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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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