Smart Manufacturing In Taiwan
ROI is the key.
YC is a senior manager with 20 years of experience in Taiwan's smart manufacturing for semiconductor and materials industries.
The Conversation
YC: Today, in manufacturing, we see tools as a way to solve problems. No matter how many new tools emerge, the key is whether they can address the issues we face. What we need is mature solutions that can deliver results quickly. We need to find the most mature solutions that can immediately show benefits to the leadership, which may or may not require cutting-edge technology.
Amy: In Taiwan, we often hear terms like "smart manufacturing" and "digital transformation." Among the diverse manufacturing companies, which industry do you see as leading in smart manufacturing?
YC: Let’s look at it from a ROI (Return On Investment) perspective. Semiconductor companies are definitely the front runners. This is intuitive because they have the strongest incentives - wafers are costly, so even small improvements bring substantial returns. Investing in AI quickly pays off.
Stella: Who is after the semiconductor companies?
YC: The second tier for AI development are more vertical industries that can more easily gather data.
Stella: Which industries are most challenging?
YC: The biggest challenge remains with small-batch, high-variety production like accessories and footwear. It’s hard to automate and gather data for such varied, low-volume production. Everyone wants to build AI, but they need to evaluate investment versus returns to determine their pace.
Amy: It always ties back to ROI.
YC: Exactly. In Taiwan, our core principle in the age of AI remains the same: lower costs, increase efficiency (降本增效). We’re not pursuing AI just for the sake of it; instead, we're watching closely for the point where there’s a clear payoff. We’re always monitoring for that crossover point and evaluating which technologies are ready for introduction next.
The Cocoons
Taiwan’s manufacturing landscape spans from semiconductor giants like TSMC and MediaTek to traditional industries like textiles. While high-tech sectors grab headlines with revolutionary AI upgrades, traditional manufacturers are quietly embracing AI in their own way. These industries aren’t rushing in—they’re taking a very practical approach, by closely tracking ROI (Return On Investment) of latest technologies, and waiting for the right moment when AI investments pay off.
LLM In Manufacturing
YC shared insights on how Large Language Models (LLMs) offer transformative potential in manufacturing, particularly in non-production tasks and domain knowledge capture.
For non-production tasks, LLMs are already proving beneficial. In manufacturing companies, office-based staff spend significant time on tasks like writing code, drafting meeting summaries and reports, or answering queries. LLMs can automate much of this work, streamlining operations and allowing employees to focus on high-impact tasks. The introduction of tools like ChatGPT has reduced coding costs, enabling manufacturing companies to tackle these tasks with far greater efficiency. This practical implementation of LLMs for knowledge work and administrative tasks offers immediate improvements and a clear return on investment.
On the production side, the potential for LLMs is immense but still developing. Manufacturing companies hold valuable domain knowledge, much of it currently stored in the minds of experienced engineers and specialists. By fine-tuning LLMs to incorporate this expertise, manufacturers could create a repository of Standard Operating Procedures (SOPs) and troubleshooting strategies, making these insights accessible for problem-solving on the production floor. However, the technology isn’t yet ready to capture and reliably deploy this knowledge at scale. The high cost and technical requirements of customizing LLMs for complex domain-specific tasks mean that companies are still experimenting with solutions.
An application in Traditional Sectors - TextileGPT
TextileGPT is a prime example of how Taiwan’s traditional industries, like textiles, are beginning to adopt advanced AI solutions. Morale AI, the consulting firm behind TextileGPT, strategically partnered with the Taiwan Textile Research Institute (TTRI) to leverage the institute’s extensive knowledge and data resources. This collaboration ensures that the LLM (Large Language Model) they developed can effectively incorporate decades of industry-specific data and expertise, making it a robust and tailored tool for the textile industry.
Morale AI’s decision to partner with TTRI wasn’t random—it was an intentional move to streamline data collection and enhance adoption. TTRI, often seen as Taiwan’s “innovation hub” for textiles, has built a centralized database, amassing over 20 years of information on textile materials, proprietary terms, international standards, and even patent data. By working with TTRI, Morale AI gained direct access to this valuable and organized knowledge library, allowing itself to seamlessly pull from a wide range of textile-specific knowledge without starting from scratch. This existing data repository makes it easier to embed reliable, domain-specific insights into LLM, giving manufacturers immediate, practical benefits without the need for complex data gathering.
TextileGPT also offers two key applications: knowledge-based Q&A and direct factory integration. For Taiwanese textile manufacturers relocating abroad, TextileGPT is invaluable, breaking down language barriers and enabling easy, multi-lingual access to company procedures and domain-specific knowledge. Additionally, the model's integration with TTRI’s knowledge base enables it to stay up-to-date with the latest research and regulations, making TextileGPT a dynamic resource that continually supports the industry’s shift toward digital transformation. By bundling TextileGPT with TTRI’s existing web service, Morale AI has made adoption straightforward for manufacturers, who are already familiar with the platform.
The Global Manufacturing Race
As Taiwan’s manufacturing scene dives into AI, each industry is finding its sweet spot on the ROI curve. Taiwan’s AI journey is part of a global trend: manufacturers worldwide are closely watching as AI takes shape, each bringing its own twist. The U.S. is making data-driven moves (e.g. GE’s “Digital Twin” technology), or Germany’s going precision-first in engineering (e.g. Bocsh’s high-precision milling machines), and Asia’s diverse industries are carving their own paths forward. Manufacturing companies across regions are racing not just for innovation, but for higher margins, and the latest AI developments shine a light on new opportunities to boost production efficiency.
As we grow accustomed to constant headlines about AI innovations, it’s fascinating to see how different manufacturers in Taiwan are bringing AI from a buzzword to the factory floor. In Taiwan, they stay true to their core principle: lower costs and increase efficiency—降本增效.
In this global manufacturing race, those who can apply AI in the most practical ways, with precise control over ROI, will stand out.