Major project Manufacturing, Robotics & Industry 4.0 Case Studio

Supply chain demand and late-delivery risk model

Inspired by publicly reported work at Nestlé

Forecast demand and predict which orders will ship late, then show planners where to act first.

The brief

Nestlé has written about upgrading its digital core so it can use AI and automation at scale across its operations. Build a supply chain analytics product of your own on an open supply chain dataset: forecast demand by product and region, predict which orders will be delivered late, and give planners a ranked list of risks with reasons. Good looks like: forecasts that beat a seasonal baseline, a late-delivery model judged on recall at a fixed alert budget, explanations per order, and a planner dashboard.

Read the case first

What Nestlé has said in public about this kind of work, on its own pages:

Make it your own

Plan for an Indian food business: a dairy cooperative, a spice exporter or a bakery chain, with monsoon delays and festival peaks. Add one risk the dataset does not have (weather or road closures) as synthetic data and show what it changes.

AITeky has no link with Nestlé, which has not reviewed or endorsed this brief. The brief only draws on what the company has published, uses open data rather than its data, and is a learning exercise: don't present your project as Nestlé's work or system.

What you will build

Explore orders, products, markets and shipping modes
Forecast monthly demand by product and region
Predict late delivery with gradient boosting
Explain risk per order with SHAP
Rank where planners should act first
Build a planner dashboard and a weekly summary

Free, open data to build it with

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