The SCM thesis Smarter, Faster, Leaner: Optimizing the End-to-End Supply Chain was authored by Javiera Arancibia and Fernanda Esparza and supervised by Dr. María Jesús Sáenz (mjsaenz@mit.edu) and Dr. Jaime Macías Aguayo (jmacias@mit.edu). For more information on this research, please contact the thesis supervisors.
A planning bottleneck in the age of agility
A global consumer goods company faced a growing challenge: planning its end-to-end supply chain with speed and flexibility. Its optimization system, based on mixed-integer programming (MIP), took nearly two hours to generate each plan, limiting the ability to react to real-time changes. Demand fluctuations, raw material delays, or last-minute production maintenance all required a faster response than the system could provide.
In an industry where responsiveness can drive competitive advantage, this delay had a cost. The company needed a planning solution that could maintain quality while dramatically reducing execution time
When every second counts
The legacy planning model covered every aspect of the supply chain, from purchasing raw materials to coordinating production across two plants and managing distribution to regional warehouses. This comprehensive approach, while effective in scope, became a bottleneck due to its computational complexity. Each time the planning team updated demand forecasts, which happened twice daily, they faced a system that simply couldn’t keep up. In some cases, the planning team had to stop the program mid-run and work with a sub-optimal solution.
Our project set out to change that. The goal was ambitious yet clear: maintain the integrity and quality of planning outputs while drastically reducing the time needed to generate them. We tackled this challenge using a two-pronged strategy. First, we broke down the planning problem into smaller, independent segments to reduce the computational load. Then, we implemented a hybrid approach that combined Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), two powerful metaheuristic techniques capable of navigating complex problems with a large number of variables.
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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