Manufacturing Breakthrough Blog
Tuesday March 3, 2015
The answer from last week's question:
In my last posting, "Processes: Are they in control or not?" I explained the concept of what being “in control” means and why it is so important. Before we discuss another of the Six Sigma tools, let’s answer the question that I asked at the end of my last posting. You will recall the question and the multiple choice answers as follows:
In our example using the control chart, what do you think happened on the range chart?
- The range chart went out-of-control (10%)
- The range chart shifted but did not go out-of-control (40%)
- The range chart showed no indication of an out-of-control (50%) condition
- I don’t have a clue
Here is the range chart before and after the guides became loose and after they were retightened. So based upon what you see in these two charts, what would your conclusion be or which answer would you have selected?
Neither of the range charts (i.e. before or after the guides became loose) went out of control, so choice number 1 is not a viable option. Option 3, the range chart showed no indication of an out of control condition, is actually true. Option 2, the range chart shifted but did not go out of control, is clearly the best description of what took place. The key take-away from this example is that, at least in this example, the range chart would not have detected this out of control condition even though the X-bar chart did.
The negative impact of high variation
Everyone has heard of the bell-shaped curve, so named because of its familiar shape, but exactly what is it telling us? We know that even though we try hard to make every part exactly the same, sometimes things don’t come out like we want. It’s because of the variation that exists in every process. But even though variation exists, what we want to happen is for our parts to be reasonably the same and that as many parts as possible are within customer-imposed specification limits for key variables. If most parts are within the range of the specification, we say our process is “capable.” As we observed in my last posting, we also want our processes to be in control. So what’s the difference between being “capable” and being “in control?”
Simply stated, being in control means that only common cause variation is present within the process. Being capable means that all parts produced by a process are within the specification limits supplied by the customer. The drawing above represents data collected on one of the key variables for a part produced by a manufacturer. Let’s say, for example, that one of the key variables for this part is its thickness and as such, it has an upper and lower spec limit. The manufacturer is producing this part on two different machines. We just said that not all parts produced will be exactly the same thickness, so as we collect more and more data points for thickness, the data will naturally arrange itself into a pattern. In this case, we see the familiar bell shaped curve, also known as the normal distribution curve.
The positive impact of low variation
In order for a process to be capable, it must produce parts that are inside the specification limits most of the time. If we look at the two curves above, we see two very different bell shaped curves, with each curve representing data plotted from each of the two machines making the same part. Because the two normal distribution curves are so different, we can state with certainty that the variation between the two machines is significantly different. In other words, because Machine B’s curve is much wider than Machine A’s, we can say with certainty that there is much more variation associated with Machine B. In fact, since quite a few of the data points appear to be outside of the acceptable specification limits, we can state with confidence that defective product is being made on Machine B. We can also say that this machine, in its current configuration, is not capable. It’s important to note that a machine can be in control, but not capable. Conversely, a machine can be in control, but not capable.
If it’s too thick, hopefully it can be reworked and made thinner; but if it’s too thin, then it is probably scrap. So if this problem is being caused at the constraint, in a process step that is feeding the constraint, or even a process step after the constraint, then right away we see that finding and eliminating the root cause is a clear way to increase the effective capacity of the constraint and of this total process. If we could reduce the variation to the same level as Machine A then we have an automatic gain in throughput. The overarching conclusion is, Machine A exhibits much less variation than Machine B and is said to be “capable.” It should be clear to everyone that we want our processes to be both “in control” and “capable.”
In my next posting we will discuss another very useful tool in the Six Sigma tool kit, the Causal Chain. As you will see in my next posting, the Causal Chain is a very effective problem-solving tool. As always, if you have any questions or comments about any of my postings leave me a message in the message box and I will respond.
Until next time,