Advanced Analytics & Big Data in Supply Chain Management

Advanced Analytics & Big Data in Supply Chain Management

AI and ML-driven applications as integral planning tools in supply chain management

Advanced analytics describe a wide range of analysis methods – such as heuristics, mathematical solver-based optimization methods, artificial intelligence (AI), and machine learning (ML) – that are used in supply chain management.

Advanced analytics in supply chain management is no longer limited to analysing historical data through descriptive analytics (the question of “what happened?”), but focuses on predictive (“what will happen?”) and prescriptive analytics (“how can I achieve it?”). By using analytics with regard to the future, planning scenarios can be mapped and – based on predefined and/or prioritized/ranked goals such as maximum customer service level, customer segmentation, margin-optimised production programme, etc. – automatically executed.

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.

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Dr. Martin Kohl
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