Paul Mackie explains how Atlas utilizes generative AI to uncover relationships in data, aiding in root cause analysis for supply chain issues.
In a recent webinar for Atlas customers, he demonstrated how Atlas can suggest adjustments based on understocked items and negative forecast biases, allowing planners to proactively address challenges. This capability in Atlas enhances the planners’ efficiency by providing insights that were previously time-consuming to uncover.
- Full Transcript
AI in Atlas is very good at uncovering relationships in your data, that you may not be able to see directly, or might require a little bit more effort to uncover. AI can be a very powerful tool in analyzing your data and understanding those relationships.
The idea with generative AI is to produce some sort of an understanding based on your data of what might be the underlying dynamics and give you suggestions, especially about root causes and areas, that you might choose to focus.
So Atlas is able to help you manage by exception via the to do list and understand your planning process, and what needs to be done next.
These days, with generative AI, we can also go deeper into root cause analysis.
What's really good about generative AI is that it'll give you a sort of natural language summary of the root causes that we're seeing of this forecast error.
Without having to go into too much detail about analyzing all of the possible ins and outs the Atlas AI capabilities will help uncover that relationship, that root cause, and it'll write it out to you in plain language, using generative AI.