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Identification and Impact Assessment of High-Priority Field Failures in Passenger Vehicles Using Evolutionary Optimization

  • Abhinav Gaur
  • Sunith Bandaru
  • Vineet Khare
  • Rahul Chougule
  • Kalyanmoy Deb
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 201)

Abstract

This paper presents a method for prioritizing field failures in passenger vehicles based on their potential for improvement in the Customer Satisfaction Index (\({\text{ CSI}}_{QSR}\)). \({\text{ CSI}}_{QSR}\) refers to Customer Satisfaction Index pertaining to quality, service and reliability of the vehicle and is referred to as simply ‘CSI’ in this paper. A novel method for quantitative modeling of the CSI function using an evolutionary approach was presented in [3]. Such a CSI function can be used to capture individual customer’s perception of a vehicle model as well as to compare overall CSI of multiple vehicle models. This work is firstly aimed at improving the previous modeling technique and validating it against Consumer Reports reliability ratings. More importantly, it presents a procedure for identifying high impact field failures based on their CSI Improvement Potential (CIP). These high priority field failures can then be further studied for root cause analysis.

Keywords

Customer satisfaction index Quantitative modeling Evolutionary optimization Field failures 

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Notes

Acknowledgments

The financial support and vehicle related data provided by India Science Lab, General Motors R&D are greatly appreciated. Authors thank Dr. Prakash G. Bharati, Dr. Pulak Bandyopadhyay and Dr. Pattada A. Kallappa for helpful discussions.

References

  1. The American Customer Satisfaction Index. ACSI (2010). www.theacsi.org.Google Scholar
  2. JDPower.com. J.D. Power and Associates (2010). www.jdpower.com.Google Scholar
  3. Bandaru, S., Deb, K., Khare, V., Chougule, R.: Quantitative modeling of customer perception from service data using evolutionary optimization. In: Proceedings of the 13th annual conference on Genetic and evolutionary computation, pp. 1763–1770. ACM (2011).Google Scholar
  4. ConsumerReports.org. Consumers Union of U.S., Inc. (2010). www.consumerreports.org.Google Scholar
  5. ConsumerReports. ConsumersReports.org (2010).Google Scholar
  6. Deb, K., Agarwal, S., Pratap, A., Meyarivan, T.: A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6(2), 182–197 (2002).Google Scholar
  7. Deb, K., Gupta, S.: Understanding knee points in bicriteria problems and their implications as preferred solution principles. Engineering optimization (2011).Google Scholar
  8. J.D. Power/What Car? 2011 UK Vehicle Ownership Satisfaction Study (2011). www.jdpower.com.Google Scholar
  9. Lomax, R.: An introduction to statistical concepts for education and behavioral sciences. Lawrence Erlbaum (2001).Google Scholar
  10. Robinson, J., Chukova, S.: Estimating mean cumulative functions from truncated automotive warranty data. In: Communications of the Fourth International Conference on Mathematical Methods in Reliability, Methodology and Practice, pp. CD-ROM (4 pages). Santa Fe, New Mexico, USA (2004).Google Scholar

Copyright information

© Springer India 2013

Authors and Affiliations

  • Abhinav Gaur
    • 1
  • Sunith Bandaru
    • 1
  • Vineet Khare
    • 2
  • Rahul Chougule
    • 2
  • Kalyanmoy Deb
    • 1
  1. 1.Kanpur Genetic Algorithms LaboratoryIndian Institute of TechnologyKanpurIndia
  2. 2.India Science LabGeneral Motors Global R&DBangaloreIndia

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