Recursive AI for Last Mile Delivery

Though complex logistics operations are often time-sensitive, distribution efficiency is often at stake due to staff shortage and the lack of optimization. Imagine being a liquefied petroleum gas (LPG) distributor delivering gas canisters to households in Japan’s rural areas. Many factors—such as employee turnover, driver shortages, and unexpected demand surges—can cause shipment delays, missed deliveries, or even supply shortages. Ensuring reliable, timely deliveries is critical for supporting households, especially during winter months when heating is critical.

Recursive AI for Last Mile Delivery addresses these challenges by integrating two AI models: a demand forecasting model and a scheduling model. The demand forecasting AI model combines diverse internal data (e.g., customer consumption patterns, IoT data, order history) and external data (e.g., AI-forecasted weather conditions, holiday calendars) to accurately forecast demand for each customer over the next 14 days.

Using the demand forecast and advanced clustering algorithms, the AI scheduling model prioritizes deliveries based on location and urgency, generating AI-optimized routes. These routes account for factors such as optimal delivery days, driver-specific variables (e.g., workload, truck capacity, speed), and traffic conditions.

The system also incorporates manual adjustments made by drivers using real-world insights, such as inaccessible roads, driveways, or agricultural areas that require special routing. These inputs further enhance the model’s accuracy and adaptability.

Recursive AI for Last Mile Delivery can be tailored to businesses across various industries that depend on precise delivery timing. Whether you deliver LPG to households, supply restaurants with ingredients, or distribute goods to retail stores, Recursive ensures timely deliveries while lowering operational costs and maintaining customer satisfaction.

Highlights

• Demand Forecasting AI Model: Accurately predicts demand for each customer over the next 14 days, ensuring uninterrupted supply.
• AI Scheduling Model: Optimizes and schedules delivery routes, saving fuel and reducing maintenance costs.
• Distribution Center Console: Integrates seamlessly with existing software, allowing operators to manage and monitor delivery plans efficiently without needing to adapt to new systems.
• Driver Mobile App: Provides real-time updates and AI-optimized routes, enabling drivers to execute deliveries effectively without requiring extensive local knowledge.
• Environmental Impact Reduction: Minimizes unnecessary trips and fuel consumption, reducing CO2 emissions and promoting greener operations.

Implementation