Data-Driven Solutions for Green Production Integrating Resource Efficiency Assessment in Manufacturing Systems
Typ
Examensarbete för masterexamen
Master's Thesis
Master's Thesis
Program
Product development (MPPDE), MSc
Publicerad
Författare
Gonzales, Juan
Nguyen, Thommy
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
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.