Dijital Depolama Alanlarında Verimliliğin Artırılması: Lojistik Yönetiminde SWARA Tabanlı Değerlendirme ve Yapay Sinir Ağı ile Geçerleme

Yıl 2025, Cilt: 9 Sayı: 3, 1110 - 1134, 19.09.2025

Mustafa Hakan Saldı , Gonca Reyhan Akkartal

https://doi.org/10.30586/pek.1650559

Öz

Bu araştırma, lojistik yönetiminde dijital depolama alanlarının verimliliğini iyileştiren faktörleri incelemek amacıyla SWARA (Adım Adım Ağırlık Değerlendirme Oran Analizi) yöntemi uygulanarak gerçekleştirilmiştir. Tedarik zincirlerinin artan karmaşıklığıyla birlikte, envanter depolamanın dijital çözümlerle optimize edilmesi, operasyonel performansın geliştirilmesi ve maliyetlerin indirgenmesi açısından kritik öneme sahiptir. Bu çalışmada, beş temel kriter değerlendirilmiştir: Otomasyon Seviyesi, Veri Analitiği Yetkinliği, İşgücü Eğitimi, Envanter Yönetimi Verimliliği ve Enerji Verimliliği. Bu kriterlerin uzmanlar tarafından nasıl önceliklendirildiğini ortaya çıkarmak amaçlanmıştır. Her kriter, genel depolama etkinliği üzerindeki etkisine göre değerlendirilmiştir. SWARA yöntemi kullanılarak, bu faktörler önceliklendirilmiş ve böylece lojistik yöneticilerinin en etkili olanlara odaklanmaları sağlanmıştır. Bu çalışma, dijitalleşme yolunda organizasyonların farkındalığını artırırken, yönetim bilişim sistemleri (MIS) ile bilgi ve iletişim teknolojileri (ICT) altyapılarını en iyi şekilde harmonize etmelerini sağlamayı hedeflemektedir. SWARA yöntemi uygulandıktan sonra, elde edilen sonuçlar SPSS’te çok katmanlı algılayıcı (multilayer perceptron) algoritması ile Yapay Sinir Ağları (Neural Networks) kullanılarak test edilmiştir. Çalışmanın bulguları, karar vericilere kaynak kullanımını iyileştirme, lojistik süreçleri daha verimli hale getirme ve giderek dijitalleşen bir ortamda sürdürülebilir ve uzun vadeli uygulamaları destekleme konusunda uygulanabilir içgörüler sunmaktadır.

Anahtar Kelimeler

Dijital Depolama , Lojistik Yönetimi , Nicel Yöntemler

Enhancing Efficiency in Digital Storage Fields: A SWARA-Based Evaluation with Neural Network Validation in Logistics Management

Öz

This research is designed to investigate the factors to enhance efficiency in digital storage areas within logistics management, by performing the SWARA (Step-wise Weight Assessment Ratio Analysis) method. With the developing complexity of supply chains, optimizing inventory storage through digital solutions is critical for improving operational performance as well as reducing costs. In this study five key criteria are evaluated: Automation Level, Data Analytics Capability, Workforce Training, Inventory Management Efficiency, and Energy Efficiency to emerge how the conditions are being ranked by the experts. Each criterion is assessed for its impact on overall storage effectiveness. By applying the SWARA method, the factors are prioritized, which can allow logistics managers to focus on the most effective ones which can promote awareness of the organizations in digitalization path while structuring their management information systems (MISs) to leverage their information and communications technology (ICT) infrastructures. After processing SWARA, the results of the method are tested with Neural Networks through multilayer perceptron in SPSS. The findings offer actionable insights, enabling decision-makers to implement targeted improvements that enhance resource utilization, streamline logistics processes, and support sustainable and long-term practices in an increasingly digital landscape.

Anahtar Kelimeler

Digital Storage , Logistics Management , Quantitative Methods

Kaynakça

• Aka, D. Ç. (2024, April 30). Evaluation of the change in perspectives of SME executives towards the Industry 4.0 process and opportunities in digital transformation with the SWARA method. Erciyes Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 45-53. https://doi.org/10.18070/erciyesiibd.1276967
• Almakayeel, N. (2023, May 8). Evaluating and ranking SCPMS enablers using ISM and SWARA. Applied Sciences, 13(9). https://doi.org/10.3390/app13095791
• Biçer, B., Sayılı, E., Ağaçhan, M., Dündar, B., Doğantay, S. C., Kazancoglu, Y., & Pala, M. Ö. (2023). Facility layout design for dangerous goods containers in the warehouse. Towards Industry 5.0, 807-817. https://doi.org/10.1007/978-3-031-24457-5_64
• Chakraborty, S., Raut, R. D., Rofin, T., & Shankar, C. (2023, December). A comprehensive and systematic review of multi-criteria decision-making methods and applications in healthcare. Healthcare Analytics, 4. https://doi.org/10.1016/j.health.2023.100232
• Demir, S., Paksoy, T., & Koçhan, Ç. G. (2021). Logistics 4.0: SCM in Industry 4.0 era (Changing patterns of logistics in Industry 4.0 and role of digital transformation in SCM). In T. Paksoy, Ç. G. Koçhan, & S. S. Ali (Eds.), Logistics 4.0 Digital Transformation of Supply Chain Management (pp. 15-26). CRC Press Taylor & Francis Group. Retrieved from https://api.pageplace.de/preview/DT0400.9781000245103_A40924601/preview-9781000245103_A40924601.pdf
• Dimitriev, A. (2019). Digital technologies of transportation and logistics systems visibility. Strategic Decisions and Risk Management, 10(1), 20-26. https://doi.org/10.17747/2618-947X-2019-1-20-26
• Ding, Y., & Jain, N. (2022). Research on logistics management information system of electronic commerce based on computer information technology. In Cyber Security Intelligence and Analytics (pp. 569-576). The International Conference on Cyber Security Intelligence and Analytics. https://doi.org/10.1007/978-3-030-96908-0_71
• Dixit, V. K., Malviya, R. K., Kumar, V., & Shankar, R. (2024, March). An analysis of the strategies for overcoming digital supply chain implementation barriers. Decision Analytics Journal, 10. https://doi.org/10.1016/j.dajour.2023.100389
• Guo, S., Zhang, Y., & Zhao, X. (2023). Design and research of IoT-based logistics warehousing and distribution management system. Cyber Security Intelligence and Analytics, 173, 319-328. https://doi.org/10.1007/978-3-031-31775-0_33
• Guo, Y. (2023). Optimization of logistics industry organization management system in digital intelligence era. Innovative Computing Vol 2- Emerging Topics in Future Internet, 1045, 332-338. https://doi.org/10.1007/978-981-99-2287-1_47
• Helo, P., & Thai, V. V. (2024, September). Logistics 4.0 – digital transformation with smart connected tracking and tracing devices. International Journal of Production Economics, 275. https://doi.org/10.1016/j.ijpe.2024.109336
• Karagöz, B., & Çağlar, B. (2024). Evaluation of technology-based sustainable practices in logistics service providers by content analysis and SWARA method. Verimlilik Dergisi, 58(3), 359-374. https://doi.org/10.51551/verimlilik.1349615
• Kern, J. (2021). The digital transformation of logistics. In M. Sullivan & J. Kern (Eds.), The Digital Transformation of Logistics: Demystifying Impacts of the Fourth Industrial Revolution (pp. 361-403). Wiley-IEEE Press. https://doi.org/10.1002/9781119646495.ch25
• Koleshnia, Y., & Zhaldak, H. (2021). Digital technologies in logistics. 43-51. https://doi.org/10.17512/znpcz.2021.3.04
• Korucuk, S., Aytekin, A., Ecer, F., Karmaşa, Ç., & Zavadskas, E. K. (2022, December 8). Assessing green approaches and digital marketing strategies for twin transition via Fermatean fuzzy SWARA-COPRAS. Fuzzy Set Theory and Its Applications in Decision Making, 11(12). https://doi.org/10.3390/axioms11120709
• Kumar, A., Mâni, V., Jain, V., Gupta, H., & Venkatesh, V. (2023, January). Managing healthcare supply chain through artificial intelligence (AI): A study of critical success factors. Computers & Industrial Engineering, 175. https://doi.org/10.1016/j.cie.2022.108815
• Mihai, N. (2022). Digital logistics: Historical background and current key development trends. Digital Technologies in the Contemporary Economy, 8-26.
• Mohamed, M., Elsayed, A., & Voskoglou, M. (2024, March 27). Collaboration of vague theory and mathematical techniques for optimizing healthcare by recommending optimal blockchain supplier. Sustainable Machine Intelligence Journal. https://doi.org/10.61356/smij.2024.66105
• Mohamed, M., Salam, A., Ye, J., & Yong, R. (2024, April). A hybrid triangular fuzzy SWARA-MAROCS approach for selecting optimal and smart logistic enterprise based on IoT, blockchain, and UAVs. Multicriteria Algorithms with Applications, 4, 1-15. https://doi.org/10.61356/j.mawa.2024.4241
• Nabeeh, N. A., & Tantawy, A. A. (2023). A neutrosophic model for blockchain platform selection based on SWARA and WSM. Neutrosophic and Information Fusion, 1(2), 29-43. https://doi.org/10.54216/NIF.010204
• Neher, A. (2021). Logistics management in an IoT world. In M. Sullivan & J. Kern (Eds.), The Digital Transformation of Logistics: Demystifying Impacts of the Fourth Industrial Revolution (pp. 27-40). https://doi.org/doi.org/10.1002/9781119646495.ch3
• Noor, S., Tajik, O., & Golzar, J. (2022). Simple random sampling. Sampling Method | Descriptive Research, 1(2), 78-82. https://doi.org/10.22034/ijels.2022.162982
• Özdağoğlu, A., & Bahar, S. (2022, January 21). Logistics 4.0 and smart supply chain management. 163-183. https://doi.org/10.1108/978-1-80117-326-120211012
• Pansare, R., Yadav, G., & Arturo Garza, J. R. (2023, April 28). Assessment of sustainable development goals through Industry 4.0 and reconfigurable manufacturing system practices. Journal of Manufacturing Technology Management, 34(3), 383-413. https://doi.org/10.1108/JMTM-05-2022-0206
• Pekarčíková, M., Trebuňa, P., Kliment, M., Edl, M., & Rosocha, L. (2020). Transformation the logistics to digital logistics: Theoretical approach. International Scientific Journal about Logistic, 7(4), 217-223. https://doi.org/10.22306/al.v7i4.174
• Ronaghi, M. H. (2021, September). A blockchain maturity model in agricultural supply chain. Information Processing in Agriculture, 8(3), 398-408. https://doi.org/10.1016/j.inpa.2020.10.004
• Salkin, C., Öner, M., Üstündağ, A., & Çevikcan, E. (2018). A conceptual framework for Industry 4.0. In A. Üstündağ & E. Çevikcan (Eds.), Industry 4.0: Managing the Digital Transformation (pp. 3-23). https://doi.org/10.1007/978-3-319-57870-5_1
• Stanujkic, D., Karabasevic, D., & Zavadskas, E. K. (2015). A framework for the selection of a packaging design based on the SWARA. Inžinerinė Ekonomika-Engineering Economics, 26(2), 181-187. https://doi.org/10.5755/j01.ee.26.2.8820
• Şahin, Y., & Bozkurt, Y. (2022). Multi-criteria decision support with SWARA and TOPSIS methods to the digital transformation process in the iron and steel industry. In N. M. Durakbasa & M. Gençyılmaz (Eds.), Digitizing Production Systems (pp. 309-322). ISPR: The International Symposium for Production Research. https://doi.org/10.1007/978-3-030-90421-0_26
• Taş, M. A., & Akcan, S. (2021, November 2). Selecting a green, agile and Industry 4.0 supplier with the fuzzy-SWARA-BWM integrated method. https://doi.org/10.21203/rs.3.rs-365657/v1
• Ugochukwu, N. A., Goyal, S., & Arumugam, S. (2022, June 8). Blockchain-based IoT-enabled system for secure and efficient logistics management in the era of IR 4.0. Manufacturing Methods and Characterization of Nanomaterials for Industry 4.0, 1-10. https://doi.org/10.1155/2022/7295395
• Ugochukwu, N. A., Goyal, S., Rajawat, A. S., Islam, S. M., He, J., & Aslam, M. (2022). An innovative blockchain-based secured logistics management architecture: Utilizing an RSA asymmetric encryption method. Supply Management and Mathematical Logistics, 10(24). https://doi.org/10.3390/math10244670
• Volodymyr, R., & Prus, Y. (2023, December). Digital technologies in logistics and supply chain management. Facta Universitatis Series Economics and Organization. https://doi.org/10.22190/FUEO230517012R
• Voronova, O., Khareva, V., & Koshkin, I. (2021). Optimization of logistics through introduction of digital technologies in the company’s supply chain. 246, 573-586. https://doi.org/10.1007/978-3-030-81619-3_65
• Wang, Y., & Pettit, S. (2016). E-logistics: Managing your digital supply chains for competitive advantage.
• Woschank, M., König, A., & Miklautsch, P. (2021, March). Digitalization in industrial logistics: Contemporary evidence and future directions. https://doi.org/10.13140/RG.2.2.31301.01764
• Yang, J., Ma, X., Crespo, R. G., & Martínez, O. S. (n.d.). Special issue: Blockchain for logistic industry. https://doi.org/10.1002/asmb.2577