Artificial Intelligence–Driven PDCA Cycle for Continuous Environmental Management in the Manufacturing Industry
Keywords:
PDCA Cycle, Artificial Intelligence (AI), Environmental Management, Manufacturing IndustryAbstract
The global manufacturing industry is increasingly confronted with significant environmental pressures arising from climate change, inefficient resource utilization, and pollutant emissions that adversely affect ecosystems, human health, and long-term business sustainability. Within the context of international policy frameworks and standards, including the Sustainable Development Goals (SDGs), Environmental, Social, and Governance (ESG) principles, and the ISO 14001 environmental management standard, organizations are required to enhance environmental management systems that are measurable, monitorable, and capable of continuous improvement.
This article is a conceptual paper that proposes an integrated framework combining Artificial Intelligence (AI) with the PDCA (Plan–Do–Check–Act) cycle to support continuous environmental management in the manufacturing industry. The proposed framework explains the role of AI across each stage of the PDCA cycle: environmental data analysis and predictive planning (Plan), process control and real-time monitoring through digital technologies (Do), performance evaluation using data analytics and environmental dashboards (Check), and root cause analysis together with continuous process improvement (Act).
The proposed conceptual framework provides a systematic perspective for enhancing environmental management practices in industrial organizations through the utilization of advanced data analytics and digital technologies. Such integration can contribute to improving resource efficiency, reducing greenhouse gas emissions, and strengthening organizational competitiveness within the broader context of sustainable industrial development and the principles of Industry 5.0.
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