La Poste Optimizes Colissimo Flows in Real Time - Modern Data Stack Recording available

La Poste Group is using Pathway’s real-time data processing capabilities to optimize the flows of its well-known Colissimo business line. Jean-Paul Fabre, the Head of Technological innovation at La Poste, and Claire Nouet, the co-founder of Pathway, discussed the collaboration during the Modern Data Stack summit in Paris.

Learn how La Poste Group deployed ‘operational speed’ AI to forecast more accurately, assess disruption and automate key processes, improving operations in a meaningful way! (The video is in French, recap below!)

Challenges Faced by La Poste

• La Poste handles a high volume of packages across 17 industrial platforms, has more than 400 truck movements daily, and 16 million+ unused data points.
• Need to provide platform operators with real-time information on truck arrival times, origins, and destinations.
• Requirement to improve efficiency to avoid congestion and incidents.
• Need to identify anomalies in real-time

Key Objectives and Benefits

• Reduce costs: Pathway's solution is more cost-effective than existing outsourced solutions, with savings reinvested in functional improvements.
• Leverage data: La Poste generates approximately 16 million geolocation points annually but was not fully utilizing this data. Pathway helps make sense of this data and turn it into actionable insights.
• Simplify infrastructure: Pathway enables easier integration of different acquisition platforms and sensors and facilitates predictive calculations and rapid prototyping.

How Pathway Works

• Network Identification: Pathway identifies nodes (e.g. locations where trucks stop for a significant time) within the network. It differentiates between relevant nodes (e.g., platforms) and irrelevant ones (e.g., driver rest stops).
• Route Analysis: Pathway determines the primary routes between platforms and identifies alternative routes. This helps La Poste understand if drivers are using preferred (e.g., tolled) routes or opting for alternative routes.
• Data Integration: Pathway concentrates real-time geolocation data and historical data on a single platform. This creates a digital twin of the network, which can be used for real-time monitoring and analysis.
• Real-time data processing: Data scientists can work with real-time data in Jupiter Notebooks, and code can be moved directly into production, improving productivity.
• Anomaly Detection: Uses machine learning to detect anomalies with security implications.
• GPS Data Enhancement: Pathway automatically creates polygons based on GPS quality to filter out errors and false positives caused by signal fluctuations and metallic buildings.