Inventory & Procurement Optimization for an E-Commerce leader — at the scale of hundreds of thousands of SKUs

The Opportunity

Operating at the scale of hundreds of thousands of SKUs — spanning private label, marketplace, and third-party brands — the company's procurement function was under immense pressure. Demand volatility driven by flash sales, seasonal peaks, and promotional campaigns made traditional reorder-point methods unreliable. Working capital was trapped in slow-moving and dead stock, while fast-moving SKUs faced recurrent stock-outs that directly impacted GMV and customer satisfaction. Vendor quality was inconsistent, with no systematic way to track sellable-vs-unsellable ratios at the supplier level. Inventory ageing costs were estimated but never precisely measured — making it impossible to quantify the true cost of overstock decisions.

• 01Hundreds of thousands of active SKUs across private label and third-party brands — each with distinct demand patterns, lead times, and margin profiles.
• 02Working capital locked in slow-moving and dead inventory with no visibility into ageing cost at the SKU or vendor level.
• 03Stock-outs on high-velocity SKUs directly eroding GMV and customer lifetime value during peak periods.
• 04Vendor quality inconsistencies — no structured framework to evaluate sellable-vs-unsellable ratios or hold suppliers accountable.

The Solution

We architected a comprehensive Inventory Ageing and Purchase Order analytics platform on AWS — ingesting transactional data from the company's OMS, WMS, and vendor systems into a purpose-built data warehouse. The engineering layer handled deduplication, SKU normalization across catalog variants, and time-series alignment for accurate ageing calculations. A Power BI front-end delivered real-time visibility across procurement, enabling category managers and procurement leads to move from reactive firefighting to strategic, data-informed purchasing decisions.

• 01Sellable vs. non-sellable inventory at vendor and SKU level.
• 02Inventory ageing by quantity and cost — bucketed to prioritize action on slow-moving stock.
• 03Historically non-performing SKUs to inform smarter future purchases.
• 04Pareto analysis of the long-tail SKU base — vendor consolidation and PO rationalization.
• 05Automated reorder recommendations based on velocity, lead time, and demand seasonality — replacing manual spreadsheet-based PO planning.
• 06Vendor scorecard integration — tracking fill rate, defect ratio, and lead-time adherence at the supplier level.

The Impact

Real-time visibility across procurement reshaped how the team made buying decisions — freeing significant working capital while protecting availability across the long tail. Category managers moved from weekly review cycles to continuous, exception-driven monitoring. Dead stock was identified and liquidated systematically rather than discovered during quarterly audits. Vendor negotiations became data-backed, with fill-rate and quality metrics replacing anecdotal feedback.

• 01Stock-outs reduced by 24% across the portfolio.
• 02Holding inventory value reduced by 17% — significant working capital freed.
• 03Procurement operations streamlined through automation — reactive firefighting replaced with proactive strategy.
• 04Vendor negotiation leverage strengthened through transparent, data-backed performance scorecards.
• 05Dead stock identification accelerated from quarterly audits to continuous, automated flagging — enabling faster liquidation and markdown decisions.