The lot quality units of measure for an attributes acceptance sampling plan depend on whether you choose to count defective items or defects.
The lot size is the population that you collect your samples from when you decide whether to accept or reject the entire lot.
Often, the lot size is chosen to be convenient for shipping and handling for both the supplier and consumers. For example, a convenient lot size might be an entire shipment. Because sampling plans assume homogeneity of parts in a lot, the units that comprise a lot should be produced under the same process conditions. Also, larger lots are generally more economical to inspect than a series of smaller lots.
The consumer and supplier should agree to the highest defective rate or defect rate that is acceptable (AQL). The consumer and supplier should also agree to the highest defective rate or defect rate that the consumer will tolerate in an individual lot (RQL).
To protect the producer, the risk of rejecting a lot that has acceptable quality must be low. To protect the consumer, the risk of accepting a lot that has poor quality must be low.
In acceptance sampling, the sample size is the number of items that are randomly chosen from a single lot for inspection.
The acceptance number is the maximum number of defects or defectives that are allowed in a sample from an acceptable lot.
The probability of accepting lots at the AQL should be close to 1 – α. The probability of accepting lots at the RQL should be close to β. The probability of rejecting is 1 – the probability of accepting.
The average outgoing quality level represents the relationship between the quality of the incoming material and the quality of the outgoing material, assuming that rejected lots will be 100% inspected and all defective items will be replaced or reworked.
You must specify the lot size in order to calculate the AOQ and AOQL.
In this example, when the average incoming quality level is 1.5% defective, the average outgoing quality is 1.42% defective. When the average incoming quality level is 10.0% defective, the average outgoing quality is 0.956% defective. The incoming quality is worse than the outgoing quality because rejected lots will be 100% inspected and will have all nonconforming units replaced or reworked.
You must specify the lot size in order to calculate the ATI.
The operating characteristic (OC) curve shows the ability of an acceptance sampling plan to distinguish between good and bad quality lots. The OC curve plots the probability of accepting lots that have different incoming quality levels for each sampling plan.
In this example, if the actual % defective is 1.5%, you have a 0.957 probability of accepting this lot based on the sample and a 0.043 probability of rejecting it. If the actual % defective is 10%, you have a 0.097 probability of accepting this lot and a 0.903 probability of rejecting it.
You must specify the lot size in order to create an AOQ curve.
In this example, when the average incoming quality level is 1.5% defective, the average outgoing quality is 1.42% defective. When the average incoming quality level is 10.0% defective, the average outgoing quality is 0.956% defective. The incoming quality is worse than the outgoing quality because rejected lots will be 100% inspected and will have all nonconforming units replaced or reworked.
The worst average outgoing defect level (AOQL) of 2.603% defective occurs when the incoming quality level is 4.3% defective.
You must specify the lot size in order to create an ATI curve.