The core of South Korea’s industrial strength is still manufacturing. In vital sectors like shipbuilding, batteries, and household appliances, the competitive edge is primarily shaped within factories. Even when using identical machinery and labor, the variation in efficiency and quality hinges not on the product itself but on the method of production.
When engaging with manufacturing companies on-site, similar questions often come up: “How much will production efficiency increase if we adopt artificial intelligence (AI)?” This is not an inappropriate question, but it doesn’t fully capture the transformations happening in factories today. In reality, a more common concern emerges: When data is limited, should we build the infrastructure first, or start with small AI projects? Should implementation be postponed until human-level accuracy is achieved? Although this approach seems careful, it often results in delayed action. While infrastructure is being developed, on-site problems remain unaddressed, and individual tasks fail to contribute to overall organizational performance. Waiting for perfection hinders progress, leaving companies in a situation where they are preparing for AI without experiencing any improvement in competitiveness.
A notable example for such companies is Foxconn, a worldwide electronics manufacturing firm well-known for its partnership with Apple. Foxconn, in collaboration with BCG, adopted a strategy to completely restructure factory operations. Rather than seeing AI as a means to boost efficiency in particular tasks, it began by asking the fundamental question: “How should factories be managed?”
◇ Foxconn’s Selected Strategy: ① Reimagining the Factory Operational Model
Via the “Genesis” initiative, Foxconn integrated physical processes, digital twins, and AI-driven decision-making mechanisms into one unified system. Manufacturing facilities evolved from being centered around human labor to a framework where AI consistently enhances efficiency. At first, a “Human-in-the-loop” strategy was utilized, with humans actively engaging with AI systems to support decisions, allowing for quick on-site implementation and simultaneous learning.
The most significant transformation occurred in the design phase. In the past, factories were constructed first, with problems addressed through trial operations. However, Foxconn now designs processes initially within a digital twin setting, identifies the best conditions using AI simulations, and then builds the physical factory. Changes during the operational stage are also evident. AI agents manage routine tasks like defect correction, process synchronization, and performance enhancement, while humans concentrate on unusual scenarios and complex decisions. This framework evolved into a cross-factory learning system. The central model is implemented in global centers, each factory relearns from data, and outcomes are collected back at the center. As a result, knowledge and expertise among factories are exchanged instantly, and operational methods are consistently refined in response to changes in materials, machinery, products, and other environments.
Factories are no longer static systems but developing entities that can learn and modify themselves. This is not just a move towards automation; it represents a core transformation in the way manufacturing facilities function.
◇ Foxconn’s Selected Strategy: ② Focus on Direction and Speed, Not Perfection
What holds greater significance in Foxconn’s situation is its method of decision-making. The secret to success isn’t about choosing a particular AI, but rather about determining how factories should function. Before implementing AI, Foxconn first outlined the path for its AI operational model. This strategy was set by CEO Young Liu, not by the industry itself. Particular targets like yield, quality, productivity, and cost structure were connected to the direction of AI application, using AI as a tool to meet these goals.
Another distinction lies in the pace. Foxconn did not hold back for complete readiness. Even when there was inadequate data, it gathered information and simultaneously learned through AI. At first, human input supported outcomes while AI was quickly implemented. This approach is essential for rapid AI integration. Similar trends are observed among foreign battery material companies. Despite resistance caused by initial data limitations and infrastructure challenges, once top management clearly defines the direction and pushes for implementation, AI-driven decision-making is swiftly embraced. Consequently, quality and yield improve rapidly, leading to positive changes in productivity and cost structures. In the end, the speed of AI transformation depends not on technology but on the determination of leadership.
◇ In the end, Competitive Advantage Arises from ‘Reengineered Factories’
The conditions confronting South Korean manufacturing are growing more intricate. In the face of competition from China, firms need to achieve both cost efficiency and superior quality, yet there is a decline in skilled workers, global production facilities are increasing, and market demands are becoming more unpredictable. Specifically, as global expansion reaches areas such as the U.S., Europe, and Southeast Asia, differences in organizational cultures and operational practices across regions make previous Korean production techniques hard to implement directly. Moreover, rising demand fluctuations and supply chain risks are greatly increasing the uncertainty in production activities. Under these circumstances, traditional operational approaches based on experience and instinct are no longer sufficient to sustain competitiveness. The challenge lies not with productivity but with the fundamental way factories are managed.
Currently, manufacturing firms find it challenging to postpone decisions. The decision lies between merely integrating AI into current processes or completely reimagining factory operations around AI. AX (AI transformation) in manufacturing goes beyond digitalization. It involves a shift in operational approaches where machinery, procedures, employees, and data are integrated into one system. Although this transition is not simple, once initiated, the competitive advantage grows quickly. The challenge that companies are currently facing is not due to a lack of technology—adequate technology is already available. What matters is how swiftly they define a path and move towards implementation. Simply observing early adopters or beginning with infrastructure development makes it hard to match the speed of change. Now, companies need to adopt a structure that allows them to implement strategies on-site, gain knowledge through execution, and incorporate the outcomes back into their operations.
South Korea’s manufacturing edge continues to stem from its factories. The sole distinction is that these facilities are now managed differently than in the past. Modern factories are being restructured around artificial intelligence, and the pace of this transformation will shape future competitive advantages.
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