Towards Industry 4.0: Efficient and Sustainable Manufacturing Leveraging MTEF – MTEF-MAESTRI Total Efficiency Framework

Introduction

The continuous population growth has led to increased concerns regarding the protection of the environment and resource scarcity by our society and economy. In this context, the concept of sustainability was defined in 1987 in a Report of the World Commission on Environment and Development as “development that meets the needs of the present without compromising the ability of future generations to meet their own needs” (U. Nations, 1987). Since then, the interest and concern about such subjects has aroused growing interest and the sustainability concept was never as meaningful and important as it is nowadays (Fercoq et al., 2016). Likewise, the industrial sector has the concept of sustainable manufacturing, which comprehends a significant number of objectives. The most quoted definition is given by the U.S. Department of Commerce: “the creation of manufactured products that use processes that minimize negative environmental impacts, conserve energy and natural resources, are safe for employees, communities, and consumers and are economically sound” (U.S. Department, 2014).

Hence, sustainable development supported by resource efficiency - based on the principle that the efficient and effective material and energy use can reduce natural resource inputs and waste or pollutant outputs, thus avoiding environmental degradation, and eco-efficiency assessments is nowadays a clear priority for the European industry as it is largely dependent on resources imports from international markets. Moreover, European industry is also accounted for more than a quarter of total energy consumption in 2010 in Europe.

Despite the fact that the concept of sustainability might be understood intuitively, yet, its quantitative evaluation for production systems is a complex task and not intuitive. Therefore, in order to improve sustainability and resource efficiency, the appropriate sustainability assessment and performance evolution should be carried out.

Due to the complexity related with sustainability assessment, this has become a rapidly developing topic with a growing number of concepts and tools being developed during the last decades. This has been particularly relevant for manufacturing industries, main consumers of natural resources (Garetti, 2012). The response to these challenges must encompass suitable approaches enabling industries to be supported on capabilities of the novel approaches to improve production processes and to ensure resource efficiency in the delivery of high-value-added products, while maintaining a superior economic and sustainable performance.

However, it is not enough to have proper tools and to assess resource efficiency and eco-efficiency on an off-line analysis basis, as such assessments monitored in an on-line way enable an overview of the current status and support continuous analysis capabilities. So, in order to improve resources efficiency and eco-efficiency with an “on the spot” information-decision, rather than after the major inefficiencies and costs have taken place, one first has to be able monitor data in real time in order to analyse relevant results for the resource efficiency assessment.

Additionally, the process itself needs to be known in detail and to be somehow documented. One way is to model business processes with a specific language, for instance the Business Process Modelling and Notation (BPMN). BPMN help as a means of communication between technical and non-technical personnel, the models can also be executed in process engines and it is one of the standards used today in industry. In order to be able to analyse data in a much deeper way, such data coming from sensors and production systems need to be mapped to process steps and annotated automatically. This way, and in the scope of the new industrial paradigm named of “Industry 4.0”, the process can be monitored in real-time and the data can be used for a more detailed analysis. In this sense, advances towards Industry 4.0 and overall control of manufacturing systems will lead to a significant increase of available information. Industry 4.0 leverages on the concept of Internet-of-Things (IoT), which incorporates several opportunities related to tracking resources, through IoT system can reveal hidden inefficiencies, costs, and ultimately provide sufficient information, with higher quality, to set goals and make informed decisions to enhance resource efficiency and eco-efficiency performance. Smart systems tend to be better than humans in capturing and communicating data, since errors related to human errors registering and managing the data may be avoided.