Statistical Quality Control

Statistical quality control

Statistical quality control is the use of statistical techniques and methods to monitor and maintain the quality of goods and services, as well as the production process. The tools and procedures used in statistical quality control enable businesses to find solutions for products and services issues, discover and fix potential problems in internal systems, and monitor the process behavior. Some of the tools and techniques used in statistical quality control include:

  • Check sheet
  • Cause and effect diagram
  • Histogram
  • Control chart
  • Scatter diagram
  • Pareto chart
  • Stratification
  • Defect maps
  • Event logs
  • Progress centers
  • Process flowcharts
  • Randomization

Statistical quality control methods

There are two methods used in statistical quality control. These include:

  • Acceptance sampling: Assume that a company receives a shipment of goods from a manufacturer. A sample will be taken from the goods and the number of faulty items will be counted. If the amount of faulty items is not high, the entire shipment will be accepted. On the other hand, if there is a high amount of defective items, then the entire shipment will be rejected. Decisions have to be made on whether to accept good quality goods or reject poor quality goods. And to make such decision, the company receiving the goods has to do some sampling. Also, during sampling, the likelihood of erroneous decisions has to be put into consideration. The error of accepting poor quality goods causes a problem for the company, in this case, the consumer. The probability of such an error is referred to as the consumer’s risk. Similarly, the error or rejecting good quality goods will cause a problem for the manufacturer, in this case, the producer. The likelihood of this error to occur is called the producer’s risk.
  • Statistical process control: This method uses sampling and statistical procedures to monitor and track the quality of a continuing process like the production of goods. Information is represented in graphics such as control charts to understand whether the variation in the process output is as a result of common causes or out of the ordinary transferable or assignable causes. If the variations are due to assignable causes, then the entire process is adjusted to restore the output back to the standard quality levels. Control charts are categorized based on the data they contain. For example, -chartis applied in instances where the quality of the output is measured using the sample mean. Quantitative data like temperature, weight, and length can be tracked using an -chart. The variability of the process will be tracked using an R-chart. In situations where the output quality is determined by the proportion or number of defects  in a sample, monitoring can be done using a p-chart or an np-chart.

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Structure of a control chart

All control charts are built in a similar fashion. For instance, the center line in the -chart matches the process mean when the process is running optimally and producing an output of a great quality. The perpendicular line (the vertical axis) of the chart represents the measurement scale for the variable being studied. The upper control limit, also known as the upper horizontal line and the lower control limit (lower horizontal line) are selected so that when the process is running optimally, the value of the sample mean falls between both control limits. For the best results, the control limits should be set at about three standard deviations below and above the process mean.

Significance of statistical quality control

Statistical quality control is essential because it uses mathematical formulas and techniques to monitor the quality of a product, service, or process. Unlike manual monitoring, statistical control reduces errors associated with human judgement and enhances manufacturing productivity. Here are more benefits of statistical quality control to businesses:

  • Provides reliable means of identifying errors at inspection
  • Leads to a more uniform and better quality of production
  • Improves the relationship between the business and the customer
  • Minimizes inspection costs
  • Minimizes the number of rejects
  • Points out trouble spots and bottlenecks
  • Helps manufacturing companies determine the capability of their manufacturing processes
  • Results in the production of better products, which enhances customer satisfaction

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