National Pingtung University of Science and Technology / Assistant Professor Yu-Hsin Huang

 Pain Points Solved 

When driving digital transformation and implementing AI, enterprises often face the following pain points:
1.    Difficult AI Integration & Poor Adaptation: Over-reliance on purely technology-driven approaches neglects the dynamic complexity of "human-AI collaboration," leading to rigid systems and resistance from frontline workers.
2.    Unanticipated Risks from Automation: Current AI systems often lack dynamic adaptability. When facing sudden variations or extreme conditions, they can inadvertently trigger complex systemic failures, as described in Normal Accident Theory.
3.    Lack of Resilience Evaluation for Sustainable Operations: Traditional management models struggle to quantify and prevent new types of risks arising from human-AI interactions, making it difficult to meet ESG requirements for social responsibility, worker well-being, and operational safety.
Value Delivered:
This technology eliminates these blind spots. By proactively identifying workflow variations through human factors engineering, it transforms AI from a "disruptor" into an "enabler." It not only ensures the safety and stability of smart manufacturing and high-risk systems but also significantly enhances the enterprise's dynamic resilience against unforeseen disruptions.

 Technology Introduction 

This technology is an "AI Integration and System Resilience Optimization Solution" tailored for complex systems and high-risk industries, such as smart manufacturing and healthcare automation.
Departing from traditional static and linear risk assessments, this technology constructs an advanced dynamic human-AI collaboration analysis model. It systematically quantifies and visualizes the "variability" and "potential vulnerable nodes" within operational workflows, accurately predicting the cascading effects and systemic failures that may arise post-AI implementation.
Consequently, this technology enables enterprises to develop highly fault-tolerant and dynamically adaptable standard operating procedures (SOPs) and collaborative fail-safe mechanisms prior to system deployment. This ensures efficient synergy between AI systems and human operators, while significantly enhancing the overall operational stability and safety against unanticipated disruptions, thereby achieving the goals of human-centric, high-resilience sustainable smart manufacturing.

▲「Framework of Human Factors Driven High-Resilience AI Collaboration Optimization」 : This diagram illustrates the core technical logic. We systematically analyze operational workflows and human factors data (e.g., cognitive workload, operational variability) to precisely identify dynamic risk nodes associated with AI integration. Based on this, we proactively design fault-tolerant collaboration mechanisms and SOPs, enhancing the overall system's dynamic resilience against unforeseen disruptions.

 Application Examples 

1. Resilience Analysis and Evaluation of AI Integration in Healthcare Systems Applied to highly complex and dynamic environments such as hospital nursing operations, this technology evaluates workflow variability prior to the implementation of AI assistance systems. By analyzing the cognitive workload and operational dynamics of frontline medical staff, it ensures that AI systems complement healthcare professionals. Without interfering with their flexible decision-making, it enhances the overall resilience and safety of the healthcare system.
2. Resilience Analysis and Evaluation of AI Integration in Major Chemical Plants Targeting leading large-scale chemical plants in Taiwan, this technology utilizes the Safety-II perspective to analyze near-miss events and daily operations before integrating AI monitoring and assistance systems into critical processes. By decoding the latent flexibility and successful coping strategies of frontline workers in maintaining system safety, it ensures that the AI integration perfectly aligns with and strengthens the protective mechanisms of human workers. This assists enterprises in shifting from passive failure prevention to proactively enhancing human-AI collaborative resilience.

 Related Links 

None

 Patent Name and Number 

None

 Industry-Academia / Tech Transfer Partner 

Industry-Academia Cooperation: Taiwan Nylok Corporation, 

 Honors and Awards  

None

 Technical Contact  

Joan Li, Project Manager

National Pingtung University of Science and Technology
Tel: +886 8-7703202 ext. 6571
Email: joanli@mail.npust.edu.tw