Transportation planning in supply chains refers to the systematic process of designing, coordinating, and managing the movement of goods from suppliers to manufacturers, warehouses, distribution centers, and finally to customers in the most efficient, reliable, and cost-effective manner. It involves decisions about transportation modes, routing, carrier selection, shipment consolidation, scheduling, and capacity utilization, all while balancing service levels, cost constraints, and risk factors. In modern supply chains, transportation planning is not a static, one-time activity but a dynamic, data-driven function that continuously adapts to changing demand patterns, network structures, regulatory environments, and external disruptions. As supply chains become more global, complex, and exposed to uncertainty, transportation planning has evolved from a purely operational task into a strategic capability supported by advanced data analytics. At its core, transportation planning aims to ensure that products reach the right place, at the right time, in the right quantity, and at the lowest possible total cost without compromising reliability. This requires the integration of demand forecasts, inventory policies, production plans, and distribution strategies. Poor transportation planning can lead to increased logistics costs, delivery delays, stockouts, excess inventory, and reduced customer satisfaction. Conversely, effective transportation planning enhances visibility, improves coordination across supply chain partners, and enables organizations to respond proactively to variability and risk. Data analytics plays a central role in enabling this effectiveness by transforming raw transportation data into actionable insights that support informed decision-making. Geographic Information System analytics has emerged as a particularly powerful tool in transportation planning within supply chains. GIS analytics involves the capture, storage, visualization, and analysis of spatial and geographic data related to locations, routes, infrastructure, and environmental factors. In a supply chain context, GIS analytics allows planners to map suppliers, production facilities, warehouses, ports, distribution centers, and customer locations on a geographic platform. This spatial visualization provides immediate insights into network structure, distances, connectivity, and regional dependencies that are difficult to grasp through traditional tabular data alone. By overlaying transportation networks such as roads, railways, shipping lanes, and air routes, GIS analytics supports more accurate route planning and mode selection. One of the key contributions of GIS analytics to transportation planning is enhanced risk assessment. Transportation risks often have a strong spatial dimension, including exposure to natural disasters, geopolitical instability, infrastructure bottlenecks, congestion, and environmental constraints. GIS analytics enables organizations to overlay risk-related data such as flood zones, earthquake-prone regions, weather patterns, political boundaries, and crime statistics onto supply chain maps. This helps identify vulnerable routes, facilities, and regions where transportation disruptions are more likely to occur. For example, a supply