IKH

Uses of Sampling in Quality Control

Finally, let’s go through the fourth use case of sampling, i.e., its use in quality control. Quality control is a process followed at manufacturing sites, where batches of the manufactured product are regularly checked to ensure that they meet the standards the company would like them to meet. Since it will be very expensive and time-consuming to check each and every product manufactured, companies typically just check a few randomly selected products and decide for the entire batch based on that.

Hence, you will need to apply the sampling theory here too. Think about it: you are deciding the quality of the entire batch on the basis of one or two samples. So, let’s listen to Ujjyaini as she explains how sampling is used in quality control.

Let’s say you’re inspecting a batch of bolts to assess their quality. You decide to check every 1000th bolt and see whether it is manufactured as per the desired quality or not. Since all the bolts you inspect turn out to be good, you decide that there are no defects in the batch.

However, there is a problem with this approach. What if the 6th product made by the machine is defective, and then, every 1000th product the machine makes after that is defective? In that case, the defective pieces will have ID numbers as follows: 6, 1006, 2006, 3006, and so on. However, since you’re only checking every 1000th product, i.e., ID numbers 1000, 2000, 3000, etc., you will never find the defective piece.

The point is if the defects occur in a pattern, then your best chances to catch them are if you randomly select batch numbers. If your selection has any trend to it, you risk matching the pattern of the defects and missing out.

Hence, it is always advisable to use a table of random numbers to decide which batches you’re going to inspect.

Table of random numbers

61424204198654600517
90222279930495266762
50349711466766886523
02429100050821625906
90519197611537043882
20631619881966086624
89990787331644727932

Note: Using this table will not ensure that you will detect the defective pieces, but it will make that more likely.

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