tuje Turkish Journal of Engineering 2587-1366 Murat YAKAR 10.31127/tuje.1813047 Information Modelling, Management and Ontologies Bilgi Modelleme, Yönetim ve Ontolojiler Enhancing Agile Manufacturing Efficiency: A Markov-Based Approach to Flexibility Optimization https://orcid.org/0000-0001-9186-0660 Omoniyi Ezekiel B Bells University of Technology https://orcid.org/0009-0001-6185-7492 Anyaeche Christopher Osita https://orcid.org/0000-0002-1506-5347 Periola Ayodele https://orcid.org/0000-0002-6573-4668 Onokwai Anthony 05 01 2026 10 2 450 465 10 29 2025 01 20 2026 Copyright © 2017, Turkish Journal of Engineering 2017 Turkish Journal of Engineering

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.

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