Case Study – Arla Foods Demand Sensing

Case Study – Arla Foods Demand Sensing

Demand sensing is a concept for capturing and modelling various big data demand signals into intelligence to foresee short-term sales demand patterns and demand changes to proactively adapt your operational supply chain planning. Reduction of scrap, increased customer service levels, lower inventories, improved replenishment decisions and reduced time spent by the planners are the business benefits of a more accurate short-term sales forecast. This session examines how to utilize the functionality of IBP Demand Sensing to create, evaluate, and monitor a short-term forecast for being right on the mix at the right place at the right time

- Understand common barriers to creating an accurate short-term forecast, and step through use case examples that illustrate how to avoid or overcome these
- Learn how to use SAP IBP Demand Sensing functionality to calculate a sensed short term forecast using pattern recognition and predictive analytics
- See how Arla Foods uses IBP Demand Sensing, and get real life insights in best practices for implementation and which pitfalls to avoid
- Get tips for taking advantage of SAP IBP and how it in combination with SAP APO can reduce forecast errors.
- Discover where is Demand Sensing is applicable – does it make sense in all situation and industries?

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