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Data Analytics and Machine Learning in the Aviation Industry Supply Chain
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Orientador(es)
Resumo(s)
Amid growing global volatility, the aviation industry's supply chain faces new challenges, like
supplier unreliability, geopolitical disruptions, and unpredictable lead times for critical parts.
These issues demand proactive approaches that go beyond traditional procurement analytics.
This thesis examines how data analytics and machine learning can be leveraged to anticipate
and mitigate such disruptions, utilizing real procurement data from a global aviation company.
Can machine learning forecast the unexpected in the new aviation supply chain? Could a datadriven approach reshape how we manage the supply chain? These questions guide the
development of an artifact that supports decision-making through machine learning.
Following the CRISP-DM methodology, the research integrates unsupervised clustering
techniques to segment supplier behavior and supervised regression models to predict part
delivery lead times. The Random Forest model achieved strong predictive performance (R² =
0.92; MAE = 0.35 days), while agglomerative clustering revealed interpretable supplier groups
that reflect a new way to manage supplier relationships in the supply chain. The results show
that machine learning techniques can improve strategic supplier management and short-term
operational planning in the aviation industry. This research contributes to the growing body
of literature on data-driven supply chain optimization, providing a scalable foundation for
future integration into business intelligence planning systems.
Descrição
Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Business Intelligence
Palavras-chave
Machine Learning Supply Chain Aviation Data Analytics Regression Clustering SDG 4 - Quality education SDG 9 - Industry, innovation and infrastructure