Strategic Supplier Selection in Smart Manufacturing: A Bipolar Fuzzy VIKOR Approach

Öz

Smart manufacturing increasingly requires supplier-selection approaches that account for technological readiness, strategic alignment, sustainability, and uncertainty in expert judgment. This research develops a decision-support framework that integrates signed bipolar fuzzy encoding with entropy-weighted VIKOR to evaluate suppliers across six Industry 4.0-aligned criteria using a seven-point complementary bipolar scale. The results identify the agile domestic mid-sized supplier, characterized by strong innovation capability and strategic fit, as the best-ranked alternative. However, the acceptable-advantage condition is not satisfied; therefore, the technologically advanced multinational supplier and the innovation-driven technology startup also remain within the formal compromise set. Sensitivity analysis demonstrates that supplier preference depends substantially on strategic priorities. The multinational supplier becomes the preferred alternative under equal and technology-focused weighting, whereas the agile domestic supplier performs best when sustainability and strategic fit receive greater emphasis. The technology startup becomes more competitive when technological priorities dominate but remains constrained by its financial and sustainability weaknesses. These findings demonstrate that strategic supplier selection in smart manufacturing should not be reduced to identifying a universally superior supplier. Instead, the proposed framework provides a transparent connection between bipolar linguistic evaluations and compromise-based ranking while helping managers identify a defensible shortlist that reflects strategic priorities, overall performance, and criterion-specific trade-offs.

Anahtar Kelimeler

• strategic management
• smart manufacturing
• supplier selection
• Industry 4.0
• digital transformation

Akıllı Üretimde Stratejik Tedarikçi Seçimi: Bipolar Bulanık VIKOR Yaklaşımı

Öz

Akıllı üretim ortamları, teknolojik yeterlilik, stratejik uyum, sürdürülebilirlik ve uzman değerlendirmelerindeki belirsizliği birlikte dikkate alan tedarikçi seçim yaklaşımlarına giderek daha fazla ihtiyaç duymaktadır. Bu araştırmada, Endüstri 4.0 ile uyumlu altı kriter kapsamında tedarikçilerin değerlendirilmesi amacıyla işaretli bipolar bulanık kodlama ile entropi ağırlıklı VIKOR yöntemini bütünleştiren bir karar destek çerçevesi geliştirilmiştir. Uzman değerlendirmeleri yedi düzeyli tamamlayıcı bipolar ölçek kullanılarak elde edilmiştir. Bulgular, güçlü inovasyon yeteneği ve stratejik uyumuyla öne çıkan çevik, yerli ve orta ölçekli tedarikçinin en iyi sıralanan alternatif olduğunu göstermektedir. Bununla birlikte, kabul edilebilir avantaj koşulunun sağlanmaması nedeniyle teknolojik açıdan gelişmiş çok uluslu tedarikçi ile inovasyon odaklı teknoloji girişimi de biçimsel uzlaşık çözüm kümesinde yer almaktadır. Duyarlılık analizi, tedarikçi tercihinin stratejik önceliklere önemli ölçüde bağlı olduğunu ortaya koymaktadır. Çok uluslu tedarikçi eşit ve teknoloji odaklı ağırlıklandırma koşullarında öne çıkarken, sürdürülebilirlik ve stratejik uyuma daha fazla önem verildiğinde çevik yerli tedarikçi en iyi seçeneğe dönüşmektedir. Teknoloji girişimi ise teknolojik önceliklerin arttığı koşullarda daha rekabetçi hâle gelmekle birlikte finansal sağlamlık ve sürdürülebilirlik açısından sınırlılıklar göstermektedir. Bulgular, akıllı üretimde stratejik tedarikçi seçiminin evrensel olarak üstün tek bir tedarikçinin belirlenmesine indirgenmemesi gerektiğini göstermektedir. Önerilen çerçeve, bipolar dilsel değerlendirmeler ile uzlaşık sıralama arasında şeffaf bir bağlantı kurarken yöneticilere stratejik öncelikleri, genel performansı ve kriter bazlı ödünleşimleri yansıtan savunulabilir bir kısa liste sunmaktadır.

Anahtar Kelimeler

• stratejik yönetim
• akıllı üretim
• tedarikçi seçimi
• Endüstri 4.0
• dijital dönüşüm

Kaynakça

1. Akram, M., Shumaiza, & Rodríguez Alcantud, J. C. (2023a). Extended VIKOR method based with complex bipolar fuzzy sets. In M. Akram, Shumaiza, & J. C. Rodríguez Alcantud, Multi-criteria decision making methods with bipolar fuzzy sets (pp. 93–122). Springer. https://doi.org/10.1007/978-981-99-0569-0_4
2. Akram, M., Shumaiza, & Rodríguez Alcantud, J. C. (2023b). VIKOR method with trapezoidal bipolar fuzzy sets. In M. Akram, Shumaiza, & J. C. Rodríguez Alcantud, Multi-criteria decision making methods with bipolar fuzzy sets (pp. 67–91). Springer. https://doi.org/10.1007/978-981-99-0569-0_3
3. Baah, C., Acquah, I. S. K., & Ofori, D. (2022). Exploring the influence of supply chain collaboration on supply chain visibility, stakeholder trust, environmental and financial performances: A partial least squares approach. Benchmarking: An International Journal, 29(1), 172–193. https://doi.org/10.1108/BIJ-10-2020-0519
4. Behera, D. K., & Beura, S. (2023). Supplier selection for an industry using MCDM techniques. Materials Today: Proceedings, 74, 901–909. https://doi.org/10.1016/j.matpr.2022.11.291
5. Chai, N., Zhou, W., & Jiang, Z. (2023). Sustainable supplier selection using an intuitionistic and interval-valued fuzzy MCDM approach based on cumulative prospect theory. Information Sciences, 626, 710–737. https://doi.org/10.1016/j.ins.2023.01.070
6. Chauhan, C., Kaur, P., Arrawatia, R., Ractham, P., & Dhir, A. (2022). Supply chain collaboration and sustainable development goals (SDGs): Teamwork makes achieving SDGs dream work. Journal of Business Research, 147, 290–307. https://doi.org/10.1016/j.jbusres.2022.03.044
7. Demiralay, E., & Paksoy, T. (2022). Strategy development for supplier selection process with smart and sustainable criteria in fuzzy environment. Cleaner Logistics and Supply Chain, 5, 100076. https://doi.org/10.1016/j.clscn.2022.100076
8. Dubois, D., & Prade, H. (2008). An introduction to bipolar representations of information and preference. International Journal of Intelligent Systems, 23(8), 866–877. https://doi.org/10.1002/int.20297

9. Ghadimi, P., Wang, C., Lim, M. K., & Heavey, C. (2019). Intelligent sustainable supplier selection using multi-agent technology: Theory and application for Industry 4.0 supply chains. Computers & Industrial Engineering, 127, 588–600. https://doi.org/10.1016/j.cie.2018.10.050
10. Hosseini Dolatabad, A., Heidary Dahooie, J., Antucheviciene, J., Azari, M., & Razavi Hajiagha, S. H. (2023). Supplier selection in the Industry 4.0 era by using a fuzzy cognitive map and hesitant fuzzy linguistic VIKOR methodology. Environmental Science and Pollution Research, 30(18), 52923–52942. https://doi.org/10.1007/s11356-023-26004-6
11. Khulud, K., Masudin, I., Zulfikarijah, F., Restuputri, D. P., & Haris, A. (2023). Sustainable supplier selection through multi-criteria decision making (MCDM) approach: A bibliometric analysis. Logistics, 7(4), 96. https://doi.org/10.3390/logistics7040096
12. Lee, K. M. (2000). Bipolar-valued fuzzy sets and their operations. In Proceedings of the International Conference on Intelligent Technologies (pp. 307–312).
13. Nafei, A., Azizi, S. P., Edalatpanah, S. A., & Huang, C.-Y. (2024). Smart TOPSIS: A neural-network-driven TOPSIS with neutrosophic triplets for green supplier selection in sustainable manufacturing. Expert Systems with Applications, 255, 124744. https://doi.org/10.1016/j.eswa.2024.124744
14. Opricovic, S., & Tzeng, G.-H. (2004). Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS. European Journal of Operational Research, 156(2), 445–455. https://doi.org/10.1016/S0377-2217(03)00020-1
15. Puška, A., Božanić, D., Nedeljković, M., & Janošević, M. (2022). Green supplier selection in an uncertain environment in agriculture using a hybrid MCDM model: Z-numbers–fuzzy LMAW–fuzzy CRADIS model. Axioms, 11(9), 427. https://doi.org/10.3390/axioms11090427
16. Resende, C. H., Geraldes, C. A., & Lima Junior, F. R. (2021). Decision models for supplier selection in Industry 4.0 era: A systematic literature review. Procedia Manufacturing, 55, 492–499. https://doi.org/10.1016/j.promfg.2021.10.067
17. Riaz, M., & Tehrim, S. T. (2021). A robust extension of VIKOR method for bipolar fuzzy sets using connection numbers of SPA theory-based metric spaces. Artificial Intelligence Review, 54, 561–591. https://doi.org/10.1007/s10462-020-09859-w
18. Sachdeva, N., Shrivastava, A. K., & Chauhan, A. (2021). Modeling supplier selection in the era of Industry 4.0. Benchmarking: An International Journal, 28(5), 1809–1836. https://doi.org/10.1108/BIJ-12-2018-0441
19. Sahoo, S. K., & Goswami, S. S. (2024). Green supplier selection using MCDM: A comprehensive review of recent studies. Spectrum of Engineering and Management Sciences, 2(1), 1–16. https://doi.org/10.31181/sems1120241a
20. Sahoo, S. K., Goswami, S. S., & Halder, R. (2024). Supplier selection in the age of Industry 4.0: A review on MCDM applications and trends. Decision Making Advances, 2(1), 32–47. https://doi.org/10.31181/dma21202420
21. Sahu, A. K., Sharma, M., Raut, R. D., Sahu, A. K., Sahu, N. K., Antony, J., & Tortorella, G. L. (2023). Decision-making framework for supplier selection using an integrated MCDM approach in a lean-agile-resilient-green environment: Evidence from the Indian automotive sector. The TQM Journal, 35(4), 964–1006. https://doi.org/10.1108/TQM-12-2021-0372
22. Shannon, C. E. (1948). A mathematical theory of communication. Bell System Technical Journal, 27(3), 379–423. https://doi.org/10.1002/j.1538-7305.1948.tb01338.x
23. Stanujkic, D., Karabasevic, D., Zavadskas, E. K., Smarandache, F., & Brauers, W. K. M. (2019). A bipolar fuzzy extension of the MULTIMOORA method. Informatica, 30(1), 135–152. https://doi.org/10.15388/Informatica.2019.201
24. Sudusinghe, J. I., & Seuring, S. (2022). Supply chain collaboration and sustainability performance in circular economy: A systematic literature review. International Journal of Production Economics, 245, 108402. https://doi.org/10.1016/j.ijpe.2021.108402
25. Tarigan, Z. J. H., Siagian, H., & Jie, F. (2021). Impact of internal integration, supply chain partnership, supply chain agility, and supply chain resilience on sustainable advantage. Sustainability, 13(10), 5460. https://doi.org/10.3390/su13105460
26. Thanh, N. V., & Lan, N. T. K. (2022). A new hybrid triple bottom line metrics and fuzzy MCDM model: Sustainable supplier selection in the food-processing industry. Axioms, 11(2), 57. https://doi.org/10.3390/axioms11020057
27. Tufail, F., & Shabir, M. (2022). VIKOR method for MCDM based on bipolar fuzzy soft β-covering-based bipolar fuzzy rough set model and its application to site selection of solar power plant. Journal of Intelligent & Fuzzy Systems, 42(3), 1835–1857. https://doi.org/10.3233/JIFS-211223
28. Youn, S., Yang, M. G. M., Hong, P., & Park, K. (2013). Strategic supply chain partnership, environmental supply chain management practices, and performance outcomes: An empirical study of Korean firms. Journal of Cleaner Production, 56, 121–130. https://doi.org/10.1016/j.jclepro.2011.09.026
29. Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338–353. https://doi.org/10.1016/S0019-9958(65)90241-X
30. Zekhnini, K., Cherrafi, A., Bouhaddou, I., Benghabrit, Y., & Garza-Reyes, J. A. (2020). Supplier selection for smart supply chain: An adaptive fuzzy-neuro approach. In Proceedings of the 5th North American International Conference on Industrial Engineering and Operations Management, Detroit, Michigan, USA, August 10–14, 2020.
31. Zhang, W.-R. (1994). Bipolar fuzzy sets and relations: A computational framework for cognitive modeling and multiagent decision analysis. In Proceedings of the First International Joint Conference of the North American Fuzzy Information Processing Society Biannual Conference, the Industrial Fuzzy Control and Intelligent Systems Conference, and the NASA Joint Technology Workshop on Neural Networks and Fuzzy Logic (pp. 305–309). IEEE. https://doi.org/10.1109/IJCF.1994.375115