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AI-led forecasting can outperform broad manual intervention when baseline patterns are strong.
Forecast Value Add analysis within Demand Sensing helps planning teams measure where planner adjustments improve accuracy and where they do not.
Targeted exception management can improve planner productivity by focusing effort where human expertise matters most.
Shared forecast governance helps organizations define when to trust AI and when to intervene.
A global industrial and technology manufacturer with a large, complex product portfolio used e2open Demand Sensing to evaluate how planners and AI-generated forecasts could work together more effectively.
The company needed to understand whether manual forecast adjustments were improving accuracy or consuming planner effort without adding measurable value.
Using Forecast Value Add analysis on the e2open Demand Sensing baseline, the organization compared its AI-led forecast with the planner-adjusted consensus forecast, revealing where planners created value, where AI should be trusted, and where targeted intervention could unlock a 30% to 50% planner productivity opportunity.
When should planners trust AI forecasting over their own judgment?
As AI-led forecasting becomes more embedded in supply chain planning, organizations face a practical operating question: when should planners intervene, and when should they trust the forecast engine?
For this manufacturer, planners were applying judgment and cross-functional business knowledge input to an already strong AI-generated baseline. The opportunity was to make that collaboration measurable, repeatable, and focused on the exceptions that mattered most.
Do manual forecast adjustments actually improve accuracy?
Manual planning effort is valuable only when it improves the forecast. The company needed a reliable way to measure whether planner adjustments improved or degraded accuracy against actual demand, especially when planners were adjusting 39% of planning buckets.
Planner effort was spread broadly across the forecast instead of targeted to high-value exceptions.
Manual adjustments reduced forecast accuracy overall, with the consensus forecast at 67% MAPE compared with 62% MAPE for the AI-generated Demand Sensing baseline.
The organization lacked a shared measurement system for deciding when human intervention added value.
Planning teams needed governance to determine when AI should lead and when business context should override the baseline.
Using Forecast Value Add (FVA) analysis to measure planner impact
The company used Forecast Value Add analysis within e2open Demand Sensing to compare the AI-generated baseline forecast with the planner-adjusted consensus forecast, measuring the impact of manual intervention in MAPE points.
With e2open Demand Sensing, the manufacturer established an AI-led baseline forecast for longer-range planning, then applied Forecast Value Add analysis to see where planner judgment improved accuracy and where the baseline performed better at scale.
Created an AI-led baseline forecast for longer-range planning with e2open Demand Sensing.
Compared baseline and consensus forecasts to measure whether planner adjustments improved or worsened accuracy.
Identified where human expertise added measurable value and where AI should be trusted.
Shifted planner effort from broad manual intervention to targeted, evidence-based exceptions.
Established a practical governance model for planner-AI collaboration.
The results: forecast accuracy, MAPE, and the productivity opportunity
By applying Forecast Value Add analysis to the Demand Sensing baseline, the company made planner-AI collaboration measurable and actionable, revealing where manual intervention improved forecast outcomes and where it reduced accuracy.
62% MAPE for the AI-generated Demand Sensing baseline forecast.
67% MAPE for the planner-adjusted consensus forecast.
39% of planning buckets manually adjusted, indicating significant planner effort.
-5% total Forecast Value Add, showing that manual adjustments reduced accuracy overall.
30% to 50% planner productivity opportunity by targeting intervention where it pays off.
Application used:
e2open Demand Sensing
Forecast Value Add analysis within Demand Sensing gave the company a shared measurement system for deciding when to trust the forecast engine and when planners should intervene.
Why it matters
Modern supply chain planning depends on knowing when to scale AI forecasting and when to apply human expertise. This case study shows how e2open Demand Sensing can use Forecast Value Add analysis to turn that decision from a judgment call into a measurable planning discipline.
Ready to make planner-AI collaboration measurable?
Read the case study to see how a global manufacturer used e2open Demand Sensing and Forecast Value Add analysis to uncover where planners should intervene, where AI should lead, and how planning teams can work smarter at scale.
FAQs
What is Forecast Value Add analysis in Demand Sensing?
Forecast Value Add analysis measures whether manual forecast adjustments improve or worsen accuracy compared with actual demand. In this case study, the manufacturer applied the analysis to the Demand Sensing baseline to determine where planner intervention created value and where the AI-generated forecast performed better.
How did e2open Demand Sensing support the manufacturer?
E2open Demand Sensing produced the AI-led baseline forecast used for longer-range planning and supported Forecast Value Add analysis to compare that baseline with the planner-adjusted consensus forecast.
Why does planner-AI collaboration matter in demand planning?
Planner-AI collaboration helps supply chain teams combine scalable pattern recognition with human business context. The strongest results come when planners focus on exceptions where their knowledge improves the outcome.
What results did the manufacturer achieve?
The analysis found that the AI-led baseline forecast achieved 62% MAPE compared with 67% MAPE for the planner-adjusted consensus forecast, while identifying a 30% to 50% planner productivity opportunity.
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