Leveraging AI to Simultaneously Reduce Stockouts and Inventory

The Business & The Challenge

A national consumer goods company with $1.3 billion in annual revenue, managing over 150,000SKUs in an omnichannel environment, faced mounting challenges:

• High stock investment paired with frequent stockouts
• Inventory coverage models failed to consider demand uncertainty, delivery time,     and shipping frequency
• Lacked integration of internal and external variables influencing inventory     decisions

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The critical question:
How to reduce stockouts while optimizing inventory levels across a massive product portfolio?

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Approach Details

Falconi deployed a data-and AI-driven strategy focused on realigning inventory decisions with operational and market realities.

Core Implementation Steps:

1. Comprehensive Variable Mapping
   
    
   • Included both internal (OTIFs, stock turns) and external (supplier lead times, demand variability) data
 
2. Multi-Segmented Demand Modeling
   
    
   • Used AI to model and forecast demand, distribution, and purchasing needs
    
   • Applied segmentation analytics for more accurate optimization
 
3. Process Reorganization
   
    
   • Adjusted inventory processes to align with model outputs and optimal thresholds
 
4. Pilot Sprints & Rollout Plan
   
    
   • Deployed controlled pilot implementations
    
   • Structured rollout for broader deployment

Model integrated planning modules across demand, distribution, and purchasing.

📊 Results Achieved

KPI                                                    Outcome

Stockout Reduction                 +$50M in gains

Inventory Reduction                -$39M over 25 months

Inventory Level  Reduction   -25%

Revenue Impact                         +5% increase

📉 Clear evidence of non-linear optimization: substantial inventory drops led to both reduced stockouts and higher revenue.

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Why It Worked

• Falconi’s model incorporated multi-factor AI forecasting, not just historical averages
• Segmentation allowed targeted strategies by SKU type and risk profile
• Pilot rollout mitigated risk and allowed for adaptive scaling
• The client transitioned from reactive inventory practices to proactive, AI-informed planning

📘 Conclusion

This case highlights how Falconi helped a large consumer goods company cut $39M in excess inventory, eliminate $50M in stockout-related losses, and still grow revenue by5%. Through advanced analytics and AI modeling, Falconi enabled a sustainable transformation in inventory management — proving that smarter planning can simultaneously save costs and boost performance.

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