Digital manufacturing systems: a framework to improve social sustainability of a production site

The topic of digital manufacturing is increasingly emerging in industry. One of the main scope of data digitalization is achieving more efficient factories. Different techniques and tools under the Industry 4.0 paradigm were already discussed in literature. These are aimed mostly at boosting company efficiency in terms of costs and environmental footprint. However, from a sustainability point of view, the social theme must be equally considered. While energy flows or costs can be already monitored in a production plant, this is not valid for data related to human effort. Monitoring systems aimed at supervising factory social sustainability were not already discussed in literature. The aim of this paper is to propose a method to acquire social related data in a production plant. The method is supported by a smart architecture within the concept of IoT factory. Such architecture permits to monitor the parameters that could influence social sustainability in a production site. After a discussion on production plants facilities and features, the parameters that need to be considered to guarantee socially sustainable manufacturing processes are identified. A set of sensors controls these data taken from different sources, including operator vital signs. Operations as well as humans are monitored. Data acquired by sensors are collected by a central server. A decision maker can interpret the data and improve the production system from a social point of view, implementing corrective actions. Data can be exploited not only for social assessments but even for other analyses on the production system. Guaranteeing social sustainability could boost the factory productivity. A case study is included in the paper: smart sensors are implemented in a production line to understand the operations efficiency in terms of social sustainability. (C) 2017 The Authors. Published by Elsevier B.V.