Research Article

Enhancing Agile Manufacturing Efficiency: A Markov-Based Approach to Flexibility Optimization

Volume: 10 Number: 2 May 1, 2026

Enhancing Agile Manufacturing Efficiency: A Markov-Based Approach to Flexibility Optimization

Abstract

In today’s increasingly dynamic and competitive manufacturing environment, agility has become a defining factor for operational success, particularly in enhancing efficiency and responsiveness. Attaining the needed agility necessitates a high degree of operational flexibility. This study applies a Quasi-Birth Death (QBD) Markov model to analyze volume, delivery, and routing flexibility in a 3-dimensional-Computer Numerical Control (3D-CNC) machining system. With system transition modeling among various operational states, the work quantifies the impact of flexibility on aggregate machine output in an agile production environment. An eight-state Markov chain model is formulated to characterize manufacturing profiles of multi-standard 3D printer networks, considering the probabilistic behaviour for evaluating system flexibility. Results show that flexibility parameter optimization enhances manufacturing responsiveness, cost-effectiveness, and system flexibility, ensuring flexible production methods in turbulent Manufacturing settings. MATLAB simulations reveal that strategic state changes primarily shifting from high to medium or low flexibility tend to produce significant gains in performance, increasing machine efficiency up to 91.2%. This research contributes to agile manufacturing theory by developing a computational model for real-time flexibility control, offering data-driven insights for maximizing operational efficiency and decision-making in flexible manufacturing systems.

Keywords

References

  1. Mishra, R. (2018). Configuration of volume flexibility in Indian manufacturing firms: Evidence from case studies. International Journal of Quality & Reliability Management, 35(5), 1093–1107
  2. Çakmak, Z. (2023). Adapting to environmental change: The importance of organizational agility in the business3 landscape. Florya Chronicles of Political Economy, 9(1), 67–87.
  3. Dubey, R., & Gunasekaran, A. (2015). Agile manufacturing: Framework and its empirical validation. The International Journal of Advanced Manufacturing Technology, 76(9), 2147–2157.
  4. Kaur, S. P., Kumar, J., & Kumar, R. (2017). The relationship between flexibility of manufacturing system components, competitiveness of SMEs and business performance: A study of manufacturing SMEs in Northern India. Global Journal of Flexible Systems Management, 18(2), 123–137.
  5. Brand, M., Tiberius, V., Bican, P. M., & Brem, A. (2021). Agility as an innovation driver: Towards an agile front end of innovation framework. Review of Managerial Science, 15(1), 157–187.
  6. Kaushal, A., Vardhan, A., & Rajput, R. (2016). Flexible manufacturing system: A modern approach to manufacturing technology. International Refereed Journal of Engineering and Science, 5(4), 16–23.
  7. Soydan, Z., Şahin, F. İ., & Acaralı, N. (2024). Advancements in polymeric matrix composite production: A review on methods and approaches. Turkish Journal of Engineering, 8(4), 677–686. https://doi.org/10.31127/tuje.1468998
  8. Wallner, B., Trautner, T., Pauker, F., & Kittl, B. (2021). Evaluation of process control architectures for agile manufacturing systems. Procedia CIRP, 99, 680–685.

Details

Primary Language

English

Subjects

Information Modelling, Management and Ontologies

Journal Section

Research Article

Publication Date

May 1, 2026

Submission Date

October 29, 2025

Acceptance Date

January 20, 2026

Published in Issue

Year 2026 Volume: 10 Number: 2

APA
Omoniyi, E. B., Anyaeche, C. O., Periola, A., & Onokwai, A. (2026). Enhancing Agile Manufacturing Efficiency: A Markov-Based Approach to Flexibility Optimization. Turkish Journal of Engineering, 10(2), 450-465. https://doi.org/10.31127/tuje.1813047
AMA
1.Omoniyi EB, Anyaeche CO, Periola A, Onokwai A. Enhancing Agile Manufacturing Efficiency: A Markov-Based Approach to Flexibility Optimization. TUJE. 2026;10(2):450-465. doi:10.31127/tuje.1813047
Chicago
Omoniyi, Ezekiel B, Christopher Osita Anyaeche, Ayodele Periola, and Anthony Onokwai. 2026. “Enhancing Agile Manufacturing Efficiency: A Markov-Based Approach to Flexibility Optimization”. Turkish Journal of Engineering 10 (2): 450-65. https://doi.org/10.31127/tuje.1813047.
EndNote
Omoniyi EB, Anyaeche CO, Periola A, Onokwai A (May 1, 2026) Enhancing Agile Manufacturing Efficiency: A Markov-Based Approach to Flexibility Optimization. Turkish Journal of Engineering 10 2 450–465.
IEEE
[1]E. B. Omoniyi, C. O. Anyaeche, A. Periola, and A. Onokwai, “Enhancing Agile Manufacturing Efficiency: A Markov-Based Approach to Flexibility Optimization”, TUJE, vol. 10, no. 2, pp. 450–465, May 2026, doi: 10.31127/tuje.1813047.
ISNAD
Omoniyi, Ezekiel B - Anyaeche, Christopher Osita - Periola, Ayodele - Onokwai, Anthony. “Enhancing Agile Manufacturing Efficiency: A Markov-Based Approach to Flexibility Optimization”. Turkish Journal of Engineering 10/2 (May 1, 2026): 450-465. https://doi.org/10.31127/tuje.1813047.
JAMA
1.Omoniyi EB, Anyaeche CO, Periola A, Onokwai A. Enhancing Agile Manufacturing Efficiency: A Markov-Based Approach to Flexibility Optimization. TUJE. 2026;10:450–465.
MLA
Omoniyi, Ezekiel B, et al. “Enhancing Agile Manufacturing Efficiency: A Markov-Based Approach to Flexibility Optimization”. Turkish Journal of Engineering, vol. 10, no. 2, May 2026, pp. 450-65, doi:10.31127/tuje.1813047.
Vancouver
1.Ezekiel B Omoniyi, Christopher Osita Anyaeche, Ayodele Periola, Anthony Onokwai. Enhancing Agile Manufacturing Efficiency: A Markov-Based Approach to Flexibility Optimization. TUJE. 2026 May 1;10(2):450-65. doi:10.31127/tuje.1813047
Flag Counter