The SCM thesis From Chaos to Coordination: Rethinking Inbound Logistics was authored by Paula Constanza Servideo Fischer and Anshuman Kandaswamy, and supervised by Dr. Josué C. Velázquez Martínez (josuevm@mit.edu) and Dr. Benedict Jun Ma. For more information on this research, please contact the thesis supervisors.
Every day across global supply chains, a quiet inefficiency persists. Multiple trucks from the same company crisscross cities, picking up inventory from the same suppliers with half-empty vehicles. The result? Inflated transportation costs, unnecessary emissions, and a web of missed opportunities. But what if coordination, not chaos, defined inbound logistics? Our project set out to challenge this fragmented reality. By mapping supplier overlaps and simulating optimized pickup patterns, we developed a data-driven playbook for proactive inbound coordination. The insight was simple yet powerful: collaboration does not mean compromise; it means fuller loads, fewer trucks, and a more efficient network.
The High Cost of Siloed Logistics
Large-scale inbound networks, while vast, are often highly fragmented. An analysis of a representative network spanning 200 suppliers in China and 22,000 shipment records highlighted a common inefficiency: business units sourcing from the same geographic areas operated independently. This operational structure, common throughout the industry, creates a reliance on costly, reactive spot freight and leads to underutilized transport assets. The challenge lies not in a lack of data, but in synthesizing it to reveal actionable consolidation opportunities.
An innovative framework for network consolidation
Our novel framework moves beyond simple route mapping to model the complex interplay of cost, efficiency, and service levels. A volume-weighted distance algorithm was created to analyze the network and identify optimal consolidation points. The analysis revealed that just three strategic hubs could handle 75% of the total inbound volume, cutting inland transport distance by 50%. The model was enhanced with a dynamic fleet simulation which engineered demand variability to determine the ideal mix of contract and spot freight, targeting 80% truck utilization. This integrated approach projects potential cost savings of up to 60% and an 80% reduction in spot freight dependency.
Smarter routing, better results
When fragmented networks operate as one, they unlock a new level of operational excellence. The core finding is that the greatest productivity gains originate from a single source: integrated planning. By coordinating pickups across previously siloed business units, companies can achieve dramatic efficiencies without needing to invest in a costly infrastructure overhaul. This data-driven blueprint builds a more resilient supply chain. It empowers organizations to move away from a reactive dependence on the volatile spot freight market, offering protection from service disruptions and price shocks during peak demand. Furthermore, the model is dynamic; it allows for continuous stress-testing against real-world variables like shifting demand patterns, making it a viable and future-proof tool for strategic decision-making.
For more about this capstone project, and to see the full results of this research, visit the Supply Chain Management Review online at SCMR.com.
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