Industrial Wire Distributor Doubles Inventory Turns with Supply Chain Digital Twin on Azure

In wholesale distribution, inventory optimization across a multi-warehouse network is a core competitive lever — excess stock ties up working capital while stockouts erode customer relationships. This mid-market industrial wire distributor, operating 12 warehouses and managing over 6,000 active SKUs, faced a compounding data quality crisis: years of poor order routing had degraded the historical data inside their ERP. The system's item/site classification rules, built on that corrupted history, systematically misallocated stock — signaling replenishment at locations with no real demand while starving high-demand sites. Stockouts and overstock coexisted across the network, purchasing decisions grew increasingly strained, and management lacked reliable visibility into true demand patterns or network optimization opportunities.

Persepta deployed its Inventory & Network Optimization Solution Accelerator as a Supply Chain Digital Twin built on Microsoft Azure and Microsoft Fabric. Rather than attempting to remediate years of corrupted ERP history, the solution constructed an independent virtual replica of the business grounded in true demand signals — bypassing the flawed data layer entirely. Optimization models ran against the digital twin to simulate inventory strategies and network configurations before any real-world commitment, giving supply chain personnel a risk-free environment to evaluate purchasing decisions at scale. Automated intelligent order routing, driven by the optimized twin, progressively fed cleaner transaction data back into the ERP, creating a self-improving feedback loop. The implementation was scoped to fit mid-market IT consulting budgets, avoiding the enterprise-scale infrastructure typically associated with digital twin deployments.

The distributor doubled inventory turns across its full 12-warehouse network — a step-change in working capital efficiency. Inventory placement was realigned to actual demand patterns rather than the corrupted ERP signals that had driven misallocation for years. Purchasing decisions previously strained by unreliable system recommendations became more automated and accurate, reducing workload on key supply chain personnel.

Key outcomes:

• 2x inventory turns across the 12-warehouse network
• Optimized inventory levels — stock repositioned to match true site demand
• Reduced working capital costs through leaner, better-positioned inventory
• Streamlined purchasing workflow — automation reduced manual decision burden on the supply chain team

• Corrupted ERP history is a known pitfall in multi-site distribution; a digital twin that models true demand independently can break the self-reinforcing data cycle without requiring a full ERP remediation.
• Simulation-first decision-making lets supply chain teams stress-test inventory and network strategies before committing resources, materially reducing rollout risk.
• Automated order routing compounds returns over time: it drives operational efficiency while progressively improving the quality of transactional data.
• Azure-based digital twin architectures can deliver meaningful ROI within mid-market IT budgets — enterprise-scale infrastructure is not a prerequisite for network optimization.