In this video from our webinar: Turning AI into Results: Practical Applications Across Supply Chain Planning, Zac Nemitz from John Galt Solutions explores the benefits of AI improving key processes across supply chain planning, such as demand sensing. Zac breaks down how AI brings sharper focus to short-term demand signals, helping organizations detect fluctuations early and respond faster, with more confidence.
Demand sensing complements traditional demand planning by uncovering patterns across vast, complex datasets. With AI, planners can identify non-linear relationships between demand drivers like pricing changes, weather patterns, inflation, or even competitor activity – that would otherwise go unnoticed. This deeper visibility enables faster, smarter decisions in an increasingly volatile and fast-moving market.
The benefits of AI-enabled demand sensing include:
- Improved forecast accuracy through real-time data analysis
- Faster response to demand disruptions and market shifts
- Enhanced visibility into short-term demand drivers
- Better inventory optimization and reduced out-of-stocks
- Clearer insights into price elasticity and promotional impact
- More efficient use of internal and external data sources
- Stronger alignment between short-term sensing and long-term planning
As supply chains grow more complex, driven by omnichannel commerce and shifting consumer expectations, AI helps cut through the noise, revealing actionable insights that keep businesses agile and competitive.
The Atlas Planning Platform brings these capabilities to life by embedding advanced AI directly into supply chain planning. By continuously learning from data and uncovering hidden demand patterns, Atlas empowers organizations to elevate forecasting accuracy, improve responsiveness, and unlock smarter, end-to-end planning decisions.
Watch the webinar to learn more and explore how Atlas can help you unlock AI to improve demand sensing and other key areas of supply chain planning to deliver real value: Turning AI into Results: Practical Applications Across Supply Chain Planning
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Zac Nemitz: Demand sensing, if you're not familiar, so this is, you know, just a way to focus on the short term and predict any sort of immediate fluctuations in demand or things that I need to potentially react to relative to my plan. So it's a complementary component to demand planning.
You're essentially trying to find relationships in these large data sets, so a similar concept to the clustering and decision trees. But, you know, I may have non-linear connections that are derived, and these non-linear connections could lead to influence on my demand volume.
These are just some examples of some demand drivers, so, like, inflation, how much, you know, sun is up during a day, any sort of, like, price, you know, changes, things like that.
You could even consider, and one of the cool things with, you know, this AI support is there's so much potential with additional data here. So, you know, think about things like IoT connection.
Maybe you're looking at adoption rates from a new product perspective. Can you look at competitor pricing and competitor promotional data and understand what that might mean relative to your demand outlook? But again, the main value that AI-driven demand sensing is proving is that you're improving that accuracy of your forecasts, and you're getting visibility into potential disruptions to allow those quicker operational responses.