From "approximately" to inventory we can audit.
An industrial parts distributor with many thousands of SKUs had a count that drifted every quarter. Maker–checker on Shipider closed the drift inside two operational cycles.
What was wrong before.
Long-tail SKU catalogs are unforgiving. Visually similar parts, identical packaging, dozens of variants per family. The team had been running on a legacy system for years, and the count had quietly drifted from operational truth — by enough that wholesale audits regularly returned uncomfortable findings.
- ✕Visually similar parts → mis-picks
- ✕Count drifted every quarter
- ✕Audits returned uncomfortable findings
- ✕No structural blocker on wrong picks
Why Shipider.
The team needed two things: a system that could enforce maker–checker on every consequential movement, and a barcode-scan workflow that could not be bypassed at the bench. Shipider had both, built in.
How it actually went.
Highest-confusion aisle migrated first. Maker–checker on every count.
Full SKU catalog brought in with barcodes and per-part metadata.
System surfaced specific drift sources for the first time — they could be fixed individually instead of estimated away.
By the second operational cycle, count matched reality at audit precision.
The shape of the new normal.
Count matches reality at audit precision.
Catalog scale handled without manual intervention.
No more uncomfortable findings.
Scan-to-verify makes wrong picks structurally hard.
My checker caught two errors on the first day. The platform paid for itself before our trial ended.
The next move.
The team is rolling out cycle-count automation to a second site this year and plans to publish their internal accuracy KPIs externally.
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