How to Run an Inventory Data Readiness Audit Before Investing in AI

The fastest way to waste money on AI forecasting is to point it at data that isn't ready. Good models need good inputs; messy or missing data produces confident, wrong forecasts. A data readiness audit answers a simple question before you invest: can your data support better forecasting today, and where will it pay off first?

Why data readiness comes first

Forecasting quality is capped by data quality. Buying a platform before checking your data just automates the same bad inputs — now with a license fee. An audit de-risks the investment and usually surfaces quick wins you can act on regardless of what you do next.

The data an audit looks at

• Sales / shipment history — ideally 2–3 years, at transaction or daily grain.
• Current inventory and locations — on-hand by SKU and site.
• Supplier lead times — and how much they actually vary.
• Product and location hierarchy — categories, substitutes, DCs.
• Promotions, price changes and known one-off events.

Common data problems (and quick fixes)

• History that nets out returns or hides stockouts, so true demand is understated.
• Inconsistent SKU or location identifiers across systems.
• Lead times stored as static averages rather than actuals.
• Discontinued or substitute items muddying the history.

Most of these are fixable, and knowing about them up front is half the battle.

What 'ready' actually looks like

You don't need perfect data — you need enough clean, granular history to learn patterns, plus an honest map of the gaps. 'Ready' means you can forecast a meaningful slice of the catalog reliably and have a plan to improve the rest over time.

What the audit delivers

A good audit leaves you with a prioritized roadmap: which categories are ready to forecast now, an estimate of the cash trapped in overstock and revenue lost to stockouts, the data fixes that matter most, and a recommended pilot. It's yours to keep whether or not you work with us.

Doing it in about two weeks

With read-only exports from your ERP and WMS, this is typically a two-week exercise — not a quarter-long project. You come out knowing the size of the prize and the smallest first step to capture it.