Sürdürülebilir Kalkınma Amaçları
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- MakaleAçık Erişim2025Adoption of AI-based order picking in warehouse: benefits, challenges, and critical success factors
Rad, Fakhreddin Fakhrai; Oghazi, Pejvak; Onur, İzmir; Kordestani, Arash
This research assesses the adoption of artificial intelligence (AI)-based batch order picking in a warehouse, focusing on benefits, challenges, and critical success factors. Using customer order data and simulation, the study employs a quantitative approach, combining mathematical and statistical estimations with qualitative examinations centered on interviews with the warehouse staff, its Enterprise Resource Planning (ERP) developer, and the AI developer. The findings of this mixed-method study reveal that the AI-based order-picking system (AI-based system) has improved order-picking efficiency by reducing travel distance and time. Nevertheless, challenges hinder maximum utilization of the system. In addition, the research highlights critical success factors and other benefits of adopting the system tailored to warehouse management. Understanding the lessons learned in this research is essential for businesses seeking to adopt AI to enhance their efficiency.
- MakaleYalnız künye2016Logistics center site selection by ANP/BOCR analysis: A case study of Turkey
Peker, Iskender; Baki, Birdogan; Tanyas, Mehmet; Murat Ar, Ilker
Site selection for logistics centers is one of the most strategic decisions in logistics. This study addresses the problem of logistics center site selection, using Analytic Network Process/Benefits, Opportunities, Costs and Risks, which is a multi-criteria decision making technique. The decision problem is applied to the city of Trabzon, which is an important city of the Eastern Black Sea Region in Turkey. First, the results are evaluated in terms of each sub-network and each group and, next, are consolidated for each alternative. They yield that Alternative A is the most appropriate logistics center location of all in Trabzon. Furthermore, the sensitivity analysis results emphasize that the decision model is robust.