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A Novel Analysis Framework of 4.0 Production Planning Approaches – Part I

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Service Oriented, Holonic and Multi-Agent Manufacturing Systems for Industry of the Future (SOHOMA 2021)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 987))

Abstract

The emergence of the Fourth Industrial Revolution has brought enterprises to review their production planning processes. Characterized by important technologies, this revolution provides managers and planners with multiple means to increase productivity, get added value from data mining processes and become more agile. This paper, divided into two parts, aims at proposing an analysis framework for conducting a literature review of the production planning approaches developed during the Fourth Industrial Revolution. This first part of the paper presents the analytical framework, the research methodology and the results of the systemic review. The proposed framework characterizes the approaches in terms of the addressed production planning activity, the planning horizon, the company size, the dimension of agility and the employed means.

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References

  1. CEFRIO PME 2.0: CEFRIO: Prendre part à la révolution manufacturière? Du rattrapage technologique à l’industrie 4.0 chez les PME, Analysis Report (2016)

    Google Scholar 

  2. Kohler, D., Weiz, J.: Industry 4.0: Les défis de la transformation numérique du modèle industriel allemand, Industry, 176 éd. La Documentation française, Paris (2016)

    Google Scholar 

  3. Arnold, J. T., Chapman, S.N., Clive, L.M.: Introduction to materials Management. New Jersey (2008). https://doi.org/10.1017/mdh.2014.75

  4. Dallasega, P., Rojas, R.A., Rauch, E., Matt, D.T.: Simulation based validation of supply chain effects through ICT enabled real-time-capability in ETO production planning. Procedia Manuf. 11, 846–853 (2017). https://doi.org/10.1016/j.promfg.2017.07.187

    Article  Google Scholar 

  5. Erol, S., Sihn, W.: Intelligent production planning and control in the cloud – towards a scalable software architecture. In: 10th CIRP Conference on Intelligent Computation in Manufacturing Engineering, pp. 571–576. https://doi.org/10.1016/j.procir.2017.01.003 (2016)

  6. Georgiadis, P., Michaloudis, C.: Real-time production planning and control system for job-shop manufacturing: a system dynamics analysis. Eur. J. Oper. Res. 216, 94–104 (2012). https://doi.org/10.1016/j.ejor.2011.07.022

    Article  MathSciNet  MATH  Google Scholar 

  7. Kumar, S., Purohit, B.S., Manjrekar, V., Singh, V., Kumar Lad, B.: Investigating the value of integrated operations planning: a case-based approach from automotive industry. Int. J. Prod. Res. 7543, 1–22 (2018). https://doi.org/10.1080/00207543.2018.1424367

    Article  Google Scholar 

  8. Lanza, G., Stricker, N., Moser, R.: Concept of an intelligent production control for global manufacturing in dynamic environments based on rescheduling. In: IEEE International Conference on Industrial Engineering and Engineering Management, pp. 315–319. Malaysia (2014). https://doi.org/10.1109/IEEM.2013.6962425

  9. Jiang, Z., Jin, Y., Mingcheng, E., Li, Q.: Distributed dynamic scheduling for cyber-physical production systems based on a multi-agent system. IEEE Access 6, 1855–1869 (2017). https://doi.org/10.1109/ACCESS.2017.2780321

    Article  Google Scholar 

  10. Rajabinasab, A., Mansour, S.: Dynamic flexible job shop scheduling with alternative process plans: an agent-based approach. Int. J. Adv. Manuf. Technol. 54, 1091–1107 (2011). https://doi.org/10.1007/s00170-010-2986-7

    Article  Google Scholar 

  11. Pellerin, R.: The contribution of Industry 4.0 in creating agility within SMEs. In: Proceedings of the 2018 IRMBAM Conference, pp. 1–10. Nice, France (2018)

    Google Scholar 

  12. APICS: The Association of Operations (2011). http://www.apics.org/docs/default-source/industry-content/apics-ombok-framework.pdf?sfvrsn=c5fce1ba_2

  13. Lemieux, A.A., Pellerin, R., Lamouri, S., Carbone, V.: A new analysis framework for agility in the fashion industry. Int. J. Agil. Syst. Manag. 5, 175–197 (2012). https://doi.org/10.1504/IJASM.2012.046904

    Article  Google Scholar 

  14. Alberts, F., Hammond, J., Weil, D.: Power to the Edge: Command and Control in the Information Age. Washington DC Report (2003). http://edocs.nps.edu/dodpubs/org/CCRP/Alberts_Power.pdf

  15. Ruessmann, M., et al.: Industry 4.0: The Future of Productivity and Growth in Manufacturing (2015). https://inovasyon.org/images/Haberler/bcgperspectives_Industry40_2015.pdf

  16. Geisberger, E., Broy, M.: Agenda CPS: Integrierte Forschungsagenda Cyber Physical Systems. Springer Acatech (2012)

    Book  Google Scholar 

  17. Berger, C., Zipfel, A., Braunreuther, S., Reinhart, G.: Approach for an event-driven production control for cyber-physical production systems. In: 12th CIRP Conference on Intelligent Computation in Manufacturing Engineering, pp. 349–354. Elsevier (2019). https://doi.org/10.1016/j.procir.2019.02.085

  18. Tang, L., et al.: Online and offline based load balance algorithm in cloud computing. Knowl.-Based Syst. 138, 91–104 (2017). https://doi.org/10.1016/j.knosys.2017.09.040

    Article  Google Scholar 

  19. Wandt, R., Friedewald, A., Lödding, H.: Simulation aided disturbance management in one-of-a-kind production on the assembly site. In: IEEE International Conference on Industrial Engineering and Engineering Management, pp. 503–507 (2012). https://doi.org/10.1109/IEEM.2012.6837790

  20. Tobon Valencia, E., Lamouri, S., Pellerin, R., Forget, P., Moeuf, A.: Planification de la production et Industrie 4.0: Revue de la littérature. In: CIGI-Qualita, Montréal (2019)

    Google Scholar 

  21. Xu, Y., Chen, M.: Improving just-in-time manufacturing operations by using internet of things based solutions. I. In: 9th International Conference on Digital Enterprise Technology, pp. 326–331. Elsevier (2016). https://doi.org/10.1016/j.procir.2016.10.030

  22. Xu, Y., Chen, M.: An internet of things based framework to enhance just-in-time manufacturing. J. Eng. Manuf. 232, 2353–3263 (2017). https://doi.org/10.1177/0954405417731467

    Article  Google Scholar 

  23. Lin, P., Li, M., Kong, X., Chen, J., Huang, G.Q., Wang, M.: Synchronization for smart factory – towards IoT-enabled mechanisms. Int. J. Comput. Integr. Manuf. 31, 624–635 (2018). https://doi.org/10.1080/0951192X.2017.1407445

    Article  Google Scholar 

  24. Nonaka, Y., Suginishi, Y., Lengyel, A., Nagahara, S., Kamoda, K., Katsumura, Y.: The S-model: a digital manufacturing system combined with autonomous statistical analysis and autonomous discrete-event simulation for smart manufacturing. In: International Conference on Automation Science and Engineering, CASE 2015. pp. 1006–1011. Gothenburg, Sweden (2015). https://doi.org/10.1109/CoASE.2015.7294230

  25. Qu, S., Wang, J., Govil, S., Leckie, J.O.: Optimized adaptive scheduling of a manufacturing process system with multi-skill workforce and multiple machine types: an ontology-based, multi-agent reinforcement learning approach. In: 49th CIRP Conference on Manufacturing Systems, CIRP-CMS 2016, pp. 55–60. Elsevier Stuttgart, Germany (2016). https://doi.org/10.1016/j.procir.2016.11.011.

  26. Shamsuzzoha, A., Toscano, C., Carneiro, L.M., Kumar, V., Helo, P.: ICT-based solution approach for collaborative delivery of customized products. Prod. Plan. Control. 27, 280–298 (2016). https://doi.org/10.1080/09537287.2015.1123322

    Article  Google Scholar 

  27. Zhu, X., Qiao, F., Cao, Q.: Industrial big data-based scheduling modeling framework for complex manufacturing system. Adv. Mech. Eng. 9, 1–12 (2017). https://doi.org/10.1177/1687814017726289

    Article  Google Scholar 

  28. Asadzadeh, L.: A local search genetic algorithm for the job shop scheduling problem with intelligent agents. Comput. Ind. Eng. 85, 376–383 (2015). https://doi.org/10.1016/j.cie.2015.04.006

    Article  Google Scholar 

  29. Helo, P., Phuong, D., Hao, Y.: Cloud manufacturing – Scheduling as a service for sheet metal manufacturing. Comput. Oper. Res. 110, 208–219 (2019). https://doi.org/10.1016/j.cor.2018.06.002

    Article  MathSciNet  MATH  Google Scholar 

  30. Waschneck, B., et al.: Optimization of global production scheduling deep reinforcement learning. In: 51st CIRP Conference on Manufacturing Systems. pp. 1264–1269. Stockholm, Sweden (2018). https://doi.org/10.1016/j.procir.2018.03.212

  31. Saif, U., Guan, Z., Wang, C., He, C., Yue, L., Mirza, J.: Drum buffer rope-based heuristic for multi-level rolling horizon planning in mixed model production. Int. J. Prod. Res. 57, 3864–3891 (2019). https://doi.org/10.1080/00207543.2019.1569272

    Article  Google Scholar 

  32. Shim, S., Park, K., Choi, S.: Sustainable production scheduling in open innovation perspective under the fourth industrial revolution, J. Open Innov., 4(4), 42; https://doi.org/10.3390/joitmc4040042

  33. Klein, M., et al.: A negotiation-based approach-based production International for scheduling. In: 28th Int. Conference on Flexible Automation and Intelligent Manufacturing, FAIM 2018, pp. 334–341, Elsevier Columbus, USA (2018). https://doi.org/10.1016/j.promfg.2018.10.054

  34. Denno, P., Dickerson, C., Anne, J.: Dynamic production system identification for smart manufacturing systems. J. Manuf. Syst. 48, 1–11 (2018). https://doi.org/10.1016/j.jmsy.2018.04.006

    Article  Google Scholar 

  35. Leusin, M.E., Kück, M., Frazzon, E.M., Maldonado, M.U., Freitag, M.: Potential of a multi-agent system approach for production control in smart factories. IFAC-Papers on-line 51(11), 1459–1464 (2018). https://doi.org/10.1016/j.ifacol.2018.08.309

    Article  Google Scholar 

  36. Kuhnle, A., Röhrig, N., Lanza, G.: Autonomous order dispatching in the semiconductor industry using reinforcement learning, In: 12th CIRP Conference on Intelligent Computation in Manufacturing Engineering, pp. 391–396, Elsevier, Naples, Italy (2019). https://doi.org/10.1016/j.procir.2019.02.101

  37. Gräler, I., Pöhler, A.: Intelligent Devices in a Decentralized Production System Concept. In: 11th CIRP Conference on Intelligent Computation in Manufacturing Engineering. pp. 116–121 (2018). https://doi.org/10.1016/j.procir.2017.12.186

  38. Kubo, R.H., Asato, O.L., Dos Santos, G.A., Nakamoto, F.Y.: Modeling of allocation control system of multifunctional resources for manufacturing systems. In: 12th IEEE Int. Conf. on Industry Applic., INDUSCON (2016). https://doi.org/10.1109/INDUSCON.2016.7874596

  39. Lanza, G., Stricker, N., Peters, S.. Ad-hoc rescheduling and innovative business models for shock-robust production systems. In: Forty Sixth CIRP Conference on Manufacturing Systems, pp. 121–126. Elsevier (2013). https://doi.org/10.1016/j.procir.2013.05.021

  40. Li, Q., Wang, L., Shi, L., Wang, C.: A data-based production planning method for multi-variety and small-batch production. In: IEEE 2nd Int. Conf. on Big Data Analysis, ICBDA, pp. 420–425. Beijing (2017). https://doi.org/10.1109/ICBDA.2017.8078854

  41. Biesinger, F., Meike, D., Kraß, B., Weyrich, M.: A digital twin for production planning based on cyber-physical systems: A case study for a cyber-physical system-based creation of a digital twin. In: 12th CIRP Conference on Intelligent Computation in Manufacturing Engineering, pp. 355–360. Elsevier (2019). https://doi.org/10.1016/j.procir.2019.02.087.

  42. Graessler, I., Poehler, A.: Integration of a digital twin as human representation in a scheduling procedure of a cyber-physical production system. In: IEEE International Conference on Industrial Engineering and Engineering Management, pp. 289–293. Singapore (2017). https://doi.org/10.1109/IEEM.2017.8289898

  43. Fang, C., Liu, X., Pei, J., Fan, W., Pardalos, P.M.: Optimal production planning in a hybrid manufacturing and recovering system based on the internet of things with closed loop supply chains. Oper. Res. Int. J. 16(3), 543–577 (2015). https://doi.org/10.1007/s12351-015-0213-x

    Article  Google Scholar 

  44. Illmer, B., Vielhaber, M.: Virtual validation of decentrally controlled manufacturing systems with cyber-physical functionalities, 51st CIRP Conf. on Manufacturing Systems Virtual, pp. 509–514. Elsevier Stockholm (2018). https://doi.org/10.1016/j.procir.2018.03.195

  45. Meyer, G.G., Hans Wortmann, J.C., Szirbik, N.B.: Production monitoring and control with intelligent products. Int. J. Prod. Res. 49, 1303–1317 (2011). https://doi.org/10.1080/00207543.2010.518742

    Article  Google Scholar 

  46. Um, J., Choi, Y.C., Stroud, I.: Factory planning system considering energy-efficient process under cloud manufacturing. In: 47th CIRP Conf. on Manufacturing Systems, pp. 553–558. Elsevier, Windsor, Canada (2014). https://doi.org/10.1016/j.procir.2014.01.084

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Author information

Authors and Affiliations

  1. Square Flow & Co., Neuilly-sur-Seine, France

    Estefania Tobon Valencia

  2. LAMIH, Arts et Métiers ParisTech, Paris, France

    Samir Lamouri

  3. Polytechnique Montréal, Montréal, Canada

    Robert Pellerin

  4. Exxelia, Paris, France

    Alexandre Moeuf

Authors
  1. Estefania Tobon Valencia
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  2. Samir Lamouri
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  3. Robert Pellerin
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  4. Alexandre Moeuf
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Corresponding author

Correspondence to Estefania Tobon Valencia .

Editor information

Editors and Affiliations

  1. UPHF, LAMIH UMR CNRS 8201, Polytechnic University Hauts de France, Valenciennes cedex 9, France

    Damien Trentesaux

  2. Faculty of Automatic Control and Computer Science, University Politehnica of Bucharest, Bucharest, Romania

    Theodor Borangiu

  3. Polytechnic Institute of Bragança, Research Centre in Digitalization and Intelligent Robotics (CeDRI), Bragança, Portugal

    Paulo Leitão

  4. Faculty of Engineering, Department of Industrial Engineering, Pontificia Universidad Javeriana, Bogota, Colombia

    Jose-Fernando Jimenez

  5. Facultad de Ingeniería, Universidad de La Sabana, Chia, Colombia

    Jairo R. Montoya-Torres

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Tobon Valencia, E., Lamouri, S., Pellerin, R., Moeuf, A. (2021). A Novel Analysis Framework of 4.0 Production Planning Approaches – Part I. In: Trentesaux, D., Borangiu, T., Leitão, P., Jimenez, JF., Montoya-Torres, J.R. (eds) Service Oriented, Holonic and Multi-Agent Manufacturing Systems for Industry of the Future. SOHOMA 2021. Studies in Computational Intelligence, vol 987. Springer, Cham. https://doi.org/10.1007/978-3-030-80906-5_9

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