Integrating Predictive Analytics Into Manufacturing Finance: A Case Study On Cost Control And Zero-Carbon Goals In Automotive Production

Abstract

Planning of future costs is essential to any organization, especially those committed to a zero-carbon future. This case study describes the development of a joint-level cost predictive analytics model in support of carbon footprint reduction goals. Realistically complex predictive analytics research using Enterprise Resource Planning data and machine learning in a large-scale multinational automotive company is presented, and the challenges of extracting generalizable conclusions to the industry at large from the context of a particular company are discussed. We aim to illuminate the benefits deriving both from collaboration between the finance and operational teams and from breaking the cost objects down to the level of component sub-complexing typically used by manufacturing engineers undertaking product cost estimation studies. The model was found to uncover cost reduction opportunities relating to assembly lines in the case studies designed jointly by manufacturing engineers acting as domain experts. The work has direct implications for predictive analytics research related to sustainability goals, and, in particular, the ambitious zero-carbon goals in many smart manufacturing industries.