AI in Logistics: How PostNL Optimised Route Planning and Delivery Operations

Inefficient Routing: Sub-optimal delivery order and increased fuel wastage were a direct result of manual processes and heuristic-based routing.

Unpredictable Workloads: Absence of demand forecasting would lead to understaffing or overstaffing, completely missing the peak delivery periods.

Poor Last-mile Visibility: Once parcels were dispatched from the warehouse, there was virtually no real-time visibility of where the parcels were..

Delayed Deliveries: Customer sentiments were negatively impacted due to shifting (rerouting) of plans, inaccurate delivery windows, or ETA promises.

High Operational Costs: The costs and duration it took to deliver yielded were significantly high due to the underutilisation of available resources.

Customer Dissatisfaction: The drop-off in repeat orders was a result of receiving parcels after set timelines or not receiving them at all.

AI-Powered Route Planning: Employed PostNL’s logistics data alongside AI models to enhance delivery routing to focus on clusters, traffic patterns, and prioritized deliveries.

Automatic Traffic Avoidance: The system was able to change routes in real-time to accommodate traffic congestion, road detours, or other delays that were bound to happen while ensuring timely and faster deliveries.

Optimized Delivery Windows: The AI examined customer data to determine their preferred times for delivery and provided suggestions for time slits with higher likelihoods of successful first-attempt deliveries.

Assisted Interaction & Monitoring: Customer-facing functionalities such as Delivery SMS updates, emails, and automated real-time updates were integrated to keep the recipients posted throughout the process until the package reached their hands.

Unified Team Command & Control Tool: Operations teams had access to a centralized dashboard where they could supervise fleet activity, receive real-time KPIs, and take action to fix any problems related to deliveries ahead of time.

Discovery & Planning: We assessed the client’s logistics flow and delivery pain points across regions.

Partnership with PostNL: Integrated PostNL’s route optimisation APIs and delivery insights.

Custom AI Layer Development: Developed a custom AI layer using the client’s operational data combined with PostNL’s historical delivery performance.

Testing & Deployment: Ran parallel testing on key regions and gradually scaled nationwide.

Continuous Optimisation: The AI continues to learn and improve route suggestions based on new data.

25% Reduction in Delivery Time: Smarter route mapping reduced time per delivery, especially in high-traffic zones.

30% Cost Savings: Reduced fuel usage, better route planning, and fewer failed deliveries contributed to lower operational costs.

40% Improvement in Customer Satisfaction: Real-time updates, accurate ETAs, and improved delivery experience led to better NPS scores.

Scalability & Future Readiness: The modular AI in logistics system now supports rapid onboarding of new regions and fleets.

AI/ML Frameworks: TensorFlow, Python, Scikit-learn for predictive analytics and route optimisation.

Data Infrastructure: Google Cloud, Docker, and PostNL’s logistics APIs enabled secure, scalable integration.

APIs & System Integration: Real-time GPS, order management systems, and customer communication platforms were connected seamlessly.

Compliance & Security: GDPR-compliant architecture ensured safe handling of customer and delivery data.