The term “Industry 4.0” refers to the integration of digital technologies into industrial environments. New technologies have had a profound impact on manufacturing, as intelligent systems generate exponentially increasing amounts of data and require a higher level of workforce expertise.

The objective of smart manufacturing is to increase sustainability, safety, and competitiveness through coordinated, efficient processes that address complex challenges with agility. While AI clearly plays a vital role in operational efficiency, its broader contribution across the manufacturing life cycle remains underexplored.

In an article published in the IEEE Transactions on Engineering Management, researchers examine the potential of artificial intelligence (AI) to facilitate sustainable development in global smart manufacturing, addressing a significant literature gap. 

The study explores how AI can be harnessed to achieve both environmental and operational objectives in smart manufacturing. By asking, “How can AI technologies be leveraged to advance sustainability in smart manufacturing across key operational domains?”, the researchers investigated AI's potential to optimize energy use, improve predictive maintenance, manage emissions, and enhance supply chain transparency. 

Main Findings

The authors first outline the methodology employed to analyze qualitative data gathered from interviews with global experts in smart manufacturing, AI, and sustainability, before moving on to the main findings and implications. The researchers discussed four main areas affected by AI:

  • AI-Driven Energy Optimization in Smart Manufacturing: Before adopting AI, small- and medium-sized enterprises (SMEs) typically managed energy consumption using manual record-keeping and sporadic audits. These traditional methods led to delays, inaccuracies, and significant inefficiencies in identifying areas for improvement. AI enables real-time energy optimization, emissions tracking, and adaptive resource use, while also improving supply chain sustainability and helping firms meet international standards. 
  • AI-Enhanced Maintenance and Resource Optimization: Manufacturers traditionally used reactive “run-to-failure” and routine, time-based maintenance, which led to inefficiencies, downtime, wasted resources, and higher energy consumption. AI-driven maintenance strategies have transformed these practices by enabling real-time, condition-based monitoring. This allows manufacturers to anticipate equipment failures, extend machinery lifespan, and reduce the need for replacements—resulting in significant energy savings and sustainability benefits.
  • AI-Enabled Sustainable Supply Chains: AI significantly improves demand forecasting and inventory management by enabling manufacturers to align production with actual market needs. Traditionally, manufacturers relied on basic historical data and static inventory strategies, which often led to overproduction, excessive inventory, and energy-intensive storage and transportation. With AI, companies can accurately predict customer demand, manufacture only what is needed, and dynamically adjust inventory levels in real time.
  • AI for Carbon Footprint and Emissions Management: Real-time emissions data from AI systems increases transparency and accountability, providing stakeholders with instant, verifiable updates on sustainability progress—a key competitive advantage as market demand for environmental responsibility grows. Beyond monitoring, AI proactively supports emissions management by simulating process changes and evaluating their impact before implementation. This positions AI as a strategic tool for sustainability governance and corporate social responsibility, extending its value beyond compliance to shaping organizational behavior and reputation

Data structure aggregating themes for the use of AI in sustainability in manufacturing.

 

Discussion

The researchers discuss the challenges and requirements for fully realizing AI’s potential in sustainable manufacturing, deepening the understanding of how AI supports sustainability in smart manufacturing. Practical AI-driven sustainability requires not only technological solutions but also systemic and institutional readiness. The authors suggest a holistic, ecosystem-based approach to integrating AI in manufacturing.

According to the researchers, the findings enrich the theoretical understanding by confirming and extending our current knowledge of how emerging AI technologies can be harnessed for sustainable outcomes, and they offer practical guidance by identifying where engineering managers and policymakers should focus to overcome adoption barriers. 

Multilevel emerging conceptual model of AI-enabled sustainability transformation in smart manufacturing.

 

Future research should address study limitations by analyzing plant-level longitudinal data and conducting a cross-country survey to test links among AI capabilities, operating practices, and sustainability outcomes. Moreover, future research should examine workforce impacts and develop common frameworks to assess AI-driven sustainability initiatives. 

The authors believe that this study has significant managerial relevance, providing engineering managers and policymakers with actionable insights on leveraging artificial intelligence (AI) to advance sustainability in manufacturing. This article demonstrates how AI can enable energy optimization, enhance predictive maintenance, improve supply chain transparency, and support carbon footprint monitoring—making it a key component of future engineering management strategies.

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