Decision-Support Framework for Evaluating AI-Enabled ESG Strategies in Sustainable Manufacturing Systems

A Decision-Support Framework for Evaluating AI-Enabled ESG Strategies in Sustainable Manufacturing

Environmental, Social, and Governance (ESG) criteria have become essential for businesses aiming to operate responsibly and sustainably. However, evaluating ESG strategies is complex due to multiple criteria and uncertainties in expert judgments. This article introduces a clear method to assess AI-driven ESG strategies using fuzzy multi-criteria decision-making tools, focusing on sustainable manufacturing systems.

Why ESG Matters for Product Development

The United Nations Sustainable Development Goals (SDGs) highlight urgent issues such as climate change, inequality, and environmental degradation. Companies need to align their strategies with these global priorities. ESG frameworks help businesses assess their environmental impact, social responsibility, and governance transparency. For product developers, integrating ESG means creating products and processes that support sustainability goals without sacrificing competitiveness.

How AI Supports ESG Strategies

Artificial Intelligence (AI) transforms how companies approach ESG challenges. AI analyzes large datasets to identify patterns and generate insights, supporting better decision-making. However, ESG evaluation involves uncertainty and conflicting criteria. Fuzzy logic-based decision-making methods handle this ambiguity effectively by incorporating imprecise data and subjective judgments into the analysis.

Key AI-Enabled ESG Strategies for Sustainable Manufacturing

• AI-Powered Predictive Analytics (A1): Uses AI to analyze real-time data, helping anticipate future trends and optimize manufacturing processes.
• Renewable Energy Integration (A2): Shifts energy sources from fossil fuels to renewables like solar, wind, and hydro, reducing emissions and supporting long-term sustainability.
• Smart Waste Management Systems (A3): Employs AI and IoT to improve waste disposal and resource management.
• Blockchain for Transparent Governance (A4): Enhances transparency and accountability in ESG practices through secure, immutable data records.
• AI-Enhanced Workforce and Community Development (A5): Addresses social challenges such as education, healthcare, and employment using AI tools.
• Sustainable Supply Chain Optimization (A6): Improves supply chain efficiency and reduces environmental impact with AI-driven insights.
• Generative AI for Eco-Friendly Innovation (A7): Supports designing sustainable products and optimizing operational resource use.

Applying Fuzzy TOPSIS to Rank ESG Strategies

Traditional decision-making models struggle with the vagueness and subjectivity in ESG evaluations. The Fuzzy Technique for Order Preference by Similarity to Ideal Solution (Fuzzy TOPSIS) combines fuzzy logic with the TOPSIS method to better handle these challenges.

This approach ranks ESG strategies by comparing how close each option is to an ideal solution, while accounting for uncertainty in expert assessments. It provides product developers and decision-makers with a structured and transparent way to prioritize initiatives that bring the most value in sustainability.

Study Findings and Practical Insights

The analysis highlighted renewable energy integration as the top ESG strategy, reflecting its critical role in reducing carbon footprints. AI-powered predictive analytics and sustainable supply chain optimization followed closely, emphasizing data-driven efficiency and operational improvements.

These insights suggest that combining energy transition efforts with intelligent data use can significantly advance sustainability in manufacturing.

Next Steps for Product Development Teams

Incorporate AI-enabled ESG strategies into your product planning and manufacturing processes to boost sustainability credentials and meet growing stakeholder expectations. Consider tools like fuzzy MCDM methods for making informed choices when multiple ESG initiatives compete for attention.

For those interested in deepening their AI and sustainability knowledge, exploring comprehensive AI courses can be a valuable step. Check out Complete AI Training’s latest courses to build skills that support smarter, greener product development.