In large companies, inbound logistics is often managed independently by different business divisions. This fragmented approach leads to repetitive pickups at suppliers, inefficient route planning, underutilized vehicles, and high transportation costs. This capstone, developed in collaboration with a multinational industrial company, proposes two distinct methodologies: (i) locate optimally located consolidation hubs, (ii) create an optimization heuristic to coordinate pickups at suppliers. The first model uses historical shipment data, including trip frequency and cargo weight, to develop a facility location model using the p-median approach and identify the optimal number and location of consolidation hubs. The second model incorporates demand variability to simulate a 100-year time horizon and create a heuristic to find the optimal pickup schedule. The results reveal that placing three consolidation hubs, located in Shanghai, Qingdao, and Guangzhou, can reduce distance travelled by approximately 50%. In addition, increasing pickup frequency at suppliers improves full truckload utilization but reduces flexibility and increases reliance on spot trucks. An optimal balance is achieved at 80% contracted truck utilization. This capstone shows how a data-driven, cross-divisional approach to inbound logistics can reduce inefficiencies, improve transportation planning, and enable a more scalable and cost-effective supply chain.