Adaptive Control Charts: Recent Developments and Extensions

Psarakis, Stelios

doi:10.1002/qre.1850

Cited by 53 publications

(40 citation statements)

References 71 publications

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“…The capability regarding each single product characteristic is described by a generic univariate PCI ( gs (1) ). The capability of the set of six product characteristics is described with a generic multivariate PCI for sets of six product characteristics ( gs (6) ) that complies with both the cascading and the detection of modications properties.…”

Section: Comparing Similar Production Processesmentioning

confidence: 99%

“…In this sense, SPM can increase the competitiveness of a machining process by increasing the utilized tool life and decreasing instances of part damage from excessive tool wear or tool breakage [5]. For all these reasons, industries are interested in measuring their product characteristics [6].…”

Section: Introductionmentioning

confidence: 99%

“…The lower level includes six sets of single product characteristics. In step 4, we proceed as follows: Looking at the evolution of the PCI of the upper level (P CI (6) ) is enough to detect that the capability of the process is worsening from week to week (i.e., week 2 is worse than week 1, week 3 is worse than week 2). Thus, we suggest monitoring the PCI of the upper level of the hierarchical structure.…”

mentioning

confidence: 99%

“…The upper level is a set including all product characteristics, and the lowest level includes six sets of single product characteristics as it can be seen in table 1. In step 4 we suggest proceeding as follows: The decision maker has to look at the value of the PCIs of the upper level of the hierarchical structure (P CI (6) ) for the three plants. Doing this, he or she will nd that plant A presents the better capability, followed by plants B and C. If he or she wants to go deeper into the topic, he or she can use the values of the PCIs in the lowest level of the hierarchical structure.…”

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confidence: 99%

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Monitoring high complex production processes using process capability indices

de-Felipe

Benedito

2017

Int J Adv Manuf Technol

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The increasing demand and the globalization of the market are leading to increasing levels of quality in production processes, and thus, nowadays multiple product characteristics must be tested because they are considered critical. In this context, decision makers are forced to interpret a huge amount of quality indicators, when monitoring production processes. This fact leads to a misunderstanding as a result of information overload. The aim of this paper is to help practitioners when monitoring the capability of processes with a huge amount of product characteristics. We propose a methodology that reduces the amount of data in capability analysis by structuring hierarchically the multiple quality indicators obtained in the quality tests. The proposed methodology may help practitioners and decision makers of the industry in three aspects of statistical process monitoring: To identify the part of a complex production process that presents capability problems; to detect worsening over the time in multivariate production processes; and to compare similar production processes. Some illustrative examples based on dierent kinds of production processes are discussed in order to illustrate the methodology. A case of study based on a real production process of the automotive industry is analyzed using the proposed methodology. We conclude that the proposed methodology reduces the necessary amount of data in capability analysis; and thus, that it provides an added value of great interest for managers and decision makers.

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Section: Comparing Similar Production Processesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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confidence: 99%

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Monitoring high complex production processes using process capability indices

de-Felipe

Benedito

2017

Int J Adv Manuf Technol

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“…Amiri et al proposed an EWMA control chart with an adaptive VSS. The details of recent developments in the design of the adaptive control charts can be found in the literature …”

Section: Introductionmentioning

confidence: 99%

EWMA chart with variable sample size and sampling interval for monitoring Pareto data with estimated parameters

Cheng

Wang

2018

Quality & Reliability Eng

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The Pareto distribution has been widely used to model data in numerous areas including actuarial science, climatology, economics, finance, and reliability. The exponential weighted moving average (EWMA) chart with variable sample size and sampling interval (VSSI) is quicker than EWMA chart, EWMA chart with variable sample size (VSS), and EWMA chart with variable sampling interval (VSI) in detecting the mean shifts of a normal process. In this study, we present an EWMA‐VSSI chart to monitor the Pareto data with estimated parameters. The average time to signal (ATS) is computed by using the Markov chain approach. The results indicate that the proposed chart performs better than the other EWMA charts with fixed sample size, VSS, and VSI in terms of ATS. The design parameters of the proposed control charts are given in order to expedite the use of the chart by practitioners. Two examples are used to illustrate the application of the proposed control charts.

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The ENBIS‐14 Quality and Reliability Engineering International Special Issue

Ograjenšek

Reis

2015

Quality & Reliability Eng

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Special Issue highlights the application of statistical thinking and methods in several key areas of interest to the ENBIS community. One of them has traditionally been healthcare. In this area, Shuki Dror and Dina Margol 1 show an interesting application of the Pareto principle and quality function deployment by developing and critically evaluating a framework for analysing malpractice cases in healthcare with the ultimate goal of improving the healthcare system. Rosa Falotico et al., 2 on the other hand, strive to prevent malpractice cases by proposing the use of text mining as a tool for identifying oncological patient communication needs with the goal of improving e-health communication. Service improvement is also at the core of the paper written by Alex Kuiper and Michel Mandjes, 3 who discuss practical principles in appointment scheduling. Finally, occupational health and safety are dealt with by Aliye Ayca Supciller and Nilsu Abali 4 within the scope of risk analysis.Another key area is manufacturing. Manoj Dora and Xavier Gellynck 5 present an interesting case study in the SME framework: an application of Lean Six Sigma -an effective hybrid method that combines the variability elimination tools from Six Sigma with the waste elimination practices from lean manufacturing -to reduce variability and waste in the production process of a food processing company. The challenges of manufacturing are also dealt with by Alberto Pasanisi et al. 6 who present a discrete modelling formalism for describing the reliability of equipment for which the relevant information is the number of cycles or solicitations it can endure. The proposed approach is particularly useful for low (decelerated) ageing conditions.The third key area is transportation. Pasquale Erto et al. 7 outline a procedure for predicting and controlling the ship fuel consumption and, consequently, the ship energy efficiency. At the core of the procedure is a multiple linear regression model exploiting the navigation information, which is usually available on modern ships to support managerial decision-making.Design of experiments has always been a much discussed topic at ENBIS conferences. In her paper, Ulrike Grömping 8 first evaluates the existing methods for assessing the effect significance in half-normal effects plots from fractional factorial two-level experiments and then proposes a new one. Peter Zemroch, 9 in his contribution, deals with the computerized generation of fractional-replicate designs that allow for more freedom in the definition of the number of experiments to conduct.Two papers address the topic of process optimization in multiresponse contexts. Francesco Adamo et al. 10 explore a robust approach based on a mixed-response surface model and apply it to a smart grid infrastructure, while Nikolaus Rudak et al. 11 extend the joint optimization method -a method devoted to finding the process settings leading to pre-specified targets in the responsesto a case in which the quality characteristics are correlated and then apply it to a the...

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Adaptive Control Charts: Recent Developments and Extensions

Cited by 53 publications

References 71 publications

Monitoring high complex production processes using process capability indices

Monitoring high complex production processes using process capability indices

EWMA chart with variable sample size and sampling interval for monitoring Pareto data with estimated parameters

The ENBIS‐14 Quality and Reliability Engineering International Special Issue

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