To many quality engineers and managers, process capability is a jumbled confusion of ideas expressed in jargon that only the anointed can understand.
Imagine the following scene. The boss rushes into the quality director’s office. He’s obviously distraught.
(Boss enters, walking quickly from stage right.)
Boss: “Jane, we’ve got a serious problem. Our biggest customer just called. Their assembly line is shut down because the last batch of XYZ-50’s that we shipped won’t fit into their assembly fixtures. What happened?”
(Jane, sitting at her desk, puts down her pen and looks up at her boss. She shakes her head in dismay.)
Jane: “I knew this would happen sooner or later, boss. The problem is that our customer requires us to provide a Cpk of 1.33 or higher. But the formula they make us use assumes normality, and the XYZ-50 has a skewed distribution. If we center the process to maximize Cpk, then the tail area extends beyond the specification limit .”
(Boss exits, stage right, shaking his head and wearing a puzzled expression.)
I fear that when the quality profession talks about process capability, this is how we sound to others. To many quality engineers and managers, process capability is a jumbled confusion of ideas expressed in jargon that only the anointed can understand. Let me try to clear the air on the subject.
Process capability is about one thing, and one thing only: quality. It answers the simple question, “Can you meet my requirements?” Ideally the customer would like a simple answer, yes or no. Unfortunately, this is not possible due to one or more of the following:
Inspection is not perfect; even 100-percent inspection won’t guarantee 100-percent quality. Explaining this becomes complicated.
All processes vary, and the variation must be analyzed using statistical methods that always predict at least an occasional failure. The statistics virtually always get complicated.
Measurement isn’t perfect, so even if a process did have zero variation, our measurements would still vary. This means that we might accidentally ship a defective item even if we measure it carefully. Not only that, our measurements of a particular item might be somewhat different from our customer’s measurements. Explaining how two trained people using the same type of instruments can check the same item and get different results can get complicated.
We or our customer might not properly understand the requirements. Human communication is always complicated.
Yet it’s really not complicated at all. In fact, the customer’s question can be answered easily, and the answer is: no. For all of the reasons listed, and many more, we cannot guarantee that we will always deliver a product or service that meets the customer’s requirements as understood by the customer.
So, now what? The best approach is also the most radical: Be honest. Tell customers about how many items they are likely to receive, on average, that will not meet the requirements. This cuts right to the heart of the matter. It tells customers what they want to know. It works for variables data and attributes data. If control charts are being used, the estimate can be obtained directly from the process average (for attributes data) or the process average and standard deviation (for variables data). The count can be adjusted to include sorting operations, inspection error, measurement error and all of the other factors that influence what the customer receives.
If our process is extremely good, we can tell the customer that, while we can’t guarantee perfection, we can provide quality in the near-perfect range. One good way of quantifying this is to use parts-per-million quality statements. For example: “Our return rate on this item is three returns per million items in service per year.” Most people can easily understand this statement. A customer ordering up to several thousand items will probably, and accurately, interpret this to mean “zero defects.”
If our process is less capable, stating the expected number of defective items that the customer will receive might result in a shock to both the employees and the customer. This may provide the incentive needed to improve quality.
High-volume production is another area where stating process capability as expected defectives can provide insights. A defect rate of 1/10 percent sounds pretty good. But a can line may produce in excess of 1,000 cans per minute, so a reject rate of 1/10 percent would result in the production of 1,440 defective cans per day. If the defect is major, say a leaking can that could damage many cases of product in a warehouse or truck, even a defective rate of one in a million might not be acceptable; it would result in several serious problems each week. For such processes, parts per billion quality may be required.
If a process is not in statistical control for unknown reasons, there is no way to state the process capability with any degree of precision. The best option is to tell the customer what the expected defectives will be (based on the historical data) and hope for the best.
The key to good customer relations is clear communication. The easiest way to get the point across is to tell the customer what level of product or service quality to expect, using plain language.
Measurement Systems Analysis
This course covers the subject of measurement systems analysis. You will learn the basic principles of variation and several important techniques to help you understand, quantify, and improve the error of measurement inherent in all measurement systems.
It will take you about 12 hours to complete this course. You have 30 days from the date of purchase to complete the course. Upon completion you will receive a certificate of completion and you will be awarded 1.2 CEUs by The Pyzdek Institute.