Integrating Statistical Process Control and Engineering Process Control

Montgomery, Douglas C.; Keats, J. Bert; Runger, George C.; Messina, William S.

doi:10.1080/00224065.1994.11979508

Cited by 169 publications

(82 citation statements)

References 6 publications

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“…The main goal is to compensate for the e!ects of systematic (nonrandom) disturbances in the process, to keep the process on target. APC is applied in cases where successive observations are related in time, and where the characteristic tends to drift dynamically [17,9]. To control the process, very frequent or continuous measurements of a product or process characteristic are taken and compared with a target value.…”

Section: Working Area Of Apcmentioning

confidence: 99%

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An integrated approach to process control

Schippers

2001

International Journal of Production Economics

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The control of production processes is the subject of several disciplines, such as statistical process control (SPC), total productive maintenance (TPM), and automated process control (APC). Although these disciplines are traditionally separated (both in science and in business practice), their goals have a great deal of overlap. Their common goal is to achieve optimal product quality, little downtime, and cost reduction, by controlling variations in the process. However, single or separated parallel applications may be not fully e!ective. This implies the need for an integrated approach to de"ne, describe and improve the control of production processes. This paper discusses how controls from disciplines such as SPC, TPM and APC can be seen as a coherent set of e!orts directed to the technical control of production processes. To achieve this, an integrated process control (IPC) model is introduced. The model provides a structure to get an overview of the functions of controls and their interrelations. It shows that there is no one best way to control a process: the optimal set of controls depends on the situation. The main contingencies are brie#y addressed. The possibilities to use the model for prescribing, describing and improving control are illustrated. Finally, implications for business practice are discussed.

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Section: Working Area Of Apcmentioning

confidence: 99%

“…In other cases APC may be able to correct the e!ects of disturbing factors, but does not remove the actual disturbing process factor. To detect and remove causes of variation, it may be necessary to use an additional SPC system [17,19].…”

Section: Working Area Of Apcmentioning

confidence: 99%

An integrated approach to process control

Schippers

2001

International Journal of Production Economics

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“…Following Deming 1 , the funnel experiment has become a popular subject of several papers that deal with SPC strategies 6,11,27,28,30,31 . Boardman and Boardman 6 analyzed the variance of the hit processes subject to Deming's compensation rules.…”

Section: Introductionmentioning

confidence: 99%

“…It is well known that feedback policies, as well as more complicated control theory techniques that are not considered in this paper, often create nonlinear dynamics of the controlled observations [6][7][8][9][10][11] . Sometimes the structure of these dynamics may be established by the observed trajectories of the output.…”

Section: Introductionmentioning

confidence: 99%

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The funnel experiment: The Markov‐based SPC approach

Singer

Ben‐Gal

2007

Quality & Reliability Eng

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The classical funnel experiment was used by Deming to promote the idea of statistical process control (SPC). The popular example illustrates that the implementation of simple feedback rules to stationary processes violates the independence assumption and prevents the implementation of conventional SPC. However, Deming did not indicate how to implement SPC in the presence of such feedback rules. This pedagogical gap is addressed here by introducing a simple feedback rule to the funnel example that results in a nonlinear process to which the traditional SPC methods cannot be applied. The proposed method of Markov-based SPC, which is a simplified version of the context-based SPC method, is shown to monitor the modified process well.

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Improving automatic‐controlled process quality using adaptive principal component monitoring

Tsung

1999

Qual. Reliab. Engng. Int.

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Manufacturing quality is an important factor in global market competition. The implementation of statistical process control (SPC) techniques is necessary if a manufacturing firm desires to be competitive in the global market. With advances in sensing and data capture technologies, large volumes of data are routinely being collected in automatic‐controlled processes. There is a great need for SPC techniques for variation reduction and quality improvement in these processes. This paper focuses on SPC schemes that are based on a combination of the process outputs and automatic control actions using adaptive principal component monitoring (APCM). These schemes are more efficient in detecting process changes for automatic‐controlled processes. Copyright © 1999 John Wiley & Sons, Ltd.

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Integrating Statistical Process Control and Engineering Process Control

Cited by 169 publications

References 6 publications

An integrated approach to process control

An integrated approach to process control

The funnel experiment: The Markov‐based SPC approach

Improving automatic‐controlled process quality using adaptive principal component monitoring

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