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Simulation in Manufacturing: Review and Challenges

Open access
Mourtzis, D., Doukas, M., Bernidaki, D.·2014·Procedia CIRP

Abstract

Simulation comprises an indispensable set of technological tools and methods for the successful implementation of digital manufacturing, since it allows for the experimentation and validation of product, process and system design and configuration. Especially in todays’ turbulent manufacturing environment, which is affected by megatrends such as globalisation and ever-increasing requirements for higher degree of product customisation and personalisation, the value of simulation is evident. This keynote paper investigates the major milestones in the evolution of simulation technologies and examines recent industrial and research applications and findings. Based on this review, the identification of gaps in current practices is presented, and future trends and challenges to be met on the field are outlined. The considered simulation methods and tools include CAx, Factory layout design, Material and Information flow design, Manufacturing Networks Design, Manufacturing Systems Planning and Control, Manufacturing Networks Planning and Control, Augmented and Virtual Reality in product and process design, planning and verification (ergonomics, robotics, etc.). The evolution, advances, current practices and future trends of these technologies, industrial applications and research results are discussed in the context of the contemporary manufacturing industry.

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Metadata

Authors
Mourtzis, D., Doukas, M., & Bernidaki, D.
Venue
Procedia CIRP
Publisher
Elsevier BV
Published
2014
Volume
25
Pages
213-229
ISSN
2212-8271 (ISSN-L)
OpenAlex
W2013150564
Cited by
356
References
0
FWCI
13.77
Type
journal article
Language
en
Open access
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CoChat. (2026). Citation check: Simulation in Manufacturing: Review and Challenges. https://app.cochat.ai/papers/10.1016/j.procir.2014.10.032
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Mourtzis, D., Doukas, M., & Bernidaki, D. (2014). Simulation in Manufacturing: Review and Challenges. Procedia CIRP. https://doi.org/10.1016/j.procir.2014.10.032

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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.