Supply chain & logistics.

Inventory and supply continuity

AI helps improve demand anticipation, prioritise flows at risk and refine the trade-off between service level, capital tied up and operational continuity.

• Recurring stockouts or overstocks despite the S&OP cycle;
• Forecasts insufficiently reliable by SKU, site or customer;
• Supplier or carrier delays identified too late;
• Stock / customer service trade-offs insufficiently supported by data.

01: Demand forecast & tied-up capital

Anticipating demand helps improve the trade-off between inventory level, service level and working capital requirement.

In a distribution case, forecast error was reduced from 35% to 19%, a 46% reduction compared with the initial method. Total impact was estimated at approximately €250k per year, combining cash release and margin recovered from avoided stockouts.

02: Supplier delays & supply continuity

Prioritising flows at risk helps focus preventive actions on shipments where the economic exposure justifies intervention.

In a pilot based on freight data, the model made it possible to prioritise a minority of at-risk shipments and translate logistics delay into a financial trade-off: delay probability, expected avoidable cost, action cost and expected net gain.

03: Safety stock and service level

Predictive models can help adjust safety stock according to demand variability, customer criticality and the real cost of a stockout.

The objective is not to increase or reduce stock uniformly, but to identify the SKUs where capital tied up is not justified, and those where stockout risk is commercially unacceptable.

When should a diagnosis be initiated?

A diagnosis is relevant when three conditions are met:

• sales, order or shipment history is available;
• measurable impact on inventory, customer service, working capital or supply continuity;
• ability to integrate the results into S&OP, purchasing, inventory or transport trade-offs.

Qualify a logistics use case