Discrete simulation software ranking – a top list of the worldwide most popular and used tools

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.

Most of scientific works related to tools comparison analyze only a small set of tools and usually evaluating several parameters separately, avoiding to make a final judgment, due to its subjectivity.

Simulation languages have been replaced by simulation software packages/tools. High market prices of simulation tools in the past decades, added to other factors like: ease of construction of a simulation tool; the emerging graphics facilities; the wide field of applications and the absence of strong standards or languages; lead to a large, or may be too large, tools offer (Dias, 2005).

Thus, for instance, in the Industrial Engineering Magazine (1993/July) there is a list of 45 commercial simulation software products. The sixth biannual edition of simulation software compiled by James J. Swain in 2003 identified about 60 commercial simulation products, 55 in 2005, 48 in 2009, 43 in 2013, 55 in 2015 (Swain, 1991-2015). The annual 2004 SCS edition — “M&S Resource Directory” lists 60 simulation products (Klee, 2004). In the “Simulation Education Homepage” (Simulation tools list by William Yurcik) there were more than 200 simulation products, including non-commercial tools.

This work started with Swain´s list, removing non discrete event simulation environments, and adding some tools found in more than one list sources.

As aforementioned, this tools comparison was performed previously in 2006 and 2011, and is now extended with more parameters and relevant changes are discussed.

In this scenario of such a large simulation tools’ offer it is unfeasible to perform a consistent experiment. The comparison, based on features or characteristics is also very difficult or non-conclusive because most of them have similar features lists.

The measure here called “popularity” was the way that we found to overcome those difficulties, identifying the tools that are potentially the best or most used. Choosing a popular simulation tool may bring benefits in two different perspectives:

  • If you are a company, it is easier to find simulation specialists with know-how on a popular tool;
  • If you are a simulation specialist, it is easier to find companies working with a popular tool.

The second way includes educational purposes because students should be the future simulation specialists. Nevertheless, popularity should never be used as a unique parameter for simulation tools selection. If so, new tools, would never gain market share — and this is a generic risk, not a simulation particularity. Therefore, the popularity may be seen as a significant “blind” factor to be used in conjunction with direct evaluation mechanisms like features comparison and experimentation.

Product names in this paper are trademarks or registered trademarks of their respective owners.

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