## 1 Introduction  
As a result of the German government's pioneer effort to innovate manufacturing systems [1], the Fourth Industrial Revolution was designated Industry 4.0 (14.0) for the first time in 2011, on the occasion of the Hannover Fair [2]. Despite the term's extensive nature, a widely accepted definition can be found in [3], where it is described as \"a set of technologies based on digitisation and interconnection of all production units present within an economic system\". In order to improve the efficiency and profitability of manufacturing systems [4] by integrating activities involving human beings, machines and data [2], 14.0 relies on enabling technologies such as big data, Internet of Things (IoT) and simulations. Therefore, factories are progressively becoming smart factories, the main features of which are the capacity to accept on-the-fly changes to the ongoing manufacturing processes, provide remote access to every single resource and autonomously organise production tasks [5]. Nevertheless, in spite of 14.0's indisputable benefits, the high initial investments, the uncertainty about its profitability and the risk of cyber-attacks, still prevent companies from embracing the transition [2]. Tightly related to 14.0 is the concept of Cyber-Physical System (CPS) [6], coined by [PERSON] at a workshop on CPSs, hosted by the National Science Foundation (NSF) in 2006 [7]. According to [8], \"_CPSs are systems of collaborating computational entities which are in intensive connection with the surrounding physical world and its on-going processes, providing and using, at the same time, data-accessing and data-processing services available on the internet_\". Whenever applied for manufacturing purposes, CPSs are referred to as Cyber-Physical Production Systems (CPPSs) [9]. Most of the proposed CPPSs aim to bring dynamic and flexible solutions using adaptable and reconfigurable distributed control systems [10; 11]. Usually, these distributed control systems are modelled as modular components and designed using Multi-Agent Systems (MASs) approaches [12]. Although the benefits and improvements introduced by these solutions are known and understandable by the manufacturers, it is still difficult for these approaches to be adopted and used in real industrial environments. In the literature, some authors already identified barriers as critical in adopting these approaches [13]. One of the main obstacles is the difficulty to predict the self-organised behaviour of such systems and the impossibility to understand how the system will evolve [14]. Hence, this research aims to study the utilisation of Digital Twins (DTs) to mitigate this aspect. The authors believe that a simulation-based DT can be used to predict the system's behaviour and performance. To do that, it must use the production line's current status and the production plan in order to simulate the system to different time horizons. The main innovation associated with the previous concepts is related to the digital world and its components. On this regard, an identical concept to what is now referred to as DT was originally suggested by [PERSON] on the occasion of an industry lecture on Product Lifecycle Management (PLM). According to him, a DT was no more than a virtual entity capable of mirroring its physical counterpart [15]. It is worth mentioning that, in spite of this definition still being suitable, a considerable amount of authors mistakenly insist on stating that a DT includes the components from both the cyber and the physical worlds. According to [16], three designations are possible depending on the integration level between the physical asset and its digital corresponding. In case the data exchange between both entities is manually performed, the term Digital Model is adopted. If only the physical device is able to automatically update its counterpart, the term Digital Shadow is used instead. The full DT level implies that the cyber entity is capable of influencing its physical twin and vice versa.