# Statistical Process Control And An Application

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```					       Statistical Process Control And An
Application

Berna YAZICI and Sevil ŞENTÜRK
Science Faculty, Department of Statistics, Eskisehir, TURKEY

(PhD , Research Assistant in Department of Statistics )

Abstract: Statistical process control provides use of the statistical principals
and techniques at every stage of the production. So producer gets the cheapest
and the most useful products. On the other hand, statistical process control
aims to control quality characteristics on the methods, machine, products,
equipments both for the company and operators with magnificent seven.
In this study, statistical process control is examined and its magnificent seven
is explained. The method is applied on data set about a food company’s
product weight and the result are interpreted.

Keywords: Process control, check sheet, pareto diagram, cause and effect
diagram, defect and concentration diagram, scatter diagram, control charts.

1.   Introduction
Statistical process control is used to describe the variability that can be controlled or
cannot be controlled. This variability is also called common cause or special cause.
Common cause occurs with the nature of the process. It exists in all processes and it is
the variability from the system. Special cause is not the part of the process. It exists
almost all processes because of some certain reasons.

If there is not a variability because of special causes, that means the process is
statistically under control. For a process that is statistically under control, the researcher
can conclude that, it has a definable identification and a definable capability. In a
process that is under control, by removing all special causes that are noticed until then,
the remaining variability would come from common causes. After taking the process
under control, the next stage improves the process. The only target for the production is
to get the statistically control, and to reduce the variables in the same time. Because as
the variables reduce, the cost is going to be less, too.

901
Statistical Process Control And An Application
Berna Yazici and Sevil Şenturk

2.    The Tools of Statistical Process Control
Statistical process control aims to produce the products in the most economic and
useful way by using statistical principles and techniques at every stage of the
production. In this manner, statistical process control aims faithfulness to the standards,
provides the fitness of the specifications that have been determined earlier. It is used to
reduce the defected products as much as possible.

Statistical process control is powerful collection of problem-solving tools useful in
achieving process stability and improving capability through the reduction of
variability. These tools, often called magnificent seven are; histogram, check sheet,
pareto diagrams, cause and effect diagram, defect and concentration diagram, scatter
diagram and control charts.

3.    Application Study
In this study, the weight of product in food sector has been examined. The upper
specification limit of the product is 200 grams and the lower specification limit of the
product is 194 grams. In packing process 25 samples are drawn. All samples include 5
packets. The X and S charts have been constructed for the sample in question.

Mean and standard deviation control charts show that, the deviations, in mean and
distribution are not out of 3-sigma limits for the process characteristic of interest. The
mean for the sample is 206.8, but the upper specification limit is 200. So, the mean is
larger than the specification limit. The expected mean is 197, but the mean for the
sample is 206.8. That means the packets are filled more than they must be. Calculated
standard deviation 3.863 is much more than expected standard deviation 0.75. So the
variability is bigger than it expected. 6 points in a row are above the centreline in
control charts.

The process seems under control, but because of the situations described above, the
process is not under control:

The factory produces heavier packets, so the cost for the factory is more. Because of
the reasons mentioned above, the researcher can conclude that there are some special
causes. So those special causes must be defined and removed.

References

[1]      NEWBOLD, P., İşletme İktisat İçin İstatistik, Literatür Yayıncılık İstanbul, p.983, 2000.
[2]      DEMING, E. W., Krizden Çıkış, Arçelik A.Ş., İstanbul, p.418, 1998.
[3]      AKIN, B., ISO 9000 Uygulamasında İşletmelerde İstatistik Proses Kontrol Teknikleri, Bilim
Teknik Yayınevi, İstanbul, p.150, 1996.
[4]      MONTGOMERY, D.C., Introduction to Statistical Quality Control, 3rd Ed., J. Wiley, New York,
P.677, 1996.
[5]      DUMAN, G., Handbook of Statistics, Tusaş Uçak-Motor Sanayi, Eskişehir, 1997.
[6]      GÜMÜŞOĞLU, Ş., İstatistiksel Kalite Kontrolü ve Toplam Kalite Yönetim Araçları, Beta Basım
Yayım A.Ş., p.171, 2000.
[7]      FARNUM, N.R., Modern Statistical Quality Control and Improvement, Duxbury Press, Belmont,
California, p.500, 1994.
[8]      BURNAK, N., Toplam Kalite Yönetimi Ders Notları, T.C. Osmangazi Universitesi, Mühendislik
Mimarlık Fak., Eskişehir, p.148, 1996.

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