Multicriteria evidential reasoning decision modelling and analysis—prioritizing voices of customer

Abstract

In this paper, a new methodology is investigated to support the prioritization of the voices of customers through various customer satisfaction surveys. This new methodology consists of two key components: an innovative evidence-driven decision modelling framework for representing and transforming large amounts of data sets and a generic reasoning-based decision support process for aggregating evidence to prioritize the voices of customer on the basis of the Evidential Reasoning (ER) approach. Methods and frameworks for data collection and representation via multiple customer satisfaction surveys were examined first and the distinctive features of quantitative and qualitative survey data are analysed. Several novel yet natural and pragmatic rule-based functions are then proposed to transform survey data systematically and consistently from different measurement scales to a common scale, with the original features and profiles of the data preserved in the transformation process. These new transformation functions are proposed to mimic expert judgement processes and designed to be sufficiently flexible and rigorous so that expert judgements and domain specific knowledge can be taken into account naturally, systematically and consistently in the transformation process. The ER approach is used for synthesizing quantitative and qualitative data under uncertainty that can be caused due to missing data and ambiguous survey questions. A new generic method is also proposed for ranking the voices of customer based on qualitative measurement scales without having to quantify assessment grades to fixed numerical values. A case study is examined using an Intelligent Decision System (IDS) to illustrate the application of the decision modelling framework and decision support process for prioritizing the voices of customer for a world-leading car manufacturer.

Keywords:

• evidential reasoning
• multiple criteria decision analysis
• voices of customer
• ranking

Acknowledgements

This work was funded by General Motors Company (GM) under the Grant no: GM ISL-105/CL-06-08 and also partially supported by the UK Engineering and Physical Science Research Council (EPSRC) under the Grant no: EPSRC EP/F024606/1. The authors are grateful for the technical support to this research by the many staff members of the GM R&D labs in both India and the US. The detailed and constructive comments from the anonymous reviewers are greatly appreciated.