A fuzzy logic based approach for modeling quality and reliability related customer satisfaction in the automotive domain

Chougule, Rahul; Khare, Vineet R.; Pattada, Kallappa

doi:10.1016/j.eswa.2012.08.032

Cited by 26 publications

(12 citation statements)

References 20 publications

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“…e Àu u tÀ1 du (2) p and q are the shape parameters of the probability distribution. They are expressed in terms of the mean (m X ), and standard deviation (s X ) of the random variable (X).…”

Section: Beta 1 Distributionmentioning

confidence: 99%

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Fault tree analysis of proton exchange membrane fuel cell system safety

Collong

Kouta

2015

International Journal of Hydrogen Energy

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Section: Beta 1 Distributionmentioning

confidence: 99%

“…These first vehicles must comply with the safety integrity obligations. Indeed, safety is a major user expectations of automotive vehicles [1,2].…”

Section: Introductionmentioning

confidence: 99%

Fault tree analysis of proton exchange membrane fuel cell system safety

Collong

Kouta

2015

International Journal of Hydrogen Energy

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“…However, for every visit, service and repair data are produced and can be used for further analysis. [12,13] Therefore, the essential question for the automotive industries is: Can dissatisfied customers be classified based on data that is produced during every service visit?…”

Section: Introductionmentioning

confidence: 99%

Can machine learning techniques predict customer dissatisfaction? A feasibility study for the automotive industry

Meinzer

Jensen

Thamm

et al. 2016

AIR

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The automotive industry is in the strongest competition ever, as this sector gets disrupted by new arising competitors. Providing services to maximum customer satisfaction will be one of the most crucial competitive advantages in the future. Around 1 Terabyte of objective data is created every hour today. This volume will significantly grow in the future by the increasing number of connected services within the automotive industry. However, customer satisfaction determination is solely based on subjective questionnaires today without taking the vast amount of objective sensor and service process data into account. This work presents an industrial application that fills this lack of research and thus provides a solution with a high practical impact to survive in the tough competition of the automotive industry. Therefore, the work addresses these fundamental business questions: 1) Can dissatisfied customers be classified based on data that is produced during every service visit? 2) Can the dissatisfaction indicators be derived from service process data? A machine learning problem is set up that compared 5 classifiers and analyzed data from 19,008 real service visits from an automotive company. The 105 extracted features were drawn from the most significant available sources: warranty, diagnostic, dealer system and general vehicle data. The best result for customer dissatisfaction classification was 88.8% achieved with the SVM classifier (RBF kernel). Furthermore, the 46 most potential indicators for dissatisfaction were identified by the evolutionary feature selection. Our system was capable of classifying customer dissatisfaction solely based on the objective data that is generated by almost every service visit. As the amount of these data is continuously growing, we expect that the presented data-driven approach can achieve even better results in the future with a higher amount of data.

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“…A satisfação (PARASURAMAN et al, 1985;PARASURAMAN, 2004;CHOUGULE;KHARE;PATTADA, 2013) do cliente de serviço pode ser expressa pela comparação da percepção do serviço prestado com as expectativas do serviço desejado A qualidade dos serviços tornou-se uma exigência para as organizações manterem-se bem sucedida e assim tornarem-se competitiva. Muitos pesquisadores (ASUBONTENG ET AL., 1996;MALHOTRA, 2002;GANGULI;ROY, 2010) têm enfatizado a importância de iniciativas de melhoria da qualidade que resultaram em vantagem competitiva (SHEKARCHIZADEH; RASLI; HON-TAT, 2011) sustentável.…”

Section: Introductionunclassified

Satisfação Do Cliente Em Serviços Bancários

Stefano

Godoy

2013

SBIJ

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RESUMOO objetivo desse trabalho é avaliar a interação entre o cliente e o prestador de serviço bancário. A pesquisa possui natureza qualitativa, realizando o estudo com base na opinião de pessoas relacionadas à prestação do serviço. Natureza quantitativa, em função do relacionamento causal entre variáveis e não entre processos. Os resultados mostraram que os clientes estão satisfeitos com os serviços prestados pela agência bancária. Por outro lado, um ponto importante, que chama atenção é a percepção sob a ótica dos funcionários de linha de frente que não coincide com a dos clientes, resultado apresentado na discussão dos dados e conclusões deste trabalho.Palavras-chave: Agência Bancária, Linha de Frente, Satisfação, Clientes, Qualidade. ABSTRACT The aimed of this study is to evaluate the interaction between customer

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A fuzzy logic based approach for modeling quality and reliability related customer satisfaction in the automotive domain

Cited by 26 publications

References 20 publications

Fault tree analysis of proton exchange membrane fuel cell system safety

Fault tree analysis of proton exchange membrane fuel cell system safety

Can machine learning techniques predict customer dissatisfaction? A feasibility study for the automotive industry

Satisfação Do Cliente Em Serviços Bancários

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