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Decision-Making Process Development for Industry 4.0 Transformation

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Języki publikacji
EN
Abstrakty
EN
Development of design characteristics based dynamic decision support framework is presented in the current study, to facilitate the decision makers in the transformation of system in the industry 4.0 paradigm. The model development is designed for a robust decision-making approach to integrating human and machine knowledge to adopt smart technologies and system design. The system is based on prioritization of the industry 4.0 design principles and characteristics including flexibility, self-adaptability, self-reconfigurability, context awareness, decision autonomy, and real-time capabilities. It has been revealed from an industrial field study, the companies facing difficulty to transform the system, and systematics approach needed to overcome the challenge. A decision support process has been developed as an integrated approach to embedding human knowledge. The developed process has been validated using Technique for Order of Preference by Similarity to Ideal Solution, the results depict the operational flexibility, has been most crucial transformation characteristics prioritized using the Analytical Hierarchical Process. The developed process has the capability to help the system development and estimate the factors involved in the transformation.
Twórcy
autor
  • Faculty of Industrial Engineering, Department of Engineering Management, University of Engineering and Technology Taxila, 47080 Taxila, Pakistan
autor
  • Faculty of Industrial Engineering, Department of Engineering Management, University of Engineering and Technology Taxila, 47080 Taxila, Pakistan
  • Faculty of Industrial Engineering, Department of Engineering Management, University of Engineering and Technology Taxila, 47080 Taxila, Pakistan
Bibliografia
  • 1. Milisavljevic-Syed J., et al. Decision-Based Design of Networked Manufacturing Systems (NMS), in Architecting Networked Engineered Systems. 2020, Springer; 41–70.
  • 2. Milisavljevic-Syed J., et al. Architecting Networked Engineered Systems: Manufacturing Systems Design for Industry 4.0. 2020: Springer Nature.
  • 3. Qu Y., et al. Integrating fuzzy Kano model and fuzzy analytic hierarchy process to evaluate requirements of smart manufacturing systems. Concurrent Engineering. 2019; 27(3): 201–212.
  • 4. Mejjaouli S., Albathi R. Fuzzy AHP and Linear Programming Based Decision Support System for Logistics Service Providers Allocation.
  • 5. Kaya S.K., Aycin E. An integrated interval type 2 fuzzy AHP and COPRAS-G methodologies for supplier selection in the era of Industry 4.0. Neural Computing and Applications, 2021; 1–21.
  • 6. Fallahpour A., Olugu E.U., Musa S.N. A hybrid model for supplier selection: integration of AHP and multi expression programming (MEP). Neural Computing and Applications, 2017; 28(3): 499–504.
  • 7. Bruck B.P., et al. A Decision Support System to Evaluate Suppliers in the Context of Global Service Providers. in Proceedings of the 23th International Conference on Enterprise Information Systems (ICEIS 2021). 2021.
  • 8. Jung K., et al. Performance challenges identification method for smart manufacturing systems. 2016: US Department of Commerce, National Institute of Standards and Technology.
  • 9. Lee Y.T., et al. A classification scheme for smart manufacturing systems’ performance metrics. Smart and sustainable manufacturing systems. 2017; 1(1): 52.
  • 10. Kibira D., et al. Methods and tools for performance assurance of smart manufacturing systems. 2015: US Department of Commerce, National Institute of Standards and Technology.
  • 11. Lu Y., K.C. Morris, and S. Frechette. Standards landscape and directions for smart manufacturing systems. in 2015 IEEE international conference on automation science and engineering (CASE). 2015. IEEE.
  • 12. Tao F., et al. Advanced manufacturing systems: socialization characteristics and trends. Journal of Intelligent Manufacturing, 2017; 28(5): 1079–1094.
  • 13. Davis J. Cyberinfrastructure in chemical and biological process systems: impact and directions. in Arlington, VA, NSF workshop report; 2006.
  • 14. Park H.-S., Tran N.-H. Autonomy for smart manufacturing. Journal of the Korean Society for Precision Engineering. 2014; 31(4): 287–295.
  • 15. Cheng K. Keynote presentation – 2: Smart tooling, smart machines and smart manufacturing: Working towards the Industry 4.0 and beyond. in 2015 21st International Conference on Automation and Computing (ICAC). 2015. IEEE.
  • 16. Lafou M., et al. Convertibility indicator for manual mixed-model assembly lines; 2014.
  • 17. Park J., Lee J. Presentation on Korea smart factory program. In: Proceedings of the international conference on advances in production management systems, Tokyo, Japan 2015.
  • 18. Davis J., et al. Smart process manufacturing: An operations and technology roadmap. Smart process manufacturing engineering virtual organization steering committee, Los Angeles, CA, Tech. Rep; 2009.
  • 19. Papazoglou, M.P., van den Heuvel W.-J., Mascolo J.E. A reference architecture and knowledge-based structures for smart manufacturing networks. IEEE Software. 2015; 32(3): 61–69.
  • 20. Karayalcin I.I. The analytic hierarchy process: Planning, priority setting, resource allocation: Thomas L. SAATY McGraw-Hill, New York, 1980, xiii+287 pages,£ 15.65. North-Holland; 1982.
  • 21. Jadoon T.R., et al. Sustaining power production in hydropower stations of developing countries. Sustainable Energy Technologies and Assessments. 2020; 37: 100637.
  • 22. French S., Xu D.L. Comparison study of multiattribute decision analytic software. Journal of Multi‐Criteria Decision Analysis. 2005; 13(2–3): 65–80.
  • 23. Von Winterfeldt D., Edwards W. Decision analysis and behavioral research; 1993.
Uwagi
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-96ffdd8b-001c-474c-b457-d2bb6e23954d
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