Analytics · 3PL & Warehousing
Order fulfillment and pick accuracy analytics
A 3PL tracked fulfillment accuracy and cycle time by client to hold service levels as order volume rose.
Microsoft FabricPower BIAzure Data FactorySemantic model
- Pick accuracy
- 99.4%
- illustrative
- Error source
- Located
- per step
- Cycle time
- Tracked
- pick to ship
- View
- Per client
- shared
Illustrative scenario · example figures
The challenge
Mispicks and short ships drove client complaints, but errors were only counted after a claim, with no view of where in the pick-pack-ship flow they came from.
What we built
We modeled order flow from pick to ship, measuring accuracy, cycle time, and error source by client, zone, and picker.
- Accuracy and cycle time by client, zone, and picker.
- Error source analysis across pick, pack, and ship.
- A client-facing service view with trend.
Outcomes
- Errors are located at the step that causes them.
- Accuracy is measured continuously, not after a claim.
- Client reviews run on a shared service view.
Where the data came from
WMSOrder management systemShipping manifestsReturns data
Data is drawn from the systems the operation already runs on and delivered in the client’s own tenant.
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