Using simulation to study the influence of Overall Equipment Effectiveness on energy consumption of a single production machine

In recent years, manufacturing companies have been forced to focus their attention on the effective and efficient use of energy, partly due to the huge and continuous increase of energy costs, and partly due to the enforcement of environmental laws and regulations. However, efforts made to pursue energy efficiency have proved themselves to be highly remunerative in more than one way, as recent studies have demonstrated that they often end up in additional benefits, such as operational efficiency increases. Furthermore, other investigations have suggested the existence of a tight relation between Overall Equipment Effectiveness losses and energy consumption, so that actions taken to reduce the firsts may have consequences on the second, a condition that shall be taken into account while evaluating their feasibility and priority. In order to deeply analyse such mutual influence, a simulation model to evaluate and estimate system efficiency losses, meaning both Overall Equipment Effectiveness losses and energy efficiency losses of a single production machine has been created, and is presented as a first step towards the definition of a more extended model that will consider the behaviour of a whole production line and allow the creation of new Operations and Maintenance policies and practices based on energy efficiency considerations. This work turns out to be highly innovative from a scientific point of view, given the scarceness and incompleteness, both in commercial and academic literature, of simulation models considering energy and/or environmental aspects of manufacturing systems, and the limited number of simulation models considering them in conjunction with Overall Equipment Effectiveness. In addition, it can be also particularly useful for practitioners, helping to develop general awareness of the criticality of energy efficiency in any aspect of production systems and to start considering and handling it as a key parameter to drive Operations and Maintenance decisions.