Demand Planning Data Readiness Checklist: Is Your History Fit to Forecast?
Forecast models only reflect the history they are given. This checklist covers the data checks that matter most before you fit a model or load a planning tool: coverage, definition of demand, outliers, master data and ownership.
0 of 32 items complete
10 critical items still open - these are the highest-risk gaps.
History coverage
What counts as demand
Outliers and events
Master data and hierarchy
Process and ownership
Fix all critical items before trusting any statistical forecast. Non-critical gaps reduce accuracy but rarely invalidate the result on their own.
Automated data quality checks
Want these checks run against your ERP tables each cycle? Ask for an optional expert review.
Optional. The tool and every export work without this. No spam.
How to use the checklist
Go through the groups with your planning lead and an ERP data owner. Mark each item as done, partly done or not done, and record who will fix gaps. Treat the critical items as gates: if history does not reconcile to finance or demand is not defined, stop and fix that before modelling. Export the completed list as evidence for a planning tool implementation or an audit of your forecast process.
What good demand data looks like
Good history is complete, consistent and explainable. Each period has a value, including zero. The definition of demand is written down, so everyone knows whether returns, cancellations and transfers are in. Unusual spikes have a recorded reason. Items that changed part number keep their history. When these hold, simple methods perform well. When they do not, even advanced models fail.
Handling outliers and lost sales
Do not delete unusual points silently. Mark them, state a rule such as replacing a month with the median of surrounding months when an order exceeds a set multiple of normal, and keep the original. Stock-outs hide true demand in shipment history. Flag those periods and consider uplifting them, or use order history instead of shipments to avoid the problem.
- Keep original and cleansed series
- Write the cleansing rule down
- Review the largest items by hand
- Flag stock-out periods
Common mistakes
Teams forecast on shipments and then wonder why the model repeats past shortages. Others forget unit of measure conversions, so cases and pieces are added together. A third problem is superseded part numbers that split history across two items and make both look like poor candidates for modelling. Fix these at source in master data, not in the forecast sheet.
Frequently Asked Questions
How much history do I need for demand planning?
Three years of monthly data is a good target for seasonal items, with two years as a practical minimum. Stable non-seasonal items can be forecast with less, but accuracy statistics need enough periods to be meaningful.
Should I forecast from orders or shipments?
Orders by requested date are closer to true demand because shipments reflect what you could supply. Shipments are acceptable if you flag stock-outs. Whichever you pick, document it and keep it consistent.
How do I treat a large one-off order?
Review it with sales. If it will not recur, mark it as non-recurring and replace or smooth it in the cleansed history, keeping the original. If similar orders recur, leave it in and model it as an event.
Who should own demand history quality?
Assign one named person, typically a demand planning lead working with an ERP data steward. Quality problems found in the forecast should be corrected at source in the ERP, otherwise they return every cycle.
If data checks like these are run manually before each cycle, Netray can run them against your ERP tables and list the failures with the query used.
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