A Comparison of Statistical Process Control and Engineering Process Control

Box, George E. P.; Coleman, David E.; Baxley, Robert V.

doi:10.1080/00224065.1997.11979740

Cited by 35 publications

(14 citation statements)

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“…This is an important issue because of an interest in involving operators in quality improvement (Box et al 1997). The ease of interpretation makes the X chart particularly suitable for shop floor use, because it can be understood by all personnel involved, including the process operator, inspector and manager (Murdoch 1979).…”

Section: Comparison In Simplicity For Understanding Implementation Amentioning

confidence: 99%

The X control chart for monitoring process shifts in mean and variance

Yang

Lee

et al. 2012

International Journal of Production Research

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Control charts are widely used in statistical process control (SPC) to monitor the quality of products or production processes. When dealing with a variable (e.g., the diameter of a shaft, the hardness of a component surface), it is necessary to monitor both its mean and variability (Montgomery 2009[Montgomery, D.C., 2009. Introduction to statistical quality control. New York: John Wiley & Sons.]). This article studies and compares the overall performances of the X chart and the 3-CUSUM chart for this purpose. The latter is a combined scheme incorporating three individual CUSUM charts and is considered as the most effective scheme for detecting mean shift and/or standard deviation shift in current SPC literature. The results of the performance studies reveal two interesting findings: (1) the best sample size n for an " X chart is always n ¼ 1, in other words, the simplest X chart (i.e., the " X chart with n ¼ 1) is the most effective "X chart for detecting and/or ; (2) the simplest X chart often outperforms the 3-CUSUM chart from an overall viewpoint unless the latter is redesigned by a difficult optimisation procedure. However, even the optimal 3-CUSUM chart is only slightly more effective than the X chart unless the process shift domain is quite small. Since the X chart is very simple to understand, implement and design, it may be more suitable in many SPC applications, in which both the mean and variance of a variable need to be monitored.

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Section: Comparison In Simplicity For Understanding Implementation Amentioning

confidence: 99%

The X control chart for monitoring process shifts in mean and variance

Yang

Lee

et al. 2012

International Journal of Production Research

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“…The idea of statistical control charts is that a reference distribution that can be attained by simply combining observations from rational subgroups where they are taken over short periods of time in which that the process is considered to be stable [8]. A continuous comparison of the current process observations will be compared to the control limits that is calculated based on the reference distribution can lead to a detection of assignable causes such as out of control points and patterns.…”

Section: Statistical Process Control (Spc)mentioning

confidence: 99%

“…In order to implement a control chart successfully, a practitioner will ask three main questions [8]:…”

Section: Statistical Process Control (Spc)mentioning

confidence: 99%

Implementation of SPC-EPC Scheme to Lessen and Control Production Disruptions in Chlor-Alkali Industry

Smew¹,

Al-Mutawa²,

Issawy³

2020

Lean Manufacturing and Six Sigma - Behind the Mask

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The quality of products in the industry can be improved by monitoring the manufacturing process and adjusting/optimizing the process input variables based on the output deviation from the target. SPC stands for statistical process control, and it is used to monitor processes in order to identify any assignable causes of variation. EPC stands for engineering process control, and it is concerned with adjusting systems inputs to keep the system output on target using different types of controllers such as integral controllers and PID controllers. Combining SPC and EPC as a unified framework proved to be effective in reducing production disruption in manufacturing industries. The main objective of this project is to redesign the production and quality control system in a chemical batch processing company, anonymous company, by developing an engineering process controller (EPC) in order to adjust the chemical process inputs to keep the output chemical concentration on best optimized target while monitoring the system using the sensitive time weighted control charts (SPC), in order to eliminate the process assignable causes of variation, improve system elements life, and reduce overall system wide costs.

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“…According to [5], especially under unstable conditions, engineering processes are better monitored using methods based on automatic process control. This approach, however, does not necessary provide opportunities for studying the root causes of the problems.…”

Section: Modelling Engineering Systemsmentioning

confidence: 99%

An application of statistical modelling on power generating asset performance analysis at the Asam-asam steam power plant of South Kalimantan

Mursadin

2018

E3S Web Conf.

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Abstract.A power generating system in a steam power plant is a complex one. This system involves a large number of variables containing information concerning the operation conditions. It can also be seen as an important asset in both power generation and energy portfolios. At the Asam-asam power plant of South Kalimantan, the complexity of the system has led to difficulties in explaining the apparently steady decrease in the output power. The amount of data collected is simply too big and the dimension too high for analysis purposes based on conventional thermodynamics. This study was performed to tackle the problem using statistical modelling. This approach can accommodate empirical behaviors of the variables and the probabilistic nature of the system. Information obtained from this modelling can be valuable for various purposes. The method consists of literature review, model development, data collection and analysis, and model fitting. Generalized additive models were chosen. Data were available from the company as observed from more than 140 variables. The resulting model identifies variables significantly related to the output power and locate subsystems whose fluctuating behaviors are usually ignored in a conventional thermodynamic analysis. A direction for future research is recommended.

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A Comparison of Statistical Process Control and Engineering Process Control

Cited by 35 publications

References 0 publications

The X control chart for monitoring process shifts in mean and variance

The X control chart for monitoring process shifts in mean and variance

Implementation of SPC-EPC Scheme to Lessen and Control Production Disruptions in Chlor-Alkali Industry

An application of statistical modelling on power generating asset performance analysis at the Asam-asam steam power plant of South Kalimantan

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