Enhancing big data for greentelligence across the production value chain

Abstract

The big data concept has been explosive, revealing, and transformative across manufacturing industries as it provides deeper insights into manufacturing operations for decision-making. However, big green data (BGA), a dedicated subset of big data, is not adequately structured for comprehensive sustainability analysis, particularly in smart factories. With our proposed green data balance (GDB), there will be accountability for each input and output composition in a production unit within the production value chain (PVC). Data will be exhaustively and accurately collected in each workshop to help uncover unknown issues in a production value chain while facilitating the development of sustainability metrics or index systems. Additionally, a structured big green data system will fuel “greentelligence”, using intelligent systems and technologies to speed up digitalization toward sustainable manufacturing by measuring, tracking, and minimizing adverse environmental impacts. Lastly, with the support of the cognitive intelligence data analytic system (CIDAS), real-time and near real-time comprehensive sustainability analytics can be performed, leading to Self-X metacognitive adjustments and corrective actions.