Demand Planning Data Readiness Checklist (Free)

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10 critical items still open - these are the highest-risk gaps.

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History coverage

At least 24 months of history exist for items with seasonalityCRITICALAt least 36 months exist for items where you want seasonal indices to be stableHistory is stored at the same time bucket you will forecast inMonths with no sales are present as zero, not missingCRITICALThe first and last period of each item are verified against sales reportsItems launched or discontinued inside the window are flaggedHistory totals reconcile to finance within an agreed toleranceCRITICAL

What counts as demand

You have decided between orders by requested date and shipments by ship dateCRITICALReturns and credit notes are netted or excluded by a stated ruleCRITICALCancelled order lines are removedIntercompany and inter-plant transfers are excluded from external demandFree of charge, sample and warranty shipments are tagged and handledLost sales from stock-outs are flagged or estimatedBackorders are counted once, in the period of the original request

Outliers and events

Large one-off orders are reviewed and marked as non-recurringCRITICALPromotions and price changes are recorded as events with datesPlant shutdowns, strikes and supply disruptions are markedPandemic, tariff or customer loss periods have an agreed treatmentOutlier cleansing rules are written down and applied consistentlyCRITICALOriginal and cleansed history are both retained

Master data and hierarchy

Every item has one base unit of measure and conversion factors are correctCRITICALSuperseded part numbers are linked so history carries forwardCRITICALProduct family, brand and category hierarchy has no orphansCustomer and region hierarchy is defined for aggregationItem status (active, new, phase-out, obsolete) is maintainedLead times and planning bucket settings exist for every forecasted item

Process and ownership

A named owner is accountable for history qualityThe forecast lag used for accuracy reporting is agreedCRITICALEvery forecast version is stored so accuracy can be measured laterThe data refresh schedule fits the planning calendarA baseline statistical forecast exists to measure manual overrides againstExceptions found in data checks have a route to correction at source

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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Demand Planning Data Readiness Checklist: Is Your History Fit to Forecast?