Evaluation of the overall environmental sustainability of products and processes in the automatic machine industry

The environmental performance of automated industrial machinery is increasingly important in sustainable manufacturing, especially for capital goods with long service lives and energy-intensive operation. Although Life Cycle Assessment (LCA) is widely used to evaluate environmental impacts, existing approaches often lack specificity for complex machines operating under dynamic industrial conditions. This dissertation assesses the environmental sustainability of an industrial automatic washing machine for pharmaceutical applications and proposes methodological improvements to make LCA more representative for such systems. The study applies a cradle-to-grave LCA approach, with particular attention to the use phase, which is identified as the dominant contributor to the overall environmental profile. A parameterised LCA model is developed to account for key variables such as electricity supply structure, operating conditions, cycle parameters and detergent formulation. Sensitivity analysis is performed for alternative electricity mixes to evaluate how different energy source compositions influence the results, while Monte Carlo uncertainty analysis is applied to detergent scenarios to assess the robustness of comparative outcomes. A detailed inventory model is developed using the manufacturer’s Bill of Materials, which includes more than 6,000 components. These components are classified by material and function through custom VBA macros and AI-assisted coding, improving inventory consistency, traceability and mapping to background datasets. A further contribution lies in the integration of function-oriented energy analysis and the Total Estimated Energy Consumption approach derived from ISO 14955. Design of Experiments and regression analysis are then integrated with the parameterised LCA model as an optimisation layer. The research provides a replicable framework combining LCA, TEEC, AI-assisted inventory processing, uncertainty analysis and DOE-based optimisation to support environmentally informed design and operation of automated machinery.