The determination of optimum process mean and screening limits based on quality loss function

Chen, Chung-Ho; Kao, Hui-Sung

doi:10.1016/j.eswa.2008.07.002

Cited by 20 publications

(5 citation statements)

References 10 publications

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“…The signal-to-noise ratio is an important measure in the Taguchi method and is developed from the quality loss function (Y. G. Cho, K. T. Cho 2008;Chen, Kao 2009;Liao, Kao 2010). In the definition of quality loss function, it has the minimum loss when the measured value is equal to the ideal value.…”

Section: Signal-to-noise Ratiomentioning

confidence: 99%

Multi Period Performance Assessment Model for the Site Property Management

Lin

Pan

2014

International Journal of Strategic Property Management

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Performance assessment is an important task for the property management company to measure the performance of site, to evaluate and rank the sites, and to trace the main indices resulting in worse performance. Therefore, this study proposes a multi period performance assessment model. A performance scale with twelve indices is adopted to measure the site performance. The raw data can be transformed to be comparable by the proposed linear preprocessing method with specification limits. The priority is ranked by the integrated method with the technique for order preference by similarity to ideal solution and the signal-to-noise ratio. Finally, the main indices resulting in worse performance can be identified by the proposed tracing method. An illustrative example of real case is adopted to demonstrate the feasibility and practicability of the proposed model. Results show the proposed model can obtain a more reliable result.

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Section: Signal-to-noise Ratiomentioning

confidence: 99%

Multi Period Performance Assessment Model for the Site Property Management

Lin

Pan

2014

International Journal of Strategic Property Management

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“…For some other models, the item is in a terminal state only if the quality characteristic is acceptable, i.e., rework is performed if x < L, or x > U, models in [3,6,11,16,27]. Other models assume that the terminal state is x ≤ U, i.e., rework is needed if x > U, models [31,34,35,38,40,44,[46][47][48]. Finally, there are models where a recurrent state is limited to the case where x < L, models [9,19,20,25,32,36,37,42,43,[49][50][51].…”

Section: Generalized Targeting Modelmentioning

confidence: 99%

“…This correlated variable is known as a surrogate variable, and it is known to be relatively easier to measure. Many studies have investigated models with surrogate variables [9,11,15,25,35,39].…”

Section: Introductionmentioning

confidence: 99%

A Generalized Process Targeting Model and an Application Involving a Production Process with Multiple Products

Abdel-Aal

Selim

2019

Mathematics

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This paper presents a generalized targeting model that subsumes most known targeting problems. In this paper, a recurrent state is defined as a condition that requires reprocessing or rework. The generalized model can accommodate one or two specifications limits and can be used for the following quality characteristics: The nominal-the-better, the larger-the-better, and the smaller-the-better. This model can be used to find the optimal mean of a quality characteristic, as well as the optimal specification limits. In addition, the paper studies the conditions under which the solution to the proposed model can provide a global solution. The paper shows that, for some of the special cases and under very general conditions, the optimal lower limit should be zero and the optimal upper limit should be infinity. This paper proves that the expected profits improve for the case where only a lower limit on the quality characteristic is used, if a recurrent state is included by adding an optimized upper limit. A special case of the model is used to study the problem of determining a common mean for multiple products, as well as the optimal upper specification limits for each product. A solution procedure for maximizing the expected profits and obtaining the optimal solution is introduced. A numerical example is presented.

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“…Darwish [10] developed an integrated and hierarchical model of optimal process targeting in a single-vendor single-buyer supply chain. Based on a quality loss function, Chen and Kao [11] computed both the optimal process mean and screening limits. A reverse programming routine that identifies the relationship between the process mean and the settings within an experimental factor space was developed by [12] and [13] .…”

Section: Introductionmentioning

confidence: 99%