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This paper documents a work on all-purpose discrete event simulation tools evaluation. Selected tools must be suitable for process design (e.g. manufacturing or services industries). Rather than making specific judgments of the tools, authors tried to measure the intensity of usage or presence in different sources, which they called "popularity". It was performed in several different ways, including occurrences in the WWW and scientific publications with tool name and vendor name. This work is an upgrade to the same study issued 5 years ago (2011), which in its turn was also an upgrade of 10 years ago (in 2006). It is obvious that more popularity does not assure more quality, or being better to the purpose of a simulation tool; however, a positive correlation may exist between them. The result of this work is a short list, of 19 commercial simulation tools, with probably the nowadays' most relevant ones.

The fourth industrial revolution, or Industry 4.0 (I4.0), originated in German, englobes many innovative features, in order to bring the concept of "smart factories". Moreover, discrete-event simulation (DES) is one of the most important areas involved in this goal. With this in mind, the purpose of this paper is to propose a research and development agenda (R&D) for DES researchers and practitioners, in order to comply with the I4.0 agenda. To achieve this, a literature review (LR) was conducted, in which 45 papers were considered relevant for this research. From their analysis, it was found that: the ability to automatically generate simulation models; the automation of data exchange between manufacturing applications and simulation tools; and visualization features, are the most essential DES features for I4.0. Thereafter, the LR focused on analysing the most recent papers in these areas of simulation, in order to propose a list of R&D items for DES researchers and practitioners.

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Supply Chains (SCs) are dynamic and complex networks that are exposed to disruption, which have consequences hard to quantify. Thus, simulation may be used, as it allows the uncertainty and dynamic nature of systems to be considered. Furthermore, the several systems used in SCs generate data with increasingly high volumes and velocities, paving the way for the development of simulation models in Big Data contexts. Hence, contrarily to traditional simulation approaches, which use statistical distributions to model specific SC problems, this paper proposed a Decision-Support System, supported by a Big Data Warehouse (BDW) and a simulation model. The first stores and integrates data from multiple sources and the second reproduces movements of materials and information from such data, while it also allows risk scenarios to be analyzed. The obtained results show the model being used to reproduce the historical data stored in the BDW and to assess the impact of events triggered during runtime to disrupt suppliers in a geographical range. This paper also analyzes the volume of data that was managed, hoping to serve as a milestone for future SC simulation studies in Big Data contexts. Further conclusions and future work are also discussed.

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