چكيده به لاتين
This study aims to identify, model, and improve business processes in a logistics hub. The research focuses on analyzing core operational processes, including unloading, sorting, and loading, as well as support processes such as security, maintenance, and human resource management. Given the significance of enhancing operational efficiency in the supply chain and reducing costs, this study leverages approaches such as business process modeling using the BPMN standard and integrating quantitative and qualitative methods to identify weaknesses and bottlenecks within the hub’s processes. Operational data were collected and analyzed using tools such as Microsoft Excel, Python, and Power BI. During the preprocessing phase, scattered data from over 260 Excel files were consolidated into a unified dataset to enable more precise analysis and real-time data visualization. Statistical analyses demonstrated reductions in operational cycle times, improvements in data recording accuracy, and increased productivity. Additionally, the development of interactive dashboards in Power BI allowed managers to monitor process performance accurately and in real time. The study’s findings indicate that employing standardized modeling methods, alongside data-driven analysis and process optimization, can significantly enhance operational efficiency, reduce time and costs, and increase customer satisfaction in logistics hubs. These achievements not only contribute to the theoretical domain of business process management but also provide practical solutions for digital transformation in the logistics industry.
