Similar to the PC revolution decades ago, all signs point to AI following suit with enhanced productivity and profitability. Productivity soared when PCs became interconnected across organizations. Manufacturing will see the same breakthrough with “embedded AI”—to help ease workforce bottlenecks with specific solutions. On the shop floor, for example, predictive-maintenance AI (see Chapter 17) can analyze sensor data to forecast equipment failures and avoid labor-sapping downtime.
AI vision systems (Chapter 7) can catch defects on production lines at a pace beyond human capabilities and without the repetition-induced fatigue and employee turnover. Collaborative robots (cobots) and automated mobile robots transport material and can assist with assembly and repetitive operations. AI’s coding capabilities extend to numerical control and other industrial equipment, speeding up setup time and productivity in hard-to-fill technical positions.
The interaction of embedded AI, agent-based AI, and machine learning across different areas of an organization holds the greatest promise in solving long-term labor shortages. AI can already let a customer snap a photo of a damaged part and identify it for replacement. Its real power will manifest when AI can also determine the part’s inventory status and locations, establish shipping terms and timing, add the part to the procurement queue to replenish once it’s sold, alert engineering that a design change for a chronic defect may be in order, and propose alternative designs.
Here is a current example involving AI across systems: the big semiconductor company AMD is using generative AI to track down the root cause of delivery delays, simplifying complex supply chain interactions to transform a complex, specialist-dependent, labor-intensive manual process into faster issue resolution and better decision-making. The system cuts the time needed for what was a 14-step process taking 20-30 minutes by 90%, saving more than 3,100 staff hours a year.
Also coming soon to these intelligent product recommendation engines is an ability to parse what can be 50-page tender documents to extract multiple configurable products for sales quotes. That not only saves time, but also enables junior staff to handle work that has previously required experienced hands.
Classroom discussion questions:
- What can AI do to improve a procurement system?
- What does “embedded AI” mean?

Temple U. Professor Misty Blessley brings up a very timely OM issue-air safety.
However, when radar and communication systems go dark, there’s no safe way to guide aircraft into these stacks or maintain proper separation. Once communication is restored, controllers must work through the resulting queues to safely sequence and clear aircraft for landing. Outages lead to flight delays and cancellations while also raising serious safety concerns. How can the skies be stabilized?
Prof. Howard Weiss survived the recent hurricane that hit Florida and shares his thoughts about losing power.
Quality control enhancement: AI can improve manufacturing quality control through vision systems trained on images and videos, accurately detecting complex product defects. Real-time monitoring identifies issues promptly to prevent future defects, and AI’s continuous learning enhances defect detection. (See Ch. 6)
When Ural Airlines Flight 1383 to Siberia suffered a technical fault with its hydraulics a few months ago, the pilots decided to divert to a closer airport. Then they discovered the defect meant the aircraft was rapidly running out of fuel and needed to land quickly. The plane, with 165 people onboard, eventually made a successful emergency landing in a farm in southern Russia. The Airbus A320 jet remains there, fenced in and under security, with Ural agreeing to pay rent for a year to the land’s owner–and then harvesting the jet for parts.
According to Toyota, its Japanese factories and their 28 assembly lines were halted due to “some multiple servers that process part orders” becoming unavailable and causing Toyota’s production order system to malfunction on August 28. The situation caused production output losses of roughly 13,000 cars daily, which threatened to impact exports to the global market.
Professor Howard Weiss, who created the free Excel OM and POM software for our text, provides a fresh view of maintenance.











