Cohort: 1 

Location: New Taipei City, Taiwan, China 

Application Title: Self-learning AI factory systems for electronics manufacturing optimization 

Team Members: 

The challenge 

Electronics manufacturing is characterised by high volumes and frequent changes in product mix. Factories must continuously adjust machine settings, production schedules and quality controls as designs evolve and demand fluctuates. While automation is widespread, many operational decisions still depend on the experience of individual technicians and engineers. 

In processes such as injection molding or printed circuit board assembly, parameter tuning is often performed manually, guided by prior experience rather than systematic feedback. When defects occur, root cause analysis typically happens after the fact, requiring engineers to review logs and test adjustments sequentially. Knowledge accumulates over time, but it is rarely formalised or shared consistently across teams or sites. As production scales, this reliance on experience-driven workflows makes it harder to maintain uniform quality and respond quickly to issues. 

Conventional improvement efforts focus on lean practices or incremental automation. These methods can raise efficiency, but they do not adapt continuously as operating conditions change, and they struggle to transfer expertise across large, distributed factory networks. 

The solution 

A shared AI layer that supports continuous learning 

Foxconn partnered with Boston Consulting Group (BCG) to develop Project Genesis, an AI platform designed to support self-learning factory operations across electronics manufacturing. 

Instead of introducing isolated optimisation tools, the initiative establishes a unified AI layer that sits alongside core production systems. This layer captures manufacturing knowledge accumulated over decades and embeds it into AI systems that assist engineers and operators during daily work. The objective is to reduce repetitive manual tuning, shorten problem-resolution cycles and improve consistency, while keeping decision authority with experienced staff. 

BCG supported the design of the platform architecture and operating model, while Foxconn integrated the system into live production environments across its global operations. 

How it works 

Embedding expert knowledge into operational workflows 

The platform supports several operational modules that share a common data foundation and learning loop. 

In injection molding, real-time sensor data from machines is analysed to recommend parameter settings that balance quality and throughput. These recommendations are informed by historical outcomes and encoded process knowledge, allowing technicians to make adjustments without relying solely on trial and error. 

In surface-mount assembly, AI systems monitor production lines and assist with diagnosing defects. When issues arise, the system proposes likely causes based on observed patterns and records corrective actions so that similar problems can be addressed more quickly in the future. 

For production planning, operators can express scheduling needs in natural language. The system translates these inputs into updated production plans that reflect current constraints and order changes. Across all modules, outcomes are fed back into the platform so that recommendations evolve as conditions shift. 

How it differs from conventional approaches 

Reducing reliance on manual optimisation 

Traditional factory optimisation relies on periodic intervention by experts, with improvements applied locally and often documented informally. As a result, lessons learned in one line or plant do not always transfer to others. 

Foxconn’s approach embeds AI directly into day-to-day workflows, allowing optimisation to occur continuously rather than episodically. Instead of manually adjusting parameters or diagnosing issues after defects appear, operators receive guidance derived from accumulated knowledge and live data. This reduces variability while preserving human oversight and accountability. 

Impact 

• 50% reduction in workload related to changeovers 
• 30% decrease in time required to resolve production issues 
• Approximately 10% reduction in overall cycle time 

Why it matters 

Supporting consistency in large-scale manufacturing 

As electronics manufacturing networks expand, maintaining consistent performance across sites becomes increasingly difficult. The collaboration between Foxconn and BCG shows how AI can support this challenge by formalising expert knowledge and applying it in real time during operations. 

By reducing reliance on manual tuning and accelerating problem resolution, the platform helps factories respond more effectively to variability while allowing experienced staff to focus on higher-value decisions.