Statistical quality control introduction

It gives an overview of Statistical quality control theory and the concepts.

1. Statistical Quality control (SQC): Concepts and overview Dr. Pankaj Das Mail id: [email protected]

2. Background The goal of every operation or production system is to generate a useful product. The product may be a service, information or a physical object. The quality built into product and process design, quality identified problems at the source, and quality made everyone’s responsibility is important. Therefore need specific tools that can help us make the right quality decisions. These tools come from the area of statistics and are used to help identify quality problems in the production process as well as in the product itself.

3. Need of SQC In every system or process, errors or uncertain aberrations may causes nonconformities which hampers the quality of produced products (outputs). SQC help to identify, measure and rectify the problems. It also help us make the right quality decisions.

4. Goals of SQC Elimination of nonconformities and their consequences. Elimination rework and wasted resources. Optimization of product cost i.e. achieve the above goals at a lowest price.

5. Definition Statistical quality control (SQC) is the term used to describe the set of statistical tools used by quality professionals. It is a general category of statistical tools used to evaluate organizational quality.

6. History SQC was pioneered by W alter A. Shewhart at Bell Lab in early 1920. Shewhart developed the control chart in 1924 and the concept of a state of statistical control. Shewhart consulted with colonel Lesile E. S imon in the application of control chart in army arsenal in 1934.

7. History W. Edwards Deming invited Shewhart to speak at the Graduate School of the U.S. Department of Agriculture and served as the editor of Shewhart's book Statistical Method from the Viewpoint of Quality Control (1939) which was the result of that lecture. Deming was an important architect of the quality control short courses that trained American industry in the new techniques during WWII.

8. History In 1988, the Software Engineering Institute in Pittsburgh, Pennsylvania, United States suggested that SPC could be applied to non-manufacturing processes, such as software engineering processes

9. Categories of SQC

10. Descriptive statistics Descriptive statistics are used to describe quality characteristics and relationships Included are statistics such as the mean, standard deviation, the range and a measure of the distribution of data.

11. Statistical process control (SPC) A statistical tool that involves inspecting a random sample of the output from a process and deciding whether the process is producing products with characteristics that fall within a predetermined range.

12. Acceptance sampling The process of randomly inspecting a sample of goods and deciding whether to accept the entire lot based on the results. The tools in each of these categories provide different types of information for use in analyzing quality .

13. Variation in quality Variation denotes the no similarity in product or its characteristics. For example, when a chipmaking machine was found to be a few feet longer at one facility than another. Variation in the production process leads to quality defects and lack of product consistency.

14. Sources of Variations Common causes of variation : Random causes that cannot be identified. Example: Difference in the average liquid content in a bottle of a soft drink. Assignable causes of variation: Causes that can be identified and eliminated. Example: Poor quality in raw materials, an employee who needs more training, or a machine in need of repair

15. 15 Types of Data Variable data Product characteristic that can be measured Examples: Length, size, weight, height, time, velocity Attribute data Product characteristic evaluated with a discrete choice Examples: Good/bad , yes/no

16. 16 Topics that need to recall for upcoming class Descriptive statistics: mean, mode, range, Standard Deviation, Standard error, Shape of Distributions. Normal distribution and its properties Confidence intervals, C ontrol limits Concept of sampling

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