Supply Chain & Logistics
Freight cost optimisation analysis
Analyse shipment data by lane, mode, carrier, and timing to identify cost reduction opportunities. Surfaces patterns in inefficient routing, underutilised consolidation, and carrier cost variances.
5–12%
reduction in freight cost per unit shipped
Opportunity assessment
Moderate impact. Meaningful cost or revenue improvement, likely noticeable at function level but modest relative to total business scale.
Moderate effort. Requires configuration, prompt engineering, and testing. A capable team can get there but expect several months.
Demanding data requirements. Relies on clean, integrated data across multiple systems. Significant data preparation work typically needed.
Very low risk. Fully internal use. A person checks everything before it goes further. Worst case is a minor internal inconvenience.
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
UPS's ORION AI route optimisation saves 100M miles annually, delivering $300–400M in cost savings per year and saving 10M gallons of fuel. Each mile removed per driver per day equates to approximately $50M in annual savings (Supply Chain Dive).
Shipment-level data with cost, weight, dimensions, origin, destination, mode, carrier, and transit time is the minimum required. This data often exists but isn't consolidated anywhere.
The AI identifies patterns and opportunities; the logistics team applies judgment on what's practically achievable given carrier relationships and service level requirements.
Rate benchmarking requires access to market rate data — either through a third-party freight marketplace or broker — which the AI alone cannot provide.
Freight optimisation often surfaces opportunities that require contract renegotiation, not just operational changes. Ensure the procurement team is involved before surfacing the analysis to operations, so recommendations can be acted on commercially.
Experiment starter: Export 12 months of shipment data including cost, mode, carrier, lane, and weight. Prompt an LLM to identify the top five lanes by cost per unit shipped and compare the cost variance between carriers on those lanes. Present the analysis to the logistics team and identify whether any opportunities are actionable in the next carrier negotiation.
Go deeper in the playbook
Section — Opportunity Identification, including the AI Opportunity Assessment ScorecardSection — RAG and Knowledge Systems: Unlocking Proprietary DataThis is an decision support opportunity — see the relevant playbook section for how to approach it.More in Supply Chain & Logistics
Supplier document review
Review supplier contracts, compliance certificates, and T&Cs against standard requirements. Flags gaps and deviations for procurement team review.
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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