Statistical quality control (SQC) is a part of quality control that uses statistical methods to analyze data from quality control charts with the aim of detecting variation or defects that may exist in items being produced by a process.
In statistical quality control, statistical techniques are used to prevent defects and improve the process of manufacturing a certain item. The first step in preventing defects is through a process known as inspection. Inspection involves visually looking over the product for any imperfections or issues that may arise. Inspectors then determine if there will be any additional action taken on the product. In order to ensure that no defective products make it to market, several of these inspections must take place throughout the entire production process.
What is Statistical Quality Control?
A Statistical Quality control system performs inspection, testing and analysis to conclude whether the quality of each product is as per laid quality standard or not. It’s called ‘‘Statistical Quality Control’’ when statistical techniques are employed to control quality or to solve quality control problem. SQC makes inspection more reliable and at the same time less costly. It controls the quality levels of the outgoing products.
What is the Statistical Process Control?
Statistical process control is a technique of quality control which uses statistical approaches to monitor and regulate a process. This helps to guarantee that the process runs effectively, generating more criterion items with less wastage.
A procedure that evaluates output compared to a benchmark and taking appropriate action when differences arise.
SPC utilizes a variety of techniques, including run charts, control charts, an emphasis towards continuous improvement, & experiment design.
Introduction to Statistical Quality Control
There are two major types of quality control: statistical and non-statistical. Statistical quality control is the process of taking measurements in order to evaluate a manufacturing process. Non-statistical quality control measures the end product itself to ensure it meets certain requirements.
This article will focus on statistical quality control, which involves sampling techniques that measure parts or products in a way that helps to determine whether they are acceptable for use or not, based on predetermined specifications.
Statistical Quality control is the process of identifying errors in a product or service and taking steps to correct them. Quality control personnel are responsible for ensuring that everything produced by an enterprise meets acceptable standards. Businesses use statistical methods to analyze data, predict outcomes and improve performance.
SQC should be viewed as a kit of tools which may influence related to the function of specification, production or inspection.
Statistical Quality Control Terminology
Statistics entails a substantial quantity of data. Or simply, the joint study of collection, analysis, interpretation and presentation of large quantities of data.
Applications of statistical techniques in order to display, understand and predict consequences over gathered data.
“ a feature of technical feasibility at lowest possible cost” , or “degree of excellence that ﬁts the requirements of the clients”. Quality is described as “the sum total of qualities and behaviors of goods and services that meet hidden and visible consumer expectations.”
An method of measuring and inspecting a particular phenomena for a product or a service, control recommends when to examine, and how much to inspect. The system provides feedback to understand the reasons for low quality and required remedial actions. The control system essentially determines the quality features of an item, compares the same with established quality criteria and differentiates between defective goods and non-defectives ones.
Quality control is a critical technique for ensuring that goods or services meet a predetermined standard of quality. Quality control is becoming a significant instrument and essential element in every successful enterprise to guarantee standard quality. Peters and Waterman identified quality as an essential component of success in 1982.
Thus, quality control is the use of suitable methods and actions to achieve, maintain, and improve the quality of goods and services, as well as to meet customer requirements for pricing, safety, availability, dependability, and usability, among others.
The approach uses statistical methods based on probability theory to set standards of quality and maintain them in the most affordable way.
Objectives of Quality Control
(1) To decide about the standard of Quality of a product that is easily acceptable to the customer.
(2) To check the variation during manufacturing.
(3) To prevent the poor quality products reaching to customer.
Advantages of Statistical Quality Control:
(1) Improvement of quality.
(2) Reduction of scrap and rework.
(3) Efficient use of men and machines.
(4) Economy in use of materials.
(5) Removing production bottle-necks.
(6) Decreased inspection costs.
(7) Reduction in cost/unit.
(8) Scientific evaluation of tolerance.
(9) Scientific evaluation of quality and production.
(10) Quality consciousness at all levels.
(11) Reduction in customer complaints.
Statistical Quality Control Techniques and Tools
The principle tools and techniques of statistical quality control are as follows :
(1) Frequency distribution.
(2) Control charts for measurement and attribute data.
(3) Acceptance sampling techniques.
(4) Regression and correlation analysis.
(5) Tests of significance.
(6) Design of experiments.
We are living in the world of statistics. Statistics are everywhere, even in sports, politics and entertainment. Statistics is not only used to measure the success or failure of an event but also for making critical decisions that can change the outcome of any project.
Statistics is a branch of mathematics dealing with data collection, analysis, interpretation, presentation and organization. Statistical quality control (SQC) is a set of activities implemented in production processes to ensure that products conform to established standards.
The quality of a product is important to the company that produces it. Quality control is the measurement and comparison of characteristics (or parameters) of a product against pre-established standards to ensure that the product meets requirements for use.
Statistical quality control involves analysis of data, both qualitative and quantitative, about processes, products, suppliers or services to assess their degree of conformance to specifications or standards. It also refers to the techniques used in the application of this analysis.
Statistical Quality Control using Minitab
Crayola is a well-known crayon manufacturer with a worldwide reputation, and they are often regarded as the world’s top manufacturer of crayons and other art materials for children. The only way Crayola has been able to achieve and retain this kind of notoriety is by ensuring that all of their goods are of superior quality. They must maintain high quality standards for almost all of their goods in order to retain their worldwide reputation as a leading company.
Now, something as basic as crayons is not the first thing that comes to mind when thinking of high quality, but being the greatest at everything in the world takes meticulous attention to detail as well as many rigorous procedures to ensure that the quality is maintained.
Crayola manufactures over 3 billion crayons each year, which equates to nearly 12 million crayons produced per day on an annual basis. This is a disproportionately large number of crayons especially goods that must adhere to very high quality requirements. Then, what is their method of doing this?
Crayola’s Data-Driven Strategy for Keeping Their Product Quality High
One of the most important factors in ensuring that an organization as productive as Crayola may keep running and produce goods that are always improving upon their high quality is to maintain an information-driven strategy that would be concentrated on constant improvements.
For example, Crayola use Minitab Statistical Software to assist them in their data analysis so that they may enhance their manufacturing line and procedures. Minitab assists the business in maintaining a comprehensive statistical analysis of their data and in visualizing the data’s progression.
Crayola adopted Lean Principles for their projects in 2008 and was able to save more than $1.5 million. Since then, the company has not looked back. Minitab has been used for a number of years to analyze and improve their statistical quality control, and they have had tremendous success with it.
Frequently Asked Questions in Statistical Quality Control
What is involved with Statistical Quality Control?
The Statistical quality control involves five steps: Design the experiment, Analyse the information, Summarize the information, Come to conclusions, Take appropriate action. In a planned experiment, researchers maintain control over the study’s circumstances.
What are the importance of Statistical Quality Control?
To evaluate a process’s stability and predictability, statistical quality control offers off-line methods.
Statistical analysis in quality control is when statistical techniques are used to assess, monitor and maintain the overall quality of goods. Over time, the findings assist operations, such as manufacturing, guarantee that the methods will create more reference implementation goods, thus generating fewer wastage.
What are the advantages of Statistical quality control?
One of the best scientific instruments, Statistical Quality Control offers the following benefits;
In this technique, just a partial outcome is examined to guarantee the quality of product, thus probing cost would be lowered significantly.
Inspection of a fractional part takes fewer time and tedious in comparison to comprehensive inquiry leading to significant increase in efficiency and output.
Easier to use:
Quality control not only lowers process variability, but also brings the manufacturing process under control. Even yet, it is difficult to use without significant professional assistance.
SQC is the most prominent method that can correctly forecast future output. To guarantee the degree of precision and significant enhancement, SQC offers a high predictability.
Prior failure detection:
Any departure from conventional control limits displays indications of risk in the underlying manufacturing process that invites required corrective measurement to be made sooner. SQC is useful in early identification of problems.
Though in comprehensive inspection, unwanted variations under quality control procedure would be discovered in the final step, but for the time being many faulty products have been generated.
In such circumstances, SQC (using chart controls) provides a visual picture of how the manufacturing process is operating and where corrective actions should be accounted for for smooth running of the process in economical way.
What are the disadvantages of statistical quality control?
The limits of statistical quality control are as follows: It cannot be used uniformly as a remedy for all quality problems; Implementation of statistical quality control is an expensive investment; and When there is a lack of widespread knowledge, a false perception of security is generated.
The use of statistical quality control is an important aspect of any business. It can ensure that products and services meet the needs of customers and achieve maximum productivity and efficiency. Statistical Process Control (SPC) can also be used in conjunction with SQC to help you monitor trends in your process over time. If you would like to learn more about how we can help your company avoid costly mistakes, please leave a comment below today!
- 1 What is Statistical Quality Control?
- 2 What is the Statistical Process Control?
- 3 Introduction to Statistical Quality Control
- 4 Statistical Quality Control Terminology
- 5 Objectives of Quality Control
- 6 Advantages of Statistical Quality Control:
- 7 Statistical Quality Control Techniques and Tools
- 8 Statistical Quality Control using Minitab
- 9 Frequently Asked Questions in Statistical Quality Control
- 10 Conclusion: