By Shai Cohen, VP Sales at

Machine Learning and predictive AI can learn from past behaviors and real-time data to provide a dynamic and, therefore, more accurate forecast. It’s no surprise that many companies have rushed to implement AI in their organization. According to an MIT Sloan review, 9 out of 10 agree that AI represents a business opportunity for their company. However, few have seen the benefits of AI in their business. In fact, 7 out of 10 companies reported that they have seen minimal or no impact from their AI implementation.

One of the key challenges in AI technology adoption is the translation of analyzed data into business value. There is a lot of information that is nice to know, and that can be presented in pretty dashboards, graphs, and notifications. Still, at the end of the day, information is only useful if it leads to actions that deliver business value.

Organizations should focus on closing the gap between ML generated information and tangible business value. For every business use case, it’s important to understand the story the data is telling and identify the relevant insights that you need to know in order to focus on the critical to dos for your business. 

Image:Brent Dykes, Effective Data StoryTelling 

For example, at Curve.Tech we use Machine Learning to help Sales & Supply Chain stakeholders make better decisions by identifying potential business risks early and suggesting possible solutions. Here are some examples:

  1. Optimized Inventory Levels – Real-time alerts on overstock and understock risks at the product, location, and client level, with suggestions for mitigation. React on time based on your lead time and the expected demand.
  2. Dynamic Lead Time Calculation – LT calculations based on actual behavior rather than predefined static categorization. Know your actual lead time to work better with your manufacturers and manage the risks of each one. Optimize stock based on the real behavior of your suppliers.
  3. Predict Impact of Supply Delays – Analyze the impact a delay in supply has on sales and see suggested actions based on inventory and client priorities. Make decisions early to mitigate risk and reduce impact.
  4. Predict Returns – Predict returns based on customer behavior and combine it as a factor for stock optimization to keep your overall inventory level accurate.
  5. Identify Trends Early – Alerts on emerging upward and downward sales trends at the product, category, and geographic levels. Adjust sales and marketing efforts, inventory, and location volumes accordingly to take advantage of new opportunities.
  6. Sales Forecast vs. Goals – Get input early in the quarter on how you are performing against your plan and budget. React on time and focus your efforts on where they are needed to meet your KPIs and stay on track.
  7. “What-If” Simulations: Based on factors that influence sales, simulate possible scenarios with actions within your control, such as price and marketing. Compare possibilities to find the best path of action.

In order to truly leverage AI, it’s important that businesses cut through the noise and consider the gap between the data, technology, and business value. When entering an AI project, make sure you start with the end goal in mind. Consider the business decisions you want to make and start working backward from there.

Do you want to hear more about how you can use machine learning for sales forecasting in your business? Reach out to us for a consultation at

0 replies

Leave a Reply

Want to join the discussion?
Feel free to contribute!

Leave a Reply

Your email address will not be published. Required fields are marked *