Forecasting & Demand Planning
ForecastingwithMeasurableConfidence
Demand forecasts that become a trusted planning input—built by pairing demand sensing, statistical models, and structured planner workflows with measurable accuracy targets.
Common Demand Planning Challenges
Planners rework the forecast after every meeting because consensus processes lack structure, wasting hours on subjective adjustments that erode accuracy.
AI and ML model outputs go unused because planners lack trust in automated forecasts, so manual overrides dominate and bias compounds over time.
Demand, procurement, and supply planning operate on disconnected horizons, creating misalignment that surfaces as expediting costs and missed commitments.
How We Improve Forecast Accuracy
Process
Create a single forecasting rhythm, map reconciliation touchpoints, and lock the measurement plan before planners adjust numbers.
Systems
Integrate demand capture, signal management, and supply planning tools to broadcast a trusted forecast.
Automation
Apply AI to detect bias drift, flag manual overrides, and surface critical exceptions with context.
Forecasting & Demand Planning KPIs We Track
Forecast accuracy
Bias / synchronization
Planned vs. actual variance
Inventory days of supply
Planning cycle time
Frequently Asked Questions
Why do demand forecasts lose accuracy over time?
Forecast accuracy degrades when there is no structured review cadence, when manual overrides accumulate without tracking, and when demand signals are not fed back into the model. We fix this by establishing a single forecasting rhythm with measurable checkpoints and automated bias detection.
How do you improve forecast accuracy with AI?
We layer AI on top of a clean planning process—not as a replacement for planners. AI detects bias drift, flags outlier overrides, and surfaces demand signals from external data. The planner stays in control but gets better inputs and automated exception alerts tied to accuracy KPIs.
What is forecast bias and how do you fix it?
Forecast bias is the consistent tendency to over- or under-predict demand. It compounds when planners adjust numbers without feedback loops. We measure bias at the SKU and category level, automate drift detection, and build reconciliation steps into the planning cadence so bias is caught and corrected before it impacts inventory or service.
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We baseline forecasting KPIs, uncover constraint hypotheses, and document the measurement plan before the first workshop ends.
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