Case Study
Driving Supply Chain Efficiency with AI-Powered Forecasting Solutions
Reduced delivery delays, optimized fleet operations, and improved customer satisfaction with AI forecasting and route planning.
Project Overview
A global logistics company grappled with inaccurate forecasts, suboptimal route planning, and last-mile inefficiencies. Legacy tools couldn’t process dynamic data in real time, causing delayed shipments and waste. Keylent built a Smart Logistics Forecasting Platform leveraging AI and real-time data (sensors, GPS, ERP) to enable proactive decision making, including predictive demand insights, optimized routing, and dynamic resource allocation.
Key Challenges
Inaccurate Demand Forecasts Static historical models failed to adapt to market, weather and event fluctuations.
Inefficient Route Planning Manual planning ignored live traffic, fuel optimization and capacity constraints.
Last Mile Delivery Delays Limited fleet visibility caused frequent delays and missed SLAs.
High Operational Costs Fuel waste, underutilized vehicles and overstaffed hubs inflated costs.
Our Solution
- Demand Forecast Engine Machine learning models use historical, market and external factors to predict SKU and regional demand.
- Real Time Route Optimization Integrated GPS, traffic, and load data generate optimal routes dynamically.
- Live Fleet Visibility & Dispatch — Central dashboard with status, ETAs, stages, and exception alerts.
- Predictive Resource Planning — AI-driven staffing, vehicle allocation, and warehouse balancing.
Key Technologies
Impact
85%
Accuracy in short-term demand forecasts
40%
Reduction in fuel consumption
60%
Faster last-mile delivery times
Client Testimonial
“Our dispatch decisions are now data-driven, delivery reliability has improved dramatically, and we’ve significantly reduced waste across the board.”
— VP of Supply Chain, Global Logistics Company