Continuous and prescriptive life cycle assessment for smart manufacturing systems

Abstract

Industry 4.0 has introduced significant potential for improving manufacturing systems through digitalization. There is huge data in manufacturing which is being collected and used to improve the performance of manufacturing systems. While most of the applications have focussed on improving the economic aspects of manufacturing, these same technologies can also be used to improve the environmental sustainability. Manufacturing industries consume large number of resources and contribute significantly to environmental impacts. To address these environmental sustainability issues, this research paper establishes a framework by integrating Life Cycle Assessment (LCA) with data analytics and plant simulations. LCA is chosen as the preferred method for assessing environmental sustainability due to its robust and science-based approach to estimate environmental impacts. In this study, we analyse data from a PCB assembly plant to capture energy consumption variations throughout the multiple work shifts, dividing into six-time intervals for manufacturing the similar parts. Then, data from each interval was used to model Gate-to-Gate LCA scenarios, to observe the variations in environmental impacts across six-time intervals. The results showed significant differences in environmental impacts and hence reinforces the need to conduct near-real-time LCA. Further, experiments using Discrete Event Simulation (DES) were conducted to examine the effects of process parameter variations (e.g., availability, cycle time) on environmental impacts for the same assembly line. The results showed significant differences in environmental impacts and hence reinforces the need to conduct prescriptive LCA using manufacturing system simulations. Overall, the proposed approaches have the potential to contribute towards achieving a dynamic LCA while improving manufacturing performance.