Selecting Key Product Characteristics to Improve the QMS in Automotive Sector

EasyChair Preprint 7822

Abstract

In the automotive industry, one of the major factors contributing to customer satisfaction is the product’s quality.
To improve this latter, companies are implementing quality management systems: QMS, which are requiring usage of quality tools especially statistical ones, to control the manufacturing processes, by monitoring the product’s characteristics. But, must we track all the characteristics to achieve the product compliance and system efficiency? This paper aims to give a solution model that will allow the organizations to select the key product characteristics that should be monitored by quality tools as they have the most influence on the product’s quality.
The goal is to use mathematics in industrial field to improve quality management systems and manufacturing processes performances.
The paper also gives architecture of the discussed problematic and a perspective on the next work which will be an application of the proposed solution in the automotive sector.

Keyphrases: Data Mining, Product characteristics identification, Quality Improvement, automotive industry, linear regression, machine learning

@booklet{EasyChair:7822,
  author    = {Laila Benzaza and Najlae Alfatehi and Abdelouahid Lyhyaoui},
  title     = {Selecting Key Product Characteristics to Improve the QMS in Automotive Sector},
  howpublished = {EasyChair Preprint 7822},
  year      = {EasyChair, 2022}}