Modelling a 20-outstation network as a digital twin — down to the SKU.
A pan-European aviation group needed to know where its inventory was actually working — and where it wasn't. We rebuilt their network as a live simulation and stress-tested it 10,000 times.
Distance and frequency matter as much as stock levels
We don't only analyse local inventories — we model the distances between outstations and the frequency of connected locations, including how those dynamics shift over time.
Inventory value share by outstation — illustrative client network:
Monthly demand Very low → Very high
Every SKU, every route, every failure mode
Our algorithm accounts for every variable in a complex inventory structure — route frequencies and the stochastic demand and lead-time patterns for each SKU.
Monthly demand Very low → Very high
Inventory optimisation, done as rigorous stress-testing
Beside the traditional approach of inventory stratification (ABC analysis) and reviewing min/max policies, we stress-test the client's own data with a proprietary, AI-powered simulation.
We extracted the required data directly from the client's systems to describe demand patterns and initial inventory, then applied our proprietary simulation to every SKU across 10,000 iterations.
- Excess stock (non-movers)
- Full inventory stratification
- Data-driven safety stock
- Backorder risk exposure
Demand = 100/month · SD = 30 · Zero-prob = 0.10 · Max = 220/month
Lead time = 4 weeks · SD = 2 · Max = 10 weeks
Initial quantity = 200 · 10,000 simulations · middle 80% spread shown
A repeatable journey, not a one-off report
Every engagement follows the same disciplined path — from raw demand data to a resilient, stress-tested inventory policy your team can actually run on.
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We forecast your future demand.
Historical demand patterns are a good start, but an accurate, AI-powered estimation of your future demand is vital to optimise your inventory.
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We re-define your inventory policy.
Once future demand is accurately determined, the next step is to classify the emerging clusters of materials and set an optimum policy for each group.
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We recommend a purchase schedule.
Despite stochastic demand patterns, complexity can be reduced to accurate and reliable purchase cycles that simplify your planning process.
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We stress-test our recommendations.
Uncertainty is inevitable. Unexpected events happen. We simulate many future scenarios to stress your inventory and test our recommendations.
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We help you manage your risk.
Based on the simulations, we help you reduce or eliminate stockout risk — which can lead to AOG — while maximising cash at hand.