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Analytics · 3PL & Warehousing

Inventory aging and dead-stock analytics

A distributor surfaced slow and dead stock across sites to free the cash and space it was consuming.

Microsoft FabricPower BIAzure SQLDAX measures
Dead stock
-21%
illustrative
Carrying cost
Quantified
per SKU
At-risk stock
Flagged
early
Sites covered
All
one view

Illustrative scenario · example figures

The challenge

Slow-moving stock accumulated quietly across sites, tying up cash and space, and nobody saw it until a warehouse ran short of room.

What we built

We built an aging model with days-on-hand, last-movement, and carrying cost by SKU and site, flagging dead and at-risk stock early.

  • Aging buckets with days-on-hand and last movement by SKU.
  • Carrying cost and space consumed by slow stock.
  • Early flags for stock trending toward dead.

Outcomes

  • Slow and dead stock is visible before it fills the warehouse.
  • Cash and space tied up in excess inventory are recovered.
  • Markdown and liquidation decisions are made on time.

Where the data came from

WMSNetSuiteSales historyCost master

Data is drawn from the systems the operation already runs on and delivered in the client’s own tenant.

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