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Application of FlexSim software for developing cyber learning factory for smart factory education and training

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Abstract

Smart factory is a manufacturing facility equipped with modern information and communication technologies, and it is considered as an innovative manufacturing paradigm in the era of 4th industrial revolution. However, conventional technology-oriented smart factory education programs often focus on specific technologies, and many undergraduates and practitioners have trouble in understanding concepts, elements and features of entire smart factory system. In order to address this problem, this paper proposes a cyber learning factory for operations management-oriented smart factory education and training, developed by applying 3D factory simulation software, FlexSim. The cyber learning factory is implemented by incorporating three key components, information system, database and virtual manufacturing facility provided by 3D factory simulation software such as FlexSim. Since overall smart factory system can be virtually implemented in a single cyber space, the cyber learning factory can provide hands-on experiences for understanding, designing and optimizing smart factory. Consequently, the cyber learning factory can be used to train both operations managers of manufacturing companies and information systems architects of IT companies, and this paper will provide significant insights into the operations management-oriented smart factory education and training.

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References

  1. Abele E, Matternich J, Tisch M, Chryssolouris G, Sihn W, ElMaraghy H, Hummel V, Ranz F (2015) Learning factories for research, education and training. Proc CiRp 32:1–6

    Article  Google Scholar 

  2. Beaverstock M, Greenwood A, Nordgren W (2017) Applied simulation – modeling and analysis using FlexSim (5th eds.), FlexSim Software Products, Inc

  3. Botta-Genoulaz V, Millet PA, Grabot P (2005) A survey on the recent research literature on ERP systems. Comput Ind 56:510–522

    Article  Google Scholar 

  4. Dallasega P, Rojas RA, Rauch E, Matt DT (2017) Simulation based validation of supply chain effects through ICT enabled real-time-capability in ETO production planning. Proc Manufact 11:846–853

    Article  Google Scholar 

  5. De Felice F, Petrillo A, Zomparelli F (2018) Prospective design of smart manufacturing: an Italian pilot case study. Manufact Lett 15:81–85

    Article  Google Scholar 

  6. Fanti MP, Iacobellis G, Rotunno G, Ukovich W (2013) A simulation based analysis of production scheduling in a steelmaking and continuous casting plant. Proc Auto Sci Eng 2013:150–155

    Google Scholar 

  7. Ghosh SM, Sharma HR, Mohabay V (2011) Study of impact analysis of software requirement change in SAP ERP. Int J Adv Sci Technol 33:95–100

    Google Scholar 

  8. Helo P, Suorsa M, Hao Y, Annussomnitisam P (2014) Toward a cloud-based manufacturing execution system for distributed manufacturing. Comput Ind 65:646–656

    Article  Google Scholar 

  9. Jia H, Yu K, Zhang J (2014) The simulation and optimization on the certain type fuel pump assembly line balance based on Flexsim. Appl Mech Mater 741:850–855

    Article  Google Scholar 

  10. Jo DS, Kim JW (2018) A survey on characteristics and application domains of 3D factory simulation technology. J Inform Syst 27(4):35–70

    Google Scholar 

  11. Kumar BS, Mahesh V, Kumar BS (2015) Modeling and analysis of flexible manufacturing system with FlexSim. Int J Comput Eng Res 5:1–6

    Google Scholar 

  12. Kumar BS, Sanjeev G, Amarnath K, Krishna SV (2016) Performance evaluation of an FMS with alternative machines using FlexSim simulation software. Int J Eng Manag Res 6(4):406–413

    Google Scholar 

  13. Kumar BS, Raju GJ, Janardhana GR (2018) Simulation modeling and analysis of flexible manufacturing systems with FlexSim software. Res J Eng Technol 9(1):85–89

    Article  Google Scholar 

  14. Kurth M, Schleyer C, Feuser D (2017) Smart factory and education: an integrated automation concept. J Serv Comput Orient Manufact 3:43–53

    Google Scholar 

  15. Kwok PK, Chan BK, Lay HY (2018) A virtual collaborative simulation-based training system, Proceedings of the 10th ACM international conference on computer modeling and simulation, 258–264

  16. Lee J (2015) Smart factory systems. Informatik-Spektrum 38:230–235

    Article  Google Scholar 

  17. Li XM (2017) Layout analysis and design of the spindle box processing workshop. Proceedings of the 23rd international conference on industrial engineering and engineering management, 177–180

  18. Lohtander M, Garcia E, Lanz M, Volotinen J, Ratava J, Kaakkumen J (2018) Micro manufacturing unit – creating digital twn objects with common engineering software. Proc Manufact 17:468–475

    Article  Google Scholar 

  19. Lohtander M, Ahonen N, Lanz M, Ratava J, Kaakkumen J (2018) Micro manufacturing unit and the corresponding 3D-model for the digital twin. Proc Manufact 25:55–61

    Article  Google Scholar 

  20. Longo F, Nicolettie L, Padovano A (2017) Smart operations in industry 4.0: a human-centered approach to enhance operators’ capabilities and competencies within the new smart factory context. Comput Ind Eng 113:144–159

    Article  Google Scholar 

  21. Macia-Perez F, Berna-Martinez JV, Marcos-Jorquera D, Lorenzo-Fonseca I, Ferrandiz-Colmeiro A (2012) A new paradigm: cloud agile manufacturing. Int J Adv Sci Technol 45:47–54

    Google Scholar 

  22. Min H, Yang Q (2018) Study on modeling and simulation based on FlexSim. Acad J Manufact Eng 16(2):149–156

    MathSciNet  Google Scholar 

  23. Peters G, Seruga J (2012) Network effects for enterprise resource planning systems. Int J Adv Sci Technol 43:105–114

    Google Scholar 

  24. Radziwon A, Bilberg A, Bogers M, Madsen ES (2014) The smart factory: exploring adaptive and flexible manufacturing solutions. Proc Eng 69:1184–1190

    Article  Google Scholar 

  25. Samaranayake P, Kiridena SB, Cai D (2014) Planning and scheduling across the supply chain: simulation-based validation of the unitary structuring technique. Proc Indust Eng Eng Manage 2014:1275–1279

    Google Scholar 

  26. Tang XY, Yang LL, Zhang JJ, Shi J, Chen LC (2013) Research on AS/RS simulation based on FlexSim. Appl Mech Mater 347:406–410

    Article  Google Scholar 

  27. Wang S, Wan J, Li D, Zhang C (2016) Implementing smart factory of Industrie 4.0: an outlook. Int J Distrib Sensor Netw 12:e3159805

    Article  Google Scholar 

  28. Yan J, Jiang P (2017) The application of simulation technology in distribution center. Appl Mech Mater 865:675–680

    Article  Google Scholar 

  29. Zhang R, Ong SK, Nee AY (2015) A simulation-based genetic algorithm approach for remanufacturing process planning and scheduling. Appl Soft Comput 37:521–532

    Article  Google Scholar 

  30. Zhou G, Mao L (2010) Design and simulation of storage location optimization module in AS/RS based on FLEXSIM. Int J Intell Syst Appl 2:33–40

    Google Scholar 

  31. Zhou J, Zhao CY, Liu ZQ, Yang Y, Li JF (2009) Operation optimization of storage and retrieval for stackers in AS/RS of raw tobacco material. Comput Integr Manuf Syst 15(4):772–776

    Google Scholar 

  32. Zhu X, Zhang R, Chu F, He Z, Li J (2014) A Flexsim-based optimization for the operation process of cold-chain logistics distribution Centre. J Appl Res Technol 12:270–288

    Article  Google Scholar 

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Acknowledgements

This research was supported by the KIAT(Korea Institute for Advancement of Technology) grant funded by the Korea Government(MOTIE: Ministry of Trade Industry and Energy). (No. N0002429).

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Authors and Affiliations

  1. Department of Industrial and Management Systems Engineering, Dong-A University, 840, Hadan 2-dong. Saha-gu, Busan, 604-714, South Korea

    Jun Woo Kim & Jin Sung Park

  2. Department of Game Engineering, Paichai University, Baejae-ro 155-40, Seo-Gu, Daejeon, South Korea

    Soo Kyun Kim

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  1. Jun Woo Kim
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Correspondence to Soo Kyun Kim.

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Kim, J.W., Park, J.S. & Kim, S.K. Application of FlexSim software for developing cyber learning factory for smart factory education and training. Multimed Tools Appl 79, 16281–16297 (2020). https://doi.org/10.1007/s11042-019-08156-1

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  • DOI: https://doi.org/10.1007/s11042-019-08156-1

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