Consumer Electronics (Appliances)
Forecasting Improvement for Fortune 500 Consumer Appliances Company
City×Product-level forecasting and dynamic distributor norms to cut unsold inventory and rationalize product–market variants.
30% average forecasting accuracy improvement
Improvement
The Problem
Demand forecasting hampered by weather-influenced variation, poor product-level accuracy, new launches, and high logistics costs, leading to value loss if products were unsold before next version.
Key Challenges
- Bad forecast accuracy at specific product levels and markets.
- Forecasting for new launches and complementary brands.
- High logistics costs for heavy products due to poor planning.
- Value loss for products not sold before next version.
- Lack of integration of color/feature variants affected by weather patterns.
Our Solution
Deployed granular city×product forecasting with dynamic distributor norms and variant rationalization recommendations.
- Intellimark Feature Engineering Engine
- Granular City×Product Forecasting
- Variant Optimization
Implementation Approach
- Granular Forecasts: city×product level for maximum accuracy.
- Dynamic Norms: distributor norms to reduce unsold inventory.
- Variant Actions: recommendations to remove specific variants from specific geographies.
Results & Impact
- 80% SKUs gained forecast accuracy.
- 30% average accuracy improvement.
- 300 product×market combinations removed.