Supply Chain & Logistics
Container load optimisation
Optimise how products are loaded into shipping containers and delivery vehicles to maximise space utilisation and minimise transport cost per unit.
5–15%
improvement in container utilisation
Opportunity assessment
Minor improvement. Small efficiency gain with limited effect on overall turnover or bottom line.
Moderate effort. Requires configuration, prompt engineering, and testing. A capable team can get there but expect several months.
Moderate data needs. Works with data most businesses hold, but will likely need consolidation, cleaning, or reformatting before use.
Low risk. Limited external exposure. A human reviews output before it reaches anyone outside the team.
Moderate people impact. Part of someone's working day changes. Requires training and some adjustment time, but roles remain broadly the same.
Tooling required
Things to consider
Accurate product dimension and weight data is the prerequisite. If your master data has approximate or missing dimensions, the optimisation will underperform.
The algorithm needs to account for real-world constraints: weight limits, fragility, stacking rules, delivery sequence (last loaded = first delivered), and temperature requirements.
A 5–10% improvement in container utilisation translates directly to fewer containers shipped. For businesses moving 500+ containers per year, the cost saving is material.
Experiment starter: Audit the utilisation of your last 20 shipments (measure actual fill versus theoretical capacity). If average utilisation is below 85%, there is meaningful optimisation opportunity. Run the same 20 shipments through a load planning tool and compare.
Go deeper in the playbook
Section — Opportunity Identification, including the AI Opportunity Assessment ScorecardSection — The Tooling LandscapeThis is an automation opportunity — see the relevant playbook section for how to approach it.More in Supply Chain & Logistics
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Purchase order exception handling
Identify anomalies in PO data — price variances, quantity mismatches, missing references — and route to the right team with context and suggested resolution.
Demand pattern summarisation
Convert sales, stock, and forward-order data into plain-language summaries for planning meetings. Identifies trends, spikes, and cover risks.
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