arXiv:2601.14031v2 Announce Type: replace-cross Abstract: Forecasting intermittent time series, which contain zeros, is a crucial challenge in supply chains as inventory policies require probabilistic forecasts to establish safety levels. Intermittent time series are commonly forecast using local models, trained individually on each time series. In the last years global models, trained on a large collection of time series, have become popular for time series forecasting. Global models are often based on neural networks or gradient boosted trees. We carry out the first study comparing state-of-
The paper 'Intermittent time series forecasting: local vs global models' highlights a critical discussion within the AI/ML community regarding optimal forecasting methods for supply chain challenges like inventory management, building on recent advances in global models.
Improved intermittent time series forecasting directly impacts the efficiency and resilience of global supply chains, enabling better inventory policies and reducing waste, which is crucial for economic stability.
This research provides a comparative analysis that can guide the adoption of more effective, potentially 'global' AI/ML forecasting models for critical business operations, shifting away from less efficient 'local' models.
- · Supply chain management software providers
- · Logistics companies
- · Retailers with complex inventory
- · AI/ML model developers
- · Companies relying on outdated forecasting methods
- · Businesses with inflexible inventory systems
Increased adoption of sophisticated AI-driven forecasting tools across various industries dealing with intermittent demand.
Enhanced supply chain resilience and reduced capital tied up in excess inventory, leading to improved operational efficiency and profitability.
A potential shift in competitive advantage towards companies that effectively leverage advanced AI forecasting, accelerating consolidation in logistics and retail sectors.
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