Developing a planning panel to forecast truck fleet size based on activity forecast

Abstract

This thesis addresses inefficiencies in SLB Algeria’s domestic logistics by developing an integrated solution composed of a planning tool, an optimization model, and a performance dashboard. The planning panel, built in Excel VBA, improves visibility over job schedules, truck allocation, and material requirements. A mixed-integer linear programming model is implemented to retrospectively determine the minimum truck fleet needed to fulfill past demand while respecting operational constraints. A Power BI dashboard visualizes key performance indicators to assess efficiency and support decision-making. Findings show that data-driven tools significantly improve planning accuracy, fleet utilization, and visibility. However, challenges remain regarding data integration, shipment consolidation logic, and tool scalability, indicating directions for future research.