Designing an Effective Process Capability Study


McLaughlin, Gregory C.   (1988, ASQC)   Reynolds Metals Co., Richmond, VA

Annual Quality Congress, Dallas TX    Vol. 42    No. 0
QICID: 3472    May 1988    pp. 524-529
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Article Abstract

The most successful Process Capability Studies utilize a wide variety of effective statistical and quality engineering principles. These include such fundamental concepts as correlation of process variables, pareto analysis, sampling, normal theory, probability, and variance components. In addition to the statistical methods, techniques such as brainstorming, team participation, creation of a plan which includes measurable objectives, and development of causal relationships are used to effectively design a successful capability study. This paper will focus on the design and execution of such a study. The investigator will accumulate information on process variability, process interrelationships, and an estimate of process capability.

A step-by-step guide detailing significant interrelationships of key measurable variables is provided. This stepwise procedure includes: establishing the criteria for identifying and selecting a process; identification of quality or performance characteristics; classification of Pareto diagrams and Ishikawa - Fishbone diagrams; creating a workable plan which incorporates the concept of quality improvement teams; preparing adequate and appropriate documentation; analyzing the data; and initiating investigation and/or corrective action.

The type of process capability study described in this paper utilizes statistical techniques which will provide information concerning both the consistency and predictability of a process. A process which is both predictable and consistent with respect to its requirements is considered "statistically in control." The aim is to understand and control process causal relationships which insure consistency of method and product. Positive feedback is provided to the investigator. The proposed procedures also incorporate sound principles of quality engineering. These methods enable the investigator to implement Statistical Process Control with an associated understanding of process variability. Possible pitfalls and problem areas, where encountered, are identified. A general discussion of corrective action procedures are included.


Chemical and process industries,Process capability studies

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