Data-Driven Fleet Optimization Using ML Algorithms and a Decision-Making Grid Framework

The most impactful factors for the cost of fleet management are maintenance expenses and fuel consumption. Traditional ways of monitoring fleet performance fail to connect raw operational data with driving habits. The current study addresses this challenge by developing an architecture of frameworks, consisting of unsupervised and supervised machine learning algorithms, statistical testing, simulation and survival analysis to discover insights that lead to key behavioral predictors. The nucleus of this complex architecture is the decision-making grid (DMG), a two-dimensional matrix that groups vehicles based on their frequency of entering the service and the cost of their repairs. It is the first integration of DMG with ML for prescriptive fleet management. The objective of the study is twofold: firstly, to build a system that classifies vehicles according to their risk profile, and secondly, to offer clear directions for changing driver patterns that most affect vehicle costs or for keeping good practices. The framework proposed by this study not only drives the optimization of operational efficiency but also contributes to a methodology that links driver profiles to costs, offering a scalable methodology for similar business contexts.