Toward AI-Enabled Production Management: A Decision-Layer Framework with Simulation-Based Structural Validation

Authors

DOI:

https://doi.org/10.20535/2521-1943.2026.10.2(109).353725

Keywords:

Decision support systems, Production management, Discrete-event simulation, Capacity planning, multi-KPI evaluation, Decision-layer coherence, AI-enabled manufacturing

Abstract

The increasing adoption of artificial intelligence and simulation in production management has largely concentrated on improving algorithmic performance at isolated decision points. However, relatively limited attention has been given to how decisions interact across strategic, tactical, and operational levels of an organization. This study adopts a decision-centric perspective to examine the structural alignment of production decisions across these interconnected layers. A decision-layer decomposition framework is proposed to explicitly distinguish strategic, tactical, and operational decision domains and to clarify how decisions at each level collectively shape overall system performance. Rather than pursuing algorithmic optimization or empirical benchmarking, discrete-event simulation is employed as a structural validation tool to examine decision coherence across layers. Using a simplified production system, the simulation experiments investigate how strategic capacity decisions propagate through tactical planning and operational execution, influencing key performance indicators such as throughput, lead time, and work-in-process. The results reveal a non-linear performance response in which throughput reaches saturation before lead time and work-in-process are fully minimized. This divergence highlights the limitations of throughput-driven capacity planning when evaluated in isolation and underscores the importance of multi-KPI assessment. By framing AI and simulation as decision-support infrastructures rather than purely optimization mechanisms, the proposed framework provides a transferable approach for evaluating decision coherence and aligning managerial objectives with operational outcomes. The study contributes to production management research by formalizing decision

References

  1. Schmidt, G., & Wilhelm, W. E. (2000). Strategic, tactical and operational decisions in multi-national logistics networks: A review and discussion of modelling issues. International Journal of Production Research, 38(7), 1501–1523. https://doi.org/10.1080/002075400188690
  2. Bilgen, B., & Ozkarahan, I. (2004). Strategic tactical and operational production-distribution models: A review. International Journal of Technology Management, 28(2), 151–171. https://doi.org/10.1504/IJTM.2004.005059
  3. Teng, S. G., & Jaramillo, H. (2005). Linking tactical and operational decision-making to strengthen textile/apparel supply chains. International Journal of Logistics Systems and Management, 1(2–3), 244–266. https://doi.org/10.1504/IJLSM.2005.005973
  4. Laínez, J. M., et al. (2008). Integrating strategic, tactical and operational supply chain decision levels in a model predictive control framework. Computer Aided Chemical Engineering, 25, 477–482. https://doi.org/10.1016/S1570-7946(08)80084-3
  5. Munoz, E., et al. (2012). Operational, tactical and strategical integration for enterprise decision-making. Computer Aided Chemical Engineering, 30, 397–401. https://doi.org/10.1016/B978-0-444-59519-5.50080-0
  6. Bashiri, M., Badri, H., & Talebi, J. (2012). A new approach to tactical and strategic planning in production–distribution networks. Applied Mathematical Modelling, 36(4), 1703–1717. https://doi.org/10.1016/j.apm.2011.09.018
  7. Kazaz, B. (1997). Integrated models for strategic and tactical decision making in manufacturing management. Doctoral dissertation, Purdue University.
  8. Khalifa, A. S. (2021). Strategy and what it means to be strategic: Redefining strategic, operational, and tactical decisions. Journal of Strategy and Management, 14(4), 381–396. https://doi.org/10.1108/JSMA-12-2020-0357
  9. Cañas, H., Mula, J., & Campuzano-Bolarin, F. (2025). Strategical and tactical supply chain optimisation for smart production planning and control 4.0. International Journal of Production Research, 63(8), 2926–2946. https://doi.org/10.1080/00207543.2024.2412828
  10. Choudhary, A., et al. (2025). An integrated fuzzy intuitionistic sustainability assessment framework for manufacturing supply chains. Annals of Operations Research, 349(2), 687–730. https://doi.org/10.1007/s10479-019-03452-3
  11. Kádár, B., Pfeiffer, A., & Monostori, L. (2004). Discrete event simulation for supporting production planning and scheduling decisions in digital factories. Proceedings of the CIRP International Seminar on Manufacturing Systems.
  12. Mahdavi, I., Shirazi, B., & Solimanpur, M. (2010). Simulation-based decision support system for controlling stochastic flexible job shops. Simulation Modelling Practice and Theory, 18(6), 768–786. https://doi.org/10.1016/j.simpat.2010.01.015
  13. Heilala, J., et al. (2010). Developing simulation-based decision support systems for customer-driven manufacturing operation planning. Winter Simulation Conference Proceedings. https://doi.org/10.1109/WSC.2010.5679027
  14. Krenczyk, D., et al. (2016). Integration of scheduling and discrete event simulation systems to improve production flow planning. IOP Conference Series: Materials Science and Engineering, 145(2), 022018. https://doi.org/10.1088/1757-899X/145/2/022018
  15. Zandieh, M., & Motallebi, S. (2018). Determination of production planning policies using discrete event simulation. Production Engineering, 12(6), 737–746. https://doi.org/10.1007/s11740-018-0843-y
  16. Santos, R., Toscano, C., & Sousa, J. P. (2021). Simulation-based approach in manufacturing system design and real-time decision making. IFAC-PapersOnLine, 54(1), 282–287. https://doi.org/10.1016/j.ifacol.2021.08.033
  17. Silva, V., et al. (2021). Simulation-based decision support system to improve material flow. Sustainability, 13(5), 2947. https://doi.org/10.3390/su13052947
  18. Mahmoodi, E., et al. (2024). Data-driven simulation-based decision support system for resource allocation in Industry 4.0. Journal of Manufacturing Systems, 72, 287–307. https://doi.org/10.1016/j.jmsy.2023.11.019
  19. Elbasheer, M., et al. (2025). Towards a digital twin for production planning. Procedia Computer Science, 253, 3078–3087. https://doi.org/10.1016/j.procs.2025.02.032
  20. Shen, Y. C., et al. (2025). Supply chain production planning and scheduling coordination using discrete event simulation. International Journal of Simulation Modelling. https://doi.org/10.2507/IJSIMM24-1-CO3
  21. Yadin, K., & Kie, C. J. (2025). Optimizing production line layout using discrete event simulation. Global Business & Management Research, 17(1).
  22. Kasie, F. M., Bright, G., & Walker, A. (2017). Decision support systems in manufacturing: A survey. Journal of Modelling in Management, 12(3), 432–454. https://doi.org/10.1108/JM2-02-2016-0015
  23. AlDurgham, M. M., & Barghash, M. A. (2008). A generalized framework for simulation-based decision support. Production Planning & Control, 19(5), 518–534. https://doi.org/10.1080/09537280802187626
  24. De Vin, L. J., et al. (2006). Information fusion for simulation-based decision support. Robotics and Computer-Integrated Manufacturing, 22(5–6), 429–436. https://doi.org/10.1016/j.rcim.2005.11.007
  25. Krause, T. (2020). AI-based discrete-event simulations for manufacturing schedule optimization. Proceedings of the International Conference on Algorithms, Computing and Systems. https://doi.org/10.1145/3423390.3426725
  26. Reiners, R., Haubitz, C. B., & Thonemann, U. W. (2025). Lead time prediction for inventory optimization with machine learning. Production and Operations Management. https://doi.org/10.1177/10591478251328630
  27. Kim, E. (2025). Inventory rationing and capacity allocation in MTS/MTO systems. International Transactions in Operational Research, 32(3), 1593–1619. https://doi.org/10.1111/itor.13521
  28. Hedvall, L., Mattsson, S.-A., & Wikner, J. (2025). Identification and selection of safety buffers. Production Planning & Control, 36(1), 92–109. https://doi.org/10.1080/09537287.2023.2252380
  29. Tappia, E., et al. (2025). Part feeding scheduling with autonomous mobile robots. International Journal of Computer Integrated Manufacturing, 38(4), 485–500. https://doi.org/10.1080/0951192X.2024.2380276
  30. Wang, S., & Jiao, R. J. (2025). Cognitive intelligent task allocation in Industry 5.0. International Journal of Advanced Manufacturing Technology, 136(3), 1717–1739. https://doi.org/10.1007/s00170-024-14890-0
  31. Saavedra Sueldo, C., et al. (2025). Simulation-based metaheuristic optimization for material handling. Journal of Intelligent Manufacturing, 36(3), 1689–1709. https://doi.org/10.1007/s10845-024-02327-0
  32. Ishida, T. (2002). An optimization algorithm for production systems. IEEE Transactions on Knowledge and Data Engineering, 6(4), 549–558. https://doi.org/10.1109/69.298172
  33. Kapanoglu, M., & Miller, W. A. (2004). Evolutionary algorithm-based decision support for flexible manufacturing. Robotics and Computer-Integrated Manufacturing, 20(6), 529–539. https://doi.org/10.1016/j.rcim.2004.07.008
  34. Kochenderfer, M. J., Wheeler, T. A., & Wray, K. H. (2022). Algorithms for decision making. MIT Press.
  35. Ramirez, A. J., et al. (2009). Applying genetic algorithms to decision making in autonomic computing. Autonomic Computing Conference Proceedings. https://doi.org/10.1145/1555228.1555258
  36. Silva, C., Ribeiro, R., & Gomes, P. (2023). Algorithmic optimization techniques for operations research problems. Springer Nature. https://doi.org/10.1007/978-3-031-54820-8_26
  37. Luo, L., & Yan, X. (2025). Scheduling stochastic hybrid flow-shops using EDA and PPO. Expert Systems with Applications, 271, 126523. https://doi.org/10.1016/j.eswa.2025.126523
  38. Oubrahim, I., & Sefiani, N. (2025). Integrated multi-criteria decision-making for sustainable supply chains. International Journal of Productivity and Performance Management, 74(1), 304–339. https://doi.org/10.1108/IJPPM-09-2023-0464
  39. Abdel-Basset, M., Mohamed, R., & Chang, V. (2025). Multi-criteria decision-making for Industry 5.0 technologies. Information Systems Frontiers, 27(2), 791–821. https://doi.org/10.1007/s10796-024-10472-3
  40. Kumar, R., & Pamucar, D. (2025). Comprehensive review of multi-criteria decision-making methods (2004–2024). Spectrum of Decision Making and Applications, 2(1), 178–197. https://doi.org/10.31181/sdmap21202524
  41. Singh, S. P., Mehta, A., & Vasudev, H. (2025). Sensitivity analysis for multiple-attribute decision making. Engineering Management Journal. https://doi.org/10.1080/10429247.2024.2383855
  42. Caprace, J.-D., et al. (2025). Multi-criteria decision-making for subsea asset decommissioning. Journal of Offshore Mechanics and Arctic Engineering, 147(5), 051401. https://doi.org/10.1115/1.4067499
  43. Radovanović, M., et al. (2025). Evaluation using spherical fuzzy AHP and grey MARCOS models. Spectrum of Decision Making and Applications, 2(1), 198–218. https://doi.org/10.31181/sdmap21202518
  44. Zhao, H., & Guo, S. (2025). Hybrid MCDM framework for urban energy system planning. Environment, Development and Sustainability, 27(6), 14223–14252. https://doi.org/10.1007/s10668-024-04491-y
  45. Abdo, H. G., et al. (2025). Multi-criteria analysis and geospatial mapping of flood vulnerability. Natural Hazards, 121(1), 1003–1031. https://doi.org/10.1007/s11069-024-06864-y

Published

2026-06-18

How to Cite

[1]
A. Karkadakattil, “Toward AI-Enabled Production Management: A Decision-Layer Framework with Simulation-Based Structural Validation”, Mech. Adv. Technol., vol. 10, no. 2(109), Jun. 2026.

Issue

Vol. 10 No. 2(109) (2026): in progress

Section

Aviation Systems and Technologies

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Copyright (c) 2026 Асвін Каркадакаттил

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