Meridian Logistics: AI-Powered Route Optimisation

The Client

Meridian Logistics Ltd is a regional distribution company based in Birmingham, operating a fleet of 45 vehicles across the Midlands. With 85 employees, they handle next-day delivery for retail and B2B clients, managing over 2,000 deliveries per day. Their operations team spent significant time manually planning routes, dealing with traffic delays, and adjusting schedules on the fly.

The Challenge

Meridian's operations director, Sarah Whitman, knew that route planning was the biggest bottleneck in their operation. Drivers were following static routes that did not account for real-time traffic, road closures, or delivery priority. Fuel costs were rising, and the operations team spent 15 hours per week manually adjusting routes. The company had looked at off-the-shelf routing software but found it either too rigid for their needs or too expensive for a business their size.

The key challenges were:

• Static route planning that ignored real-time traffic and road conditions
• 15 hours per week spent on manual route adjustments
• Rising fuel costs eating into margins
• No clear picture of which routes were inefficient and why
• Existing routing software either too rigid or too expensive

The Solution

The engagement started with an AI readiness audit to assess Meridian's data, infrastructure, and team readiness. The audit found that their existing dispatch system had clean, structured data that was ideal for AI augmentation. Their team was receptive to new technology, and their cloud infrastructure was modern enough to support an AI overlay without replacing existing systems.

Following the audit, ajairu developed an AI implementation plan with three phases:

1. Data integration (weeks 1-3): Connected the dispatch system to real-time traffic data, historical delivery records, and vehicle telemetry. Built a data pipeline that fed route information into an AI model trained on Meridian's own delivery history.
2. AI route optimisation (weeks 4-7): Implemented an AI model that generates optimised routes daily, accounting for traffic patterns, delivery windows, vehicle capacity, and driver hours. The system learns from each day's actual performance and improves its recommendations over time.
3. Team training and handover (weeks 8-10): Trained the operations team on the new system, created documentation and runbooks, and provided 30 days of post-launch support. The team can now adjust parameters and override AI suggestions when needed.

The Results

Within eight weeks of the system going live, Meridian saw a 30% reduction in manual route planning time. The AI system optimised routes for fuel efficiency, reducing average miles per delivery by 18%. Annual fuel savings are estimated at £180,000 based on the first six months of operation. The operations team now spends their time on strategic planning rather than manual route adjustments.

The system continues to learn and improve. After six months of operation, route efficiency had improved by an additional 7% as the AI model refined its understanding of Meridian's delivery patterns and traffic conditions across the Midlands.

The routing team also gets back around 15 hours a week previously lost to manual planning and re-planning. With fuel savings running at roughly £15,000 a month, the combined audit, strategy, and implementation investment paid for itself within six weeks of go-live, and every month since has been pure margin.

Client Testimonial

Services Used

This engagement combined three of our core services:

• AI Readiness Audit to assess data, infrastructure, and team readiness
• AI Strategy to define the phased implementation roadmap
• AI Implementation to build, deploy, and hand over the route optimisation system

Why It Worked

The engagement succeeded because the audit came first. Rather than jumping straight to implementation, the readiness audit identified that Meridian's data was clean and their team was ready. This meant implementation could move fast without hitting data quality or adoption blockers halfway through. The phased approach also meant the team saw incremental improvements, building confidence in the system before the full deployment.

The system was built to be owned and operated by Meridian's team. Full documentation, training, and a 30-day support period meant the operations team could run and extend the system independently after handover.