The Impact of Process Management on Productivity and Flexibility in Digitalized Production Systems Within the Context of Advanced Manufacturing Techniques
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Adem, A. (2020). The Impact of Digital Transformation in Manufacturing Systems on Work-Study Techniques. Verimlilik Dergisi.
Akhramovich, K., Serral Asensio, E., & Cetina, C. (2024). A systematic literature review on the application of process mining to Industry 4.0. Knowledge and Information Systems.
Bilgin Sarı, E., Ozveri, O., Senyay, U. E. (2019). The Impact of Industry 4.0 on Business Process Management. International Management Academy Journal.
Buri, Z. (2025). Digitalisation in the Context of Industry 4.0 and Industry 5.0. MDPI.
Castiglione, A. (2024). Managing flexibility in Industry 4.0 systems via simulation. ScienceDirect.
Dumas, M., La Rosa, M., Mendling, J., & Reijers, H. A. (2018). Fundamentals of Business Process Management (2nd ed.).
Friederich, J., Lugaresi, G., Lazarova -Molnar, S., & Matta, A. (2022). Process Mining for Dynamic Modeling of Smart Manufacturing Systems: Data Requirements. Procedia CIRP, 107, 546–551.
Gan, Z. L., Musa, S. N., & Yap, H. J. (2023). A review of the high- mix, low -volume manufacturing industry. Applied Sciences, 13(3), 1687. doi:10.3390/app13031687
Hornsteiner, M., et al. (2024). Process Mining on OPC UA Network Data. Sensors.
Hose, K., Amaral, A., Gotze, U., & Pecas, P. (2023). Manufacturing flexibility through Industry 4.0 technological concepts —Impact and assessment. Global Journal of Flexible Systems Management, 24, 271 – 289. doi:10.1007/s40171-023-00339-y
Huy, P. Q. (2025). Unveiling how business process management capabilities foster dynamic decision-making for effectiveness of sustainable digital transformation. Business Process Management Journal.
IBM. (t.y.). What is overall equipment effectiveness (OEE)? International Organization for Standardization. (2014). ISO 22400 - 2:2014—Automation systems and integration—Key performance indicators (KPIs) for manufacturing operations management —Part 2: Definitions and descriptions.
International Organization for Standardization. (2014). ISO 22400-2: Automation systems and integration—Key performance indicators (KPIs) for manufacturing operations management —Part 2: Definitions and descriptions. ISO.
International Organization for Standardization. (2015). The process approach in ISO 9001:2015 (Guidance document). ISO.
Janiesch, C., et al. (2020). The Internet of Things Meets Business Process Management: A Manifesto.
Khakpour, R., Ebrahimi, A., & Seyed -Hosseini, S.-M. (2024). Lean process mining: Adopting process mining in lean manufacturing for dynamic process mapping and avoiding waste occurrence in real time (International Journal of Lean Six Sigma). https://doi.org/10.1108/IJLSS-03-2024-0059
Koren, Y. (2018). Reconfigurable manufacturing systems: Principles, design, and future trends. Journal of Intelligent Manufacturing, 29, 467–474. doi:10.1007/s11465-018-0483-0
Leroux, M. (t.y.). OEE as a performance KPI—OEE calculation and the six big losses. ABB.
Mo, F., Rehman, H. U., Monetti, F. M., Chaplin, J. C., Sanderson, D., Popov, A., Maffei, A., & Ratchev, S. (2023). A framework for manufacturing system reconfiguration and optimisation utilising digital twins and modular artificial intelligence. Robotics and Computer-Integrated Manufacturing, 82, 102524. doi: 10.1016/j.rcim.2022.102524
Mo, F., Rehman, H. U., Monetti, F. M., Chaplin, J. C., Sanderson, D., Popov, A., Maffei, A., & Ratchev, S. (2023). A framework for manufacturing system reconfiguration and optimisation utilising digital twins and modular artificial intelligence. Robotics and Computer-Integrated Manufacturing, 82, 102524. https://doi.org/10.1016/j.rcim.2022.102524
Novochadlo, Y. M., & Paladini, E. P. (2024). The application of real- time overall equipment efficiency indicator in a medium -sized company. Brazilian Journal of Operations & Production Management, 21(2), 2042. https://doi.org/10.14488/BJOPM.2042.2024
OEE.com. (t.y.). OEE factors: Availability, Performance, and Quality.
Zhu, L., Johnsson, C., Varisco, M., vd. (2018). Key performance indicators for manufacturing operations management —Gap analysis between process industrial needs and ISO 22400 standard. Procedia Manufacturing, 25, 82–88.
Sener, U., et al. (2020). Industry 4.0: Digital Transformation and Productivity. Productivity Magazine.
Ullah, A., & Younas, M. (2024). Development and application of digital twin control in flexible manufacturing systems. Journal of Manufacturing and Materials Processing, 8(5), 214. doi:10.3390/jmmp8050214
Weckenborg, C., Schumacher, P., Thies, C., & Spengler, T. S. (2024). Flexibility in manufacturing system design: A review of recent approaches from Operations Research. European Journal of Operational Research, 315(2), 413–441. doi: 10.1016/j.ejor.2023.08.050
Weinzierl, S., et al. (2024). Machine Learning in Business Process Management (arXiv preprint).
Yelles-Chaouche, A. R., et al. (2021). Reconfigurable manufacturing systems from an optimisation perspective: A focused review of literature. International Journal of Production Research, 59(21), 6400–6418.
Zhu, L., Johnsson, C., Varisco, M., & et al. (2018). Key performance indicators for manufacturing operations management —Gap analysis between process industrial needs and ISO 22400 standard. Procedia Manufacturing, 25, 82–88.