Abstract
The fourth industrial revolution proposes digitization and networking by providing more productive, intelligent, controllable, and transparent factory environment. Although organizations have increasing attention to such a paradigm, changes in production and automation technologies led organizations to consider various areas. The necessity of individual qualifications and skills will eventually require more qualified managers. Furthermore, in a high technological environment which requires expertise on new materials, machines and information need more skilled labour. The increased complexity of workspaces eventually resulted in a need for a high level of education for the employees. The presented study contributes to research by providing a framework to structure an education scale in the context of Industry 4.0. Preliminary work for an I 4.0 education scale through an extensive literature review and academic reviews were provided.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Grzelczak, A., Kosacka, M., Werner-Lewandowska, K.: Employees competences for Industry 4.0 in Poland–preliminary research results. DEStech Transactions on Engineering and Technology Research, (icpr) (2017)
Aulbur, W., Bigghe, R.: Skill development for Industry 4.0: BRICS skill development working group. Roland Berger GMBH (2016)
Gebhardt, J., Grimm, A., Neugebauer, L.M.: Developments 4.0 prospects on future requirements and impacts on work and vocational education. J. Techn. Educ. 3(2), 117–133 (2015)
Hecklau, F., Galeitzke, M., Flachs, S., Kohl, H.: Holistic approach for human resource management in Industry 4.0. Procedia Cirp 54, 1–6 (2016)
Pinzone, M., Fantini, P., Perini, S., Garavaglia, S., Taisch, M., Miragliotta, G.: Jobs and skills in Industry 4.0: an exploratory research. In: IFIP International Conference on Advances in Production Management Systems, pp. 282–288. Springer, Cham, September 2017
Prifti, L., Knigge, M., Kienegger, H., Krcmar, H.: A Competency Model for “Industrie 4.0”. Employees (2017)
Fitsilis, P., Tsoutsa, P., Gerogiannis, V.: Industry 4.0: required personnel competences. Industrey 4.0 3(3), 130–133 (2018)
Gittler, T., Relea, E., Corti, D., Corani, G., Weiss, L., Cannizzaro, D., Wegener, K.: Towards predictive quality management in assembly systems with low quality low quantity data - a methodological approach. Procedia CIRP 79, 125–130 (2019)
http://sapevents.be/AnalyticsBriefings/presentations/SAP_PQM.pdf
Mannino, S., Scampicchio, M.: Nanatechnology and food quality control. Vet. Res. Commun. 31(1), 149–151 (2007)
Kuswandi, B., Futra, D., Heng, L.Y.: Nanosensors for the Detection of Food Contaminants, In: A. Grumezescu, A. Oprea (eds.) Nanotechnology Applications in Food. Academic Press (2017)
Manić, M., Miltenović, V., Stojković, M., Banić, M.: Feature models in virtual product development. Strojniski Vestnik/J. Mech. Eng. 56(3) (2010)
Ale Ebrahim, N., Ahmed, S., Taha, Z.: Modified stage-gate: A conceptual model of virtual product development process. Afr. J. Mark. Manag. 1(9), 211–219 (2009)
Berg, L.P., Vance, J.M.: Industry use of virtual reality in product design and manufacturing: a survey. Virtual Reality 21(1), 1–17 (2017)
da Silva, G.C., Kaminski, P.C.: Selection of virtual and physical prototypes in the product development process. Int. J. Adv. Manuf. Technol. 84(5–8), 1513–1530 (2016)
Lemu, H.G.: Study of capabilities and limitations of 3D printing technology. In: AIP Conference Proceedings, vol. 1431, No. 1, pp. 857–865. AIP, April 2012
Dimitrov, D., Van Wijck, W., Schreve, K., De Beer, N.: Investigating the achievable accuracy of three dimensional printing. Rapid Prototyping Journal 12(1), 42–52 (2006)
Zhong, R.Y., Xu, X., Klotz, E., Newman, S.T.: Intelligent manufacturing in the context of industry 4.0: a review. Engineering 3(5), 616–630 (2017)
Li, X., Lu, R., Liang, X., Shen, X., Chen, J., Lin, X.: Smart community: an Internet of Things application. IEEE Commun. Mag. 49(11), 68–75 (2011)
Doil, F., Schreiber, W., Alt, T., Patron, C.: Augmented reality for manufacturing planning. In: Proceedings of the Workshop on Virtual Environments 2003, pp. 71–76. ACM, May 2003
Nee, A.Y.C., Ong, S.K., Chryssolouris, G., Mourtzis, D.: Augmented reality applications in design and manufacturing. CIRP Ann. 61(2), 657–679 (2012)
Mourtzis, D., Vlachou, E., Zogopoulos, V., Fotini, X.: Integrated production and maintenance scheduling through machine monitoring and augmented reality: an industry 4.0 approach. In: IFIP Advances in Information and Communication Technology, pp. 354–362 (2017). https://doi.org/10.1007/978-3-319-66923-6_42
Mendoza, M., Mendoza, M., Mendoza, E., González, E.: Augmented reality as a tool of training for data collection on torque auditing. Procedia Comput. Sci. 75, 5–11 (2015)
Hakkarainen, M., Woodward, C., Billinghurst, M.: Augmented assembly using a mobile phone. In: 2008 7th IEEE/ACM International Symposium on Mixed and Augmented Reality (2008)
Wirz, R., Marin, R. (n.d.). Remote programming of an Internet tele-lab for learning visual servoing techniques: a case study. In: 2004 IEEE International Conference on Systems, Man and Cybernetics
Marin, R., Sanz, P.J., Nebot, P., Wirz, R.: A multimodal interface to control a robot arm via the web: a case study on remote programming. IEEE Trans. Industr. Electron. 52(6), 1506–1520 (2005)
Shrouf, F., Ordieres, J., Miragliotta, G.: Smart factories in industry 4.0: a review of the concept and of energy management approached in production based on the Internet of Things paradigm. In: 2014 IEEE International Conference on Industrial Engineering and Engineering Management (2014)
Primiceri, P., Visconti, P.: Solar-powered LED-based lighting facilities: an overview on recent technologies and embedded IoT devices to obtain wireless control, energy savings and quick maintenance. J. Eng. Appl. Sci. ARPN 12(1), 140–150 (2017)
Fleischmann, H., Kohl, J., Franke, J., Reidt, A., Duchon, M., Krcmar, H.: Improving maintenance processes with distributed monitoring systems. In: 2016 IEEE 14th International Conference on Industrial Informatics (INDIN) (2016)
Yamato, Y., Fukumoto, Y., Kumazaki, H.: Predictive maintenance platform with sound stream analysis in edges. J. Inf. Process. 25, 317–320 (2017)
Yano, K., Akitomi, T., Ara, K., Watanabe, J., Tsuji, S., Sato, N., Moriwaki, N.: Profiting from IoT: The key is very-large-scale happiness integration. In: 2015 Symposium on VLSI Circuits (VLSI Circuits) (2015)
Zangl, G., Oberwinkler, C.P.: Predictive data mining techniques for production optimization. In: Proceedings of SPE Annual Technical Conference and Exhibition (2004)
Bastos, P., Lopes, I., Pires, L.C.M.: Application of data mining in a maintenance system for failure prediction. In: Safety, Reliability and Risk Analysis: Beyond the Horizon: 22nd European Safety and Reliability, vol. 1, pp. 933–940 (2014)
Cheng, C.-W., Yao, H.-Q., Wu, T.-C.: Applying data mining techniques to analyze the causes of major occupational accidents in the petrochemical industry. J. Loss Prev. Process Ind. 26(6), 1269–1278 (2013)
Eaton, C., Deroos, D., Deutsch, T., Lapis, G., Zikopoulos, P.C.: Understanding Big Data: Analytics for Enterprise Class Hadoop and Streaming Data. Mc Graw-Hill Companies (2012). 978-0-07-179053-6
Sagiroglu, S., Sinanc, D.: Big data: a review. In: 2013 International Conference on Collaboration Technologies and Systems (CTS), pp. 42–47. IEEE, May 2013
Gantz, J., Reinsel, D.: Extracting value from chaos. IDC iView, pp. 1–12 (2011)
Dean, J., Ghemawat, S.: Mapreduce: simplified data processing on large clusters. Commun. ACM 51(1), 107–113 (2008)
Chen, M., Mao, S., Liu, Y.: Big data: A survey. Mobile networks and applications 19(2), 171–209 (2014)
Bakshi, K.: Considerations for Big Data: Architecture and Approach. In: Aerospace Conference IEEE, Big Sky Montana, March 2012
Negash, S.: Business intelligence. Commun. Assoc. Inf. Syst. 13, 177–195 (2004)
Rohloff, R.: Health-care BI: a tool for meaningful analysis. Healthc. Financ. Manag. 65(5), 100–108 (2011)
Foshay, N., Kuziemsky, C.: Towards an implementation framework for business intelligence in healthcare. Int. J. Inf. Manage. 34(1), 20–27 (2014)
Mettler, T., Vimarlund, V.: Understanding business intelligence in the context of healthcare. Health Inf. J. 15(3), 254–264 (2009)
Chen, H., Chiang, R.H., Storey, V.C.: Business intelligence and analytics: From big data to big impact. MIS Q. 36(4) (2012)
Kalogirou, S.A.: Artificial intelligence for the modeling and control of combustion processes: a review. Prog. Energy Combust. Sci. 29, 515–566 (2003)
Mellit, A., Kalogirou, S.A., Hontoria, L., Shaari, S.: Artificial intelligence techniques for sizing photovoltaic systems: a review. Renew. Sustain. Energy Rev. 13(2), 406–419 (2009)
Helu, M., Hedberg Jr., T.: Enabling smart manufacturing research and development using a product lifecycle test bed. Procedia Manuf 1, 86–97 (2015)
Stopp, S., Wolff, T., Irlinger, F., Lueth, T.: A new method for printer calibration and contour accuracy manufacturing with 3D-print technology. Rapid Prototyping J. 14(3), 167–172 (2008)
Dev-Anand, M., Selveraj, T., Kumanan, S., Ajith-Bosco-Raj, T.: Robotics in online inspection and quality control using moment algorithm. Adv. Prod. Eng. Manag. 7, 27–38 (2012)
Mellit, A., Kalogirou, S.A.: Artificial intelligence techniques for photovoltaic applications: A review. Prog. Energy Combust. Sci. 34(5), 574–632 (2008)
Kusiak, A.Y., Sunderesh, D., Heragu, S.: Expert systems and optimization. IEEE Trans. Software Eng. 15(8), 1017–1020 (1989)
Kodi, A.K., Louri, A.: Energy-efficient and bandwidth reconfigurable photonic networks for high-performance computing (hpc) systems. IEEE J. Sel. Top. Quantum Electron. 17(2), 384–395 (2011)
Gerhardt, B., Griffin, K., Klemann, R.: Unlocking Value in the Fragmented World of Big Data Analytics. Cisco Internet Business Solutions Group, June 2012. http://www.cisco.com/web/about/ac79/docs/sp/InformationInfomediaries.pdf
Russom, P.: Big data analytics. TDWI Best Pract. Report, Fourth Q. 19(4), 1–34 (2011)
Singh, S., Singh, N.: Big data analytics. In: 2012 International Conference on Communication, Information & Computing Technology Mumbai India. IEEE, October 2011
Shen, W.M., Hao, Q., Yoon, H.J., Norrie, D.H.: Applications of agent-based systems in intelligent manufacturing: an updated review. Adv. Eng. Inform. 20(4), 415–431 (2006)
Zijm, W.H.M.: Towards intelligent manufacturing planning and control systems. OR-Spectrum 22(3), 313–345 (2000)
Kanawaday, A., Sane, A.: Machine learning for predictive maintenance of industrial machines using IoT sensor data. In: 2017 8th IEEE International Conference on Software Engineering and Service Science (ICSESS), pp. 87–90. IEEE, November 2017
Poon, T.C., Choy, K.L., Chow, H.K.H., Lau, H.C.W., Chan, F.T.S., Ho, K.C.: A RFID case-based logistics resource management system for managing order-picking operations in warehouses. Expert Syst. Appl. 36(4), 8277–8301 (2009)
Huang, G.Q., Zhang, Y.F., Chen, X., Newman, S.T.: RFID-enabled real-time wireless manufacturing for adaptive assembly planning and control. J. Intell. Manuf. 19(6), 701–713 (2008)
Zhang, Y.F., Jiang, P., Huang, G.: RFID-based smart Kanbans for Just-In-Time manufacturing. Int J Mater. Prod. Tech. 33(1–2), 170–184 (2008)
Zhong, R.Y., Lan, S., Xu, C., Dai, Q., Huang, G.Q.: Visualization of RFID-enabled shopfloor logistics Big Data in Cloud Manufacturing. Int. J. Adv. Manuf. Tech. 84(1–4), 5–16 (2016)
Qu, T., Lei, S.P., Wang, Z.Z., Nie, D.X., Chen, X., Huang, G.Q.: IoT-based real-time production logistics synchronization system under smart cloud manufacturing. Int. J. Adv. Manuf. Tech. 84(1–4), 147–164 (2016)
Selcuk, S.: Predictive maintenance, its implementation and latest trends. Proc. Inst. Mech. Eng. Part B: J. Eng. Manuf. 231(9), 1670–1679 (2016)
Sallam, R.L., Richardson, J., Hagerty, J., Hostmann, B.: Magic Quadrant for Business Intelligence Platforms. Gartner Group, Stamford (2011)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Appendix
Appendix
I 4.0 Level 2 Skill Gap Identification Form for Genetic Algorithms (GA) Sub-Dimension
Question | 1 (not sufficient) | 2 | 3 | 4 | 5 (sufficient) | |
---|---|---|---|---|---|---|
1 | Knowledge about general terminology about the GA | |||||
2 | Ability to list the coding methods | |||||
3 | To provide the solution procedure of GA | |||||
4 | Knowledge about the crossover rate in genetic algorithm? | |||||
5 | Knowledge about GA selection operator | |||||
6 | Knowledge about mutation methods and procedures |
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Kaymaz, Y., Kabasakal, İ., Çiçekli, U.G., Kocamaz, M. (2020). A Conceptual Framework for Developing a Customized I 4.0 Education Scale: An Exploratory Research. In: Durakbasa, N., Gençyılmaz, M. (eds) Proceedings of the International Symposium for Production Research 2019. ISPR ISPR 2019 2019. Lecture Notes in Mechanical Engineering. Springer, Cham. https://doi.org/10.1007/978-3-030-31343-2_18
Download citation
DOI: https://doi.org/10.1007/978-3-030-31343-2_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-31342-5
Online ISBN: 978-3-030-31343-2
eBook Packages: EngineeringEngineering (R0)