TY - JOUR T1 - Optimizing Human-Centric Warehouse Operations: A Digital Twin Approach Using Dynamic Algorithms and AI/ML TT - İnsan Merkezli Depo Operasyonlarının Optimizasyonu: Dinamik Algoritmalar ve AI/ML Kullanarak Dijital İkiz Yaklaşımı AU - Arslan, Erhan PY - 2025 DA - February Y2 - 2024 DO - 10.51551/verimlilik.1524701 JF - Verimlilik Dergisi PB - Ministry of Industry and Technology WT - DergiPark SN - 1013-1388 SP - 119 EP - 138 IS - PRODUCTIVITY FOR LOGISTICS LA - en AB - Purpose: This study aims to develop a versatile and adaptive system that optimizes manual warehouse operations through the integration of Digital Twin technology and AI/ML models.Methodology: The framework combines Digital Twin technology with advanced AI/ML analytics to dynamically adjust operational strategies based on real-time data collected from warehouse activities.Findings: A prototype implementation demonstrated significant improvements, including a 28.6% reduction in average picking time, a 20% improvement in inventory turnover, an increase in demand forecasting accuracy from 85% to 92%, and a reduction in labor costs by 15%.Originality: This research uniquely applies Digital Twin technology to manual warehouse environments, showcasing its effectiveness in enhancing operational efficiency without the need for full automation. KW - Digital Twin KW - Warehouse KW - Optimization KW - Artificial Intelligence KW - Machine Learning N2 - Amaç: Bu çalışmada, Dijital İkiz teknolojisi ve Yapay Zekâ/Makine Öğrenmesi modellerinin entegrasyonu yoluyla manuel depo operasyonlarını optimize eden çok yönlü ve uyarlanabilir bir sistem geliştirmeyi hedeflenmiştir. Yöntem: Çerçeve, depo faaliyetlerinden toplanan gerçek zamanlı verilere dayanarak operasyonel stratejileri dinamik olarak ayarlamak için Dijital İkiz teknolojisini gelişmiş Yapay Zekâ/Makine Öğrenimi analitiğiyle birleştiriyor. Bulgular: Prototip uygulaması, ortalama toplama süresinde %28,6'lık bir azalma, stok devir hızında %20'lik bir iyileşme, talep tahmin doğruluğunda %85'ten %92'ye bir artış ve işçilik maliyetlerinde %15'lik bir azalma dahil olmak üzere önemli iyileştirmeler gösterdi.Özgünlük: Bu araştırma, Dijital İkiz teknolojisini manuel depo ortamlarına benzersiz bir şekilde uygulayarak, tam otomasyona ihtiyaç duymadan operasyonel verimliliği artırmadaki etkinliğini ortaya koyuyor. CR - AIM Consulting. (2023). “AI for Supply Chain Optimization: Improve Demand Forecasting”, https://www.aimconsulting.com/ai-for-supply-chain-optimization, (Accessed: 20.06.2023). CR - Amazon. (2021). “Optimizing Warehouse Operations with Digital Twins. Amazon Blog”, https://www.aboutamazon.com/news/operations/optimizing-warehouse-operations-with-digital-twins, (Accessed: 15.07.2023). CR - Aylak, B.L., İnce, M., Oral, O., Süer, G., Almasarwah, N., Singh, M. and Salah, B. (2021). “Application of Machine Learning Methods for Pallet Loading Problem”, Applied Sciences, 11(18), 8304. CR - Aylak, B.L. (2022). “Warehouse Layout Optimization Using Association Rules”, Fresenius Environmental Bulletin, 31(3A), 3828-3840. CR - Boschert, S. and Rosen, R. (2016). “Digital Twin—The Simulation Aspect”, Mechatronic Futures, Springer. CR - Breiman, L. (2001). “Random Forests”, Machine Learning, 45, 5-32. CR - Chen, L., Cui, H. and Shi, F. (2019). “Reinforcement Learning in Predictive Control for Smart Warehouse Logistics”, Computers & Industrial Engineering, 137, 106072. CR - Chicaiza, D., Loaiza, R. and Burbano, D. (2020). “Augmented Reality Applications for Warehouse Management: A Literature Review”, Procedia Manufacturing, 42, 319-325. CR - Cortes, C. and Vapnik, V. (1995). “Support-Vector Networks”, Machine Learning, 20, 273-297. CR - DHL. (2023). “Leveraging AI for Predictive Maintenance in Warehousing”, https://www.dhl.com/content/dam/dhl/global/core/documents/pdf/glo-core-digital-twins-in-logistics.pdf, (Accessed: 03.07.2023). CR - Exotec. (2023). “Implementing Digital Twins for Enhanced Warehouse Efficiency”, https://www.exotec.com/digital-twins-for-enhanced-warehouse-efficiency, (Accessed: 12.06.2023). CR - Forbes. (2023). “Digital Twins for Warehouses: Transforming Efficiency and Productivity” https://www.forbes.com/sites/forbestechcouncil/2023/06/22/digital-twins-for-warehouses/, (Accessed: 10.06.2023). CR - Fuller, A., Fan, Z., Day, C. and Barlow, C. (2020). “Digital Twin: Enabling Technologies, Challenges and Open Research”, IEEE Access, 8, 108952-108971. CR - Ghiassi, M. and Saidane, H. (2018). “A Dynamic Artificial Neural Network Model for Forecasting Time Series Events”, International Journal of Forecasting, 21(2), 341-362. CR - Glaessgen, E. and Stargel, D. (2012). “The Digital Twin Paradigm for Future NASA and U.S. Air Force Vehicles”, AIAA SciTech Forum, https://ntrs.nasa.gov/api/citations/20120008178/downloads/20120008178.pdf. CR - Graves, S.C. and Yücesan, H.C.S. (2009). “Strategic Safety Stock Placement in Supply Chains with Demand and Lead-Time Uncertainty”, Management Science, 46(5), 739-750. CR - Grieves, M. (2002). “Concept of Digital Twin”, Product Lifecycle Management Conference, https://www.plm.automation.siemens.com/global/en/our-story/glossary/digital-twin/24465. CR - Grieves, M. and Vickers, J. (2017). “Digital Twin: Mitigating Unpredictable, Undesirable Emergent Behavior in Complex Systems” Transdisciplinary Perspectives on Complex Systems, Springer. CR - Gu, J., Goetschalckx, M. and McGinnis, L.F. (2007). “Research on Warehouse Design and Performance Evaluation: A Comprehensive Review”, European Journal of Operational Research, 203(3), 539-549. CR - Ivanov, D., Dolgui, A., & Sokolov, B. (2020). Handbook of Ripple Effects in the Supply Chain. Springer International Series in Operations Research & Management Science, Switzerland. DOI: 10.1007/978-3-030-14302-2. CR - Kaber, D.B. and Riley, S.M. (2017). “Task Design and Workload Management in Manual and Automated Warehouses: A Human Factors Perspective”, Applied Ergonomics, 60, 35-45. CR - Kardinal. (2023). “Machine Learning and Route Optimization: Towards Flawless Deliveries”, https://www.kardinal.ai/machine-learning-and-route-optimization, (Accessed: 05.06.2023). CR - Kritzinger, W., Karner, M., Traar, G., Henjes, J. and Sihn, W. (2018). “Digital Twin in Manufacturing: A Categorical Literature Review and Classification”, IFAC-PapersOnLine, 51(11), 1016-1022. CR - Kumar, P., Kumar, S. and Kumar, S. (2020). “Predictive Maintenance of Industrial Equipment Using Machine Learning”, International Journal of Advanced Research in Computer Science, 11(3), 75-81. CR - McKinsey & Company. (2022). “Improving Warehouse Operations Digitally”, https://www.mckinsey.com/capabilities/operations/our-insights/improving-warehouse-operations-digitally, Accessed: (18.06.2023). CR - PepsiCo. (2020). “Digital Twin Technology in PepsiCo's Distribution Centers”, PepsiCo Annual Report, https://www.pepsico.com/sustainability/digital-transformation, Accessed: (22.06.2023). CR - Rashid, A. and Rattenbury, S. (2018). “Scalable Machine Learning for Real-Time Inventory Optimization”, Journal of Business Research, 89, 201-213. CR - Tao, F., Cheng, J., Qi, Q., Zhang, M., Zhang, H. and Sui, F. (2016). “Digital Twin-Driven Product Design, Manufacturing and Service with Big Data”, The International Journal of Advanced Manufacturing Technology, 94, 3563-3576. CR - Uhlemann, T.H., Schock, C., Lehmann, C., Freiberger, S. and Steinhilper, R. (2017). “The Digital Twin: Demonstrating the Potential of Real Time Data Acquisition in Production Systems”, Procedia Manufacturing, 9, 113-120. CR - WarehouseBlueprint. (2023). “Optimizing Warehouse Operations with the Power of Digital Twin, SketchUp, and Power BI”, https://www.warehouseblueprint.com/blog/optimizing-warehouse-operations-with-digital-twin, (Accessed: 30.06. 2023). UR - https://doi.org/10.51551/verimlilik.1524701 L1 - https://dergipark.org.tr/en/download/article-file/4107877 ER -