The digital transformation of manufacturing has accelerated the adoption of reconfigurable production systems (RPS). RPS promise modularity, scalability and agility in response to increasing product variety and market volatility. To fully realize these benefits, it is essential to align high-level planning with real-time shop floor execution through intelligent coordination across system layers.

This paper proposes a cognitive-intelligent decision-making framework to support data-driven planning, scheduling and execution in an RPS environment. The framework aligns digital infrastructure with decision intelligence by integrating knowledge graph-based product-process modeling, blockchain-enabled information services and adaptive scheduling algorithms. It further incorporates sociotechnical system modeling to model and analyze human behavior dynamics on the shop floor. Core research topics include variety management via extended GBOMO, trusted information sharing through a blockchain-IaaS system, manufacturing load balancing using data-driven and game-theoretical approaches, and supervision–compliance synergy modeling with fuzzy game-theoretic approach. These research topics establish a coherent architecture for resilient, reconfigurable, and human-centric manufacturing decision support.