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Using AI to reproduce human-like behavior for demand forecasting

Since 2009, the Streamline team has delivered AI-based planning solutions to multiple industries. We’ve learned the benefits and constraints of AI technology, and currently bring that value directly to you in a reliable and insightful solution. Our article in Forbes.com, Overcoming Supply Chain Challenges With AI: What Large Manufacturers Need To Know, describes the competitive advantages that AI brings to supply chain planning.

What is extremely useful about AI is its effective ability to find non-trivial solutions you may not think of–even after crunching numbers in Excel for years. We apply AI to one of the critical parts of the supply chain planning process: demand forecasting. Our goal is to improve forecast accuracy based on recognition of demand patterns – such as trends and level changes – earlier and more efficiently than other methods allow. Also, our goal is to reproduce decisions you would choose by analyzing individual demand patterns, and also avoid critical errors that may result from using an inappropriate AI methodology or strategy.

AI technology encompasses a broad range of techniques: machine learning and expert systems, among others. During the evolution of Streamline, we’ve implemented a combination of approaches to provide a somewhat conservative but stable and reliable forecast, rather than a more focused result that is potentially unstable due to sensitivity to minor input perturbations. Our AI strategy includes testing with millions of different patterns and combinations of input parameters, providing confidence in the reliability of forecasts generated by Streamline.

The bottom line? Streamline uses AI to reproduce human-like behavior for demand forecasting. Our forecasting is based on a combination of AI techniques and strategies proven to be effective, efficient, and result in bottom line value.

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  • Up to 56X ROI in one year. 100% ROI in the first 3 months.
  • Up to 90% reduction in time spent on forecasting, planning, and ordering.