Fuzzy relative importance of customer requirements in improving product development

Bencherif, Fateh; Mouss, Leïla Hayet; Meguellati, M.

doi:10.1109/icmsao.2013.6552663

Cited by 3 publications

(4 citation statements)

References 13 publications

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“…Some solutions then appeared for resolving the problems. [29]. There are many types of fuzzy numbers such as trapezoidal fuzzy number, L-R type fuzzy number, etc.…”

Section: B Resolving Ecs Negative Correlationmentioning

confidence: 99%

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Fuzzy Alpha-cuts to capture customer requirements in improving product development

Bencherif¹,

Mouss²,

Benaicha³

2013

2013 11th International Symposium on Programming and Systems (ISPS)

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Quality Function Deployment is a tool to develop and design the quality in product and improve competitiveness advantages in the market. In developing new products and projects, we receive the needs from the customer, pass it around a corporate communication circle, and eventually return it to the customer in the form of the new product. First, needs and languages received from customer are often ambiguous, imprecise, and uncertain causing deviated studied results, and in a disregarding of the voice of customer. Second, to improve quality and solve the uncertainty in product development, numerous researchers try to apply the fuzzy set theory to product development. Their models usually focus only on customer requirements or on engineering characteristics. The subsequent stages of product design are rarely addressed. The correlation between engineering characteristics and benchmarking analysis disregarded in most of Quality Function Deployment practices related researches. This commonly upsets the consequences to delay and failed project development. Aiming to solve these three issues, the objective of this paper is to improve the accuracy of Quality Function Deployment, optimize and develop the customer requirements approach to attenuate risks in subsequent phases and in manufacturing process to increase industrial performance. This approach is based on Fuzzy sets theory and Alpha-cut operations, Pairwise comparison method, and fuzzy ranking and clustering method, and on theory of inventive problems solving (TRIZ).

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“…Some solutions then appeared for resolving the problems. [29]. There are many types of fuzzy numbers such as trapezoidal fuzzy number, L-R type fuzzy number, etc.…”

Section: B Resolving Ecs Negative Correlationmentioning

confidence: 99%

“…The ECs with negative correlation are the source of conflict problems which shall be resolved; otherwise it would impact the performance of product development [29]. What often happens in optimizing ECs is that some ECs deliver one function but also cause harm, act against another ECs.…”

Section: B Resolving Ecs Negative Correlationmentioning

confidence: 99%

Fuzzy Alpha-cuts to capture customer requirements in improving product development

Bencherif¹,

Mouss²,

Benaicha³

2013

2013 11th International Symposium on Programming and Systems (ISPS)

Self Cite

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“…According to the authors, this approach was effective in using the knowledge of the QFD team and accomplishing decision-making in the new product development process (Li et al, 2012). Bencherif, Mouss and Benaicha (2013) proposed using the theory of inventive problem solving for resolving negative correlations among ECs. In this approach, negatively correlated ECs were replaced with positive ones.…”

Section: Literature Reviewmentioning

confidence: 99%

“…An improvement of one may worsen the other one. They require special planning or breakthrough attempts since they represent bottlenecks in product design (Li, Tang, & Luo, 2010) and adversely affect the performance of the product (Bencherif et al, 2013;Iqbal et al, 2015). On the other hand, positive correlations indicate that ECs are mutually supportive.…”

Section: Correlation Issues Among Ecsmentioning

confidence: 99%

Integrating Quality Function Deployment with Fuzzy Cognitive Maps for Resolving Correlation Issues in the Roof Matrix

Emel¹,

Petriçli²,

KAYGULUOĞLU³

2022

Ege Akademik Bakis (Ege Academic Review)

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The roof matrix represents correlations among engineering characteristics (EC) in the house of quality (HoQ) in Quality Functions Deployment (QFD). Correlations are usually measured qualitatively and omitted in the analysis. However, ignoring them may cause duplication of effort, decreased product performance, and unsatisfied customer requirements (CR). Hence, this paper intends to propose an approach to considering the correlations quantitatively. Fuzzy Cognitive Maps (FCM) were used for this purpose. Additionally, Axiomatic Design (AD), for examining relationships between CRs and ECs, and Fuzzy Analytic Hierarchy Process (FAHP) with the Extent Analysis (EA) were used for checking the consistency of the evaluations. The proposed approach was applied in a sheet metal die-making company for ranking CRs and ECs. Results show that FCM enables analysing the quantitative roof matrix practically. The square roof matrix that supports FCM's adjacency matrix structure successfully represents asymmetric relationships among ECs. Integrating the correlations into the analysis resulted in a change in the final ranking. It also helped determine the most manageable ECs, better satisfiable CRs, and most critical/least manageable ECs.

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Complex network to enhance characterization analysis in modelling product development process

Bencherif

Mouss

2020

African Journal of Science, Technology, Innovation and Developm

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Fuzzy relative importance of customer requirements in improving product development

Cited by 3 publications

References 13 publications

Fuzzy Alpha-cuts to capture customer requirements in improving product development

Fuzzy Alpha-cuts to capture customer requirements in improving product development

Integrating Quality Function Deployment with Fuzzy Cognitive Maps for Resolving Correlation Issues in the Roof Matrix

Complex network to enhance characterization analysis in modelling product development process

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