Your cart is currently empty!
This thread recently appeared in myASQ:
Question by Aju: Hello Team, We have a situation where we did double sampling on Incoming parts from a Supplier(Machine shop). The lot was accepted and released to Inventory. These parts then later got assigned to WO and during assembly, the Manufacturing team found one non-conforming part (missing slots). manufacturing initiated NCR and Investigation found out the root cause to be supplier issue. and the probability of finding the defective during sampling was less than 20%. A week later 2 more NCR opened for a similar issue (same supplier). The supplier was notified and requested Root cause and corrective action from their end. What’s the best course of action here to avoid non-conforming parts ending up in manufacturing. Tightened sampling/100% Inspection?
Reply Jake: The general practice for us is to revert to 100% inspection for the next three deliveries. If there are no issues, then we revert to sampling plan, if there are issues, we maintain the 100% until we get 3 lots with no issues. Once we get three lots with no issues, we double the sample size for the next three lots, increase to Level III, then normal sample after that.
Here is my reply to Aju and Jake. Acceptance sampling was once widely used in quality control. In fact, it defined quality control. The “other quality control” was process control. Mostly statistical process control (SPC) but also other tools that helped identify and correct root causes of problems. Acceptance sampling focuses on output, looking at product after it’s been produced. I.e., on after effects. Process control monitors the inputs to the production process and through the use of statistical methods it determines the best course of action to prevent future non-conformances. If there are statistical signals then an immediate investigation into the cause of a change ensues and corrective action is taken on the spot. If no signals exist but non-conformances still occur, action is focused on redesigning the process itself.
In fact, it can be proven mathematically that acceptance sampling is futile if a process is in statistical control!
What are your thoughts? I’m truly interested. Please comment below.
4 responses to “An Acceptance Sampling Question”
A big problem I see all the time in this situation is confusing attributes and variables.
Critical dimensions (variables) can be measured periodically per plan and be in statistical control while a parts may have a cosmetic defect that may be caught or missed by visual inspection and not found until after it leaves the shop.
Inexperienced management will slap an AQL inspection on both the dimensions and visuals greatly increasing cost while doing little to reduce defectives. Management does not push for an understanding of root cause, does not review the PFMEA for gaps, and basically not understanding the process.
I have one comment and a suggestion.
COMMENT: if the supplier and their machinists know their customer is doing incoming sampling inspection, it’s quite possible they’ll pay less attention to internal quality improvement and gladly leave it up to the customer to catch mistakes.
SUGGESTION: If the presence of the slots is critical to quality and if their absence cause a lot of collateral damage, I wouldn’t ask for a Root Cause, Corrective Action plan, I’d ask for the supplier’s Error-Proofing Plan. The logic here is to preclude the usual “operator training” or “improve work instructions” CA response.
Good idea, Steve. What about also requiring the supplier to perform SPC and then pulling verification samples to verify measurements from the samples fall within the control limits of the supplier’s control charts?
One thing that I don’t understand is how people still do not realize acceptance sampling is not effective at modern quality levels. There is always a sliding scale probability of accepting defective lots. AS is only effective for catching large defect rates. It cannot catch the occasional defect expected even under traditional SPC control. Remember, AS was invented in the 1930’s when. 5% was a good defect rAte.