Challenge
Inventory policies are typically built using static assumptions — average demand, fixed lead times, and service targets translated into safety stock formulas. These models break down in real environments where: • Demand is volatile (CPG promotions, automotive demand swings) • Supply is uncertain (industrial components, pharma APIs) • Network dependencies amplify variability Most companies end up overcorrecting: building buffers everywhere, or cutting too aggressively and paying the price in service. This leads to a structural imbalance: • Excess inventory in slow-moving SKUs or regions • Stockouts in high-demand nodes • Capital tied up inefficiently • Service levels that fluctuate unpredictably In pharma, this can mean expiry risk. In automotive, line-down risk. In industrial, delayed project fulfillment.
Solution
VYAN models inventory as a network-level decision under uncertainty, not a node-level calculation. It evaluates stocking strategies across: • Demand variability distributions • Supply uncertainty • Multi-echelon dependencies • Service and margin constraints Rather than calculating static safety stock, it simulates how inventory positions behave across future scenarios and identifies policies that balance risk, cost, and service dynamically.
