Statistical Quality Control (SQC)

Building on the four tenets of QC, statistics were added to map the results of part inspection.
The use of statistical methods of production monitoring and part inspection became known as statistical quality control (SQC), wherein statistical data are collected, analyzed, and interpreted to solve quality problems.

The primary concern of individuals involved in quality is monitoring and control of variation in the product being produced or service being provided.
The prevention of defects by applying statistical methods to control the process is known as statistical process control (SPC).

To manufacture products within specifications, the processes producing the parts need to be stable and predictable.
A process is considered to be under control, when the variability from one part to another or from one service to another is stable and predictable.

Statistical process control emphasizes the prevention of defects.
Prevention refers to those activities designed to prevent defects, defectives, and nonconformance in products and services.

The most significant difference between prevention and inspection is that with prevention, the process – rather than solely the product- is monitored, controlled, and adjusted to ensure correct performance.

By using key indicators of product performance and statistical methods, those monitoring the process are able to identify changes that affect the quality of the product and adjust the process accordingly.
Emphasis shifts away from inspecting quality into a completed product or service toward designing and manufacturing quality into the product or service.
The responsibility for quality moves from the inspectors to the design and manufacturing departments.

Statistical process control also seeks to limit the variation present in the item being produced or the service being provided.
While it once was considered acceptable to produce parts that fell somewhere between the specification limits,
statistical process control seeks to produce parts as close to the nominal dimension as possible and to provide services of consistent quality from customer to customer.

Statistical process control can be used to help a company meet the following goals:

1-To create products and services that will consistently meet customer expectations and product specifications
2-To reduce the variability between products or services so that the results match the desired design quality
3-To achieve process stability that allows predictions to be made about future products or services
4-To allow for experimentation to improve the process and to know the results of changes to the process quickly and reliably
5-To further the long-term philosophy of continual improvement
6-To minimize production costs by eliminating the costs associated with scrapping or reworking out-of-specification products
7-To place the emphasis on problem solving and statistics
8-To support decisions with statistical information concerning the process
9-To give those closest to the process immediate feedback concerning current production
10-To assist with the problem-solving process
11-To increase profits
12-To increase productivity

Positive Results of Statistical Process Control

1-Uniformity of Output
2-Reduced Rework
3-Fewer Defective Products
4-Increased Output
5-Increased Profit
6-Lower Average Cost
7-Fewer Errors
8-Predictable, Consistent Quality Output
9-Less Scrap
10-Less Machine Downtime
11-Less Waste in Production Labor Hours
12-Increased Job Satisfaction
13-Improved Competitive Position
14-More Jobs
15-Factual Information for Decision Making
16-Increased Customer Satisfaction
17-Increased Understanding of the Process
18-Future Design Improvements