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Analytics · Manufacturers & Distributors

Cross-border inventory positioning analytics

A distributor with stock on both sides of the border analyzed where to hold inventory to balance duty, freight, and service.

Microsoft FabricPower BIAzure Machine LearningSemantic model
Positioning
Optimized
cost vs service
Freight and duty
Lower
illustrative
Service
Held
with less stock
Network
Both sides
of the border

Illustrative scenario · example figures

The challenge

Inventory was positioned by habit, so the business paid duty and freight to move stock that would have served the customer better from a different location.

What we built

We modeled demand, duty, freight, and service by location to recommend where each product should be held across the cross-border network.

  • Demand and service modeled by location and product.
  • Duty and freight cost of each positioning option.
  • Positioning recommendations that balance cost and service.

Outcomes

  • Stock is positioned on cost and service, not habit.
  • Duty and freight spent moving the wrong stock is cut.
  • Service levels hold with less inventory in the network.

Where the data came from

NetSuiteDemand dataDuty tablesFreight rates

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

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