Heuristic Approach to Improve Expected Time of Arrival Prediction for Global Intermodal Transport Networks - Singh, Arun (Digital Repository of Theses, 2024)
This research endeavours to delve into the intricate dynamics of managing Estimated Time of Arrivals (ETA) of shipments arriving to the destination in the global supply chain ecosystem. In today's interconnected world, where buyers and suppliers are scattered across continents, the accuracy of estimated departure and arrival predictions holds immense significance. These forecasts serve as critical benchmarks around which various operational decisions are made, ranging from transport planning to inventory management and sales forecasting. At the heart of this research lies a central question: What are the accuracies of current ETA predictions of intermodal shipments, and how can they be improved? After literature review and analysis of various reports, it was found that the ETA prediction accuracy is least when it comes to Ocean moves. When main move of transport is Air or Rail, the accuracy of the ETA prediction is on a higher side. But in a move that contains Dray-Ocean-Dray, the accuracy predictions drop drastically. To address this challenge comprehensively, the study aims to analyse the existing practices employed by Ocean carriers and Logistics service providers in estimating departure and arrival times. A multitude of factors, including weather conditions, port congestion, vessel schedules, and unforeseen disruptions, influence the accuracy of the ETA predictions. Against this backdrop, the research aims to unravel the operational realities faced by stakeholders involved in managing supply chain logistics across the globe. Armed with a deep understanding of existing practices and operational challenges, the research endeavours to develop a more robust approach to estimate predictions. By leveraging insights gleaned from data analysis and industry expertise, the study seeks to propose innovative solutions that enhance prediction accuracy and reliability. The research methodology encompasses a multifaceted approach, combining qualitative and quantitative analysis to gain a comprehensive understanding of the research subject. Data will be collected from various sources, including Ocean carriers, Logistics service providers, industry reports, and academic literature. The findings of this research are expected to have significant implications for stakeholders across the global supply chain ecosystem. By shedding light on the accuracies of current ETA predictions and proposing strategies for improvement, the study aims to empower buyers, suppliers, carriers, and logistics service providers with the knowledge and tools needed to optimize supply chain operations.