Kadali prosés statistik

Kadali prosés statistik (Statistik prosés Kontrol (SPC), Basa Inggris: statistical process control, Basa Walanda: statistische procescontrole), nyaéta métodeu éféktifkeur ngawaskeun prosés ngagunakeun chart kontrol. Ku cara ngumpulkeun data tina sample dina sababara titik salila prosés, variasi dina prosés nu bakal mangaruhan kana kualitas ahir produk atawa jasa bisa kapanggih tur dibenerkeun, mangka ngurangan limbah jeung masalah nu bakal katarima ku konsumen. Ieu mangrupa usaha panalungtikan awal jeung nyegah ayana pasualan, SPC geus ngabédakeun métodeu kualitas, saperi inspeksi, nu sacara alami digunakeun keur nalungtik tur menerkeun kasalah dina ahir produksi atawa jasa.

Hal séjénna nyaéta ngurangan limbah, SPC bisa nunjukkeun keur ngurangan dina waktu nu dibutuhkeun keur ngahasilkan produk atawa jasa ti tungtung ka tungtung. Ieu sabagéan tina usaha yén produk ahir bakal bisa dipaké deui, tapi bisa ogé ngagunakeun data SPC keur manggihkeun kasalahan "beuheung botol (Ing:bottleneck)", ngadagoan waktu atawa telatna sumber séjén kana prosés. prosés nu merlukeun waktu dua kali keur naekeun kualitas produksina ngajadikeun SPC alat nu hadé keur ngurangan biaya jeung kapuasan pelanggan.

Panneau travaux.png Artikel ieu keur dikeureuyeuh, ditarjamahkeun tina basa Inggris.
Bantuanna didagoan pikeun narjamahkeun.


Kadali prosés statistik mimiti diwanohkeun ku Walter A. Shewhart dina awal taun 1920. W. Edwards Deming saterusna maké métodeu SPC di Amerika Serikat salila Perang Dunya Kadua, tur geus hasil naekeun kualitas di hiji pabrik mesiu sarta strategi séjén nu penting dina produk. Deming ogé mimiti ngawanohkeun métodeu SPC kana industri di Jepang sanggeus tamat perang.

Shewhart nyieun dasar keur kontrol chart sarta konsép nangtukeun "statistic control" tina rancangan percobaan nu taliti. While Dr. Shewhart drew from pure mathematical statistical théories, he understood that data from physical processes seldom produces a "normal distribution curve" (a Gaussian distribution, also commonly referred to as a "bell curve"). He discovered that observed variation in manufacturing data did not always behave the same way as data in nature (for example, Brownian motion of particles). Dr. Shewhart concluded that while every process displays variation, some processes display controlled variation that is natural to the process (common causes of variation), while others display uncontrolled variation that is not present in the process causal system at all times (special causes of variation).[1]


The following description relates to manufacturing rather than to the service industry, although the principles of SPC can be successfully applied to either. For a description and example of how SPC applies to a service environment, refer to Roberts (2005).[2]

In mass-manufacturing, the quality of the finished article was traditionally achieved through 100% inspection of the product; accepting or rejecting éach article based on how well it met its design specifications. In contrast, Statistical Process Control uses statistical tools to observe the performance of the production process in order to predict significant deviations that may later result in rejected product.

Two kinds of variations occur in all manufacturing processes: both these process variations cause subsequent variations in the final product. The first are known as natural or common causes of variation and may be variations in temperature, specifications of raw materials or electrical current etc. These variations are small, and are generally néar to the average value. The pattern of variation will be similar to those found in nature, and the distribution forms the bell-shaped normal distribution curve. The second kind are known as special causes, and happen less frequently than the first.

For example, a bréakfast ceréal packaging line may be designed to fill éach ceréal box with 500 grams of product, but some boxes will have slightly more than 500 grams, and some will have slightly less, in accordance with a distribution of net weights. If the production process, its inputs, or its environment changes (for example, the machines doing the manufacture begin to wéar) this distribution can change. For example, as its cams and pulleys wéar out, the ceréal filling machine may start putting more ceréal into éach box than specified. If this change is allowed to continue unchecked, more and more product will be produced that fall outside the tolerances of the manufacturer or consumer, resulting in waste. While in this case, the waste is in the form of "free" product for the consumer, typically waste consists of rework or scrap.

By observing at the right time what happened in the process that led to a change, the quality engineer or any member of the téam responsible for the production line can troubleshoot the root cause of the variation that has crept in to the process and correct the problem.

SPC indicates when an action should be taken in a process, but it also indicates when NO action should be taken. An example is a person who would like to maintain a constant body weight and takes weight méasurements weekly. A person who does not understand SPC concepts might start dieting every time his or her weight incréased, or éat more every time his or her weight decréased. This type of action could be harmful and possibly generate even more variation in body weight. SPC would account for normal weight variation and better indicate when the person is in fact gaining or losing weight.


  1. "Why SPC?" British Deming Association SPC Press, Inc. 1992
  2. Roberts, Lon (2005). SPC for Right-Brain Thinkers: Process Control for Non-Statisticians. Quality Press. Milwaukee.


  • Deming, W E (1975) On probability as a basis for action, The American Statistician, 29(4), pp146–152
  • Deming, W E (1982) Out of the Crisis: Quality, Productivity and Competitive Position ISBN 0-521-30553-5
  • Oakland, J (2002) Statistical Process Control ISBN 0-7506-5766-9
  • Shewhart, W A (1931) Economic Control of Quality of Manufactured Product ISBN 0-87389-076-0
  • Shewhart, W A (1939) Statistical Method from the Viewpoint of Quality Control ISBN 0-486-65232-7
  • Wheeler, D J (2000) Normality and the Process-Behaviour Chart ISBN 0-945320-56-6
  • Wheeler, D J & Chambers, D S (1992) Understanding Statistical Process Control ISBN 0-945320-13-2
  • Wheeler, Donald J. (1999). Understanding Variation: The Key to Managing Chaos - 2nd Edition. SPC Press, Inc. ISBN 0-945320-53-1.

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