Hybrid AI Framework Improves Supply Chain Forecasting and Optimization
A recent study published on arXiv (2604.21567) introduces a Hybrid AI Framework for Demand-Supply Forecasting and Optimization (HAF-DS). This framework combines an LSTM-driven demand forecasting component with a mixed integer linear programming (MILP) optimization layer. By utilizing embedding-based feature representation and recurrent neural networks, it effectively reduces both forecasting inaccuracies and operational expenses. Testing on datasets related to textile sales and supply chains reveals substantial improvements compared to traditional methods that function independently. This research focuses on enhancing supply chain resilience and efficiency in sectors characterized by fluctuating demand and unpredictable supply, particularly in textiles and personal protective equipment (PPE).
Key facts
- arXiv paper ID: 2604.21567
- Proposes Hybrid AI Framework for Demand-Supply Forecasting and Optimization (HAF-DS)
- Integrates LSTM-based demand forecasting with MILP optimization
- Jointly minimizes forecasting error and operational cost
- Tested on textile sales and supply chain datasets
- Addresses supply chain resilience in textiles and PPE industries
- Traditional forecasting and optimization approaches operate in isolation
- Framework uses embedding-based feature representation and recurrent neural architectures
Entities
Institutions
- arXiv