Managing logistics customer service under uncertainty: An integrative fuzzy Kano framework

Flórez-López, Raquel; Ramón-Jerónimo, Juan Manuel

doi:10.1016/j.ins.2012.03.004

Cited by 50 publications

(13 citation statements)

References 60 publications

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“…Compared to the traditional Kano model, the fuzzy Kano method allows the respondents to express their ideas in a more flexible and reasonable manner. The Kano model has been applied in new product development [26], new service creation [41], and logistics customer service [42].…”

Section: Literature Reviewmentioning

confidence: 99%

A Hybrid Fuzzy Analytic Network Process Approach to the New Product Development Selection Problem

Chyu

Fang

2014

Mathematical Problems in Engineering

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New product development selection is a complex decision-making process. To uphold their competence in competitive business environments, enterprises are required to continuously introduce novel products into markets. This paper presents a fuzzy analytic network process (FANP) for solving the product development selection problem. The fuzzy set theory is adopted to represent ambiguities and vagueness involved in each expert’s judgment. In the proposed model, the fuzzy Kano method and fuzzy DEMATEL are employed to filter criteria and establish interactions among the criteria, whereas the SAM is applied to aggregate experts’ opinions. Unlike the commonly used top-down relation-structuring approach, the proposed FANP first identifies the interdependence among the criteria and then the identified relationships are mapped to the clusters. This approach is more realistic, since the inner and outer relationships between criteria are simultaneously considered to establish the relationships among clusters. The proposed model is illustrated through a real life example, with a comparative analysis using modified TOPSIS and gray relation analysis in the synthesizing phase. The concluded results were approved by the case company. The proposed methodology not only is useful in the case study, but also can be generally applied in other similar decision situations.

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Section: Literature Reviewmentioning

confidence: 99%

A Hybrid Fuzzy Analytic Network Process Approach to the New Product Development Selection Problem

Chyu

Fang

2014

Mathematical Problems in Engineering

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“…Historically, over the years this phase has been one of the most neglected areas of the transaction process in managerial practice (Mesjasz-Lech, 2015). Recently, it has gained in importance and is the subject of theoretical considerations and practical solutions mainly in the sphere of distribution (Czubała, 2001), marketing (Kotler and Keller, 2012), marketing logistics (Christopher and Peck, 2012;Erokhina et al, 2018) and logistics (Ciesielski, 2006;Florez-Lopez and Ramon-Jeronimo, 2012;Panayides, 2007). Both in literature and in economic practice, there is a tendency to integrate them and to treat the post-sale phase of a product or service much more broadly than in relation to traditional marketing only.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Post-transaction Phase of Logistics Customer Service in the Management of Companies Providing Transport Services

Kadłubek¹

2020

ERSJ

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“…It designed to improve quality development [1]. It is an effective tool for PD to incorporate and translate needs in product [16]. As shown in Figure 1, the Four-Phase method consists of concept planning, design, process planning, and production planning phases [1].…”

Section: Concepts For Customer Requirementsmentioning

confidence: 99%

“…The complete product planning table is illustrated in Figure 7. Step 2: Resolving negative correlations between ECs In this stage, before calculating the final importance of ECs, the PDT translates in technical language the developed CRs into ECs [16]. According to the acquired CRs and ECs, the PDT establishes the relationship matrix between CRs and ECs [3], and the correlation matrix between pairs of ECs, as illustrated in Fig.4 Right.…”

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)

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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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Managing logistics customer service under uncertainty: An integrative fuzzy Kano framework

Cited by 50 publications

References 60 publications

A Hybrid Fuzzy Analytic Network Process Approach to the New Product Development Selection Problem

A Hybrid Fuzzy Analytic Network Process Approach to the New Product Development Selection Problem

Post-transaction Phase of Logistics Customer Service in the Management of Companies Providing Transport Services

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

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