Estimating Import Lead Times Using Business Intelligence and Machine Learning Within the CRISP-DM Framework: A Case Study in Oil and Gas Services Industry

This study explores the estimation of import lead times in the oil and gas services industry by combining Business Intelligence (BI) and Machine Learning (ML) within the CRISP-DM (Cross-Industry Standard Process for Data Mining) framework. Addressing the challenge of limited data availability, we employ data synthesis techniques alongside machine learning algorithms to model and predict lead times accurately across diverse import scenarios. The methodology begins with a dual perspective on the problem, encompassing both business and technical requirements, followed by systematic data collection, cleansing, and preparation. BI tools facilitate the visualization of key insights, allowing for enhanced data comprehension and performance tracking. Machine learning models are then applied to predict lead times under varying import conditions, optimizing decision-making in terms of transport mode, entry port, and customs handling. The final solution is deployed through an interactive chatbot interface, streamlining access to lead time estimates for import-export specialists. This integrated approach enables proactive lead time management, cost optimization, and greater operational efficiency within import processes, providing a strategic advantage in the industry.