Data-Driven Solutions for Green Production Integrating Resource Efficiency Assessment in Manufacturing Systems

Abstract

In response to sustainable development efforts by the United Nations 2020 Agenda, industries are aiming towards more sustainable production. The European Commission has created the classification system called EU taxonomy, establishing the definition of sustainability and sustainable activities. Consequently, European manufacturers are seeking opportunities to reduce their environmental impact, creating the need to understand how their resources are utilized. Resource efficiency methods enable the assessment of resource usage but require high data quality and availability, making the implementation difficult. One of the main challenges for resource efficiency assessment is data completeness and reliability, especially at a process level, in tandem with a lack of standardized data collection methods resulting in the implementation of RE assessment being difficult. Despite the issues, there are still opportunities and benefits of using already available data in manufacturing systems with proper indicator selection having data characteristics in mind. This project aims to leverage available factory data, select indicators based on available data, and integrate resource efficiency in manufacturing systems to identify opportunities for greener production with a resource efficiency method in line with the EU taxonomy. This project showcases a case study implementing resource efficiency assessment in an automotive plant with an assessment design that includes multiple methods to be aligned with stakeholder priorities and indicates inefficiencies of resource usage. A selection method was devised as the project’s core, designed to be general and adaptable for other cases. However, assessment methods are inherently different and data quality is a critical factor in implementing them, not only stakeholder preferences of the company. The study utilized existing data to assess resource efficiency and proposed automated data handling for future assessments to streamline the process and reduce the execution time.