Üretimde Hazırlık Sürecinin Optimize Edilmesi: Yeni Bir SMED Tabanlı Çerçeve

Abstract

Üretici şirketler, çeşitli müşteri taleplerini karşılamak için ekonomik olarak küçük miktarlarda üretim yapmaya çalışmaktadır. Ancak, ürün çeşitliliğinin artması daha fazla hazırlık görevi ve daha uzun üretim süreleri ile oluşturur. Bu soruna yönelik yaygın bir çözüm, Tekli Dakikalarda Kalıp Değişimi (SMED: Single-Minute Exchange of Dies) tekniğini kullanarak hazırlık sürelerini azaltmaktır. Bu çalışma, paralel işgören kullanımını içeren SMED metodolojisi için yeni bir yol haritası sunmaktadır. Yol haritası iki ana aşamadan oluşur: ilk olarak, SMED'in ayrıntılı uygulaması, ardından bir çizelgeleme modeli aracılığıyla paralel işçilerin atanması. Klasik SMED uygulamasıyla, hazırlık süresi 67 dakikadan 49 dakikaya düşürülmüştür. SMED katma değer sağlamayan süreleri azaltırken, üretim süresini optimize etmek ve makine kullanımını en üst düzeye çıkarmak için paralel işçiler atanması gerekmektedir. Paralel işgören kullanımı için geliştirilen çizelgeleme modeli, maksimum yayılım süresini yaklaşık %48 oranında azaltmıştır. Bu yol haritası, paralel işçi çizelgelemesini ilk kez SMED ile entegre ederek ve hazırlık süreçleri için etkili bir matematiksel model önererek literatüre katkıda bulunmaktadır. Bir diğer önemli yenilik ise modelin hazırlık görevleri arasındaki öncelik ilişkilerini hesaba katma yeteneğidir. Önerilen SMED tabanlı yol haritası, bir elektrik-elektronik şirketindeki basmalı düğmeli anahtar ürün ailesinin hazırlık sürecine uygulanmıştır. Uygulama, hazırlık sürelerinin azaltılmasına ve iş prosedürlerinin iyileştirilmesi ile yeni yaklaşımın etkinliğini göstermiştir.

Keywords

• Hazırlık süreci
• SMED
• Çizelgeleme
• Paralel işgören

Optimizing Setup Process in Manufacturing: A Novel SMED-Based Framework

Abstract

Manufacturing companies strive to economically produce small batches to meet diverse customer demands. However, small batch production often leads to higher setup times and increased unit costs. A common solution to this issue is reducing setup times using the Single-Minute Exchange of Dies (SMED) technique. This study introduces a new roadmap for the SMED methodology, incorporating parallel worker utilization. The roadmap consists of two phases: a detailed SMED implementation and a scheduling model integrating parallel workers. This study contributes to the literature by integrating parallel worker scheduling with SMED for the first time and proposing an efficient mathematical model for setup processes. Another key innovation is the model’s ability to account for precedence relationships between setup tasks. The proposed SMED-based roadmap was applied to the setup process of a push-button switch product family in an electrical-electronics company. The setup time was reduced from 67 minutes to 48 minutes through SMED implementation. While SMED reduces non-value-added times, optimizing the makespan requires assigning parallel workers to maximize machine utilization. The developed scheduling model reduced the maximum makespan by about 48%. The application led to reduced setup times and improved work procedures, demonstrating the effectiveness of the new approach.

Keywords

• Setup Process
• SMED
• Scheduling
• Parallel Workers

References

1. Afonso, M., Gabriel, A. T., & Godina, R. (2022). Proposal of an innovative ergonomic SMED model in an automotive steel springs industrial unit. Advances in Industrial and Manufacturing Engineering, 4, Article 100075. https://doi.org/10.1016/j.aime.2022.100075
2. Allahverdi, A., Ng, C. T., Cheng, T. C. E., & Kovalyov, M. Y. (2008). A survey of scheduling problems with setup times or costs. European Journal of Operational Research, 187(3), 985-1032. https://doi.org/10.1016/j.ejor.2006.06.060
3. Braglia, M., Di Paco, F., Frosolini, M., & Marrazzini, L. (2023). Quick changeover design: A new Lean methodology to support the design of machines in terms of rapid changeover capability. Journal of Manufacturing Technology Management, 34(9), 84-114. https://doi.org/10.1108/JMTM-12-2022-0430
4. Dan, Y., & Tao, J. (2021). Knowledge worker scheduling optimization model based on bacterial foraging algorithm. Future Generation Computer Systems, 124, 330-337 https://doi.org/10.1016/j.future.2021.05.028
5. Fonda, E., & Meneghetti, A. (2022). The human-centric SMED. Sustainability, 14(1), Article 514. https://doi.org/10.3390/su14010514
6. Gilmore, M., & Smith, D. J. (1996). Set-up reduction in pharmaceutical manufacturing: An action research study. International Journal of Operations and Production Management, 16(3), 4-17. https://doi.org/10.1108/01443579610110459
7. Juárez-Vite, A., Corona-Arment, J. R., Rivera-Gómez, H., Montaño-Arango, O., & Medina-Marín, J. (2023). Application of the SMED methodology through folding references for a bus manufacturing company. International Journal of Industrial Engineering and Management, 14(3), 232-243. https://doi.org/10.24867/IJIEM-2023-3-335
8. Junior, R. G. P., Inácio, R. H., da Silva, I. B., Hassui, A., & Barbosa, G. F. (2022). A novel framework for single-minute exchange of die (SMED) assisted by lean tools. International Journal of Advanced Manufacturing Technology, 119, 6469–6487. https://doi.org/10.1007/s00170-021-08534-w

9. Khakpour, R., Ebrahimi, A., & Seyed-Hosseini, S. M. (2024). SMED 4.0: A development of single minute exchange of die in the era of Industry 4.0 technologies to improve sustainability. Journal of Manufacturing Technology Management, 35(3), 568-589. https://doi.org/10.1108/JMTM-08-2023-0333
10. Kose, Y., Civan, H. N., Ayyildiz, E., & Cevikcan, E. (2022). An interval valued Pythagorean fuzzy AHP-TOPSIS integrated model for ergonomic assessment of setup process under SMED. Sustainability, 14(21), Article 13804. https://doi.org/10.3390/SU142113804
11. Liu, M., Liu, X., Chu, F., Zhang, E., & Chu, C. (2021). Service-oriented robust worker scheduling with motivation effects. International Journal of Production Research, 59(8), 2328-2351. https://doi.org/10.1080/00207543.2020.1730998
12. Mohammad, A., Hamja, A., & Hasle, P. (2024). Reduction of changeover time through SMED with RACI integration in garment factories. International Journal of Lean Six Sigma, 15(2), 201-219. https://doi.org/10.1108/IJLSS-10-2021-0176
13. Moxham, C., & Greatbanks, R. (2001). Prerequisites for the implementation of the SMED methodology: A study in a textile processing environment. International Journal of Quality and Reliability Management, 18(4), 404–414. https://doi.org/10.1108/02656710110386798
14. Narula, S., Puppala, H., Kumar, A., Luthra, S., Dwivedy, M., Prakash, S., & Talwar, V. (2023). Are Industry 4.0 technologies enablers of lean? Evidence from manufacturing industries. International Journal of Lean Six Sigma, 14(1), 115-138. https://doi.org/10.1108/IJLSS-04-2021-0085
15. Ozsoydan, F. B. (2025). Reinforcement learning enhanced swarm intelligence and trajectory-based algorithms for parallel machine scheduling problems. Computers and Industrial Engineering, 203, Article 110948. https://doi.org/10.1016/j.cie.2025.110948
16. Pessan, C., & Néron, E. (2011). Setup tasks scheduling during production resettings. International Journal of Production Research, 49(22), 6787-6811. https://doi.org/10.1080/00207543.2010.519922
17. Pinedo, M. L. (2022). Scheduling: Theory, algorithms, and systems, (6th ed.). Springer. https://doi.org/10.1007/978-3-031-05921-6
18. Rosa, C., Silva, F. J. G., Ferreira, L. P., & Campilho, R. (2017). SMED methodology: The reduction of setup times for steel wire-rope assembly lines in the automotive industry. Procedia Manufacturing, 13, 1034-1042. https://doi.org/10.1016/j.promfg.2017.09.110
19. Rosin, F., Forget, P., Lamouri, S., & Pellerin, R. (2020). Impacts of industry 4.0 technologies on lean principles. International Journal of Production Research, 58(6), 1644-1661. https://doi.org/10.1080/00207543.2019.1672902
20. Ruiz-Torres, A. J., Ablanedo-Rosas, J. H., Mukhopadhyay, S., & Paletta, G. (2019). Scheduling workers: A multi-criteria model considering their satisfaction. Computers and Industrial Engineering, 128, 747–754. https://doi.org/10.1016/j.cie.2018.12.070
21. Ruiz-Torres, A. J., Alomoto, N., Paletta, G., & Pérez, E. (2015). Scheduling to maximise worker satisfaction and on-time orders. International Journal of Production Research, 53(9), 2836-2852. https://doi.org/10.1080/00207543.2015.1005764
22. Ruiz-Torres, A. J., Mahmoodi, F., & Kuula, M. (2017). Quality assurance laboratory planning system to maximize worker preference subject to certification and preference balance constraints. Computers & Operations Research, 83, 140–149. https://doi.org/10.1016/j.cor.2017.02.002
23. Saad, S. M., Bahadori, R., Bhovar, C., & Zhang, H. (2023). Industry 4.0 and Lean Manufacturing – a systematic review of the state-of-the-art literature and key recommendations for future research. International Journal of Lean Six Sigma, 15(5), 997-1024. https://doi.org/10.1108/IJLSS-02-2022-0021
24. Sahin, R., & Kologlu, A. (2022). A case study on reducing setup time using SMED on a turning line. Gazi University Journal of Science, 35(1), 60-71. https://doi.org/10.35378/gujs.735969
25. Shingo, S. (1985). A revolution in manufacturing: The SMED system. Productivity Press.
26. Tosun, Ö., Marichelvam, M. K., & Tosun, N. (2020). A literature review on hybrid flow shop scheduling. International Journal of Advanced Operations Management, 12(2), 156-194. https://doi.org/10.1504/IJAOM.2020.108263
27. Villacís, S. A., & Burneo, P. S. (2020). UAVs’ efficient assembly: Lean manufacturing implementation in an UAVs’ assembly company. International Journal of Industrial Engineering and Management, 11(4), 237-252.
28. Wang, T. C., & Liu, C. C. (2014). Optimal work shift scheduling with fatigue minimization and day off preferences. Mathematical Problems in Engineering, 2014, Article 751563. https://doi.org/10.1155/2014/751563
29. Xie, F., Li, K., Chen, J., Xiao, W., & Zhou, T. (2025). An adaptive large neighborhood search for unrelated parallel machine scheduling with setup times and delivery times. Computers and Operations Research, 177, Article 106976. https://doi.org/10.1016/j.cor.2025.106976
30. Yazici, K., Gokler, S. H., & Boran, S. (2021). An integrated SMED-fuzzy FMEA model for reducing setup time. Journal of Intelligent Manufacturing, 32(6), 1547–1561. https://doi.org/10.1007/s10845-020-01675-x
31. Zhang, H., Lv, S., Xin, D., & Jin, H. (2025). A genetic algorithm enhanced with neighborhood structure for general flexible job shop scheduling with parallel batch processing machine. Expert Systems with Applications, 265, Article 125888. https://doi.org/10.1016/j.eswa.2024.125888