Challenge
Prior to engagement, the retailer’s supply chain suffered from batch-based planning cycles, siloed data, and manual intervention. Demand planners relied on moving averages and rule-based heuristics, delivering forecast accuracy of just 65% at the SKU–store level. This imprecision drove a 10% stockout rate—translating into $120 million in lost sales—and forced planners to hold 18% excess inventory, imposing $45 per-unit annual carrying costs. With over 2,000 stores across 15 countries, the lack of end-to-end visibility and slow reaction to promotion spikes and regional demand shifts eroded both customer satisfaction and margin.
Solution
Company implemented a cloud-native supply chain platform on Azure: a unified data lake ingested point-of-sale, ERP, promotions, and weather feeds. An ensemble of gradient-boosted trees and LSTM networks produced rolling 30-day forecasts at SKU–store granularity. Dynamic replenishment algorithms optimized order quantities and cadence, balancing stock-out risk against carrying cost. A serverless Control Tower—built with Azure Functions, Cosmos DB, and Power BI Embedded—provided real-time KPIs, anomaly alerts, and what-if scenario planning. Automated pipelines retrained models daily using the latest sales and inventory data, ensuring drift remained below 2% per quarter [1][2].
Results
- Forecast accuracy: increased from 65% to 92% across pilot regions, reducing forecast error (MAPE) from 35% to 8% [1].
- Stockout reduction: declined by 45%, restoring $54 million in previously lost sales annually [1].
- Inventory optimization: on-hand levels fell by 25%, cutting annual carrying costs by $6 million [2].
- Cost savings: logistics and distribution costs dropped by 12% through optimized routing and order consolidation, saving $12 million per year [2].
- Planner productivity: improved by 30%, as automated algorithms replaced manual adjustments for over 80% of replenishment orders.
Introduction & Business Context
With over 2,000 stores in 15 countries and an average daily transaction volume exceeding 5 million items, the retailer grappled with both stockouts in high-velocity products and overstock in slower movers. Traditional monthly planning cycles, manual spreadsheet adjustments, and siloed systems led to demand planners spending up to 60% of their time on data cleanup rather than analysis.
Customer experience was directly impacted: 10% of demand went unfulfilled on the shelf, while excess inventory tied up $150 million in working capital. Executive leadership prioritized an evidence-based overhaul to reduce lost sales, improve inventory turns, and enhance planner productivity.
Advanced Demand Forecasting
The project began by consolidating historical point-of-sale, promotions, pricing, markdown, and external signals (weather, holidays) into an Azure Data Lake. A hybrid modeling strategy combined gradient-boosted decision trees for baseline seasonality with LSTM networks for short-term event spikes.
Models were trained at SKU–store granularity on five years of data using Azure Machine Learning. A rolling-window backtest validated a mean absolute percentage error (MAPE) of 8% over a 30-day horizon—versus 35% under legacy methods—delivering actionable insights for each store’s replenishment cycle [1].
Inventory Optimization & Dynamic Replenishment
Using forecast outputs, automated replenishment algorithms solved multi-stage optimization: minimizing stockout risk while controlling carrying costs. Constraints included lead times, order minimums, and service-level targets. The engine generated daily order recommendations, dynamically adjusting to actual sales and shipments.
Integration with the retailer’s ERP via Azure Functions automated purchase order creation for 80% of SKUs, reducing manual order adjustments by planners and ensuring compliance with vendor minimums and promotions.
AI-Enabled Control Tower
A serverless Control Tower—built on Cosmos DB and Power BI Embedded—provided real-time dashboards: fill-rate heatmaps, inventory-turn anomalies, and cost-service trade-off sliders. Anomaly detection (z-score alerts) flagged unexpected demand surges or supply disruptions, triggering email and Teams notifications to supply-chain managers.
What-if scenario tools allowed executives to simulate promotional impacts, supply delays, and currency fluctuations, with instant recalculation of forecast, inventory, and cost KPIs.
Pilot Deployment & Validation
The six-month pilot covered 200 stores in three regions. Forecast accuracy rose from 65% to 92% (MAPE down to 8%), and daily stockout rates fell from 10% to 5%. Inventory on hand declined by 25%, freeing $6 million in working capital.
Manager surveys reported a 30% reduction in manual planning effort and a 4.5/5 satisfaction rating for the Control Tower’s usability. The pilot’s success secured board approval for global rollout.
Business Impact & Next Steps
Year-one savings: totaled $18 million—$54 million in recovered sales, $6 million in lower carrying costs, and $12 million in logistics efficiencies. Planner productivity gains freed 5 full-time equivalents for strategic supply-chain initiatives.
Phase 2 roadmap: will integrate real-time supplier ETA feeds, IoT-driven in-store inventory scans, and generative-AI chat interfaces to support exception management and collaborative demand planning.
Lessons Learned & Conclusion
- Embed AI at the core of planning: framing analytics as a foundational capability rather than an add-on ensures high user adoption and measurable ROI.
- Continuous retraining & feedback loops: daily model refreshes and close user-feedback integration are critical to maintain forecast fidelity and algorithm relevance.