Manufacturing

We know it too well, the meeting started two hours ago, intermission for a second round of coffee but the question still hasn’t changed: “How are we going to solve …?”.

Every issue you can imagine was thrown into a blender over the last year dumped onto manufacturing in a torrential downpour. Thankfully, we’re an industry of Do’ers and we don’t give up. Whether deciding how to safely bring jobs back, mitigate supply and shipping issues, and now, finding labor to fulfill demand, we’ve all found creative solutions to survive.

As we create these solutions, we’ve realized they are more often temporary than not. The problem situations and circumstances change, requiring new tactical approaches. Wouldn’t it be nice if solutions were responsive and evolved with your business environment? Requiring less oversight and more emphasis on actionable insights?

While we can’t provide a one size fits all solution for every manufacturing issue, we can solve Demand Forecasting to a substantial degree. By leveraging Machine Learning against 14 leading forecasting models and current production data, we can improve forecasting accuracies by up to 60%!

The industry average demand forecasting accuracy is 49%, “According to Toby Brzoznowski, the Chief Strategy Officer at LLamasoft, a rough rule of thumb is that 1% forecast improvement leads to a 2.5% reduction in the amount of inventory that needs to be held”¹. What would an improvement of 60% do for your business?

The nature of Machine Learning allows the software to be fluid and change predictions on an individual SKU-level basis using your current data. You can also feed the system 3rd party data such as stocks, weather, and other variables to find positive correlations.

Have one less problem to solve by implementing AI-driven Demand Forecasting. Use actionable insights from your data to improve operational efficiencies today!