Guiding production planning to improve manufacturing process inventory data collection and modelling

TY - GEN

T1 - Guiding production planning to improve manufacturing process inventory data collection and modelling

AU - Glisic, Marija

PY - 2023/11/15

Y1 - 2023/11/15

N2 - Over the past 30 years, the depletion of natural resources has escalated, and the necessity of discovering substitute materials and alternative energy sources has grown. Among these concerns, the manufacturing industry stands out as a significant contributor to resource consumption and global emissions. In this context, the concept of sustainable manufacturing has gained prominence as a solution to mitigate environmental impacts. Presently, the assessment of manufacturing's impact relies on the estimation of material flows and resource consumption. While this approach yields results, they tend to be generic, posing challenges for effectively reducing environmental impacts and optimizing manufacturing processes. To tackle these challenges, this thesis introduces a methodology to estimate resource consumption based on process-specific parameters. Furthermore, it elaborates on a framework designed to identify critical process parameters linked to resource consumption. Additionally, it proposes parameter-based indicators to gauge the sustainability performance of manufacturing processes and thereby address these concerns.

AB - Over the past 30 years, the depletion of natural resources has escalated, and the necessity of discovering substitute materials and alternative energy sources has grown. Among these concerns, the manufacturing industry stands out as a significant contributor to resource consumption and global emissions. In this context, the concept of sustainable manufacturing has gained prominence as a solution to mitigate environmental impacts. Presently, the assessment of manufacturing's impact relies on the estimation of material flows and resource consumption. While this approach yields results, they tend to be generic, posing challenges for effectively reducing environmental impacts and optimizing manufacturing processes. To tackle these challenges, this thesis introduces a methodology to estimate resource consumption based on process-specific parameters. Furthermore, it elaborates on a framework designed to identify critical process parameters linked to resource consumption. Additionally, it proposes parameter-based indicators to gauge the sustainability performance of manufacturing processes and thereby address these concerns.

M3 - PhD thesis

T3 - PhD Theses - Department of Mechanical and Production Engineering

PB - Aarhus Universitet

ER -