Supply Chain Capacity Allocation Modeling

With transportation costs accounting for about 65% of total logistics expenses, optimizing fleet capacity utilization is crucial for organizations seeking to improve efficiency and reduce overall costs. Since transportation directly affects the final product cost, optimization becomes essential for maintaining competitiveness. Existing literature on capacity planning in logistics is substantial, but there remains a notable lack of studies focusing specifically on the integration of LTL and parcel services within 3PL operations. The current research does not adequately explore the implications of combining these two services within a single fleet context, considering increasing e-commerce demands and the complexities of real-time capacity allocation. This gap in research highlights the need for more innovative solutions that can address the evolving challenges faced by 3PL providers. By optimizing fleet utilization through integrated service models, companies can enhance their operational efficiency, reduce costs, and better meet the demands of modern logistics networks. The main research question defined was: “How can a revenue management capacity allocation model optimize the integration of Less-Than-Truckload (LTL) and parcel services in third-party logistics (3PL) providers to enhance fleet utilization and overall revenue?” A Non-Linear Programming (NLP) approach maximized total revenue while considering truck capacity, product demand, revenue per cubic volume, and product volume probability density. Parcel data included item count, origin/destination, dimensions, weight, and shipping date. Oracle Crystal Ball validated the model, with simulations aligning to expectations.