AI-Driven Logistics: Reducing Fleet Carbon Footprint by 15%

T.L Logistics faced a dual crisis: skyrocketing fuel prices and new EU regulations penalizing high-carbon supply chains. Their legacy routing system was static, relying on pre-calculated paths that ignored real-time variables like border congestion, weather patterns, or road accidents. Dispatchers were making decisions based on gut feeling rather than data, leading to a 15% inefficiency in mileage and frequent SLA breaches. The internal IT team was proficient in SAP but lacked the specialized Data Science and Geospatial engineering skills required to build a dynamic, AI-driven solution. They needed to augment their team immediately with experts who could mathematically model complex routing problems.

GTEMAS augmented the client's engineering division with 2 Senior Data Scientists and 1 Geospatial Engineer. Integrating directly into their Scrum teams, our experts took ownership of the 'Optimization Engine'. Predictive Modeling: We built a machine learning model using historical GPS logs (3 years of data) to predict congestion patterns at specific border crossings. This model inputs real-time weather and traffic APIs to score route segments. Decision Support System: Instead of black-box automation, we built a 'Human-in-the-Loop' dashboard. The AI proposes 3 optimal routes ranked by Cost, Time, and Carbon Footprint. The dispatcher makes the final call, but the decision is 90% automated based on data. This reduced the cognitive load on dispatchers significantly.

The project transformed T.L Logistics from a traditional trucking company into a tech-enabled logistics provider. The carbon reduction metrics are now a key selling point in their B2B tenders, helping them win contracts with eco-conscious retailers.