"AI for Logistics: Route Optimisation to Demand Forecasting"

July 22, 2025•7 min read•Team 400

Logistics is a business of margins. Fuel costs, driver hours, warehouse efficiency, inventory carrying costs—small improvements compound into significant competitive advantage. AI isn't transforming logistics with robots and drones (yet). It's transforming logistics with better decisions, faster.

Here's what's actually working in Australian logistics and supply chain operations.

Route Optimisation: The Obvious Application

Everyone knows AI can optimise routes. What's less obvious is how much value remains uncaptured.

Basic route optimisation: Find the shortest path between stops. Google Maps does this.

AI-powered route optimisation: Consider all the real-world constraints:

Delivery time windows
Vehicle capacity and load configuration
Driver hours and fatigue management
Traffic patterns by time of day
Customer priority levels
Pickup and delivery coordination

Real impact: We've seen logistics operators achieve 15-25% reduction in total kilometres driven. For a fleet running 100 vehicles covering 50,000km monthly each, that's 750,000-1,250,000 fewer kilometres annually. At $1.50/km fully loaded cost, the savings are significant.

The dynamic reality: Static route plans are outdated by the first delivery. AI that re-optimises throughout the day—responding to delays, traffic changes, and new orders—captures much more value than morning-only planning.

Example: A delivery company re-optimises routes hourly based on actual progress and traffic conditions. Same-day delivery completion rates improved 18% without adding vehicles.

Demand Forecasting: The Foundation of Everything Else

Bad demand forecasts cascade through the entire supply chain. Too much inventory ties up cash and risks obsolescence. Too little means stockouts, expedited shipping, and lost sales.

AI forecasting advantages:

Incorporate multiple demand signals (not just historical sales)
Granular forecasts (SKU/location/day level, not just monthly aggregates)
Automatic feature identification (the AI finds patterns humans miss)
Rapid adaptation to changing conditions

Signals that improve forecasts:

Weather (obvious for some products, subtle for others)
Economic indicators
Competitor pricing and availability
Social media trends
Promotional calendars
Events and holidays

Measured improvements: 20-35% forecast error reduction is achievable for most businesses. Some categories improve more, some less.

Honest caveat: No forecasting system predicts major disruptions. Black swan events happen. AI improves normal-condition forecasting; it doesn't eliminate supply chain risk.

Warehouse Optimisation: Beyond Labour Scheduling

The obvious application: AI-powered labour scheduling based on predicted volumes. This works and delivers value.

The deeper applications:

Slotting optimisation: Which products go where in the warehouse? AI analyses pick patterns and optimises product placement to minimise travel time. Regular re-slotting as demand patterns change.

Pick path optimisation: Given a set of orders, what's the optimal sequence to pick? AI solves this combinatorial problem faster and better than simple rules.

Wave planning: When should orders be released to the floor? AI balances workload smoothness, carrier cutoffs, and priority requirements.

Real result: A 3PL reduced warehouse labour cost per order by 12% through AI-optimised slotting and pick path planning. No automation investment required—just smarter work sequencing.

Inventory Optimisation Across Networks

Single-location inventory optimisation is relatively straightforward. Network inventory optimisation—how much of what goes where across distribution centres, hubs, and stores—is exponentially harder.

AI network optimisation:

Demand forecasting at each location
Lead time and replenishment variability
Transfer and shipping costs between locations
Service level requirements by product/location
Total network cost minimisation

Example: A retailer with 12 distribution centres used AI to rebalance inventory positioning. Same total inventory, 15% better availability, 8% lower shipping costs to stores.

The integration challenge: This requires visibility across the network. Siloed inventory systems per location or channel undermine network optimisation.

Fleet Management and Maintenance

Predictive maintenance for fleet: Same concept as manufacturing—monitor vehicle health, predict failures, schedule maintenance before breakdowns.

Fleet AI applications:

Fault prediction from telematics data
Maintenance scheduling optimisation
Fuel efficiency analysis and driver coaching
Vehicle replacement timing optimisation

Measured impact: 20-30% reduction in roadside breakdowns. Better vehicle availability. Lower total cost of ownership.

Telematics foundation: AI fleet management requires good telematics data. If you're not capturing vehicle data continuously, start there.

Carrier Selection and Rate Optimisation

Which carrier for which shipment? The obvious answer (cheapest) isn't always right. Service levels, reliability, and total cost matter.

AI carrier optimisation:

Performance tracking by carrier, lane, and service level
Rate shopping across carriers in real-time
Mode selection (ground vs. air vs. LTL vs. parcel)
Consolidation opportunity identification

Real example: A shipper using AI carrier selection reduced freight costs 11% while improving on-time delivery. The AI learned which carriers performed well on which routes—something humans couldn't track across thousands of lane/carrier combinations.

Shipment Visibility and Exception Management

Customers expect visibility. "Where's my stuff?" shouldn't require a phone call.

AI visibility applications:

ETA prediction that's actually accurate (not just carrier-provided estimates)
Exception prediction (shipments likely to be late, damaged, or missing)
Proactive customer communication
Automated exception handling for common issues

Customer experience impact: Proactive communication about exceptions dramatically reduces customer service contacts. "Your delivery is delayed due to weather; new ETA is Thursday" beats "We'll look into it."

Operational impact: Knowing which shipments need intervention before they become problems lets teams focus effort where it matters.

Returns and Reverse Logistics

Returns are expensive and getting more expensive as e-commerce grows. AI can help optimise this neglected area.

AI returns applications:

Returns prediction (which orders are likely to be returned)
Disposition optimisation (resell, refurbish, liquidate, dispose)
Fraud detection in returns
Reverse logistics routing optimisation

Example: A retailer used returns prediction to flag high-return-probability orders for additional quality checks before shipping. Return rate dropped 8%.

Getting Started in Logistics AI

If you're a logistics operator exploring AI applications, here's the practical path our AI consultants Sydney recommend:

Assess Your Data Foundation

AI needs data. Most logistics operators have:

TMS (Transportation Management System) data
WMS (Warehouse Management System) data
Telematics and tracking data
Order and inventory data

The question: Is this data accessible, clean, and integrated? Or siloed in disconnected systems?

Data integration work often precedes AI work.

Identify High-Value Starting Points

Not all AI applications deliver equal value. Prioritise based on:

Volume: Higher transaction volumes = more data = better AI = bigger impact
Variability: Stable operations need optimisation less than volatile ones
Current performance: If you're already world-class, improvements are harder
Data availability: Some applications need data you may not have yet

Common starting points:

Route optimisation (immediate, measurable savings)
Demand forecasting (foundational for other optimisations)
Labour scheduling (quick win in warehouse operations)

Build or Buy Wisely

Some logistics AI is commodity—route optimisation algorithms have been around for decades. Others require customisation—demand forecasting models need to learn your specific business patterns.

Buy where solutions are mature and your needs are standard. Build where competitive advantage requires custom models. Partner where you need capability but not full-time resources.

Plan for Integration

AI recommendations are worthless if they can't be executed. Plan integration with:

TMS for route execution
WMS for warehouse operations
Driver apps for real-time guidance
Customer-facing systems for visibility

Measure Relentlessly

Logistics has good data for measurement. Use it:

A/B test route optimisation (randomised assignment to test vs. control)
Track forecast accuracy over time
Measure before/after on warehouse productivity
Calculate actual vs. predicted savings

The Competitive Context

The major logistics players—Amazon, DHL, FedEx—have been investing in AI for years. They have advantages in data scale and technology budgets.

Australian logistics operators can compete by:

Moving faster (less bureaucracy, quicker decisions)
Focusing on niches where local knowledge matters
Building deeper customer relationships
Being realistic about where AI adds value vs. hype

The operators winning with AI aren't trying to build Amazon-scale capabilities. They're applying AI surgically to their highest-value problems.

Next Steps

We've helped logistics operators implement AI that delivers measurable efficiency gains. Not theoretical optimisation—real systems handling real operations.

As AI consultants Sydney, we help businesses across Australia transform their logistics and supply chain operations with practical AI solutions. Let's discuss what AI could do for your logistics operation.