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The devulcanization of the rubber wastes in autoclave represent a technological variant that allows the superior utilization of rubber wastes, but with high energy consumption. The researches aimed at improving the devulcanization technology in order to obtain reclaimed rubber with superior characteristics, but also with a reduction in energy consumption. An improvement to devulcanization technology consisted in vacuuming the autoclave at the end of the devulcanization process. An increase in the degree of devulcanization of the rubber from 86.83% to 93.81% and an improvement of the physico-mechanical characteristics of the reclaimed rubber was achieved by applying this technology. The realization of the new type of regenerated rubber allowed for an increase in the degree of it use for different mixtures, from 15–20 phr to 30–40 phr without substantially affecting the physical and mechanical properties of the products. Additionally, the researche has shown that, by obtaining the new type of reclaimed rubber, the duration of the refining process has been reduced by 30%. All of this leads to a considerable reduction in energy consumption and transformation of the rubber waste reclaiming process into a sustainable one.

Increasing the sustainability of a system can be achieved by evaluating the system, identifying the issues and their root cause and solving them. Performance evaluation translates into key performance indicators (KPIs) with a high impact on increasing overall efficacy and efficiency. As the pool of KPIs has increased over time in the context of evaluating the supply chain management (SCM) system’s performance and assessing, communicating and managing its risks, a mathematical model based on neural networks has been developed. The SCM system has been structured into subsystems with the most relevant KPIs for set subsystems and their most important contributions on the increase in the overall SCM system performance and sustainability. As a result of the performed research based on the interview method, the five most relevant KPIs of each SCM subsystem and the most relevant problems are underlined. The main goal of this paper is to develop a performance evaluation model that links specific problems with the most relevant KPIs for every subsystem of the supply chain management. This paper demonstrates that by using data mining, the relationship between certain problems that appear in the supply chain management of every company and specific KPIs can be identified. The paper concludes with a graphical user interface (GUI) based on neural networks using the multilayer perceptron artificial intelligence algorithm where the most trustworthy KPIs for each selected problem can be predicted. This aspect provides a highly innovative contribution in solving supply chain management problems provided by organizations by allowing them to holistically track, communicate, analyze and improve the SCM system and ensure overall system sustainability.