Idea
HEF platform optimizes demand forecasting models for businesses needing balanced accuracy and deviation control in planning.
Research Paper
Core Innovation
This paper presents the Hierarchical Evaluation Function (HEF), a novel multi-metric evaluation method that balances overall forecast accuracy with penalties for large deviations. Unlike traditional metrics such as FMAE, HEF provides greater robustness and explanatory power, making it more suitable for strategic decision-making in complex, dynamic environments.
Market Size (TAM)
$2–10B TAM, $1–2B SAM; assumption: growing demand forecasting software market driven by supply chain digitization and AI adoption.
Potential Customers & Pain Points
- Retailers needing accurate demand forecasts for inventory management
- Supply chain managers facing costly forecast errors
- Manufacturers requiring robust multivariate time series predictions
- Strategic planners needing interpretable forecasting metrics
Business Model
SaaS platform offering HEF-based demand forecasting evaluation tools with tiered subscription plans for enterprises and consulting services.
Competitive Landscape
- Amazon Forecast
- Google Cloud AI Platform
- Microsoft Azure Machine Learning
Implementation Challenges
- Integration with existing forecasting systems
- Convincing enterprises to adopt new evaluation metrics
- Demonstrating clear ROI over traditional methods
Validation Strategy
- Develop prototype integrating HEF with popular forecasting models
- Pilot with retail and manufacturing partners to measure forecast improvements
- Publish case studies demonstrating strategic planning benefits
Research Paper Overview
Hierarchical Evaluation Function (HEF): A Multi-Metric Approach for Optimizing Demand Forecasting Models
Summary
This paper introduces HEF, a hierarchical evaluation function that improves demand forecasting by balancing global accuracy and penalizing large deviations, outperforming traditional metrics like FMAE in robustness and explanatory power. HEF suits strategic planning, while FMAE favors operational efficiency. The study offers a replicable framework for optimizing predictive models in dynamic, multivariate time series environments.