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This thesis examines the viability of remanufacturing as a means of promoting sustainable and profitable appliance recovery, with a particular focus on washing machines. The study is based on a case study of Ri-Generation, a company located in Piemonte that recovers used appliances and remanufactures them for their sale. The thesis investigates the remanufacturing landscape in Europe and identifies several obstacles to a profitable remanufacturing business, including the the lack of regulation, negative customer perceptions, and high degree of variability in the manufacturing process as well as potential solutions. The potential environmental benefits of remanufacturing are a key focus of this master's thesis: the study specifically identifies remanufacturing as a potential solution to address the issue of material scarcity and reduce CO2 emissions. To quantify the environmental impact of remanufacturing in comparison to traditional manufacturing, a life cycle assessment (LCA) analysis is conducted. The results are used to inform the overall conclusions of the study, which emphasize the potential of remanufacturing as a sustainable approach to appliance recovery. Finally, the thesis analyzes the costs and circumstances that permit the profitability of the business as well as the factors that make Ri-Generation competitive. The results of this thesis provide insights into the challenges and opportunities of remanufacturing and offer recommendations for future research and policy decisions

A maturity model for micro, small and medium enterprises (MSMEs) is introduced to assess the level of implementation of sustainability strategies and practices in this type of business. According to the literature, only a few sustainability-maturity assessment models intended for MSMEs have integrated the following three factors: environmental knowledge management, environmental strategies and good practices, and environmental management systems. The sustainability maturity model proposed here for MSMEs is capable of supporting the efforts of companies in their attempts to achieve both environmental sustainability and an improvement in their production systems. The model encompasses a four-level qualitative scale and uses supervised classification algorithms to categorize companies through data analysis techniques. After applying the model to a group of MSMEs from different productive sectors in Colombia, the results have shown that 6% of the companies were at an insufficient level, 31% were at an initial level, 45% at a developed sustainability maturity level, and 18% at a consolidated level. This result implies that the decision makers in the latter group have paid greater attention to the strengthening of sustainability progress capabilities and, hence, to the definition of a maturation route.

The emerging of the fourth industrial revolution, also known as Industry 4.0 (I4.0), from the advancement in several technologies is viewed not only to promote economic growth, but also to enable a greener future. The 2030 Agenda of the United Nations for sustainable development sets out clear goals for the industry to foster the economy, while preserving social well-being and ecological validity. However, the influence of I4.0 technologies on the achievement of the Sustainable Development Goals (SDG) has not been conclusively or systematically investigated. By understanding the link between the I4.0 technologies and the SDGs, researchers can better support policymakers to consider the technological advancement in updating and harmonizing policies and strategies in different sectors (i.e., education, industry, and governmental) with the SDGs. To address this gap, academic experts in this paper have investigated the influence of I4.0 technologies on the sustainability targets identified by the UN. Key I4.0 element technologies have been classified to enable a quantitative mapping with the 17 SDGs. The results indicate that the majority of the I4.0 technologies can contribute positively to achieving the UN agenda. It was also found that the effects of the technologies on individual goals varies between direct and strong, and indirect and weak influences. The main insights and lessons learned from the mapping are provided to support future policy., QC 20210412