Fleet Utilisation and Predictive Maintenance Dashboard for Indian Logistics Operators

Objective: To develop an interactive dashboard that analyses fleet utilisation and predicts maintenance needs to optimise operational efficiency for logistics companies in India.

Problem & Context: Indian logistics firms face significant challenges in maximising fleet utilisation and minimising vehicle downtime due to unpredictable maintenance issues. Poor asset visibility, manual tracking, and reactive servicing lead to increased costs and missed delivery deadlines in an intensely competitive industry.

Approach/Methodology: The project will involve collecting real or simulated telematics and maintenance data from commercial fleets, applying data analytics and machine learning to assess utilisation patterns, and building predictive models for maintenance needs. Stakeholder interviews and secondary research will be used to tailor the solution to Indian logistics realities.

Deliverables & Analysis: The team will deliver a Power BI/Tableau dashboard integrating fleet tracking, utilisation KPIs, and predictive maintenance alerts. The dashboard will visualise vehicle allocation, idle time, route efficiency, and forecast upcoming maintenance to prevent breakdowns. Analysis includes model performance and business scenario evaluation.

Business Impact & Decision: The dashboard enables fleet managers to make data-driven scheduling and servicing decisions, reduce unplanned downtime, and optimise asset allocation—directly impacting cost efficiency, customer satisfaction, and competitiveness in India's logistics sector.