Proximity and Decision-Making: Redefining Machine Vision in Industry 4.0 Key Highlights - Manufacturers are shifting focus from scale to proximity, producing closer to demand and responding faster to emerging needs. - Effective machine vision systems should be designed around the specific decision they support, not just defect detection. - Identifying the earliest reliable signal of a problem allows for earlier intervention, reducing waste and preventing defects. - Pattern recognition and contextual data enhance understanding of process variations, enabling proactive maintenance and quality control. - The ultimate goal is to connect visual insights directly to operational decisions, reducing response times and increasing manufacturing agility. For much of industrial history, manufacturers won by mastering scale. They built larger plants, longer production runs, standardized processes, and global supply chains designed to lower unit cost and increase consistency. That logic still matters. But it is no longer the only source of advantage. The next advantage comes from proximity. By proximity, I mean the ability to move value closer to the moment and location at which a need emerges. In manufacturing, that might mean producing closer to the customer, configuring products later, responding faster to demand, or correcting a process before a small variation becomes a larger quality problem. Machine vision sits at the center of this shift. Too often, vision systems are specified around a narrow question: Can the system detect the defect? That is an essential starting point, but it is no longer enough. One of my first clients in my early days at McKinsey was a company producing inexpensive image-capture devices that manufacturers could affordably embed throughout the production flow. Even then, the promise was clear: when manufacturers could see more of what was happening inside the operation, they could make better decisions. Those technologies have advanced dramatically since, and companies are