Events impacting supply chains are happening so fast and unpredictably that we need methods to help us keep up with and survive in a dynamic world. Here we explore how AI and machine learning are helping. We look at some of the applications and use cases that real users are doing today.
This report looks at both practical and visionary use cases, including :
- Pricing–AI/ML can ingest structured and unstructured data across various sources (spreadsheets, enterprise systems, websites), validate the quality and accuracy of the data, and help identify the best price to meet specific goals.
- Promotions–How AI/ML can analyze past promotional performance across variable factors, such as product attributes, packaging, channels, and markets to help design promotions optimized for specific goals (e.g. traffic generation, inventory optimization, etc.)
- Demand Planning and Forecasting–Using AI/ML to do demand sensing, help build a consensus forecast, and improve forecast accuracy in New Product Introductions and other scenarios.
- Inventory Management–performing multi-echelon inventory optimization that differentiates the requirements at each node.
This report concludes with a discussion of rethinking the process of getting results from systems with a data-driven strategy.