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Redefining Semiconductor Sustainability: Profet AI and Yesiang Unveil First-Ever CaaS with Domain Twin™

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Redefining Semiconductor Sustainability: Profet AI and Yesiang Unveil First-Ever CaaS with Domain Twin™

AI Data Governance Meets Regenerative Manufacturing: Preventing Millions in Yield Loss and Cutting 40% in Operating Costs

The global semiconductor industry is at a critical turning point. From national security strategies and ESG goals to the rapid adoption of AI, the industry faces unprecedented challenges and opportunities. Balancing capacity, resilience, and sustainability has become the decisive factor for global leaders.

At the 30th anniversary of SEMICON Taiwan, Yesiang—a leader in chemical filtration—announced its dual strategy of “Regenerative Manufacturing + AI Data Governance.” Together with AI startup Profet AI, Yesiang introduced the world’s first Clean Air as a Service (CaaS) subscription model, a revolutionary approach to advanced manufacturing yield assurance and sustainable supply chains.

AI-Empowered Knowledge Transfer Drives the CaaS Innovation Model

Yesiang Chairman James Chuang noted:
“Yesiang holds more than 80% market share in filter manufacturing and has accumulated deep technical expertise. But we have long been asking ourselves: how can we transform from being a ‘consumables supplier’ into a ‘smart service partner’? Our collaboration with Profet AI is a critical step in realizing that vision.”

At the core of the newly launched CaaS subscription service is Profet AI’s Domain Twin™ platform, which systematizes and digitizes cross-departmental tacit know-how into a repeatable, intelligent knowledge-transfer framework. This enables overseas fabs to replicate production capacity quickly—even without senior engineers onsite—while improving overall equipment effectiveness (OEE).

The solution fully digitizes traditional filter management, covering lifecycle prediction, contamination risk monitoring, demand forecasting, and automated scheduling—all AI-driven. With 30-day demand forecasts and automated production planning, customers can operate with zero inventory, cutting total filter usage and ownership costs by 40%. At the same time, the system automatically generates key ESG metrics, enhancing transparency and compliance in sustainability reporting.

AI in Action: Preventing Millions in Losses and Creating Operational Value

The benefits of the CaaS model have already been proven in real-world fabs. In one application, the AI model predicted AMC (Airborne Molecular Contaminant) risks three days in advance and automatically issued replacement recommendations, safeguarding 99.9% yield integrity.

In a real case, this proactive approach prevented a potential multi-million-dollar yield loss. The system also recommends optimal filter specifications and replacement cycles based on process conditions, cutting testing and planning time by 80% and significantly reducing mismatch risks.

Profet AI CEO Jerry Huang said:
“In the era of AI and sustainability, the industry is not just chasing efficiency—it is pursuing the seamless integration of yield, resilience, and ESG. Our Domain Twin™ platform was built to deeply combine domain know-how with AI technology. This collaboration with Yesiang is a benchmark case showing how AI can deliver tangible business value for sustainable supply chains.”

Looking Ahead: Building “Never-Retiring” Knowledge Assets to Accelerate Transformation

Yesiang emphasized that the true key to AI adoption lies in company culture and team mindset. Through its partnership with Profet AI, the company is transitioning from a passive supplier to an active consultant that creates value for customers.

Looking ahead, both companies will continue to advance their dual-axis strategy of “AI Data Governance” and “Regenerative Manufacturing,” while expanding the CaaS model to major semiconductor hubs in the United States, Japan, and Europe.

Profet AI will also further develop Domain Twin™ applications, helping manufacturers digitize and systematize the tacit know-how of senior engineers—turning it into “never-retiring knowledge assets.” In doing so, the company aims to accelerate global manufacturing toward a new era of data-driven, zero-carbon intelligent production.

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Digital Twin Meets Domain Twin: A New Era of Intelligent Manufacturing

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Digital Twin Meets Domain Twin: A New Era of Intelligent Manufacturing

As the manufacturing industry rapidly advances into the era of Industry 4.0, companies are adopting AI technologies at an unprecedented pace. According to Data Bridge research, AI in manufacturing is projected to grow at a CAGR of 17.20% between 2022 and 2029, with the market expected to surpass $5.3 billion by 2029.

Among the leading technologies enabling this transformation is the Digital Twin — a powerful solution that simulates physical equipment and processes using real-time data and predictive models. It supports use cases such as predictive maintenance, performance optimization, and real-time monitoring.

However, Digital Twins alone often fall short of delivering true operational intelligence, because they simulate the “what” of machine behavior but lack the ability to understand the “why” behind system performance. This is where Domain Twins come into play.

What Are Digital Twins?

A Digital Twin is a virtual representation of a physical asset, system, or process that mirrors real-time behavior using sensor data and modeling. They provide clear benefits, including:

  • Real-time monitoring of equipment
  • Predictive maintenance alerts
  • Process optimization through simulations

But despite these strengths, Digital Twins face common limitations:

  • They lack human expert judgment and reasoning
  • Over-reliance on historical data reduces adaptability to new or unexpected situations
  • High retraining costs if production conditions change

For example, a Digital Twin may flag a maintenance issue based on sensor thresholds, but it may not recognize a subtle material inconsistency—something a seasoned engineer would immediately notice.

Introducing Domain Twins: Expert Knowledge Made Scalable

To address these gaps, Profet AI introduces the concept of the Domain Twin: an AI-powered solution that digitizes expert knowledge, turning human insights into machine-interpretable rules and models.

While Digital Twins simulate machines and processes, Domain Twins simulate expert reasoning and decision-making. They work together to create a comprehensive, intelligence-driven manufacturing system.

Digital Twin vs. Domain Twin: Better Together

The reality of modern manufacturing is that human experience still bridges the gap between raw machine data and operational decisions. The relationship between Digital Twins and Domain Twins can be seen as a three-layer system:

  • Top Layer (Enterprise Applications & Digital Twin): Simulation and data analytics tools like ERP, MES, and BI systems.
  • Middle Layer (Human Expertise & Domain Twin): Engineers interpret data, applying contextual insights.
  • Bottom Layer (Equipment & Automation): Machines generate real-time data and execute production.

This synergy shows how Domain Twins complement rather than replace Digital Twins. They empower AI to not only detect anomalies but also understand the reasons behind them, and suggest explainable, actionable insights.

4 Key Manufacturing Challenges Solved by Domain Twins

1. Data Silos and Integration Barriers

Most Digital Twins can’t easily integrate with existing ERP or MES systems, creating fragmented data environments.

Domain Twin Advantage:
Standardizes and modularizes expert knowledge, enabling seamless replication across plants and breaking down data silos.

2. Tacit Knowledge Loss

Years of engineering expertise—material behaviors, process tweaks, root cause intuition—are often undocumented and not machine-readable.

Domain Twin Advantage:
Captures this hidden expertise and embeds it into models, ensuring knowledge is preserved and transferable.

3. Data Overload Without Insight

Sensors generate endless data, but without context, it’s hard to act on it effectively.

Domain Twin Advantage:
Adds expert reasoning to AI models, transforming raw data into meaningful, executable recommendations.

4. Low Trust in AI Decisions

When AI outputs are black boxes, plant managers and engineers hesitate to rely on them.

Domain Twin Advantage:
Boosts explainability through embedded expert logic, increasing trust and making AI adoption smoother and more practical.

Real-World Impact of Domain Twin Technology

Developed by Profet AI, the Domain Twin is already proving its value in industries such as:

  • Semiconductors
  • Electronics manufacturing
  • Chemicals
  • Precision manufacturing

     

Benefits achieved:

  • Shortened AI deployment time
  • Improved decision accuracy
  • Increased operational resilience

By integrating Domain Twins into manufacturing systems, these companies have enhanced their ability to adapt to disruptions, scale operations globally, and capture value from their AI investments faster.

Looking Ahead: Smarter Manufacturing Through Synergy

As Industry 4.0 matures, AI’s value in manufacturing will be defined by how well it integrates data with human expertise. Digital Twins provide the foundation. Domain Twins complete the picture.

Together, they unlock the next evolution in intelligent manufacturing—moving from passive monitoring to active, explainable, and scalable decision-making.

Final Thoughts

Profet AI’s mission is to bridge the gap between data and intelligence. By enabling Domain Twins, we’re helping manufacturers future-proof their operations with AI that truly works — not just in theory, but on the shop floor.

Interested in learning how Domain Twins can elevate your factory operations?
Contact Profet AI to explore the next milestone in AI-powered smart manufacturing.

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Crossover Talks Bangkok: How Real-World AI Projects Deliver Measurable ROI for International Businesses

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Crossover Talks Bangkok: How Real-World AI Projects Deliver Measurable ROI for International Businesses

In recent years, artificial intelligence (AI) has dominated headlines, but the real question for most business leaders remains: “Can AI truly improve my bottom line—whether through revenue growth or cost reduction?”

Take Thailand’s manufacturing sector as an example. Recent surveys show that only 17% of companies have deployed AI projects that directly improve performance. The majority are still in the planning stage, while some have yet to begin. This situation is strikingly similar to Taiwan—where most executives agree that AI is important, but face the same challenges: identifying the right use cases, proving ROI, and lacking the internal talent to lead adoption.

The Winning Formula: People × Business × Technology

At a recent AI applications seminar in Bangkok, Profet AI and HexaTech Solutions emphasized a clear message: successful AI adoption is not just about technology—it’s about combining domain expertise, organizational readiness, and the right AI tools.

Profet AI is Asia’s leading no-code AutoML platform for manufacturers, with 300+ enterprise clients across 20 industries, including over half of Nvidia’s manufacturing partners in Asia. Its greatest advantage is enabling non-IT professionals—such as production engineers—to build AI models in just 3 hours, often achieving measurable ROI within 90 days.

In our panel discussion, two international companies shared how AI adoption has proven to bring real results to their organization:

Case Study 1: Minth Group — Reducing High Defect Rates with AI

Minth Group, a global automotive parts supplier, faced a serious quality challenge. Defect rates in its automotive trim bending process reached 40–47%, slowing down production and requiring massive manpower for inspection and rework.

By adopting Profet AI’s no-code platform, Minth trained over 800 employees to build models themselves. Importantly, these were not data scientists, but frontline process engineers. Using AI, they analyzed thousands of variables—such as material strength, equipment alignment, and environmental factors—to pinpoint the root causes behind the high defect rate.

The results were impressive: in the first phase alone, Minth saved RMB 5.9M (USD 820K) annually, with potential to scale to RMB 14.74M across more operations. Beyond financial gains, the company also cultivated 38 internal AI champions, who have since launched over ten new projects—laying the foundation for long-term, replicable improvements.

Case Study 2: Chicony Power — Smarter Energy Optimization

Chicony Power, one of Taiwan’s top three power supply manufacturers, approached AI from a different angle: energy efficiency. In its factories, large chiller systems consumed enormous amounts of electricity, and scheduling decisions relied solely on engineers’ experience. Managing multiple chillers, pumps, and cooling towers manually was complex, error-prone, and often inefficient.

With Profet AI, Chicony built two interconnected models:

  1. Load forecasting—predicting cooling demand by combining weather data with production schedules
  2. Energy optimization—determining the most efficient equipment combinations and providing real-time recommendations

This system delivered 3–15% energy savings under different operating conditions. At Chicony’s scale, even small percentage gains translate into substantial cost reductions. In addition, the AI system provides early equipment warnings, preventing costly downtime, and reduces risks caused by human error—making energy management smarter and more resilient.

From Quick Wins to Enterprise Transformation

HexaTech CEO Bancha (Beam) Dhammarungruang highlighted that success begins with quick-win projects that demonstrate measurable impact, which can then be scaled across the enterprise. HexaTech’s six-step approach includes: mapping processes, prioritizing opportunities, identifying key parameters, building models, testing, and integrating with existing ERP/CRM/MES systems.

“Many think AI success depends on algorithms alone. In reality, it’s about the right combination of business priorities, talent readiness, and technology fit. Only then can AI be turned into real cash flow—from cost savings to new revenue opportunities.”
— Bancha

Lessons FromTaiwan’s Industry

Thailand’s current challenges mirror what Taiwan faced a decade ago: labor shortages, an aging workforce, and a skills gap among younger employees. AI is not only a productivity tool—it’s also a way to capture and scale expert knowledge for the next generation.

As the case studies show, the most successful AI projects share common traits:

  • Targeting clear business pain points
  • Combining domain knowledge with AI expertise
  • Embedding into daily workflows, not just running pilots
  • Developing workforce capabilities to sustain adoption

For Taiwan, moving from “trying AI” to enterprise-wide transformation requires leaders to focus less on the hype and more on embedding AI into core business strategies. Done right, AI won’t just be a buzzword—it will deliver tangible, measurable, and lasting value across manufacturing, F&B, retail, and beyond.

Want to learn how Profet AI helps manufacturers achieve fast, measurable results? Fill out the form below.

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Crossover Talks Jiaxing: Minth Group AI Smart Manufacturing in Action

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Crossover Talks Jiaxing: Minth Group AI Smart Manufacturing in Action

AI in practice—seeing is believing. Step onto the factory and witness the change.

As artificial intelligence becomes a key driver of industrial upgrading, manufacturers are rolling out digital transformation strategies. Yet the real challenge is not whether companies recognize AI’s importance, but how to make AI truly land and deliver measurable results on the shop floor.

Jointly organized by Profet AI, Minth Group, and the Ningbo Intelligent Manufacturing Industry Association, Crossover Talks Jiaxing: Minth Group AI Smart Manufacturing in Action focused on real-world implementation, bringing participants directly into the factory demonstration base to see how AI is reshaping manufacturing.

Group photo with organizers and speakers

Upgrading Core Knowledge, Turning It into Productivity

Profet AI Co-founder & CEO Jerry pointed out that manufacturing today resembles the “three suns” in the sci-fi novel The Three-Body Problem, simultaneously facing three major uncertainties: shifting global tariffs, geopolitical conflicts, and the rapid advance of AI. In this environment, resilience is the only way forward, and applying AI on the ground is the key breakthrough.

Jerry emphasized that the heart of manufacturing lies in the practical experience of frontline staff and domain experts—their skills in machine tuning, process design, and tacit knowledge represent core competitiveness. Profet AI’s mission is to transform this expertise into scalable data models and assets through Domain Twin™, enabling companies to replicate consistent processes and quality across different sites and factories—converting experience into productivity.

Profet AI CEO Jerry Huang explaining how Domain Twin helps companies optimize processes and build resilience

From Knowledge Retention to Standardization and Assetization

Profet AI Pre-sales Director Eugene added that in today’s “high-uncertainty” era, most manufacturers face three pressing challenges: insufficient process capability, overly long product R&D cycles, and the loss of critical know-how when veteran employees leave.

AI’s value should not remain in strategy papers or R&D labs; it must be applied on the ground. By converting tacit experience into explicit knowledge, trial-and-error can evolve into traceable, standardized processes. With Domain Twin™, enterprises can embed critical parameters, process logic, and best practices directly into models, enabling even new employees to ramp up quickly.

Eugene stressed that AI adoption doesn’t have to start with complex algorithms. Instead, it should begin with clear pain points, building interpretable and reusable solutions—a first step in moving from experience-driven to data-driven.

Profet AI’s Eugene sharing how to achieve knowledge retention and smart manufacturing transformation under uncertainty

From Workshops to Results: 35% Yield Improvement & Cultural Shift

Chen Yikai, Deputy General Manager of Excellence Wire Ind. Co., Ltd., shared their hands-on AI journey. Excellence Wire, a specialist in crystal heads and network jumpers, had already invested years in lean and digital initiatives. But after visiting Minth’s Future Factory in 2024, they witnessed for the first time how AI could concretely optimize processes and boost yield—prompting them to launch adoption projects.

Unlike traditional classroom training, they implemented a workshop model, involving 13 departments across the company to integrate AI into daily workflows. Despite challenges like difficult topic selection, incomplete data, and unclear outcomes, with the consulting team’s guidance they broke through barriers.

The result: In the “foil stripping” process of their C6A FTP product line, yield improved from 50% to 85%, saving about 100 hours of labor per month. More importantly, company culture shifted from experience-based decisions to data-driven decisions, with AI becoming part of the organizational DNA.

Profet AI Sales Director Coco Liu in dialogue with Chen Yikai on how AI drives both technical and cultural transformation

AI+ Driving Deep Transformation: Minth’s Component Process AI Applications

Even Zhang, Senior Digital Transformation Project Manager at Minth Group, shared the group’s transformation experience. Through systematic AI training, both management and frontline staff developed a shared language, creating a top-down and bottom-up transformation dynamic. In 2024, Minth gathered 64 proposals and successfully rolled out 10 AI projects, including one focused on yield improvement in “bending arc dimensions.”

The cross-departmental project, completed over three months, ran fully on the Profet AI AutoML platform—from data collection and model building to shop-floor validation—ultimately identifying key process factors and applying them in production, significantly enhancing stability.

Minth is also advancing short-, medium-, and long-term strategies to refine models and expand adoption across its 70+ factories worldwide. Even highlighted that the greatest challenge is building user trust in AI. Minth addressed this by introducing phased training, sharing success stories, and cultivating in-house AI “seed instructors” to empower global sites to independently drive projects, paving the way for global smart manufacturing upgrades.

Minth Group’s Even Zhang sharing real-world AI applications and global rollout strategies

From the Factory Floor, Glimpsing the Future

This industry exchange demonstrated clearly: AI is not a distant vision—it is a tool that can be transformed into real productivity on the factory floor.

The collaborations between Profet AI, Minth Group, and Excellence Wire show that AI adoption can start with concrete pain points, crystallize expertise into models, and turn pilot successes into organizational capabilities.

Amid global competition and shifting markets, manufacturing resilience stems from ongoing experimentation and continuous innovation. To make AI real, you need to step into the factory and witness it firsthand. That is the truest takeaway from Jiaxing—and a tangible roadmap toward the future of smart manufacturing.

If you would like to know more about Profet AI’s Domain Twin, please fill in the form below to request additional information or schedule a demo.

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Key AI Technologies in Manufacturing: A Comparative Analysis of Digital Twin vs. Domain Twin

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Key AI Technologies in Manufacturing: A Comparative Analysis of Digital Twin vs. Domain Twin

In recent years, the rise of Industry 4.0, smart manufacturing, AI applications, and digital transformation has made the concept of the “Digital Twin” increasingly popular in the manufacturing sector. However, as companies begin integrating AI, they encounter several challenges, including insufficient data, talent shortages, and implementation bottlenecks. In response, a new concept has started to gain attention: the “Domain Twin.”

Although the names of these two concepts are similar, their meanings are entirely different. Digital Twin addresses “visible physical problems,” while Domain Twin complements “invisible experiential knowledge.” Only by complementing each other’s strengths and weaknesses can manufacturing move from data-driven to intelligence-driven. This article explores the definitions, differences, and applications of Digital Twin and Domain Twin to help companies make informed decisions in their smart transformation strategies.

What is Digital Twin? A Virtual Replica of Equipment Data

A Digital Twin is a virtual replica of a physical device, system, or process. By connecting sensors and real-time data, it can simulate the state, behavior, and performance of its physical counterpart, helping businesses with monitoring, predictive maintenance, and process optimization.

Core features of Digital Twin include:

  • Creating a data-driven model synchronized with physical assets
  • Real-time simulation of the operation of equipment or systems
  • Commonly used in predictive maintenance, operational status monitoring, and energy efficiency analysis
  • Focused on simulating and monitoring specific machines, processes, or physical equipment

According to the Ministry of Economic Affairs, a globally renowned automobile brand implemented Digital Twin technology and, through integration across various stages from product development to mass production, was able to simulate quality, resource allocation, and process stability in advance, reducing time and cost risks. They also integrated AR for staff training, significantly improving assembly efficiency, accuracy, and on-site safety.

Thus, Digital Twin uses virtual replication and data simulation to help companies better understand equipment conditions, predict risks, and improve overall production and training efficiency. However, while Digital Twin can fully simulate equipment and processes, it cannot capture the experience, judgment logic, and tacit knowledge of seasoned workers, which is where Domain Twin comes into play.

What is Domain Twin? The Key Technology for AI to Mimic Expert Decision-Making

Domain Twin is a different concept that addresses the “human intelligence layer” missing in Digital Twin. It models professional knowledge and industry logic comprehensively, allowing AI to “learn” and reuse human experience. Using a No-Code approach, it can be rapidly applied in different but similar manufacturing scenarios.

In manufacturing, the experience and skills of senior workers are often the result of decades of accumulated wisdom. However, these valuable insights are frequently lost due to retirements or personnel changes. Profet AI’s Domain Twin is designed to solve this issue by digitizing and upgrading the expertise of senior workers in machine calibration, formula optimization, and problem-solving, transforming it into a long-lasting, valuable asset for the business.

Unlike typical AI models, Domain Twin integrates with AutoML (Automated Machine Learning) and AILM (AI Lifecycle Management) platforms to tightly link departments and processes such as R&D, production, and after-sales. This ensures fast end-to-end integration. More importantly, Domain Twin enables key data related to R&D, production, dispatch, testing, etc., to remain internal, safeguarding the company’s core technologies.

Core features of Domain Twin include:

  • Digitizing the knowledge and experience of senior workers into reusable AI model logic
  • No code required, allowing users to directly operate model templates for predictive analysis
  • Designed to address common repetitive issues in manufacturing, such as quality forecasting and defect classification
  • Helping businesses lower AI adoption thresholds, improving modeling efficiency and standardization

For example, after implementing Profet AI’s Domain Twin technology in their PCB production line, a company successfully simulated process parameters like gold and nickel plating in real time. They used AI models to predict the probability of defects and recommend optimal formulas, reducing trial production costs and error rates.
Additionally, through the integration of virtual and real simulations and built-in knowledge modules, they reduced the learning curve for new employees by 40% and accelerated implementation by 50%, creating a more flexible smart manufacturing process.

Comparing Digital Twin and Domain Twin

If Digital Twin is the “shadow” of the factory, Domain Twin is the “brain” of the engineers, because it understands logic, processes, and judgment. It can teach AI to mimic these experiences. Therefore, the focus of Domain Twin lies in virtually replicating industry knowledge and logic, enabling AI to learn and apply this knowledge quickly in various scenarios.

Profet AI’s Vision: Empowering Businesses with AI-Driven Smart Decision-Making

In summary, both Digital Twin and Domain Twin have their own strengths: the former focuses on the virtual simulation of equipment and processes, while the latter infuses human experience and professional judgment. The emergence of Domain Twin fills the gaps left by Digital Twin, making it an essential part of the manufacturing industry’s journey toward smart transformation. Only by complementing each other can these two technologies help the industry overcome transformation bottlenecks and achieve continuous optimization and growth.

At Profet AI, we believe that AI should not be the privilege of a select few experts, but a tool that every business can leverage. Through our Domain Twin solution, companies can quickly transform internal knowledge into repeatable and optimizable smart decision models, truly realizing Knowledge as a Service.

If you would like to know more about Profet AI’s Domain Twin, please fill in the form below to request additional information or schedule a demo.

 

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Crossover Talks Kaohsiung：Navigating Global Disruption with Domain Twin

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Crossover Talks Kaohsiung：Navigating Global Disruption with Domain Twin

Profet AI joined forces with Chunyao Digital to co-host our Crossover Talks in Kaohsiung. Themed “Navigating Global Disruption with Domain Twin”, the event explored structural challenges facing enterprises and proposed strategic AI-driven solutions amid global industrial shifts, where manufacturing is grappling with tariff uncertainties, geopolitical risks, talent mobility, and supply chain restructuring.

We invited manufacturing industry leaders, including former Innolux CIO Dr. Howard Hsieh, former Yageo CEO David Huang, ChipMOS Technology consultant Michael Wang, and Chunyao Digital General Manager Jeff Chi, who shared insights on overseas plant setup, technology transfer, organizational collaboration, and knowledge inheritance, highlighting how AI and the Domain Twin concept can help navigate disruptions and build sustainable advantages in smart manufacturing.

Group Photo with Event Attendees

From Expertise to Scalable Process: A Practical Path for AI in Manufacturing

In his opening remarks, Profet AI CEO and Co-Founder Jerry Huang noted that while the potential of AI is widely recognized, few truly know how to implement it. With rising tariffs and volatile exchange rates adding pressure, manufacturers are seeking breakthroughs through AI. Yet gaps in understanding, from leadership to frontline staff, often leave projects stalled at the slogan stage. James, Executive Assistant to the CEO, added that global trade shifts and “Trump tariffs” are prompting companies to consider relocating production to the U.S. or ASEAN, but data security risks, talent shortages, and the need for rapid operational ramp-up remain major hurdles.

Many manufacturers have advanced in digitalization, ERP/MES adoption, and automation, but James emphasized that the human factor is still critical. As experienced workers become scarce due to demographic and labor shifts, AI is emerging as a key enabler of transformation. Profet AI’s five modules: the Resilience Management Framework, AutoML Platform, AILM Platform, AI Studio, and AI Thinking Workshop can help companies turn strategy into execution, accelerate knowledge transfer, and scale AI applications to optimize the entire process from R&D to production.

Former Innolux CIO Dr. Howard Hsieh shared his experiences in digital transformation.

Innolux 4.0: The Three Pillars of Industrial Digital Transformation

Drawing on his years of frontline manufacturing experience, former Innolux CIO Dr Howard Hsieh shared insights from leading the company’s “Innolux 4.0” initiative. He emphasized that true digital transformation goes beyond technology adoption and requires reshaping both management capability and talent capability, all starting with determination. He noted that a culture of continuous improvement is the ultimate goal. For smart manufacturing, Howard outlined three pillars: Culture (breaking silos and fostering connections), Methodology (combining shop floor observation with data), and Technology. Research from the Artificial Intelligence Foundation shows that over 70% of Taiwan’s manufacturers are stuck at the third of four AI adoption stages, able to run projects but unable to scale them company-wide.

 

Howard believes companies do not need to wait until every condition is met before starting large-scale transformation. Instead, they should begin with an AI diagnosis. “Using AI for diagnosis is the starting point of continuous improvement,” he said. The strategy of “starting small, optimizing step by step, and then expanding” is, in his view, the key to unlocking AI’s true value.

Former Yageo CEO David Huang urges using resilience and AI to seize opportunities.

Strategic Outlook and Time Management: Keys to Thriving Beyond Technology

David Huang, former CEO of Yageo and now Founder of Jensen Capital, reflected on past financial crises, highlighting the impact of exchange rate fluctuations on profitability and the importance of turning crises into opportunities. He recommended reserving one-third of the budget for unpredictable risks such as tariff changes or currency swings to strengthen financial resilience.

He noted that Taiwan’s manufacturing sector should leverage its strong industrial base to move AI from Preventive Maintenance to Prediction, integrating it with domain expertise. The real gap, he said, is the shortage of “Domain Agents” who can develop and transfer specialized knowledge. Facing global uncertainty, companies should anticipate multiple scenarios and plan ahead for talent and facility redeployment, ensuring readiness during supply chain shifts.

Michael, former Innolux head of automation, shared challenges in overseas factory setup.

The Hidden Challenges of Overseas Factory Setup: Replicating Operations and Talent

Michael, now a consultant at ChipMOS Technology, shared the challenges he faced while leading overseas factory setups as Chief Plant Manager for Greater China at Innolux. Recalling his first relocation project over 30 years ago in China, he described hurdles such as equipment power differences, maintenance issues, and language and cultural gaps, which made SOPs hard to enforce and required hands-on training for local staff. As labor costs rose, he successfully drove automation, reducing the workforce from 6,000 to just 200 employees.

 

He emphasized that the push for AI and automation often encounters internal resistance, including doubts over ROI, difficulty in quantifying benefits, and reluctance to change. Michael believes that “the key to driving reform is the determination that as long as it doesn’t kill me, I will keep going.” He urged companies to focus on long-term operational efficiency and knowledge accumulation, as the value gained over time far outweighs short-term returns.

Jeff, GM of Chunyao Digital, urged early adoption of domain-driven AI to boost supply chains.

Data-Driven Supply Chain Upgrades: AI as the New Standard for Enterprises

At the forum, Jeff Chi, General Manager of Chunyao Digital, noted that enterprise management is a continuous process of transformation, requiring stronger data integration and collaboration to enhance supply chain resilience and adaptability. “AI accuracy can already reach 95%, but for multi-agent systems, enterprises demand even higher precision. Reliable AI must combine industry domain knowledge with reproducibility and controllability,” he said.

In the digital economy era, AI is already as important as, if not more critical than, traditional MES systems. With AI technology advancing rapidly, he strongly advised companies to plan their AI adoption timelines as early as possible, treating data and knowledge management as core assets. This approach not only boosts efficiency but also protects valuable expertise from being lost due to employee turnover, helping secure long-term competitiveness.

Learn how AI helps manufacturers navigate global disruption. Fill out the form to get event highlights, case studies, and transformation strategies from industry leaders.

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From U.S. Tariffs to Resilience: Scaling Smart Manufacturing with Domain Twin

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From U.S. Tariffs to Resilience: Scaling Smart Manufacturing with Domain Twin

Insights from Profet AI’s Frontline Experience on How Manufacturers Can Navigate Uncertainty

The United States recently implemented reciprocal tariff adjustments under Section 301 of the Trade Act, imposing additional tariffs of up to 20% on a range of Taiwanese exports. These include critical electronics manufacturing components such as chips, IC packaging materials, and PCB parts, significantly increasing cost pressures on Taiwan’s high-tech industries in the U.S. market. In particular, semiconductor products face tariffs as high as 100% unless they are manufactured at facilities located in the United States, prompting serious concern within the industry about the potential impact.

Through extensive conversations with semiconductor and electronics manufacturing clients, Profet AI has observed a growing consensus:
Even with production lines currently running at full capacity, manufacturers recognize the urgency of developing replicable, transferable process capabilities to address rising costs, shifting orders, and global customer demands—ultimately strengthening operational resilience.

The Semiconductor Industry’s Current Challenges: The Impact of Non-Exemption

Taiwan Still Excluded from Exemptions – Cost Pressures Escalate

Under the updated U.S. tariff policy, many high-tech products exported from Taiwan—including chips, materials, and key electronic components—now face a 20% duty.
While several Asian countries have been able to negotiate lower tariff rates, Taiwan remains subject to 20% tariffs, reducing the price competitiveness of domestic manufacturers in the U.S. market.

Rising Risk of Order Shifts and Diversified Supply Chain Requirements

To reduce overall supply chain costs and risks, many U.S. brand customers are asking suppliers to relocate their production to the U.S. to deal with the cost that may arise with the new tariff rates —intensifying pressure on Taiwanese manufacturers to diversify their global footprint.

Knowledge Transfer Remains a Bottleneck

Many high-tech manufacturing processes still rely heavily on the tacit knowledge and on-site judgment of experienced personnel.
Even with overseas expansion plans in place, manufacturers often struggle with incomplete knowledge transfer and inconsistent process stability, resulting in prolonged ramp-up periods and challenges in achieving reliable yields.

Domain Twin™: Building Transferable Manufacturing Strength to Address Tariff and Order Shift Pressures

Profet AI’s experience working with manufacturing clients reveals that true resilience lies not simply in relocating production, but in the ability to replicate core manufacturing capabilities quickly and effectively across locations.
Faced with rising tariffs and shifting customer demands, manufacturers that proactively develop transferable process intelligence are better positioned to maintain delivery reliability and retain long-term customer trust.

Our solution: Domain Twin™. This technology transforms critical manufacturing knowledge into replicable, deployable digital assets—enhancing consistency and efficiency across multi-site operations.

1. Digitizing Process Knowledge to Enable Replication

Domain Twin™ helps manufacturers capture and structure operational experience, parameter logic, and exception handling procedures into unified digital models—allowing tacit know-how to be standardized, managed, and applied across different production environments.

2. Cross-Site Simulation for Layout and Transfer Optimization

By simulating different regional production conditions, cost structures, and equipment configurations, Domain Twin™ enables enterprises to accurately assess transfer risks and investment requirements, accelerating decision-making and deployment.

3. Reducing Ramp-Up Time and Stabilizing Yields at New Sites

With standardized procedures and data-driven recommendations, new facilities—even those with limited experienced staff—can rapidly adopt proven process logic. This shortens time-to-yield and improves early-stage productivity and consistency.

Tariffs Are Just the Beginning—The Real Challenge Is Scaling Capability

The U.S. retaliatory tariff policy is just one part of the broader transformation pressure facing the industry.
As geopolitical tensions and trade policy uncertainties continue to grow, manufacturing competitiveness will increasingly depend not just on technical expertise, but on the ability to swiftly transfer, replicate, and stabilize operations globally.

Profet AI’s Domain Twin™ enables manufacturers to convert tacit knowledge into explicit, repeatable assets, empowering organizations to adapt rapidly, deploy efficiently, and scale manufacturing capabilities with confidence.

If you would like to know more about how our Domain Twin can help you tackle manufacturing challenges, contact Profet AI to schedule a consultation with our experts.

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