摘要：In today’s industrial environment, where concepts of smart factories are consolidating their application in companies, it is still necessary to approach management decision making from a perspective that encompasses all aspects of sustainability without losing sight of the social return to which they must contribute. In order to obtain a reliable prediction, of the operation of a Sustainable Manufacturing System (SMS) and its Social Return (SR), this paper develops a methodology and procedures that allow predicting the system performance as a whole. This will allow us to assist management decision making in industries 4.0, supported by multi-criteria methods in knowledge management, simulation, value analysis and operational research by means of:a) Study the economic, social and environmental impacts in the organization and management of the efficient operation of an SMS with the selection of strategies and alternatives in production chains to minimize and / or mitigate environmental and labor risks.b) Encourage of industrial symbiosis or eco-industries networks that create opportunities increasing eco-efficiency and the positive social return of production systems.This proposed methodology will facilitate changes in the structure of production systems in order to implement industry 4.0 paradigms through facilitator technologies such as simulation and virtual reality. This framework will allow Small and Medium Enterprises (SMEs) and other companies to address the decision-making activities that improve the economic-functional efficiency, which will lead to reduce the environmental impact and increase the positive social return of certain production strategies, considering working conditions.The proposed approach went validated, in the area of the Euroregion Galicia North of Portugal, to favour the implementation of the decision-making through the Industry 4.0 Technologies.
关键词：KeywordsOptimization MethodsSimulation ToolsSustainable ManufacturingIndustry 4.0Social ReturnSustainability Index FunctionArtificial IntelligenceIndustrial Production SystemsSocial Impact of Automation
