Strategic Expansion of South American Airports through Predictive Modeling and Optimized Distribution

This research presents an integrated decision-making framework for airport expansion projects in South America, using the Jorge Chávez International Airport as the central case study. The framework combines a predictive regression model with an optimized distribution strategy, creating a unified approach to planning and execution. The regression model determines when an expansion is needed by identifying key predictors of terminal size—most notably the number of runways, which explains 88.47% of the variation. Once expansion becomes necessary, the distribution strategy defines how to execute the project optimally through a focus on supplier selection, strategic negotiations, and gradual implementation. Together, these tools enable airport authorities and governments to proactively plan expansions, reduce unnecessary expenditures, and enhance operational and logistical efficiency across South American airport infrastructure projects.