an industry standard titled, D1-9000—“Advanced quality system,” in 1991 for Boeing Co. suppliers, and he co-authored the book, Innovative Control Charting (ASQ Quality Press, 1997).
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