AI Application to Improve Demand Forecast Accuracy

The Business & The Challenge

A global leader in design, manufacturing, and distribution of construction materials was facing major inefficiencies in supply chain planning:

• A disorganized planning process failed to match supply with actual demand
• High inventory congestion due to obsolete or expired products
• Working capital was tied up in excess stock, driving up costs and limiting flexibility

The primary objective:
Optimize working capital by improving forecasting, eliminating obsolete inventory, and aligning supply with demand.

Approach Details

Falconi executed a 3-phasetransformation leveraging advanced analytics and AI-driven modeling:

1. Problem & Data Discovery

• Mapped demand history by SKU, location, and time
• Collected relevant variables like historical pricing, weather, promotions, and distribution dynamics

2. Model Development

• Built statistical forecasting models:
   
  • Moving Average, Exponential Smoothing, ARIMA, Hierarchical, and Econometric Models
• Applied Machine Learning to isolate demand-impacting variables
• Created demand segmentation by product, category, and seasonality

3. Model Operations

• Delivered an optimized operating model
• Reorganized inventory and prioritization policies
• Implemented process changes based on AI recommendations

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Results Achieved

Key Metric                                                                Improvement

Inventory Levels                                                    -17%

Stockouts                                                                 -52%

Obsolete / Expired  Product Expenses        -46%

✅ Improved responsiveness to demand patterns
✅ Freed up capital from stagnant inventory
✅ Reduced waste and loss from expired goods
✅ More accurate planning across the product lifecycle

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Why It Worked

• Combined classical statistical models with modern ML techniques
• Segmented analysis enabled SKU-level optimization despite data noise
• Hierarchical models linked category trends to individual product behavior
• Econometric modeling separated complex influencing factors (e.g., weather, price promotions)

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Conclusion

Falconi’s AI-powered forecasting transformation empowered this global manufacturer to cut inventory and waste, boost forecast precision, and reduce stockouts by over50%. By upgrading the core planning process, Falconi helped the company regain operational control and financial agility.

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