Machine Learning Algorithms started analyzing and suggesting optimal routes for delivery of mail and parcels. In conjunction with AI and enhanced learning capabilities these route planning exercises are now adaptive and dynamic. 

1. Demand Forecasting with AI

AI models predict and prescribe the demand that may increase or decrease during a certain season. By anticipating these patterns AI can precisely tell you how much of a commodity should be in stock, where, and what type of resources should be allocated. These demand fluctuations bring the estimates closer to actuals, reduce operational costs, and enhance efficiencies. 

2. Fleet Management using AI

Coupling with IoT and sensor data, AI can provide live updates on vehicle’s status, wear and tear along with how the vehicle is driven. Large fleets incur a huge cost on driving incidents every year, sometimes resulting in litigation. Driver behavioral patterns, fatigue, and skills along with predictive maintenance of vehicles, organizations can reduce such costs and ensure adherence to safety regulations. Fuel consumption can also be optimized using all of the above-mentioned factors. In large fleets, insights into fuel consumption, incidents, and maintenance also help the decision-makers opt for the most effective brand of vehicles for the fleet.

3. Improve Customer Experience

Knowing your customer by considering their data attributes, along with chatbots has vastly improved customer services in mail and parcel delivery. These learned applications can provide a unique customer experience with live alerts, answer questions, alternate delivery options, and schedules.

4. Preventing mail and parcel Fraud

From package tempering to unauthorized delivery, AI can detect anomalies and recognize fraud that is about to happen. Enhanced surveillance and monitoring systems have made package and mail delivery more secure for consumers and postal organization employees.