As a means of improving and maintaining competitiveness
within the global market, enterprises are placing a greater emphasis on
improving the quality of products provided to the consumer. To achieve this
objective, organizations are looking for ways to improve the quality of
manufacturing processes. Many world-class organizations have adopted Statistical
Quality Control (SQC) which involves using statistical tools and techniques,
such as acceptance sampling, process capability analysis, and Statistical
Process Control (SPC), to analyze, monitor, and control the efficiency and
quality of its manufacturing processes. By improving the quality of the
manufacturing processes used in production, the quality of the end-product
increases, which in turn improves productivity and customer satisfaction.
A key concept of SQC is recognizing that process and
product variation is a normal occurrence and should be expected. The causes of
variation are categorized into two types: (1) variation that is built into the
process and cannot be corrected and (2) variation caused by external sources
that can be controlled, such as material, equipment, methods, etc. SQC can be
used to quantify process variation and determine an acceptable level of
variation for manufacturing processes required to maintain or improve the
quality of the final product. Techniques often used for Statistical Process
Control, such as histograms and control charts, can be used to analyze and
monitor the quality of manufacturing processes to reduce the amount of
defective products being produced. (See Appendix F.1.21
- Statistical Process Control)
Acceptance sampling is often used to monitor the quality
of products that are produced rapidly in large quantities. It is also used when
the inspection method renders the product unusable. Because it is less expensive
to implement and execute, acceptance sampling is often preferred over total (100
percent) sampling. Acceptance sampling can be used to determine either the
quality of the product or whether the processes used to produce the product are
operating within specified limits. There are some risks associated with using
acceptance sampling, including the chance that an acceptable lot will be
rejected or vice versa. These risks have been standardized and are expressed
terms of probability. The probability that an acceptable lot will be rejected
due to sampling is referred to as the Producer's Risk (Alpha). The Consumer's
Risk (Beta) is the probability that a lot that is defective will be mistakenly
accepted. An Operating Characteristic (OC) curve (see Figure F.10) is used to
graphically depict these numbers along with the probability that various levels
of quality will be accepted under the sampling plan.
Another benefit to be gained from implementing SQC is the
minimization of failure costs. Failure costs result from product defects and can
be categorized as either internal failure costs or external failure costs.
Internal failure costs result from defects that are detected after production is
complete but prior to the product being sent to the customer. Internal failure
costs include those associated with scrap and rework efforts, additional
inspection and testing of repaired parts, as well as the labor hours spent
trying to identify the cause of the defect. External failure costs result from
defects being identified after the product has been provided to the customer and
include the costs associated with complying with product warranties as well as
the loss of revenue due to customer dissatisfaction. SQC tools and techniques
can be used to ensure the production and delivery of high quality products,
thereby reducing failure costs.
When successfully implemented and executed, SQC tools and
techniques provide a reliable method for analyzing and controlling production
processes to ensure that quality parts are produced. When manufacturing
processes are under control, product and process variation is minimized, and the
overall quality of the end product increases. Higher quality products and
processes result in increased productivity and customer satisfaction, which
improve competitiveness within the global market.
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Juran, J. M., & Gryna, F. M. (1988). Juran's Quality Control Handbook. New York: McGraw-Hill.
Maleyeff, J. (1994, December). The Fundamental Concepts of Statistical Quality Control. Industrial Engineering.
Schuetz, G. (1996, February). Bedrock Statistical Quality Control. Modern Machine Shop.