Statistical process control
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Statistical process control (SPC) or statistical quality control (SQC) is the application of
SPC must be practiced in two phases: The first phase is the initial establishment of the process, and the second phase is the regular production use of the process. In the second phase, a decision of the period to be examined must be made, depending upon the change in 5M&E conditions (Man, Machine, Material, Method, Movement, Environment) and wear rate of parts used in the manufacturing process (machine parts, jigs, and fixtures).
An advantage of SPC over other methods of quality control, such as "inspection," is that it emphasizes early detection and prevention of problems, rather than the correction of problems after they have occurred.
In addition to reducing waste, SPC can lead to a reduction in the time required to produce the product. SPC makes it less likely the finished product will need to be reworked or scrapped.
History
Statistical process control was pioneered by
'Common' and 'special' sources of variation
Shewhart read the new statistical theories coming out of Britain, especially the work of
Application to non-manufacturing processes
Statistical process control is appropriate to support any repetitive process, and has been implemented in many settings where for example ISO 9000 quality management systems are used, including financial auditing and accounting, IT operations, health care processes, and clerical processes such as loan arrangement and administration, customer billing etc. Despite criticism of its use in design and development, it is well-placed to manage semi-automated data governance of high-volume data processing operations, for example in an enterprise data warehouse, or an enterprise data quality management system.[7]
In the 1988
The application of SPC to non-repetitive, knowledge-intensive processes, such as research and development or systems engineering, has encountered skepticism and remains controversial.[8][9][10]
In No Silver Bullet, Fred Brooks points out that the complexity, conformance requirements, changeability, and invisibility of software[11][12] results in inherent and essential variation that cannot be removed. This implies that SPC is less effective in the software development than in, e.g., manufacturing.
Variation in manufacturing
In manufacturing, quality is defined as conformance to specification. However, no two products or characteristics are ever exactly the same, because any process contains many sources of variability. In mass-manufacturing, traditionally, the quality of a finished article is ensured by post-manufacturing inspection of the product. Each article (or a sample of articles from a production lot) may be accepted or rejected according to how well it meets its design
- (1) Common causes
- 'Common' causes are sometimes referred to as 'non-assignable', or 'normal' sources of variation. It refers to any source of variation that consistently acts on process, of which there are typically many. This type of causes collectively produce a statistically stable and repeatable distribution over time.
- (2) Special causes
- 'Special' causes are sometimes referred to as 'assignable' sources of variation. The term refers to any factor causing variation that affects only some of the process output. They are often intermittent and unpredictable.
Most processes have many sources of variation; most of them are minor and may be ignored. If the dominant assignable sources of variation are detected, potentially they can be identified and removed. When they are removed, the process is said to be 'stable'. When a process is stable, its variation should remain within a known set of limits. That is, at least, until another assignable source of variation occurs.
For example, a breakfast cereal packaging line may be designed to fill each cereal box with 500 grams of cereal. Some boxes will have slightly more than 500 grams, and some will have slightly less. When the package weights are measured, the data will demonstrate a distribution of net weights.
If the production process, its inputs, or its environment (for example, the machine on the line) change, the distribution of the data will change. For example, as the cams and pulleys of the machinery wear, the cereal filling machine may put more than the specified amount of cereal into each box. Although this might benefit the customer, from the manufacturer's point of view it is wasteful, and increases the cost of production. If the manufacturer finds the change and its source in a timely manner, the change can be corrected (for example, the cams and pulleys replaced).
From an SPC perspective, if the weight of each cereal box varies randomly, some higher and some lower, always within an acceptable range, then the process is considered stable. If the cams and pulleys of the machinery start to wear out, the weights of the cereal box might not be random. The degraded functionality of the cams and pulleys may lead to a non-random linear pattern of increasing cereal box weights. We call this common cause variation. If, however, all the cereal boxes suddenly weighed much more than average because of an unexpected malfunction of the cams and pulleys, this would be considered a special cause variation.
Application
The application of SPC involves three main phases of activity:
- Understanding the process and the specification limits.
- Eliminating assignable (special) sources of variation, so that the process is stable.
- Monitoring the ongoing production process, assisted by the use of control charts, to detect significant changes of mean or variation.
Control charts
The data from measurements of variations at points on the process map is monitored using
Stable process
When the process does not trigger any of the control chart "detection rules" for the control chart, it is said to be "stable". A process capability analysis may be performed on a stable process to predict the ability of the process to produce "conforming product" in the future.
A stable process can be demonstrated by a process signature that is free of variances outside of the capability index. A process signature is the plotted points compared with the capability index.
Excessive variations
When the process triggers any of the control chart "detection rules", (or alternatively, the process capability is low), other activities may be performed to identify the source of the excessive variation. The tools used in these extra activities include:
Process stability metrics
When monitoring many processes with control charts, it is sometimes useful to calculate quantitative measures of the stability of the processes. These metrics can then be used to identify/prioritize the processes that are most in need of corrective actions. These metrics can also be viewed as supplementing the traditional process capability metrics. Several metrics have been proposed, as described in Ramirez and Runger.[13] They are (1) a Stability Ratio which compares the long-term variability to the short-term variability, (2) an ANOVA Test which compares the within-subgroup variation to the between-subgroup variation, and (3) an Instability Ratio which compares the number of subgroups that have one or more violations of the Western Electric rules to the total number of subgroups.
Mathematics of control charts
Digital control charts use logic-based rules that determine "derived values" which signal the need for correction. For example,
- derived value = last value + average absolute difference between the last N numbers.
See also
- ANOVA Gauge R&R
- Distribution-free control chart
- Electronic design automation
- Industrial engineering
- Process Window Index
- Process capability index
- Quality assurance
- Reliability engineering
- Six sigma
- Stochastic control
- Total quality management
References
- ^ Barlow & Irony 1992
- ^ Bergman 2009
- ^ Zabell 1992
- OCLC 2518026.
- ^ Deming, W. Edwards and Dowd S. John (translator) Lecture to Japanese Management, Deming Electronic Network Web Site, 1950 (from a Japanese transcript of a lecture by Deming to "80% of Japanese top management" given at the Hotel de Yama at Mr. Hakone in August 1950)
- ISBN 978-0-945320-17-3.
- ISBN 978-0-471-25383-9.
- .
- S2CID 40550515.
- ^ Raczynski, Bob (February 20, 2009). "Is Statistical Process Control Applicable to Software Development Processes?". StickyMinds.
- .
- ISBN 978-0-444-70077-3.
- S2CID 109601393.
Bibliography
- Barlow, R.E.; Irony, T.Z. (1992). "Foundations of statistical quality control". In Ghosh, M.; Pathak, P.K. (eds.). Current Issues in Statistical Inference: Essays in Honor of D. Basu. Hayward, CA: Institute of Mathematical Statistics. pp. 99–112. ISBN 978-0-940600-24-9.
- Bergman, B. (2009). "Conceptualistic Pragmatism: A framework for Bayesian analysis?". IIE Transactions. 41: 86–93. S2CID 119485220.
- S2CID 21043630.
- — (1982). Out of the Crisis: Quality, Productivity and Competitive Position. ISBN 0-521-30553-5.
- Grant, E.L. (1946). Statistical quality control. McGraw-Hill. ISBN 0-07-100447-5.
- Oakland, J. (2002). Statistical Process Control. ISBN 0-7506-5766-9.
- Salacinski, T. (2015). SPC — Statistical Process Control. The Warsaw University of Technology Publishing House. ISBN 978-83-7814-319-2.
- Shewhart, W.A. (1931). Economic Control of Quality of Manufactured Product. American Society for Quality Control. ISBN 0-87389-076-0.
- — (1939). Statistical Method from the Viewpoint of Quality Control. Courier Corporation. ISBN 0-486-65232-7.
- Statistical Process Control (SPC) Reference Manual (2 ed.). Automotive Industry Action Group (AIAG). 2005.
- Wheeler, D.J. (2000). Normality and the Process-Behaviour Chart. SPC Press. ISBN 0-945320-56-6.
- Wheeler, D.J.; Chambers, D.S. (1992). Understanding Statistical Process Control. SPC Press. ISBN 0-945320-13-2.
- Wheeler, Donald J. (1999). Understanding Variation: The Key to Managing Chaos (2nd ed.). SPC Press. ISBN 0-945320-53-1.
- Wise, Stephen A.; Fair, Douglas C. (1998). Innovative Control Charting: Practical SPC Solutions for Today's Manufacturing Environment. ASQ Quality Press. ISBN 0-87389-385-9.
- Zabell, S.L. (1992). "Predicting the unpredictable". Synthese. 90 (2): 205. S2CID 9416747.
External links
- MIT Course - Control of Manufacturing Processes
- Guthrie, William F. (2012). "NIST/SEMATECH e-Handbook of Statistical Methods". National Institute of Standards and Technology. doi:10.18434/M32189.