Joint determination of process mean and production run: A review

Tahera, Khadiza; Chan, Weng Meng; Ibrahim, Raafat N

doi:10.1007/s00170-007-1210-x

Cited by 20 publications

(6 citation statements)

References 99 publications

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“…These researchers include Hunter and Kartha (1977), Bisgaard et al (1984), Golhar and Pollock (1988), Rahim and Shaibu (2000) and Chen (2004). However, according to the recent review of process mean literature by Tahera et al (2008), most of the research to this point has focused on processes with single quality characteristics. As they note, customers typically judge product quality by more than one characteristic.…”

Section: Literature Reviewmentioning

confidence: 96%

“…In the interest of tractability, most studies to date have assumed a known and constant process variance. As Tahera et al (2008) note, however, many processes actually exhibit some degree of autocorrelation due to various influencers inherent to or impacting on the process. A number of researchers have acknowledged this reality and examined the effects of variance reduction.…”

Section: Literature Reviewmentioning

confidence: 98%

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Achieving cost robustness in processes with mixed multiple quality characteristics and dynamic variability

Boylan

Goethals

2011

IJEDPO

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The tenuous economic conditions of the past several years continue to threaten the economic existence of many companies, forcing them to re-examine ways for reducing costs without surrendering quality. A common technique for achieving high quality at minimal cost focuses on identifying the ideal process mean setting among various quality characteristics. In the design of this approach, referred to as the 'optimal process mean problem', comparatively little research has addressed the realities of mixed multiple quality characteristics and the dynamic nature of process variability. To address this gap, this paper examines situations involving mixed multiple quality characteristics, proposing a methodology to accurately predict the location of the optimal process mean vector as it shifts in response to changing process variability. This knowledge of a feasible region for the optimal vector can help to achieve robustness in cost by providing a mechanism for maintaining the most profitable process target settings.

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Section: Literature Reviewmentioning

confidence: 96%

Section: Literature Reviewmentioning

confidence: 98%

Achieving cost robustness in processes with mixed multiple quality characteristics and dynamic variability

Boylan

Goethals

2011

IJEDPO

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“…Fuzzy logic models, which are meant to integrate concepts associated with human reasoning and decision-making, were used by researchers such as Shipley et al (2004) and Tahera et al (2008b). And, researchers such as Hong et al (2006) and Tahera et al (2008a) developed optimal process target models for smaller-the-better (S-type) and larger-the-better (L-type) quality characteristics, respectively. Finally, Chen and Khoo (2009) used a quality loss and single sample inspection plan to identify the optimal process target for a serial production system.…”

Section: Literature Surveymentioning

confidence: 99%

Solving the optimal process target problem using computer-generated experimental designs

Goethals

Cho

2012

EJIE

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For any given manufacturing system, the initial setting of the process target is critical in preventing excessive product rejection and rework costs. Often referred to as the 'process target problem', the traditional approach relies first on the assumption of certain values for the process mean and variance, prior to identifying the optimal setting. This paper, in contrast, proposes integrating estimated response surface functions developed for the mean and variance based upon observations made on a given process, thus removing any assumption on the parameters. In addition, this paper considers non-standard experimental regions, where constraints may exist on the factor space or restrictions are implemented on the number of experimental runs conducted. In doing so, greater flexibility is obtained in finding solutions to process target problems and the scope of the research field is broadened. Non-linear programming methods are used to facilitate this approach, and numerical examples are provided to illustrate findings.[

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“…Recently, Hong, Kwon, Lee, and Cho (2006), Lee, Kwon, Hong, and Ha (2006), Tahera, Chan, and Ibrahim (2008), Darwish (2009), Chen and Khoo (2009), and Chen and Chang (2010 studied the optimum target problem for different production processes and inspection plans.…”

Section: Introductionmentioning

confidence: 99%

The optimum common process mean and screening limits for a production process with multiple products

Park

Kwon

Hong

et al. 2011

Computers & Industrial Engineering

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Joint determination of process mean and production run: A review

Cited by 20 publications

References 99 publications

Achieving cost robustness in processes with mixed multiple quality characteristics and dynamic variability

Achieving cost robustness in processes with mixed multiple quality characteristics and dynamic variability

Solving the optimal process target problem using computer-generated experimental designs

The optimum common process mean and screening limits for a production process with multiple products

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