Manufacturing businesses produce lots of high quality products but quality control inspection may not always be done. This has made the manufacturers look for better inspection methods who will help them to ensure that the final products are of reasonable quality. Such techniques include; statistical methods are among the most efficient methods known to increase the accuracy of manufacturing. In this particular blog post let me explain about various statistical techniques used in quality control inspection models, the importance of implementing these models and how far it is helpful in enhancing the manufacturing accuracy.

Basics of Statistical Quality Control:

A branch of quality control, more specifically a method of quality controlling, is based on the principles of statistics, and is therefore called Statistical quality control or SQC. This is a method that can help you detect the flaws by taking attention to and recording as much information during the manufacturing process. The major objective is to control variation and increase homogeneity of the product.

The two principal categories in SQC are control charts originating from Shewhart and acceptance sampling. Shewhart control charts are used to observe changes of the process and in the meantime identify if there are any variations in the process that will lead to variation in quality of the final product. Whereas in acceptance sampling plans, a sample of products is selected at random in order to judge the lot on the basis of the quality of the sample.

Statistical Techniques for Quality Control Inspection:

There are several statistical techniques used for quality inspection services that help enhance manufacturing accuracy:

a) Control Charts:

Control charts, also called Shewhart charts, are a widely used technique of statistical quality control. These charts track the level of production and in the process can signal any shift for the worse that may be affecting the quality of the product. There are several types of control charts, including:

• XBar and R charts: These charts are used in controlling the change in the mean and range of a process over time. They are appropriate when the subgroups are small and the variability in sample size or distribution of the data violate the assumption of normality.
• XBar and S charts: These charts are used to help track the mean and standard deviations of a process in the elapsed time period. They are applied when proportions are equal and samples are small as well as when data is in normal distribution.
• Individuals and Moving Range (I-MR) charts: It is used to display special characteristics as well as moving ranges of individual measurements. It is most applicable when subgroups are composed of relatively few numbers, and include only one unit of analysis.

b) Acceptance Sampling:

Acceptance sampling is a sampling process in which some quantity of the product is taken for testing and the test result decides whether the whole lot of the product is accepted or rejected. It assists the manufacturers in where to make the inspection, the number of products to be inspected as well as the acceptable quality level.

The two primary acceptance sampling plans are:

• Single Sampling Plan: This plan entails sampling one unit from the batch and using the numbers of defective units in that sample to make a decision whether to accept or reject the batch.
• Double Sampling Plan: In this plan, two samples are taken randomly from the entire batch. In case the first sample exhibits an undesirable level of defects, a second sample is collected for making the last decision on acceptance or rejection of the batch.

Benefits of Implementing Statistical Techniques in Quality Control Inspection

The benefits of using statistical techniques in quality control inspection are significant:

• Enhanced Product Quality: Manufacturers must help detect and reduce variability through statistics that will allow organisations to maintain quality within products.
• Reduced Costs: The use of statistical quality control can be greatly productive as it prevents wastage and unnecessary production of other items hence saving both time and cost.
• Better Decision Making: Statistical quality control methods offer quantitative information that enables manufacturers to make correct decisions on quality and improvement strategies.

Conclusion:

It becomes clear that quality control service inspection models, especially statistical methods, play an important role in improving the degree of accuracy of production and quality of products. These models identify and fix flaws before the manufacturing cycle starts, minimize waste, and improve products. The manufacturers may use statistical techniques to analyze the data and arrive at the right decision that has positive impacts on product quality, cost, and efficiency.