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This study presents an integrated framework that combines Sustainable Balanced Scorecards (SBSC), Data Envelopment Analysis (DEA), Rough Set Theory (RST), and Fuzzy Support Vector Machine (FSVM) to assess the sustainability performance within the semiconductor industry. Guided by the foundational principles of the Balanced Scorecard (BSC), two perspectives are adapted from financial and customer perspectives to sustainability and stakeholders, addressing inadequately explored domains such as environmental protection and social issues. Departing from a one-size-fits-all DEA model, the utilization of various combination strategies aims to provide a more nuanced understanding of why corporations attain specific efficiency levels by considering diverse inputs and outputs. Moreover, to enhance the DEA model’s capabilities with forecasting, FSVM is introduced as a complementary tool. This performance evaluation model, enriched with forecasting ability, facilitates a shift in the role of decision-makers from retrospective monitoring to proactive future planning. Managers can leverage this integrated architecture as a decision roadmap to strategically allocate limited resources, mitigate avoidable waste and losses, and work towards achieving sustainable development objectives.

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A Hybrid Artificial Intelligence-Based Model for Corporate Sustainability Evaluation and Forecasting

  • 会议论文

  • 发表日期：

    2025年

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  • Te-Min Chang,
  • Ching-Chiang Yeh,
  • Guo-Hsin Hu,
  • Sin-Jin Lin,
  • Ming-Fu Hsu,
  • Hsin-Hui Hung

摘要

This study presents an integrated framework that combines Sustainable Balanced Scorecards (SBSC), Data Envelopment Analysis (DEA), Rough Set Theory (RST), and Fuzzy Support Vector Machine (FSVM) to assess the sustainability performance within the semiconductor industry. Guided by the foundational principles of the Balanced Scorecard (BSC), two perspectives are adapted from financial and customer perspectives to sustainability and stakeholders, addressing inadequately explored domains such as environmental protection and social issues. Departing from a one-size-fits-all DEA model, the utilization of various combination strategies aims to provide a more nuanced understanding of why corporations attain specific efficiency levels by considering diverse inputs and outputs. Moreover, to enhance the DEA model’s capabilities with forecasting, FSVM is introduced as a complementary tool. This performance evaluation model, enriched with forecasting ability, facilitates a shift in the role of decision-makers from retrospective monitoring to proactive future planning. Managers can leverage this integrated architecture as a decision roadmap to strategically allocate limited resources, mitigate avoidable waste and losses, and work towards achieving sustainable development objectives.