Forecasting production with AI starts by unifying the signals you already have—orders, inventory, and on-floor events. This guide shows a practical path to forecasting without adding more spreadsheets.
Why forecasting matters
Great forecasts unlock capacity planning, reliable delivery dates, and clear staffing decisions. With a unified view across orders and inventory, you reduce last-minute surprises and stabilize throughput.
Signals to unify
Orders
Order intake is your demand signal. Normalize by product families and attributes to make it comparable over time.
Inventory
Inventory buffers, reorder points, and lead times all influence feasible schedules and realistic promises.
On-floor events
Machine availability, yield, and cycle times keep forecasts grounded in reality. Capture them consistently.
A simple model
Start with a rolling average or exponential smoothing model. Calibrate weekly; incorporate exceptions rather than chasing perfect accuracy from day one.
Precision improves fastest when the feedback loop is short and friendly for operators.
Quick wins
- Establish a single source of truth for product definitions.
- Measure stage-level cycle time and yield weekly.
- Use alerts for when WIP or inventory break thresholds.
Rollout checklist
- Define inputs and owners for each data signal.
- Publish a single dashboard with forecast, variance, and notes.
- Hold a 15-minute weekly calibration with Ops + Production.
Want help implementing this? Talk to our team.
