In the first Who’ll Stop the Rain
blog, we discussed how sampling frequency is critical to understanding process behavior and how sampling too frequently can make a control chart useless. The Creedence Clearwater Revival (we fans refer to them as CCR) song reference allowed me a chance to talk about the fact that, in the manufacturing world, too much of a good thing (data) tends to bring confusion, lack of clarity, and misunderstanding—basically, a “gully washer” of data that renders your control charts unusable (please feel free to read the first blog for a better understanding of what a gully washer is).
A good sampling plan
allows for a better understanding of process behavior, which not only negates a gully washer, but also enables monitoring and improving the process. If the sampling plan isn’t adequate, then there is no true understanding of the process behavior, and we’re just wasting our time.
Two key items make a sampling plan effective: sampling at the proper frequency
and taking a rational subgroup when sampling
Sampling at the Proper Frequency
Sampling frequency depends on how quickly a process changes, and in the first blog we discussed our tendency to sample too often. As with sampling frequency, we also tend to collect too many samples
within a subgroup, which also leads to issues with our control charts.
Sampling subgroup size
depends on how many products can be produced without process adjustments or raw material changes; but sometimes we ignore the process and follow our natural tendency, assuming more (or a larger) subgroup size is better.
One Works Just Fine, Thanks
Sometimes ONE subgroup is the answer! When a process requires adjustments, raw materials changes, or a unique setting for each product, a subgroup size of one should be used. Attempting to group samples produced under different conditions would include signals of process change that could make the control chart useless
. The goal is to understand process signals between subgroups while the subgroup itself includes the noise or normal variation of the process.
Likewise, we should use a subgroup of one when sampling from a homogeneous
batch. For example, a chemical batch could return the same sample value when measured repeatedly, or a product could return the same value. Using more than one sample per subgroup would show very little or no noise or variation within the subgroup, resulting in an overly-sensitive control chart.
One sample per subgroup is also correct when only one value represents the condition monitored, such as a daily yield or weekly defect total.
But Sometimes One is the Loneliest Number
Subgroup size should be more than one
when several parts can be produced without any process adjustments. Odd sample sizes are recommended as they have a natural center point or median.
We must be aware that even when subgroups of greater than one sample are possible, a higher number of subgroup samples will increase the sensitivity of the control chart and increase the risk of a false signal.
The time required for sampling and measurement should also be considered to avoid the high cost of oversampling.
Taking a Rational Subgroup When Sampling
After determining when to sample and how many products to sample, we must ensure that our sampling itself is rational. Each sample should be random. A random sample
is a sample in which every product within the population has an equal chance of being selected. That might sound simple and obvious, but we must be careful not to introduce bias into our sampling and risk some products having a higher probability of being selected than others.
Let’s say we are baking three pizza pies in a deep brick oven. Checking only the first pie, closest to the door, doesn’t give us any information on how the other two pies are cooking. If the oven’s temperature isn’t consistent throughout, then this type of sampling bias could result in a couple of burned, or undercooked, pizzas.
Considerations for Designing a Sampling Plan
The following are some reasonable considerations for designing a sampling plan:
- Who will be collecting the data?
Evaluate the abilities of the operator collecting the data. How much time does the operator have? Does the operator have adequate resources to collect the data?
Focus on key characteristics. Always remember that it costs money to sample.We should focus on the characteristics that are critical to controlling the process.
- Where or at what point in the process will the sample be taken?
The sample should be taken at a point in the process that allows the data to be used for process control.
- When will the process be sampled?
Samples must be taken often enough to reflect shifts in the process. A good rule of thumb is that two subgroups are taken between process shifts.
- Why is this sample being taken?
Will the data be used for product control or process control?
- How will the data be collected?
Will samples be measured or evaluated by hand, or will the data be retrieved from an automated measurement source?
- How many samples will be taken?
The sample quantity should be adequate for control without being too large and creating control limits that are “too sensitive.”
As a closing thought, and a chance to refer to another CCR song... In the first blog, we focused on stopping the rain, or deluge of data, by controlling sampling frequency. CCR wrote another rain song entitled, “Have You Ever Seen the Rain?” In it, Fogerty sings, “Yesterday and days before / Sun is cold and rain is hard / I know been that way for all my time.” My thought is this: Just because you’ve been oversampling for a while now, does not mean that has to continue.
We don’t want to oversample or sample too frequently, but we need to “see” the data to start the improvement process. Don’t spend too much time or worry over creating the “perfect” sampling plan. You’ll benefit from collecting data and understanding your process with adequate, rational sampling—so please don’t postpone the gains!
Please feel free to go back and read Part 1 of this blog: Who'll Stop the Rain? A Look at Sampling Frequency
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