Supply Chain & Logistics
Supply chain digital twin
Build a digital replica of your supply chain network to simulate disruptions, test alternative sourcing strategies, and optimise inventory deployment before making real-world changes.
10–20%
reduction in supply chain operating costs
Opportunity assessment
Significant impact. Material effect on profitability, revenue, or cost base — visible at business level.
Very difficult. Requires custom development, specialist AI expertise, or capabilities that aren't yet reliable enough for production use.
High data complexity. Needs large volumes of specialised, structured data that most businesses don't have and would be costly to build.
Very low risk. Fully internal use. A person checks everything before it goes further. Worst case is a minor internal inconvenience.
High people impact. Daily workflows change materially for a number of people. Training, communication, and active change management needed.
Tooling required
Things to consider
McKinsey research shows retailers linking planning, inventory deployment, and transportation via digital twin achieved up to 20% increase in order fulfilment and 10% reduction in labour costs (McKinsey).
Building a supply chain digital twin is a major undertaking. It requires accurate data from every node in the network — suppliers, DCs, stores, transport — integrated into a single model. Most businesses underestimate the data work by 2–3x.
Vita Coco built a digital twin of their fulfilment network with RELEX Solutions, integrating cost data to optimise their 18-month supply plan. The 'what-if' scenario planning capability was the primary value driver (RELEX Solutions).
Start with a single corridor (one supplier to one DC to a set of stores) rather than attempting the full network. Prove the model's predictive accuracy on the simple case first.
Experiment starter: Map one supplier-to-store corridor with all relevant data (lead times, costs, constraints, demand). Build a basic simulation model (even in a spreadsheet). Test three disruption scenarios and compare the model's recommendations against what you would have decided manually.
Go deeper in the playbook
Section — Opportunity Identification, including the AI Opportunity Assessment ScorecardSection — Change Management, Scaling, and AdoptionThis opportunity scores low on change complexity — the change management section covers how to land it with the team.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.
AI Transformation Playbook
Ready to assess your own opportunities?
The playbook gives you the full 150-opportunity directory, scoring tools, and 100+ templates for every stage of an AI transformation programme.