Optimized Bottleneck Management

Information on material shortages, inventory, demand, bill of materials and supply chains is spread across multiple sources and has to be consolidated manually. This creates media breaks, Excel versions, errors in inventory coverage and delays in prioritisation, escalation and action planning.

Artificial intelligence consolidates bottleneck-relevant data from different sources and connects it with the bill of materials, inventory coverage and supply logic to create a single situational picture. In supply chain disruption management, an AI system automatically identifies affected products, calculates critical path supply chain dependencies and prioritises response options for shortages and production risks. This creates a transparent, versioned decision process for managing supply chain disruption across production and logistics. Teams respond faster to disruption, document decisions clearly and handle bottlenecks with far less manual effort.