AI Edge and IoT Synergy: Creating New Dimensions of Manufacturing Intelligence

As IoT brings unprecedented visibility into factory operations, the infusion of AI at the edge is turning data into instant decisions. Together, they are shaping a smarter, more resilient, and sustainable manufacturing ecosystem.

- Sudhanshu Mittal

The role of IoT in manufacturing has been visible for quite some time now. Collecting the operational data through connected devices to generate real-time production data, performing quality assessment and reports generation, addressing worker tracking and safety and many other areas, have seen the increasing role of IoT for operational improvements in manufacturing. With this data generation, comes the data utilisation using AI. While major deployment of AI has been in cloud, the increasing capabilities of edge devices (both CPU and GPU) make it feasible to start deploying AI at the edge also. While IoT devices generate a huge amount of data providing extreme visibility in operation, the addition of AI is looking to make the edge more than a passive conduit by adding mini brain that can take decisions instantly and locally.

This addition of AI at the edge and data collection through IoT is leading to newer possibilities like real-time decision-making, customised production planning, predictive resilience through digital twins, and operational improvements that also contribute towards sustainability. The focus goes beyond operational efficiency, and we are looking at new paradigm of intelligent manufacturing ecosystem. Here we explore some of the new dimensions of this intelligent manufacturing ecosystem:

Real-Time / Near Real-Time Decision Making

Traditionally, quality checks happen after production, leading to waste and rework. With AI-powered edge analytics capabilities, these defects can be detected instantly on the production line. For example, computer vision systems running AI models on edge devices can identify surface cracks or assembly flaws in milliseconds, triggering corrective actions in assembly operations through or even without human intervention, depending upon the level of integration and automation. As a result, the quality management changes from reactive to proactive.