Latest news
Watch the recording to learn
Where AI in supply chain planning delivers value today
How forecast value add shows whether planner adjustments help or hurt
Why AI demand forecasting is the practical place to start
Supply chain problems do not all respond to the same solutions, so e2open invests across machine learning, optimization, heuristics, and agentic AI. Machine learning remains the strongest tool for forecasting, and agentic AI belongs in that mix.
Forecasting makes a strong first move because it is isolated and measurable. Companies can run a machine-generated forecast beside a traditional one, compare accuracy, and prove the difference in a single number. Some e2open customers have used AI-led forecasting to support demand planning for more than ten years.
What short-term demand sensing looks like in a CPG supply chain
Dan describes a major consumer packaged goods manufacturer using short-term demand sensing inside a six-week window, where distribution decisions determine whether everyday products reach the shelf. The forecast updates daily on point-of-sale data, warehouse withdrawals, and open orders, which keeps the process largely touchless once it is parameterized.
The customer reports:
Forecast accuracy improvement of 13% to 38%, depending on the time horizon
Consistent results across more than two years and nearly every product category
Better on-shelf availability, because product lands where demand shows up
Less buffer inventory, which returns working capital to the business
Planner time redirected from producing numbers to monitoring the solution
How supply chain planners and forecast engines divide the work
Accuracy alone does not settle the question. The strongest results come from pairing the engine with the planner, and e2open uses forecast value add to show how much value planners contribute to the baseline forecast. The uncomfortable finding is that manual adjustments degrade the forecast more often than they improve it.
That repositions planners rather than replacing them.
What's ahead: Signs a forecasting process has value left on the table
Three patterns signs that AI in supply chain planning could lift performance:
One statistical model applied across the entire product portfolio
Planners are manually choosing the best-fit forecast method product by product
Recurring manual adjustments, month after month, through every forecasting cycle
None of these look broken, which is the problem. They look like business as usual while quietly holding back both the planner and the organization.
Explore further:
Frequently asked questions
What is AI in supply chain planning?
AI in supply chain planning applies machine learning, optimization, heuristics, and agentic AI to planning decisions such as forecasting, demand sensing, supply planning, and inventory optimization. E2open uses multiple solvers rather than one, because different planning problems respond to different techniques.
Where should companies start with AI in supply chain planning?
AI demand forecasting is a practical starting point. The use case is isolated and measurable, so companies can run a machine-generated forecast beside their current approach, compare accuracy directly, and prove value in a single number before expanding to other planning processes.
Does AI demand forecasting replace supply chain planners?
No. AI in supply chain planning changes what planners spend their time on. Forecast engines handle pattern recognition at scale, and planners apply judgment where business context matters most, such as new product introductions and long-range forecasts.
What is forecast value add and why does it matter?
Forecast value add measures how much accuracy planners add to, or remove from, the baseline forecast during the consensus process. It gives supply chain planners an objective way to see which adjustments improve accuracy and which reduce it.
How does demand sensing improve on shelf availability?
Demand sensing updates the near-term forecast daily using signals such as point-of-sale data, warehouse withdrawals, and open orders. Better near-term accuracy places product closer to real demand, which supports on shelf availability while reducing the buffer inventory held for uncertainty.
E2open subscription center
Subscribe to receive e2open updates
Interested in learning more? Stay current with the latest e2open news – from company updates to thought-leadership pieces, and so much more!