Advanced Decision-Making Strategies and Technologies for Manufacturing: Case Studies, and Future Research Directions

Authors

  • Gaston Lefranc Facultad de Ingeniería, Pontificia Universidad Católica de Valparaíso, Chile
  • Mario Peña Cabrera Department of Computer Systems Engineering and Automation IIMAS, Universidad Nacional Autónoma de México
  • Roman Osorio Comparan Department of Computer Systems Engineering and Automation IIMAS, Universidad Nacional Autónoma de México
  • Ismael López-Juárez Department of Robotics and Advanced Manufacturing, Center for Research and Advanced Studies (CINVESTAV), Mexico

DOI:

https://doi.org/10.15837/ijccc.2025.1.6906

Keywords:

decision-making, digital twins, manufacturing strategies

Abstract

This article presents a review exploring decision-making strategies and technologies in the manufacturing sector, examining both traditional and emerging approaches. The article critically analyses the current landscape of decision-making in the manufacturing sector, highlighting innovative technologies and practical applications through case studies and the challenges associated with their implementation. By investigating the intersection of technological advances and strategic decisionmaking, the study provides insights for improving industrial competitiveness and identifies critical areas for future research and development.

References

[Online]. Available: www.airbus.com/en, Accesed on 4 December 2024.

[Online]. Available: acubed.airbus.com/projects/adam/, Accesed on 4 December 2024.

[Online]. Available: www.airbus.com/innovation/digital-transformation, Accesed on 4 December 2024.

[Online]. Available: www.airbus.com/en/2023-airbus-annual-report, Accesed on 4 December 2024.

Banks, J., Carson, J. S., Nelson, B. L., and Nicol, A. M. (2010). Discrete-event system simulation (5th ed.). Pearson Education.

Fillip, F.G., Leiviskä, K. (2023). Infrastructure and Complex Systems Automation. In: Nof, S.Y. (eds) Springer Handbook of Automation. Springer Handbooks. Springer, Cham. https://doi.org/10.1007/978-3-030-96729-1_27

[Online]. Available: https://corporate.ford.com/home.html, Accesed on 4 December 2024.

[Online]. Available: www.iotm2mcouncil.org/iot-library/news/connected-transportationnews/ ford-digital-twin-for-predictive-headlights/, Accesed on 4 December 2024.

[Online]. Available: fordauthority.com/fmc/ford-motor-company-plants-facilities/ford-motorcompany- spain-plants-facilities/ford-motor-company-valencia-body-and-assembly-valenciaspain/, Accesed on 4 December 2024.

[Online]. Available: www.ge.com/sites/default/files/ge-company-overview.pdf, Accesed on 4 December 2024.

[Online]. Available: https://www.gevernova.com/software/products/predictive-analytics, Accesed on 4 December 2024.

[Online]. Available: d3.harvard.edu/platform-rctom/submission/ge-and-the-industrial-internetof- things/, Accesed on 4 December 2024.

Hopp, W. J., and Spearman, M. L. (2011). Factory physics, Waveland Press,

Idowu Oluwagbenga Adesoye, Oluwaseun Samuel Akerele. Improving Decision Making With Statistics: A Case of Airbus. International Journal of Scientific Research and Engineering Development-- Volume 4 Issue 6, Nov-Dec 2021

[Online]. Available: www.intel.com/content/www/us/en/company-overview/about-intel.html, Accesed on 4 December 2024.

[Online]. Available: www.intel.com/content/www/us/en/developer/topic-technology/artificialintelligence/ frameworks-tools.html, Accesed on 4 December 2024.

[Online]. Available: panmore.com/intel-operations-management-strategy-10-decisionsproductivity, Accesed on 4 December 2024.

[Online]. Available: https://www.intel.la/content/www/xl/es/content-details/789047/intelautomated- factory-solutions-overview.html, Accesed on 4 December 2024.

[Online]. Available: https://www.intel.com/content/www/us/en/now/siloed-manufacturingdata. html, Accesed on 4 December 2024. Accesed on 4 December 2024.

[Online]. Available: https://www.idc.com/getdoc.jsp?containerId=prEUR252046924. Accesed on 4 December 2024.

Jacobs, F. R., Chase, R. B.(2011). Operations and Supply Chain Management, The McGraw-Hill Companies, Inc.,

Kusiak, A. (2017). Smart manufacturing. International Journal of Production Research, 56(1-2), 508-517. https://doi.org/10.1080/00207543.2017.1351644

Mohsen Soori, Behrooz Arezoo, Roza Dastres (2023). Digital twin for smart manufacturing, A review, Sustainable Manufacturing and Service Economics, Volume 2, 2023, 100017, ISSN 2667- 3444. https://doi.org/10.1016/j.smse.2023.100017

Raj, A., Kannan, D., and Rajalakshmi, R. (2021). Industry 5.0: The role of artificial intelligence and big data analytics. Journal of Intelligent and Fuzzy Systems, 40(1), 737-745.

[Online]. Available: https://www.siemens.com/global/en.html, Accesed on 4 December 2024.

[Online]. Available: https://ecosystem.siemens.com/techforsustainability/sustainability-asdecision- factor-in-manufacturing/overview, Accesed on 4 December 2024.

[Online]. Available: https://assets.new.siemens.com/siemens/assets/api/uuid:3d852046-82ba- 4f88-86a0-b55369359c66/final-factorytomorrow-wp.pdf. Accesed on 4 December 2024.

Tao, F., Sui, F., Liu, A., Qi, Q., Zhang, M., Song, B., . . . Nee, A. Y. C. (2018). Digital twin-driven product design framework. International Journal of Production Research, 1-19.

Womack, J. P., and Jones, D. T. (2013). Lean Thinking: Banish Waste and Create Wealth in Your Corporation. Simon and Schuster Editors, ISBN 1471111008, 400 pp.

Wang, X., Wan, J., Zhang, D., and Hussain, F. (2019). Digital twin-driven product design and manufacturing: A review. Journal of Manufacturing Systems, 53, 317-333.

Additional Files

Published

2025-01-03

Most read articles by the same author(s)

Obs.: This plugin requires at least one statistics/report plugin to be enabled. If your statistics plugins provide more than one metric then please also select a main metric on the admin's site settings page and/or on the journal manager's settings pages.