For example, let’s say that we have a lot that actually has 7.5 percent defective units, and we sample 50 and use the rejection criterion of one defective. ![]() One of the many aspects that his bosses were missing was an understanding that while over time the process might average 1-percent defective, there was no guarantee that any particular lot was. Now, as a quality professional reading that paragraph, I’ll bet you saw more bad assumptions in that paragraph than you do in political speeches of the other party (whichever party that is). I feel that our quality is terrible and should be spending more, but I just can’t compete with that argument." In fact, they say we should look at some ways to save money in the manufacture since we aren’t ‘using up’ our allowed defective margin. This indicates to them that our quality is too high, so we should not spend any money on improving quality. We have an AQL of 1 percent by contract, and they keep pointing to the fact that while we are allowed one defective per lot sample, we have a number of lots that have zero defectives in the sample. “My bosses say that I am spending too much money on quality control. He sent me an e-mail one day telling me that he had moved to a smaller new company in the same industry, and that he could use some help with an issue. ![]() I consulted and trained for a really great guy in a high-tech industry-someone who saw how the use of data could save a company money. To illustrate this, let me tell you a (somewhat simplified) true story. ![]() This is the risk that the lot contains more than the AQL even when we pass it. That’s the other risk we would need to consider-consumer’s risk. This protects the producer, but it doesn’t really address the company buying the primers. The reason is that these acceptance sampling plans were built to control for “producer’s risk.” Producer’s risk is the probability that a batch will be rejected when in fact it meets the AQL. And you will do it while knowing that it’s likely that there are more defective parts in there (on average, five per batch). I can hear you clearly across time and space: “Waaaait a minute! You mean that even though in inspection I actually found a bad part in a fairly small sample I’m going to accept the lot anyway?” (Of course, this inspection could be done at the vendor’s location as well.) Based on the binomial distribution, if you found one or less primer defective, you would accept the lot, two or more and you would reject it. For example, if you received a batch of 500 primers for ammunition and you had determined that the average acceptable quality level (AQL) is 1 percent, then you would randomly sample 50 items from the batch and test them. ![]() We can already see how this clashes with the modern understanding of quality. They constructed a series of tables and rules that would allow you to determine how large a sample to mil-std-105, take, and the number of “bad” samples allowed before you would reject the shipment. To this end, the creators of MIL-STD-105 (and its descendants, e.g., ANSI/ASQ Z1.4) used the concept of inferential statistics (based on the binomial distribution) to create a sampling scheme that could be used by a customer to determine if they should accept a shipment from a supplier. If we assume that defective product is inevitable, then we had better figure out a way for making sure we don’t get too many defective products. You can kind of see the original purpose of acceptance plans. So what’s so bad about them, and how can you help a company move away from them? And yet, hard as it is to believe, companies are still using these plans. This is totally antithetical to the modern concept of quality (conformance to target) or Six Sigma (conformance to specification). We all know (especially in this political season) that humans are addicted to their indignation high, so here’s your fix for today.īack when defective products and services were considered inevitable, the military created a standard to try to control just how much defective product was allowable before it would be rejected. Every once in a while, people ask about acceptance sampling plans and I get all riled up.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |