The Problem Statement
In the logistics and supply chain industry, the core problem is a lack of real-time, actionable intelligence. Companies operate with outdated information, using static routes that don't account for traffic, weather, or real-world events. This "efficiency black hole" leads directly to wasted fuel, missed delivery windows, unhappy customers, and a higher cost-per-mile—bleeding profit from an already thin-margin business.
The Client
A major UK logistics firm
Why Most Solutions Fail
Many firms use basic software that plans routes based on historical data or simple distance calculations. This fails because the real world is dynamic. A route that was optimal yesterday is suboptimal today. This approach leaves millions of pounds of potential savings on the table every year.
Our Strategic Solution
We solve this problem by building a "Predictive Optimisation" engine that acts as the intelligent brain of a logistics operation. 1. Real-Time Data Ingestion: We use technologies like Apache Kafka to build a pipeline that ingests live data from vehicle GPS, traffic APIs, weather feeds, and order systems. 2. Machine Learning Core: We develop custom machine learning models in Python that don't just find the shortest route, but predict the optimal route based on dozens of changing variables. 3. Actionable Intelligence: The output isn't a static report; it's a live dashboard and API that feeds real-time instructions to drivers and provides customers with accurate tracking.
Real-Time Data Ingestion
Used technologies like Apache Kafka to build a pipeline that ingests live data from vehicle GPS, traffic APIs, weather feeds, and order systems.
Machine Learning Core
Developed custom machine learning models in Python that predict the optimal route based on dozens of changing variables.
Actionable Intelligence
Created a live dashboard and API that feeds real-time instructions to drivers and provides customers with accurate tracking.
Proof Point: How We Did It
A major UK logistics firm was struggling with high fuel costs and poor delivery accuracy. We implemented our Predictive Optimisation model, creating an AI engine on AWS SageMaker that recalculated their entire fleet's routes every 60 seconds. The system immediately identified inefficiencies, cutting average route distance and time. The real-time tracking data we provided to their customer service team reduced "where is my order?" calls by over 80%.
Business Impact
Measurable Results
Annual Fuel Costs: £2.2M → £1.65M (Saved £550,000 in the first year)
Delivery Accuracy: 88% on-time → 97% on-time (Significantly improved customer satisfaction)
Cost Per Delivery: £6.50 → £5.20 (Directly improved gross margin by 20%)
ROI: 320% (The system paid for itself in under 4 months)
Key Results Summary
Annual fuel costs: £2.2M → £1.65M (Saved £550,000 in the first year)
Delivery accuracy: 88% on-time → 97% on-time (Significantly improved customer satisfaction)
Cost per delivery: £6.50 → £5.20 (Directly improved gross margin by 20%)
ROI: 320% (The system paid for itself in under 4 months)
Is Your Data Working for You?
If you're making critical operational decisions based on old information, you're leaving money on the table. Your data should be your most valuable asset. Book a strategy call. We will show you how to turn your data into a powerful engine for efficiency and growth.
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