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.
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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.
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Gaur, A., Bandaru, S., Khare, V., Chougule, R., Deb, K. (2013). Identification and Impact Assessment of High-Priority Field Failures in Passenger Vehicles Using Evolutionary Optimization. In: Bansal, J., Singh, P., Deep, K., Pant, M., Nagar, A. (eds) Proceedings of Seventh International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA 2012). Advances in Intelligent Systems and Computing, vol 201. Springer, India. https://doi.org/10.1007/978-81-322-1038-2_10
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DOI: https://doi.org/10.1007/978-81-322-1038-2_10
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