Supply Chain & Logistics
Stock replenishment recommendations
Analyse live stock levels, sales velocity, and forward demand to flag lines approaching reorder points and recommend quantities. Reduces manual intervention in routine replenishment decisions.
15–30%
reduction in out-of-stock incidents
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.
Moderate risk. Some customer or external exposure. Errors create rework or reputational impact but are recoverable.
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
Ocado's deep learning demand forecasts are up to 40% more accurate than traditional systems, with food waste at 0.49% of stock handled and over 94.5% of AI-recommended purchase orders accepted with minimal human intervention (Ocado).
Requires reliable, timely stock and sales data. If your inventory data is more than 24 hours out of date, replenishment recommendations become unreliable for fast-moving lines.
Seasonal patterns, promotions, and launches need to be factored in. A model trained on historical data will systematically under-predict demand spikes it hasn't seen before.
Start with high-volume, regular-demand lines where historical patterns are stable before extending to seasonal or promotional stock where variance is higher.
Replenishment recommendations are most valuable when they replace a routine decision — 'replenish when cover drops below 3 weeks' — not a complex one requiring commercial judgment. Define which decisions are genuinely routine and which require a human before scoping the build.
Experiment starter: For one product category, define a replenishment rule (e.g. flag when forecasted cover drops below four weeks) and run the model for 60 days. Compare actual stock-outs and overstock events against the previous 60 days. A material improvement in either metric justifies expanding to other categories.
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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