Methods for analyzing and incorporating customer feedback in automotive design and manufacturing

Abstract

One of the key focus areas of the General Motors (GM) Company's leadership is to collect, quickly analyze, and respond to customer feedback pertaining to product quality issues in newly built vehicles. This project is intended to complement the Quality team's initiative to develop a tool to combine data sources on product quality. Currently, the tool prioritizes issues based on the frequency of reported incidents, and does not integrate responses to open-ended survey questions. The objective of this project is to recommend methods in which customer satisfaction input can be used to improve product quality. We leveraged customer data and analytical tools to do three things. First, we identified sources of customer feedback across the organization to strengthen collaboration on listening to the customer. We then created a survey to assess the gap between customers and GM employees' definitions of terms such as quality, dependability, and advanced technology. Lastly, we used text analytics to provide structure to open-ended survey responses, which enabled us to identify concerns expressed by customers that were not otherwise captured using the current tool. The cross-functional approach enabled us to gather quantitative results to support observations and anecdotes of misalignments between consumers and GM employees define terms. Analysis shows that Dependability definitions are similar between employees and consumers, but that there is a significant gap for High Quality. Text analytics uncovered that customers were highly dissatisfied to discover that their vehicles did not have features they expected to be basic attributes.