Operationalising AI for manufacturing in ASEAN
Karen Kim, CEO Human Managed While the hype around Gen-AI will shift to the buzz around agentic-AI in 2025, traditional sectors such as manufacturing will still be coming to terms on deploying AI strategies to become participants of Industry 4.0 or the Fourth Industrial Revolution. This is the next phase where disruptive trends including data, connectivity, analytics, robotics and human-machine interaction is said to transform manufacturing significantly. According to a Kearney study[1], while over 70% of ASEAN manufacturers polled claimed to be “non-leaders/followers” of AI adoption, 47% believe investment in AI is “very critical” for their business. Gartner’s[2] Prism use case of GenAI in Manufacturing encourages leaders to evaluate AI use cases on a strategy that addresses both value generation and feasibility . Value parameters include increased revenue, increased efficiency, managed risk and non-financial gain. Feasibility parameters include technical feasibility, internal readiness, external readiness. So how can leaders operationalise AI, keeping value and feasibility in mind? At Human Managed, we believe it begins with an understanding of data. A framework to understand the highest impact for manufacturing businesses in the age of AI, would be to understand “3 Ops”-DataOps (Feeding AI), MLOps (Tuning AI), IntelOps (Applying AI). Data Ops (Feeding AI) In the manufacturing sector, data can be provided through assets in an interconnected ecosystem. From a range of sensors, machine data on temperature, vibrations, pressure, power consumption, operating hours etc are collected. Industrial machines, robotic arms and assembly lines can provide process data includes information on production rates, cycle times, error rates, material usage. Image and video data from cameras capture data for product dimensions and defect detection. ERP systems provide information on inventory levels, order information, transportation data. Given the vast array of data, manufacturing enterprises have to first solve for the issues around understanding the data itself. […]
