Artificial Intelligence in Supply Chain Management: Empirical Studies and Research Directions
Empowering businesses with data-driven solutions for inventory, demand, and risk management.
Innovative Data Solutions
Transforming operational insights through advanced data collection, processing, and AI-driven analytics for smarter decision-making.
Data-Driven Insights
Transforming data into actionable insights through advanced analytics and machine learning techniques.
Data Collection Phase
We gather and preprocess diverse operational data for comprehensive analysis and insights.
Prompt Engineering Phase
Creating tailored prompts to enhance inventory management, demand forecasting, and risk alerting.
Integrating historical data with real-time inputs for improved decision-making and predictive analytics.
Retrieval Augmentation
Data Solutions
Empowering businesses through data-driven insights and analytics.
Phase One
Data collection and preprocessing for operational excellence.
Phase Two
Prompt engineering and retrieval for enhanced decision-making.
In a globalized context, supply chains face uncertainties from market fluctuations, geopolitical tensions, natural disasters, and pandemics, leading to material shortages, delivery delays, and production halts. AI, particularly generative LLMs, offers unprecedented capabilities for data-driven supply chain analysis and decision-making, yet real-world adoption confronts three key challenges: (1) fusing structured and unstructured data, (2) balancing high-precision forecasting with audit-ready explanations, and (3) ensuring robustness under disruption.