This term refers to the use of historical data, domain expertise, and AI to drive more intelligent inventory decisions. It merges analytics with operational know-how, creating adaptive systems that learn from outcomes. Unlike rule-based systems, knowledge-based approaches continuously refine models using real-world feedback. They integrate human inputs like seasonal trends or product lifecycle knowledge with machine learning algorithms to improve forecasts. This hybrid approach enables inventory platforms to generate smarter reorder points, safety stock levels, and supplier performance adjustments. It helps companies manage complex supply chains with nuanced strategies tailored to specific business realities.
