The past few years taught Australian manufacturers some hard lessons about supply chains. Ships stuck in ports, components unavailable for months, customers screaming about delivery dates that kept slipping.

The response? Every tech vendor suddenly had AI-powered supply chain solutions. More visibility! Better prediction! Automatic resilience!

Some of these tools are genuinely useful. Others are dressed-up spreadsheets with AI in the marketing. Let me help you sort through it.

What supply chain AI actually does

The useful applications fall into several categories:

Demand forecasting

Predicting what customers will want so you can plan procurement and production.

Traditional forecasting uses historical patterns—last year plus or minus some percentage. AI-based forecasting can incorporate many more signals:

• Economic indicators
• Weather patterns
• Social media sentiment
• Competitor actions
• Promotional calendars

Good AI forecasting doesn’t just predict better. It also quantifies uncertainty. Instead of “we’ll sell 10,000 units,” it’s “we’ll sell between 8,000 and 12,000 with 80% confidence.” That uncertainty range matters for planning.

A beverage manufacturer I work with reduced their forecast error from 35% to 18% with AI-based forecasting. That translated directly to less inventory (lower carrying cost) and fewer stockouts (fewer lost sales).

Supplier risk monitoring

Tracking signals that indicate supplier problems before they hit you.

AI can monitor:

• News and social media about suppliers
• Financial indicators (if public)
• Industry conditions affecting supplier regions
• Shipping and logistics data
• Weather and natural disaster tracking

The goal is early warning. If a key supplier is facing financial trouble, or if the port they ship from is having labour disputes, you want to know before your shipment doesn’t show up.

This is more useful for companies with global supply chains and longer lead times. If your suppliers are all in Australia and deliver in a week, the warning wouldn’t help much anyway.

Inventory optimisation

Determining how much stock to hold where.

AI optimises across multiple factors:

• Service level requirements (what fill rate do you need)
• Demand variability
• Lead time variability
• Carrying costs
• Stockout costs
• Safety stock positioning

The maths here is genuinely complex when you have hundreds of SKUs, multiple locations, and uncertain supply and demand. AI handles that complexity better than rules-based systems.

One client reduced inventory by 22% while improving fill rates from 94% to 97%. That’s the kind of counterintuitive result that comes from better optimisation.

Transport and logistics optimisation

Routing, mode selection, and scheduling for getting products where they need to go.

AI considers:

• Multiple transport options (road, rail, sea, air)
• Time windows and constraints
• Costs and trade-offs
• Real-time conditions

For manufacturers with their own fleet or complex distribution networks, this can be significant. For those using 3PLs for everything, the 3PL should be doing this (and many do).

What’s still hype

Some vendor claims should be treated sceptically:

“Predict and avoid any disruption”

No system can predict earthquakes, pandemics, or political crises with useful accuracy. AI can help you respond faster and mitigate known risks, but it can’t see the future.

Be wary of vendors showing case studies where their system “predicted” a disruption—they may be cherry-picking, or confusing correlation with actionable prediction.

”Fully autonomous supply chain”

The idea that AI can run your supply chain without human involvement is fantasy for complex operations. AI can recommend, optimise, and automate routine decisions. But judgment calls, relationship management, and novel situations still need humans.

”Universal visibility across all partners”

Visibility depends on data from trading partners. If your suppliers don’t share data, no amount of AI on your end creates visibility. True supply chain visibility requires collaboration that’s difficult to achieve.

”Real-time end-to-end tracking”

For some supply chains (especially domestic, with modern 3PLs), this is achievable. For complex global supply chains involving small suppliers, multiple handoffs, and developing-country infrastructure? Much harder.

What Australian manufacturers should prioritise

Our specific context shapes what makes sense:

Geographic isolation matters

Long lead times from overseas suppliers amplify the value of forecasting and risk monitoring. A European manufacturer might have two-week lead times from suppliers; ours are often eight to twelve weeks.

More lead time means more forecast error, more risk exposure, and more inventory required. AI that reduces these has proportionally more value for Australian manufacturers.

Supplier concentration is common

Many Australian manufacturers depend heavily on a few overseas suppliers (often in Asia). Diversification is theoretically good but often impractical.

AI can help manage concentrated risk through better monitoring and inventory positioning, even when diversification isn’t feasible.

Domestic network optimisation

Distribution across Australian geography (sparse population, large distances) creates specific optimisation challenges. Tools designed for dense European logistics don’t always translate.

Look for solutions that work well for Australian conditions, or be prepared to customise.

Practical implementation path

If you’re looking to improve supply chain capabilities with AI:

Step 1: Fix data fundamentals

AI runs on data. Most supply chain data is a mess:

• Demand history may not separate promotions from base demand
• Lead times might be contracted values, not actual historical
• Inventory records may be inaccurate
• Cost data is often outdated

Before buying AI tools, assess and clean your data.

Step 2: Start with forecasting

Demand forecasting is usually the highest-value starting point. Better forecasts cascade through the entire supply chain—procurement, production, inventory, logistics.

It’s also relatively self-contained (you own the data) and measurable (you can track forecast accuracy).

Step 3: Add inventory optimisation

Once forecasting improves, optimise inventory positioning based on the better forecasts. These two capabilities work together.

Step 4: Consider visibility investments

Supplier risk monitoring and tracking investments make sense once the internal house is in order. They also require collaboration with trading partners, which takes time to establish.

Step 5: Evaluate advanced capabilities

Transport optimisation, scenario planning, and advanced simulation come later for most organisations. Get the basics right first.

Vendor evaluation criteria

When evaluating supply chain AI solutions:

Data requirements: What data does it need? Can you provide it? What happens if data is missing or imperfect?

Integration: How does it connect to your ERP, WMS, and other systems? Who does the integration work?

Australian experience: Has the vendor implemented for Australian conditions? Can you speak to reference customers in Australia?

Implementation support: What does implementation actually involve? What resources do you need to provide?

Ongoing cost: What’s the total cost of ownership, including licensing, maintenance, and updates?

Realistic timelines: How long until you see value? Be sceptical of claims of immediate results.

The bottom line

Supply chain AI can genuinely help Australian manufacturers manage complexity, uncertainty, and risk. But it’s not magic.

Start with realistic expectations about what AI can and can’t do. Fix your data foundation. Choose tools that fit your specific situation. And don’t believe vendor claims without validation.

Done right, supply chain AI pays for itself many times over. Done wrong, it’s an expensive distraction from fundamentals.