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Dergiler / Sakarya University Journal of Computer and Information Sciences / 2025 / Volume: 8 Issue: 4

A Hybrid Fuzzy DEMATEL–ELECTRE Framework for Evaluating the Impact of Industry 4.0 Technologies on Warehouse Management Strategy Outcomes

Ahmet Kala(SAKARYA UNIVERSITY OF APPLIED SCIENCES)0000-0002-0598-1181
Cem Özkurt(SAKARYA UNIVERSITY OF APPLIED SCIENCES)0000-0002-1251-7715
Bilal Emre Yahyaoğlu(SAKARYA UNIVERSITY OF APPLIED SCIENCES)0009-0000-5113-9959
Sayfa
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Yayın Tarihi
2025-12-29
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

The proposed framework offers a transparent, interpretable, and causally grounded decision-support model for aligning digital transformation investments with strategic warehouse performance objectives.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:Integrating Industry 4.0 (I4.0) technologies into warehouse management critically enhances strategic performance.
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Temel Bulgular & Veri:However, existing studies often overlook the causal relationships between strategic outcomes and the transparency of technology prioritization.
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Sonuç & Katkı:The proposed framework offers a transparent, interpretable, and causally grounded decision-support model for aligning digital transformation investments with strategic warehouse performance objectives.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Industry 4.0 · Warehouse management · Fuzzy DEMATEL

Abstract

Integrating Industry 4.0 (I4.0) technologies into warehouse management critically enhances strategic performance. However, existing studies often overlook the causal relationships between strategic outcomes and the transparency of technology prioritization. This study proposes a hybrid multi-criteria decision-making (MCDM) framework integrating Fuzzy DEMATEL to determine the relative weights of strategic outcomes, Fuzzy ELECTRE II to rank technologies, and SHAP-based Explainable Artificial Intelligence (XAI) to enhance model transparency and interpretability. The analysis relies on Delphi-based expert evaluations from 12 senior industrial engineers across three manufacturing firms. The results reveal that Cost Reduction (weight = 0.225), Operational Efficiency (0.097), and Inventory Management (0.115) are the most critical strategic outcomes. Artificial Intelligence, Internet of Things, and Big Data Analytics emerged as the top-ranked technologies based on ELECTRE II scores. SHAP analysis further identified Cost Reduction (SHAP value: +1.62), Customer Satisfaction (SHAP value: +0.50), and Real-time Data Processing (SHAP value: +0.40) as the primary drivers behind the technology rankings. The proposed framework offers a transparent, interpretable, and causally grounded decision-support model for aligning digital transformation investments with strategic warehouse performance objectives.

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Bu Makaleye Atıf Yap

Kala, A., Özkurt, C., Yahyaoğlu, B. E. (2025). A Hybrid Fuzzy DEMATEL–ELECTRE Framework for Evaluating the Impact of Industry 4.0 Technologies on Warehouse Management Strategy Outcomes. Sakarya University Journal of Computer and Information Sciences, Volume: 8 Issue: 4. https://doi.org/10.35377/saucis...1704169

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Kaynakça (35)

  1. G. L. Tortorella and D. Fettermann, "Implementation of Industry 4.0 and lean production in Brazilian manufacturing companies," International Journal of Production Research, vol. 56, no. 8, pp. 2975-2987, 2018. doi: 10.1080/00207543.2017.1391420
  2. M. Ghobakhloo, "The future of manufacturing industry: A strategic roadmap toward Industry 4.0," Journal of Manufacturing Technology Management, vol. 29, no. 6, pp. 910-936, 2018. doi: 10.1108/JMTM-02-2018-0057
  3. P. Zawadzki and K. Żywicki, "Smart product design and production control for effective mass customization in the Industry 4.0 concept," Management and Production Engineering Review, vol. 7, no. 3, pp. 105-112, 2016. https://www.researchgate.net/publication/309026293_Smart_Product_Design_and_Production_Control_for_Effective_Mass_Customization_in_the_Industry_40_Concept
  4. G. Ken, H. Rajagopal, and S. Anjum, "Pharmacy warehouse management system," in Proceedings of International Conference on Artificial Life and Robotics, vol. 28, pp. 663-668, 2023. doi: 10.5954/icarob.2023.os26-4
  5. K. Nuengchamnong and T. Mahamud, "Optimization of KLT warehouse management," in *International Conference Proceedings PSETN-23, CBAES-23, LEHS2-23, PSETH-23 & ICCBES-23*, Pattaya, Thailand, May 29-31, 2023. doi: 10.17758/eirai18.f0523411
  6. J. Wang, B. Yin, X. Li, and H. Cui, "Research on intelligent electricity meter warehouse management system based on IoT technology," in Second International Conference on Advanced Manufacturing Technology and Manufacturing Systems (ICAMTMS 2023), 2023. doi: 10.1117/12.2688959
  7. Z. Sun, Z. Yue, X. Sun, W. Fan, and W. Zhou, "An intelligent cargo/warehouse management system," in Proceedings of International Conference on Artificial Life and Robotics, vol. 29, pp. 818-822, 2024. doi: 10.5954/icarob.2024.os26-1
  8. Y. Fu, Y. Qie, Y. Ding, S. Ma, Y. Cao, and Y. Li, "Research on the application of passive RFID technology in warehouse management," in Second International Conference on Digital Society and Intelligent Systems (DSInS 2022), 2023. doi:10.1117/12.2673413
  9. D. Du, "RFID technology in a smart warehouse application study," in Sixth International Conference on Traffic Engineering and Transportation System (ICTETS 2022), 2023, p. 4. doi: 10.1117/12.2668451
  10. M. Phan and A. Tran, "Development a warehouse management information system," Applied Mechanics and Materials, vol. 907, pp. 131-143, 2022. doi: 10.4028/p-78ah4r
  11. X. Zhang, T. Mo, and Y. Zhang, "Optimization of storage location assignment for non-traditional layout warehouses based on the firework algorithm," Sustainability, vol. 15, no. 13, p. 10242, 2023. doi: 10.3390/su151310242
  12. S. Manoharan, D. Stilling, G. Kabir, and S. Sarker, "Implementation of linear programming and decision-making model for the improvement of warehouse utilization," Applied System Innovation, vol. 5, no. 2, p. 33, 2022. doi: 10.3390/asi5020033
  13. R. Carli, M. Dotoli, S. Digiesi, F. Facchini, and G. Mossa, "Sustainable scheduling of material handling activities in labor-intensive warehouses: A decision and control model," Sustainability, vol. 12, no. 8, p. 3111, 2020. doi: 10.3390/su12083111
  14. Z. Yao-qin, "Application of information system in warehouse management," DEStech Transactions on Computer Science and Engineering, no. cii, 2017. doi: 10.12783/dtcse/cii2017/17309
  15. W. Larutama, D. Bentar, R. Risdayanto, and R. Alvariedz, "Implementation of warehouse management system planning in finished goods warehouse," Journal of Logistics and Supply Chain, vol. 2, no. 2, pp. 81-90, 2022. doi: 10.17509/jlsc.v2i2.62840
  16. A. Jarašūnienė, K. Čižiūnienė, and A. Čereška, "Research on impact of IoT on warehouse management," Sensors, vol. 23, no. 4, p. 2213, 2023. doi: 10.3390/s23042213
  17. N. Batarlienė and A. Jarašūnienė, "Improving the quality of warehousing processes in the context of the logistics sector," Sustainability, vol. 16, no. 6, p. 2595, 2024. doi: 10.3390/su16062595
  18. D. Perkumienė, K. Ratautaitė, and R. Pranskūnienė, "Innovative solutions and challenges for the improvement of storage processes," Sustainability, vol. 14, no. 17, p. 10616, 2022. doi: 10.3390/su141710616
  19. G. May and D. Kiritsis, "Zero defect manufacturing strategies and platform for smart factories of Industry 4.0," in IFIP International Conference on Advances in Production Management Systems, pp. 142-152, 2019. doi: 10.1007/978-3-030-18180-2_11
  20. U. M. Dilberoglu, B. Gharehpapagh, U. Yaman, and M. Dolen, "The role of additive manufacturing in the era of Industry 4.0," Procedia Manufacturing, vol. 11, pp. 545-554, 2017. doi: 10.1016/j.promfg.2017.07.148
  21. L. A. Ocampo, T. A. G. Tan, and L. A. Sia, "Using fuzzy DEMATEL in modeling the causal relationships of the antecedents of organizational citizenship behavior (OCB) in the hospitality industry: A case study in the Philippines," Journal of Hospitality and Tourism Management, vol. 34, pp. 11-29, 2018. doi: 10.1016/j.jhtm.2017.11.002
  22. S. Altuntas and M. K. Yilmaz, "Fuzzy DEMATEL method to evaluate the dimensions of marketing resources: An application in SMEs," Journal of Business Economics and Management, vol. 17, no. 3, pp. 347-364, 2016. doi: 10.3846/16111699.2015.1068220
  23. Y. Beikkhakhian, M. Javanmardi, M. Karbasian, and B. Khayambashi, "The application of ISM model in evaluating agile suppliers selection criteria and ranking suppliers using fuzzy TOPSIS-AHP methods," Expert Systems with Applications, vol. 42, no. 15-16, pp. 6224-6236, 2015. doi: 10.1016/j.eswa.2015.02.035
  24. C.-T. Chen, "Extensions of the TOPSIS for group decision-making under fuzzy environment," Fuzzy Sets and Systems, vol. 114, no. 1, pp. 1-9, 2000. doi: 10.1016/S0165-0114(97)00377-1
  25. C. Li and G. Tzeng, "Identification of a threshold value for the DEMATEL method: Using the maximum mean de-entropy algorithm," in Communications in Computer and Information Science, pp. 789-796, 2009. doi: 10.1007/978-3-642-02298-2_115
  26. N. Chen and Z. Xu, "Hesitant fuzzy ELECTRE II approach: A new way to handle multi-criteria decision making problems," Information Sciences, vol. 292, pp. 175-197, 2015. doi: 10.1016/j.ins.2014.08.054
  27. R. Keshavarzfard and A. Makui, "An IF-DEMATEL-AHP based on triangular intuitionistic fuzzy numbers (TIFNs)," Decision Science Letters, vol. 4, no. 2, pp. 237-246, 2015. doi: 10.5267/j.dsl.2014.11.002
  28. J. Chen, "Improved DEMATEL-ISM integration approach for complex systems," PLoS ONE, vol. 16, no. 7, p. e0254694, 2021. doi: 10.1371/journal.pone.0254694
  29. H. Shakeri and M. Khalilzadeh, "Analysis of factors affecting project communications with a hybrid DEMATEL-ISM approach (A case study in Iran)," Heliyon, vol. 6, no. 8, p. e04430, 2020. doi: 10.1016/j.heliyon.2020.e04430
  30. S. Khan, R. Singh, A. Haleem, J. Dsilva, and S. Ali, "Exploration of critical success factors of Logistics 4.0: A DEMATEL approach," Logistics, vol. 6, no. 1, p. 13, 2022. doi: 10.3390/logistics6010013
  31. S. Esmaeili et al., "Optimizing in-store warehouse safety: A DEMATEL approach to comprehensive risk assessment," PLoS ONE, vol. 20, no. 2, p. e0317787, 2025. doi: 10.1371/journal.pone.0317787
  32. K. Hsia et al., "Development of auto-stacking warehouse truck," Journal of Robotics, Networking and Artificial Life, vol. 4, no. 4, p. 334, 2018. doi: 10.2991/jrnal.2018.4.4.17
  33. L. Abdullah, Z. Ong, and N. Rahim, "An intuitionistic fuzzy decision-making for developing cause and effect criteria of subcontractors selection," International Journal of Computational Intelligence Systems, vol. 14, no. 1, p. 991, 2021. doi: 10.2991/ijcis.d.210222.001
  34. D. Lee and S. Yoon, "Application of artificial intelligence-based technologies in the healthcare industry: Opportunities and challenges," International Journal of Environmental Research and Public Health, vol. 18, no. 1, p. 271, 2021. doi: 10.3390/ijerph18010271
  35. M. Zhou et al., "Machine learning for Industry 4.0 [From the Guest Editors]," IEEE Robotics & Automation Magazine, vol. 30, no. 2, pp. 8-9, 2023. doi: 10.1109/MRA.2023.3266618