An artificial intelligence conference tailored for logistics, transport and supply chain professionals. Romain Rissoan shows how AI sharpens forecasting, optimises routes and warehouses, and brings end-to-end reliability to your operations.
Volatile demand, rising transport costs, relentless pressure on lead times, environmental requirements: logistics and supply chain operate under continuous constraint. By nature it is one vast optimisation problem — which makes it an ideal domain for artificial intelligence, bringing visibility where uncertainty once reigned and genuine optimisation where rules of thumb once sufficed.
This AI conference dedicated to logistics illustrates concrete use cases, backed by real examples: demand forecasting, route optimisation, warehouse management, disruption anticipation, document automation. Romain Rissoan also stresses the prerequisite that is too often underestimated: data quality and availability, without which even the best models remain ineffective.
The talk addresses supply chain, transport and operations directors as well as front-line teams. The goal is a realistic picture: which use cases deliver quick value even with an imperfect information system, which ones require clean data first, and how to bring teams along in a continuous-improvement mindset rather than a technology project disconnected from the shop floor.
Use cases from forecasting to execution.
By integrating historical data, seasonality, trends and external signals, AI anticipates volumes with a precision that classical methods cannot match. Companies adjust stock levels, capacities and procurement more accurately, reduce both stockouts and overstock, and gain confidence when navigating demand volatility — turning reactive scrambling into proactive planning.
AI calculates optimal transport plans by factoring in traffic, delivery time windows, vehicle capacities and operational constraints. Kilometres travelled, costs and CO₂ emissions all fall, load factors improve, and teams receive realistic, efficient routes that can be adjusted in real time as the day's disruptions unfold.
From slotting decisions to order picking, AI optimises internal flows and operator movements. Preparation times shrink, errors decrease and warehouse productivity rises — while improving working conditions for operatives thanks to better-designed routes and processes.
By cross-referencing data across the chain, AI detects and anticipates delays, stockouts and incidents before they become critical. Teams shift from reactive, fire-fighting management to proactive oversight: they receive early warnings, can reprioritise and reorganise, and limit the impact of disruptions on customers.
Generative AI processes delivery notes, invoices, customs documents and declarations at scale: extraction, checking, reconciliation and data entry. Administrative lead times drop, errors fall and support teams are freed from repetitive tasks to focus on complex cases and relationships with carriers and clients.
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