Big Data Analytics In Heavy Vehicle Manufacturing: Advancing Planet 2050 Goals For A Sustainable Automotive Industry

Abstract

The automotive industry has embraced the shift towards more sustainable solutions both in terms of the products developed and the production methods. Given its scope, the heavy vehicle manufacturing sector has a huge opportunity to contribute towards reducing the environmental impact of vehicle production. The vehicle manufacturing process is complex, with several parts and subsystems being assembled to create the finished product. The amount of energy and materials required for vehicle manufacture has driven the industry towards identifying opportunities to reduce the carbon footprint of production processes.

One key enabler in this quest has been the ability to collect and process big data. Global trends across many industry sectors indicate an increase in the number of sensors and data collected, processed, and stored. This trend correlates with the lowering cost of sensors, advances in computing, and the increase in data with which to train machine learning models. The primary relevance lies in the manner in which machine learning techniques and related algorithms can capitalize on big data collected from the heavy automotive manufacturing industry to advance the environmental goals set forth. Indeed, this offers three high-level goals designed to support vehicle innovation desired for 2050, which ultimately addresses the need for lower carbon emissions and energy consumption.