Determining the Importance of Performance Measurement Criteria Based on Total Quality Management Using Fuzzy Analytical Network Process

Öztayşi, Başar; Kutlu, Ahmet Can

doi:10.1007/978-3-642-25658-5_48

Cited by 6 publications

(10 citation statements)

References 26 publications

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“…The outcomes of this model can be compared other studies employing different methodologies that exist in the TQM literature. FANP methodology utilized by Chen (2009) andÖ ztays¸i andKutlu (2011) to analyse the weights of TQM criteria considering interdependencies between them. The dynamic TQM factor interrelations obtained by FCM can be compared with static TQM factor weights determined by FANP.…”

Section: Resultsmentioning

confidence: 99%

“…However, to determine the most affecting CSFs of TQM is a complex and difficult task when the dynamic interactions between the CSFs are considered. TQM literature focusing on the CSFs of TQM implementation involves many empirical studies such as statistical (Saraph et al, 1989;Black and Porter, 1996;Quazi et al, 1998), analytical (Pun, 2002;Chin et al, 2002;Bayazit and Karpak, 2007) and fuzzy (Chen and Chen, 2009;Ö ztays¸i and Kutlu, 2011), but the relations among the factors in a dynamic and uncertain environment are not taken into consideration.…”

Section: Introductionmentioning

confidence: 99%

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Analyzing critical success factors of total quality management by using fuzzy cognitive mapping

Kutlu

Kadaifçi

2014

Journal of Enterprise Information Management

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Purpose – Total quality management (TQM) is a process and philosophy to achieve customer satisfaction in long term by improving the products, processes and services effectively and efficiently. TQM implementation is turning into a complex practice due to the increasing number of effective factors and key elements labelled as critical success factors (CSFs). The purpose of this paper is to analyse the relations between CSFs of TQM and to provide decision makers has a clear picture of relations by determining the most affecting – both the number of CSFs which this factor affects and the its effect degree on relevant CSFs are higher comparing to other factors – of this factors affected factors – both the number of CSFs and their effect degree on these factors are higher – that influences a successful TQM implementation. Design/methodology/approach – The paper refers to fuzzy cognitive maps (FCMs) that allow dynamic modelling of a system in consideration of a complex network structure and the effects of factors to each other. The method demonstrates causal representations between CSFs under uncertainty to represent the relations and interaction between them and performs qualitative simulations to analyse the factors that have the highest impact on continuous improvement of quality management process. The evaluations are performed by five academicians whose professions are on both the areas of TQM and FCM. Findings – FCM analysis shows how the most affecting and affected factors influence the other CSF in order to manage a successful TQM implementation. Originality/value – The critical factors of TQM implementation are in the focus of most of the empirical studies in the literature. However, none of them considers the dynamic interactions between the factors. This study employs FCM to explore the CSFs that influence the TQM implementation process considering the relations among them to observe the most affecting and affected factors based on the changes of determined CSFs.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Analyzing critical success factors of total quality management by using fuzzy cognitive mapping

Kutlu

Kadaifçi

2014

Journal of Enterprise Information Management

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“…This combined method is developed based on some research, i.e. application of FANP in requirements in the house of quality [19], An IT project selection [20], Total Quality Management [21], and selection of facility location [22]. This combined method also considers some drawbacks of ANP [23][24][25] and enhance linguistic scale which is used in a pair wise comparison matrix for FANP based on the previous one [25], [27].…”

Section: Preprocessingmentioning

confidence: 99%

Decision-Making Model for Student Assessment by Unifying Numerical and Linguistic Data

Andayani

Hartati

Wardoyo

et al. 2017

IJECE

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Learning assessment deals with the process of making a decision on the quality or performance of student achievement in a number of competency standards. In the process, teacher's preferences are provided through both test and non-test, generally in a numeric value, from which the final results are then converted into letters or linguistic value. In the proposed model, linguistic variables are exploited as a form of teacher's preferences in nontest techniques. Consequently, the assessment data set will consist of numerical and linguistic information, so it requires a method to unify them to obtain the final value. A model that uses the 2-tuple linguistic approach and based on matrix operations is proposed to solve the problem. This study proposed a new procedure that consists of four stages: preprocessing, transformation, aggregation and exploitation. The final result is presented in 2-tuple linguistic representation and its equivalent number, accompanied by a description of the achievement of each competency. The α value of 2-tuple linguistic in the final result and in the description of each competency becomes meaningful information that can be interpreted as a comparative ability one student has related to other students, and shows how much potential is achieved to reach higher ranks. The proposed model contributes to enrich the learning assessment techniques, since the exploitation of linguistic variable as representation preferences provides flexible space for teachers in their assessments. Moreover, using the result with respect to students' levels of each competency, students' mastery of each attribute can be diagnosed and their progress of learning can be estimated. Keyword:Multi criteria decision making Student assessment The linguistic 2-tuple Unifying linguistic and numeric values

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“…Fuzzy ANP is developed to address the limitation of conventional ANP which becomes less effective when dealing with uncertainty. Steps in FANP method can be observed in various studies which apply it to solve various problems [8][9][10]. The process of calculating the weight of each parameter is started by constructing pair-wise comparison matrix (PCM), for comparing between parameters using linguistic variables.…”

Section: Review Of Rating Scale and Fanpmentioning

confidence: 99%

“…The process of calculating the weight of each parameter is started by constructing pair-wise comparison matrix (PCM), for comparing between parameters using linguistic variables. In some studies, fuzzy linguistic variables used in PCM are defined differently according to their requirement [9][10][11][12]. Furthermore, the global weight of each parameter is determined using Chang's Extents Analysis approach [8,13].…”

Section: Review Of Rating Scale and Fanpmentioning

confidence: 99%

Combining rating scale and FANP to determine the important weight of learning competency for students’ assessment

Andayani¹,

Hartati²,

Wardoyo³

et al. 2016

AIP Conference Proceedings

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Abstract. Determining the important weight of learning competency is an important problem for students' assessment in credentialing and certification decision-making. Learning competencies should have importance weight that definitely affect the final decision, and can represent strengths and weaknesses of student achievement of certain competencies. The weight of learning competency is determined based on its difficulty level, which is identified by the depth, scope and complexity of material involved in it. The dependency among learning competencies must also be considered in determining the weight. Therefore, this study aims to develop a weighting method based on combining rating scale and fuzzy analytic network process (FANP) to determine the weight of learning competency. Using the proposed method, the weight of learning competency is obtained and then can be used in calculating the final score for students' assessment.

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Determining the Importance of Performance Measurement Criteria Based on Total Quality Management Using Fuzzy Analytical Network Process

Cited by 6 publications

References 26 publications

Analyzing critical success factors of total quality management by using fuzzy cognitive mapping

Analyzing critical success factors of total quality management by using fuzzy cognitive mapping

Decision-Making Model for Student Assessment by Unifying Numerical and Linguistic Data

Combining rating scale and FANP to determine the important weight of learning competency for students’ assessment

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