The transport and logistics sector is the backbone of global economic exchanges. It is subject to increasingly complex constraints: demand volatility, saturation of urban networks, environmental issues, rising energy costs, regulatory pressures, and growing expectations for traceability, speed, and flexibility.
In this highly interconnected environment, artificial intelligence plays a crucial role by providing predictive, decision-making, and autonomous capabilities to traditionally fragmented, rigid, or limited systems in their real-time adaptability. AI enables the anticipation of flows, optimization of routes, synchronization of operations, and reliability of delivery commitments, all while reducing costs, CO₂ emissions, and interruption risks.
It thus becomes a major lever for reinventing the logistics chain, and an essential tool for building intelligent, smooth, resilient, and sustainable logistics.
What NeuriaLabs brings to the transport and logistics sector
NeuriaLabs supports transport operators (road, rail, maritime, air), logistics providers, warehouse managers, industrial companies, e-commerce players, and local authorities in the modernization and intelligent automation of their logistics and mobility systems.
We develop artificial intelligence solutions capable of real-time modeling of physical flows, planning resources with fine granularity, managing operational uncertainties, and providing optimized trade-offs at each link in the chain.
Our technologies address the challenges of managing multimodal transport, visibility of flows, optimization of material and human resources, and decarbonization of logistics activities, within a framework of integrated performance and respect for contractual commitments.
Use cases in transport and logistics
Artificial intelligence enables the automation, optimization, and securing of a very large number of logistics and transport processes, including:
• Forecasting logistics flows and transport demand: anticipating volumes to be handled by product, by line, or by geographical area, integrating sales data, market data, seasonality, or exogenous events.
• Dynamic planning of routes and deliveries: real-time adjustments of journeys based on traffic conditions, delivery schedules, regulatory restrictions, weather, or unforeseen events.
• Optimization of vehicle loading: intelligent distribution of loads based on weight, volume, delivery scheduling constraints, and product compatibility.
• Orchestrating flows in warehouses: algorithmic management of receiving, storage, picking, and shipping operations based on logistical priorities and departure forecasts.
• Predictive maintenance of fleets and logistics equipment: monitoring onboard sensors to anticipate vehicle breakdowns, material handling systems, or critical technical installations.
• Automation of handling logistics anomalies: real-time detection of temperature deviations, delays, preparation errors, and automatic triggering of corrective procedures.
• Reducing the carbon footprint of transport: simulating low-impact routes, adjusting deliveries according to delivery density, consolidating flows, and optimizing fleet energy management.
Solutions developed by NeuriaLabs for the transport and logistics sector
NeuriaLabs designs and deploys artificial intelligence solutions interoperable with logistics management systems (WMS, TMS, ERP, OMS), capable of processing large and heterogeneous data in environments with high operational constraints.
Our solutions include:
• Multi-level logistics forecasting platforms: machine learning models integrating internal data (orders, histories, schedules), external data (weather, events, customs data), and real-time data (sensors, RFID, GPS), to anticipate needs and detect potential disruptions.
• Optimization systems for routes and last-mile scheduling: combinatorial optimization engines that take into account delivery windows, geography, fleet constraints, and profitability or carbon neutrality goals.
• Intelligent vehicle loading modules: optimized packing algorithms considering package dimensions, weight, unloading order, and physical or regulatory compatibility.
• Smart warehouse management tools: dynamic allocation of storage locations, adaptive sequencing of picking operations, prioritization of preparations based on customer criticality or flow value.
• Predictive maintenance solutions for fleets and facilities: integrated dashboards leveraging telemetry and maintenance data to predict breakdowns, allocate technical resources, and plan interventions based on criticality levels.
• Real-time anomaly detection engines in logistics: integrating unsupervised learning models to identify deviations from operational standards and generate contextualized alerts.
• Low-emission logistics trajectory simulators: decision support tools for constructing decarbonized logistics plans, optimizing flows based on ESG objectives, regulatory constraints, and available propulsion technologies.
All our solutions are designed to operate in critical production environments, with guarantees of real-time performance, scalability, security, and regulatory compliance (notably concerning ISO 28000, AEO, GDPR, and CSR standards).
