Applied Industrial Engineering - IE in New Technologies
Industrial Engineers have to develop productivity science, productivity engineering and productivity management for new technologies. Are they doing it effectively? No industrial engineers are not doing it adequately.
https://nraoiekc.blogspot.com/2025/07/applied-industrial-engineering-prof.html
Applied Industrial Engineering - Industrial Engineering 4.0 - Online Course Module
IE Applied to Agentic AI
AI and AI Agents are new technologies with application potential in many processes and systems. Industrial engineers have to learn those technologies and develop IE for those technologies.
Operations Function - The key areas where AI agents are making a significant impact
The Business Case
The adoption of AI is not just an upgrade to the technology stack; it is a disruption to operational processes and cost structure. With the real-time decision-making process, manufacturers are seeing improvements in operational efficiency that were very difficult to achieve with rules-engine-based automation.
The key areas where AI agents are making a significant impact include:
Autonomous manufacturing operations (Smart Manufacturing): AI agents can oversee entire production processes, ensuring robotic systems operate at peak efficiency and managing deviations in schedules. They can handle most real-time decisions, with human workers intervening only for issues requiring judgment.
Predictive maintenance: By continuously monitoring machine performance and sensor data, AI agents can predict equipment failures before they occur. This allows for scheduling maintenance. It is observed that plants significantly reduced unplanned downtime by up to 40% and cut maintenance costs by 20-25% using predictive maintenance agents.
Quality control and defect detection: AI agents can be used for real-time inspection, using machine vision, sensor fusion and anomaly detection to spot subtle defects that human inspectors might miss, especially in high-speed production. They can also adjust processes in real-time to correct issues, leading to a 30-50% reduction in defect rates.
Automated Inspection - Introduction and Bibliography
Supply Chain Agents: AI agents can predict and react to supply chain disruptions by monitoring raw material availability, adjusting production schedules, optimizing resource use and even identifying alternative suppliers. They streamline logistics, forecast demand and manage inventory, helping to avoid bottlenecks and material shortages.
Energy optimization and sustainability: Manufacturers can significantly reduce energy waste as AI agents monitor consumption across machines and make real-time adjustments to minimize usage without compromising production targets. From our observations the implementation of AI tools at our plants, this can lead to energy savings of 15-20% and supports green manufacturing objectives.
Process automation and optimization: Beyond traditional robotics, AI agents enable cognitive process automation by improving decisions and workflows that were previously manual or rule-bound. They can dynamically adjust parameters like temperature and pressure in real-time based on historical data, ambient conditions and input materials, leading to less waste, fewer mistakes and consistent quality.
Workplace safety: AI agents can monitor environmental factors and safety metrics on the factory floor, predicting potential hazards and automatically triggering safety protocols—such as shutting down machinery or alerting workers—to ensure safe operations.
Intelligent manufacturing assistants: These agents integrate design intelligence into the engineering process, using generative design algorithms to explore product variants, analyzing customer data to recommend product tweaks and evaluating manufacturability before prototyping.
End-to-end automation: Advanced "super AI agents" can manage complex, cross-functional tasks across the entire manufacturing process, from material procurement and production planning to quality control and shipment. They integrate data from all aspects of the supply chain and manufacturing floor to ensure seamless automation.
A Dilemma of Marketing Managers - The First Customer Could be an AI Agent
“How do we remain visible and persuasive when the first ‘customer’ in the funnel is not a human, but an AI agent?”
McKinsey & Company
Our research estimates that by 2030, agentic commerce could orchestrate $3 trillion to $5 trillion globally, as AI agents increasingly influence discovery, decision-making, and transactions across categories.
As AI quickly becomes the first stop in the shopping journey in Europe and among industry leaders, the strategic question is shifting to: “How do we remain visible and persuasive when the first ‘customer’ in the funnel is not a human, but an AI agent?” https://mck.co/3Q7kKIM
Interesting LinkedIn Posts on AI and Agentic AI
https://www.linkedin.com/posts/andreashorn1_anthropic-claude-skills-ugcPost-7444286437992103936-HP3D
100 AI agents
6 Imp AI Reports
"Global AI Leadership Summit- Edition 1"
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