Rockwell Automation’s Singapore Manufacturing Facility: The Global Lighthouse for the Physical AI Roadmap

Rockwell Automation’s Singapore manufacturing site was named a member of the Global Lighthouse Network by the World Economic Forum (WEF) in June 2026 to recognize its productivity achievement through data- and Artificial Intelligence (AI)-driven operations.

The Global Lighthouse Network consists of manufacturers that have demonstrated leadership in applying Fourth Industrial Revolution or Industry 4.0 (4IR) technologies at scale in transforming factories, value chains, and business models to achieve breakthrough financial, operational, and sustainability improvements.

Rockwell’s Singapore facility operates in a challenging environment, where it must navigate the high-mix, low-volume manufacturing demands with more than 1,000 Stock Keeping Units (SKUs) and over 20,000 annual changeovers, which led to quality consistency challenges and dependence on tacit worker knowledge. Nevertheless, the site managed to increase units per person-hour by 43%, reduce defects by 35%, and shorten time-to-competency by 67% by deploying more than 50 digital and AI solutions, encompassing intelligent automation, AI-enabled quality control, and predictive maintenance.

The goal of Rockwell Automation is clear: to build Factories of the Future internally and for its customers. The recognition as a Global Lighthouse reflects Rockwell’s accomplishment in the Industry 4.0 revolution, which could not be achieved without the wide deployment of Internet of Things (IoT) and Artificial Intelligence of Things (AIoT) technologies. And it also suggests that Rockwell has built a strong foundation for the Factories of the Future vision with three key phases: 1) data-driven operations; 2) AI-enabled operations; and 3) AI-embedded operations. With these three phases, the company has built strong building blocks for the 4th phase—AI-powered human-robot collaborative operations—using Physical AI. A deep dive into each phase reveals the following:

• Phase 1—Data-Driven Operations: This is a crucial first phase for both Industry 4.0 and Factories of the Future vision, as data are the bedrock and lifeblood for all of the phases. The transformation of Rockwell’s Singapore site into a highly flexible and data-driven operation indicates a robust IoT sensor fabric, achieved through widespread connectivity across its physical manufacturing assets. This, in turn, established the foundational IoT data pipelines.
• Phase 2—AI-Enabled Operations: Another focus of Rockwell’s is turning data into decisions and AI into outcomes by deploying digital and AI-enabled solutions. The autonomous decision-making capability of AIoT plays a key role in this phase as it shifts the basic IoT from a passive telemetry role to an AI-enabled active role in the industrial operations. This stage combines massive IoT datasets and the AI capability in the cloud to achieve predictive maintenance and automatic defect reduction. Nevertheless, an AI-enabled operation is not sufficient for a time-critical industrial environment; AI and machine learning models need to live on the edge.
• Phase 3—AI-Embedded Operations: The WEF highlighted that the world’s leading manufacturers have embedded intelligence into the fabric of operations, enabling faster response, continuous learning, and better performance across their value chain. The AI-embedded operations are about bringing AI and machine learning capabilities from the cloud to the edge—directly inside the controllers, gateways, and machine-level devices—to achieve edge AIoT architecture. The enablers, such as on-device Neural Processing Units (NPUs) and machine vision, allow microsecond-level automated defect rejection or emergency safety stops without waiting for round-trip latency to a centralized cloud. The ultra-low latency, embedded machine vision, and edge sensors provide the foundation for the next phase.
• Phase 4—AI-Powered Human-Robot Collaborative Operations: Phase 4 will be the breakthrough from Industry 4.0 into 5.0. Leveraging the edge AIoT architecture, Physical AI will be a key driving technology, allowing robots to dynamically share a workspace with human operators, rather than sitting behind a safety cage. To achieve human-robot collaborative operations, Vision-Language-Action (VLA) models, multi-sensor safety systems, and sensor fusion will be the key enablers, while edge AIoT delivers the core infrastructure, including sensors, edge chips, and low-latency logic. Commercially, this phase delivers clear value by enabling high-mix, low-volume manufacturing to meet consumer demand for personalization, while mitigating labor shortages driven by an aging workforce.

The market is currently transitioning to AI-enabled and AI-embedded operations by utilizing cloud AIoT and edge AIoT, respectively. ABI Research’s 1Q 2025 survey, which was conducted in the industrial sector across the United States, Germany, and Malaysia, indicated that 16% of the respondents have rolled out AI-capable hardware on a limited scale, while another 14% have been scaling the technology or have fully implemented it at all applicable sites.

The benefits and use cases of edge AIoT are indisputable, offering advantages such as low latency, privacy and security, and cost reduction. Nevertheless, the Capital Expenditure (CAPEX) that is required to deploy the technology is a common hurdle, which leads to a selective edge deployment strategy instead of blanket end-to-end deployments. Consequently, the cost-conscious environment will also affect the adoption of AI-powered human-robot collaborative operations.

Integrating AI-embedded operations and the subsequent human-robot collaborative operations is a long game for the manufacturing sector, especially in uncertain economic situations. Nevertheless, the economic and geopolitical risks are also the pressing factors that underline the need for agile operations that could be achieved by adopting edge AIoT and Physical AI. Therefore, the long-term monetary benefits of adopting the technologies need to be considered in the capital budgeting from the perspective of manufacturing companies. Hardware vendors and system integrators will also need to emphasize a forward-looking transformation roadmap, helping customers make better capital investment decisions tailored to their journey toward AI-powered human-robot collaborative operations.