Sharon Lin

Turning Tacit Knowledge into Shared Operating Logic

The functional textile industry has long depended on something that is difficult to capture in a spreadsheet: experience.

From material selection and finishing conditions to lamination performance and factory trials, many critical decisions rely on technical judgment accumulated over decades. Much of that knowledge lives within experienced technicians, long-standing supplier relationships, and the day-to-day coordination between people.

For Sharon Lin, Chief Operating Officer at Long Advance, the challenge is not to replace this way of working, but to make it more transferable.

Her focus is on gradually turning an operating model that has historically depended heavily on individual experience and person-to-person coordination into one in which knowledge can be shared, validated, and carried forward across teams.

The goal is not to standardize away craftsmanship. It is to create enough structure around technical judgment so that experience can continue to inform decisions even as teams, technologies, and market requirements change.

That shift also reflects a broader evolution in the role of Asian suppliers. Rather than remaining primarily execution suppliers, Lin believes companies with deep technical knowledge can increasingly become strategic technical partners capable of interpreting brand requirements, coordinating specialized manufacturing capabilities, and contributing earlier in the product-development process.

Making Experience Transferable Through F(SYNC)

One of the operating responses to this challenge is F(SYNC), an internal framework Long Advance is developing to organize technical knowledge, development records, and decision workflows across its operations.

The intention is not to turn decades of textile expertise into a fully automated system. Instead, F(SYNC) is designed to create a more structured way for teams to connect information that has traditionally been fragmented across documents, factory communication, development history, and individual experience.

The aim is to reduce unnecessary development iterations, improve visibility across factory trials, and make technical decisions more consistent across different production partners.

For Lin, digitization is not about replacing craftsmanship with data. It is about making decades of technical judgment transferable, turning individual experience into shared organizational knowledge.

This is particularly important in a textile ecosystem where product development frequently involves multiple specialized manufacturing partners. A decision made at one stage may affect the next, and the reasoning behind those decisions can be as important as the data itself.

By making that reasoning easier to retain and share, Long Advance is gradually building an operating model that connects experienced technical teams with a new generation of colleagues working with more structured digital tools.

Using Technology to Strengthen Human Judgment

The same philosophy shapes Lin’s approach to artificial intelligence.

Her approach to AI is pragmatic: rather than treating it as a standalone technology initiative, she focuses on where it can strengthen human judgment and shorten the distance between data and decision-making.

For Long Advance, the opportunity is not simply to automate existing tasks. It is to explore how AI can help teams organize information, compare development records, identify missing data, and bring relevant technical context into the decision workflow more efficiently.

Human judgment remains central.

In a high-performance textile environment, material decisions often involve trade-offs between waterproofness, breathability, durability, hand feel, weight, sustainability, cost, and manufacturability. These relationships cannot always be reduced to a single technical parameter.

The role of technology, in Lin’s view, is to make information easier to access and connect so that people can make better-informed decisions.

This approach is being applied across Long Advance’s network of specialized manufacturing partners and selected ESG-aligned factory partners, where technical knowledge and sustainability information increasingly need to move across organizational boundaries.

Preparing for a More Data-Driven Supply Chain

The challenge is also expanding beyond product development.

Global brands are increasingly asking suppliers to provide clearer information on material origin, chemical management, environmental performance, carbon data, and traceability. For a fragmented textile supply chain, much of this information may already exist, but it is often generated by different factories, stored in different formats, and managed by different people.

The challenge Lin sees is therefore not simply how to collect more data, but how to make that information more usable across the supply chain.

Long Advance is gradually extending its operating model to connect product development, technical knowledge, ESG information, and business requirements within a more consistent data structure.

This work is also preparing the company for a future in which product-level traceability and Digital Product Passport requirements will increasingly shape how brands and suppliers exchange data.

Rather than treating DPP as a separate compliance exercise, the company sees it as part of a broader shift toward a future operating environment in which product information needs to move more consistently between materials, factories, brands, and markets.

For Lin, this is another form of integration.

Just as a high-performance fabric depends on coordinating multiple manufacturing processes, a more transparent supply chain will depend on connecting information generated across multiple organizations.

From Execution Supplier to Strategic Technical Partner

Long Advance currently works with 30+ international brand clients, but Lin sees the company’s long-term role as extending beyond the delivery of individual materials.

The broader opportunity is to help connect technical expertise, product-development decisions, sustainability information, and operational knowledge in ways that allow Taiwan’s specialized textile ecosystem to work more cohesively.

Her approach reflects a larger question facing Asian manufacturing: as production capabilities become more widely available, where will future competitive advantage come from?

For Lin, the answer lies increasingly in the quality of decisions surrounding production.

Factories will still need to manufacture efficiently and meet demanding technical specifications. But global brands will also need partners that can interpret requirements, understand technical trade-offs, coordinate specialized capabilities, and communicate increasingly complex product and sustainability information.

In that environment, the transition from execution supplier to strategic technical partner is not simply a change in positioning. It requires a different operating model, one capable of connecting knowledge that has traditionally remained fragmented between people, processes, factories, and systems.

For Lin, the future competitiveness of Asian manufacturing will depend not only on what factories can produce, but on how effectively technical knowledge, data, and judgment can move across generations and organizations.