The Application of Data Science at Original Equipment Manufacturers: A Literature Review

Abstract

The role of data as a valuable resource has caused significant transformations in various areas of life. Data Science (DS) aims to extract knowledge from data and thus, has gained attraction from organizations aiming to optimize existing processes and uncover previously unknown potentials. DS can be beneficially integrated into the business processes of Original Equipment Manufacturers (OEMs). Therefore, in this study, a structured literature review is conducted to assess the current state-of-the-art of DS in OEMs, especially in the automotive industry and in procurement, offering valuable insights for both researchers and practitioners in DS and OEMs. Several financial, operative, and strategic potentials of DS in the context of OEMs are identified and described. Examples are operational cost reduction, supplier selection and evaluation, forecasts of product demand, and promoted collaboration between stakeholders. Nevertheless, the literature also suggests several challenges in the execution of DS projects. It was observed that OEMs face both technological and procedural obstacles in this area, including the lack of data-driven work culture, inappropriate systems, and deficits with data collection and integration. Mitigating these challenges will be valuable in improving the success rates of DS projects. Further measures to enrich the results of this article are provided. Due to the rapidly evolving character of DS, the application possibilities and challenges might change in the future.

Keywords

• Business intelligence
• data science
• literature review
• original equipment manufacturer
• procurement
• value engineering