Advanced Analytics & Big Data in Supply Chain Management
Advanced Analytics: Enabling complex end-to-end planning scenarios in Supply Chain Management
With the application of advanced analytics, a primary goal is formulated: (Data) modelling of complex end-to-end planning scenarios in supply chain management, sales & operations planning and advanced planning & scheduling:
- ML (machine learning)-based analyses of time series provide reliable forecasting information for demand planning
- Lights out planning & autonomous scheduling: turning the MRP-run into an agile real-time AI-process
- Reactive scheduling as AI-supported agile process that remains open up to the last minute
- Modelling autonomous planning through a digital twin
- AI-aided controlling of the trade-off between delivery reliability and tie-up capital in inventory management
- Integral energy management as an integral part of production planning
- (Partially) automated and self-learning AI-assisted decision-making processes including alert functionalities
- Pioneering the digital factory: we think autonomisation beyond supply chain management
Autonomisation of planning through advanced analytics
Advanced analytics helps to capture and process complex information and data structures during the planning run. With their support, a solid basis of decision-making can be issued and thus be prioritised on (pre)defined criteria in order to meet certain objectives. Thanks to their ability to act independently, it can relieve employees of routine tasks. Long-term data generated by artificial intelligence (AI) and machine learning (ML) can hence provide valuable criteria for strategic decisions.
