Using Multivariate Split Analysis for an Improved Maintenance of Automotive Diagnosis Functions

Kohl, Jens; Kotucz, Agnes; Prenninger, J.P.; Dorneich, A.; Meinzer, Stefan

doi:10.1109/csmr.2011.42

Cited by 4 publications

(4 citation statements)

References 13 publications

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“…However, our literature review proved that knowledge about previous repeat repairs was essential because these are seen as critical events. Consumers were especially dissatisfied when their cars had a high rate of repeat repairs, defined as repairs caused by a similar technical problem or a perceived identical cause (Biehal 1983;Kohl et al 2011;Meinzer et al 2010). The gap between perceived service failures such as repeat repairs and the technical image that BMW wishes to convey is particularly important (Meinzer et al 2012b).…”

Section: Extending the Capability To Match Automotive-specific Determmentioning

confidence: 96%

Translating satisfaction determination from health care to the automotive industry

Meinzer

Prenninger

Vesel³

et al. 2015

Serv Bus

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This study is based on the case of BMW, and aims to improve the determination of perceived consumer satisfaction in the automotive industry by transferring existing knowledge from the health care sector. A literature analysis of the health care sector and the automotive industry was conducted to identify the common concepts of determining satisfaction. These were the service encounter, situational factors, and sociodemographics. The practical application was tested by analyzing a contemporary survey from BMW. Based on the findings, managers responsible for customer satisfaction in after-sales services in the automotive industry could improve measurement of customer satisfaction.

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Section: Extending the Capability To Match Automotive-specific Determmentioning

confidence: 96%

Translating satisfaction determination from health care to the automotive industry

Meinzer

Prenninger

Vesel³

et al. 2015

Serv Bus

Self Cite

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“…A detailed description of the transfer of information to the automotive manufacturer's can be found in literature. [13,30] The data sources, used for labeling and feature extraction are illustrated in Figure 1.…”

Section: Data Sourcesmentioning

confidence: 99%

“…A detailed description can be found in literature. [13] The features extracted from this sources are given in Table 1.…”

Section: Diagnostic Datamentioning

confidence: 99%

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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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Preface: Intelligent & Autonomous Enterprise

Hofmann

Meinzer

2017

Digital Marketplaces Unleashed

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Using Multivariate Split Analysis for an Improved Maintenance of Automotive Diagnosis Functions

Cited by 4 publications

References 13 publications

Translating satisfaction determination from health care to the automotive industry

Translating satisfaction determination from health care to the automotive industry

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

Preface: Intelligent & Autonomous Enterprise

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