How to Optimize Logistics and Planning with Machine Learning

Many planning and logistics professionals are still basing business decisions on historical data and descriptive analytics.

While valuable insights can come from what happened in the past, transformative results come from uncovering what’s likely to happen in the future — from predictive machine learning models.

With AI-powered analytics, you can find timely and accurate predictive insights in minutes, with or without code, to:

  • Dynamically forecast demand to account for granular factors like seasonality, economic conditions, and competitors’ actions
  • Avoid stockouts or overstocking to save money and optimize sales
  • Mitigate shipping delays by identifying leading indicators of potential delays and optimizing shipping routes

Learn how to optimize your supply chain with these three machine-learning use cases.


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