# Support of Advanced Technologies in Supply Chain Processes and Sustainability Impact
## 7 Conclusions and Future Research  
As a main conclusion from our study, grounded on the 12 advanced technologies considered, it can be said that the level of adoption of SC4.0 technologies to support SCOR processes is quite low. Despite that, there are some specific advanced technologies that have a high level of implementation. This is in line with [PERSON] et al. [130], that so far there is little experience in corporate practice with respect to a purposeful and successful Industry 4.0 implementation. [PERSON] and [PERSON] [131] argue that only recently has industry 4.0 gained importance by reducing the interaction between man and machine. [PERSON] et al. [127] also state that manufacturing companies are undertaking a peculiar digital transformation path, with different approaches and related level of implementation, according to their specific needs and efforts. In relation to the suitability of the KPIs analyzed in our empirical study to assess the sustainable performance of a SC4.0, the findings show that two economic, one social and three environmental indicators stand out, when considering the number of respondents that consider them very or extremely suitable. These KPIs are \"Total annual operating costs of an organization\" (EC2), \"Total annual sales/revenues of the organization\" (EC5), \"Total number of employees given training in an organization\" (S4), \"Total annual volume of water recycled/reused by organization\" (E2), \"Total annual amount of direct GHGs emissions by an organization\" (E3) and \"Waste reduction\" (E5). The \"Total annual operating costs of an organization\" (EC2) is a performance indicator used by [PERSON] et al. [132] to evaluate the impact of automation and digitalization of SCs on the performance of manufacturing companies. The \"Total annual sales/revenues of the organization\" (EC5) is considered a profitability indicator suggested by [PERSON] and [PERSON] [73] to assess the sustainability of a SC. The social sustainability indicator \"Total number of employees given training in an organization\" (S4), has been used by companies in their corporate social responsibility reports [80]. The KPI \"Total annual volume of water recycled/reused by organization\" (E2) and the \"Total annual amount of direct GHGs emissions by an organization\" (E3) are suggested by [PERSON] and [PERSON] [39] to measure performance in sustainable supply chains. Waste reduction (E5) is an important KPI highlighted by several authors [89,90] to assess the performance reached by a SC in using advanced technologies to integrate Lean production, and Logistics. This is not in line with the results achieved by [PERSON] et al. [42] in which the authors could conclude that the plastic industrial sector from Brazil is more focused on the economic pillar. In a qualitative assessment focused on ecological and social indicators, [PERSON] et al. [133] also conclude that the \"Quantity of materials used\", and the \"Primary energy consumption\" in the ecological dimension are considered critical, as well as \"Working conditions\", on the social dimension. Other scholars, such as [PERSON] et al. [134], mention that in future researchers should consider economic, environmental, and societal aspects when evaluating the effects of the adoption of Industry 4.0 on sustainability. Besides the importance of the results and conclusions drawn from this study, some limitations are identified, such as the small sample size (10 case studies); only one company from a different SC was engaged in the study which could give a biased perception. As future research, it will be interesting to study the relationship between the implementation level of these advanced technologies and the sustainability level of digitalized SCs. That is, are the more digitalized SCs more sustainable in their behavior? It would be also interesting to analyze and test the conceptual model proposed in this study in different sectors of activities by using a cross-national survey.