AI is rapidly shifting from digital decision‑support to hands‑on operational control. As AI systems move into the physical world, they are reshaping core OM functions—especially inventory management, quality assurance, and maintenance. For operations managers, this marks a transition from AI as an analytical tool to AI as an active operational partner, writes The Orlando Sentinel (Sept. 20, 2026).
AI and Inventory Management Inventory systems (see Chapter 12 in your Heizer/Render/Munson text) are becoming increasingly autonomous. Instead of relying on periodic counts or manual updates, AI‑enabled sensors and vision systems now track materials in real time—monitoring stock levels, identifying misplacements, and even predicting shortages before they occur. Machine‑learning models can dynamically adjust reorder points, safety stock, and lot sizes based on demand variability, supplier reliability, and production schedules. The result is tighter control, lower carrying costs, and fewer stockouts.
AI‑Driven Quality Assurance Quality management (see Chapter 6) is undergoing a similar transformation. High‑resolution cameras, acoustic sensors, and thermal imaging feed continuous data streams into AI models capable of detecting defects far earlier—and more accurately—than human inspectors. Instead of sampling, AI enables 100% inspection at full production speed. These systems not only flag defects but also trace root causes by linking anomalies to machine settings, operator actions, or upstream materials. Quality becomes proactive rather than reactive.
Predictive and Autonomous Maintenance Maintenance (the topic of chapter 17) is perhaps the area where AI’s physical presence is most visible. Predictive maintenance models analyze vibration, temperature, and performance data to forecast failures before they occur. More advanced systems automatically adjust machine parameters to prevent breakdowns, optimize lubrication cycles, or balance loads across equipment. This reduces downtime, extends asset life, and stabilizes capacity planning.
The OM Challenge Ahead As AI becomes embedded in physical operations, managers must rethink process design, workforce skills, and risk management. Inventory, quality, and maintenance systems will increasingly operate autonomously—but they still require human oversight, ethical safeguards, and strategic alignment. The future OM leader must be fluent in both operational fundamentals and AI‑driven decision‑making.
Classroom Discussion Questions:
- How will AI‑enabled real‑time inventory, quality, and maintenance systems change traditional OM decision frameworks?
- What new risks arise when AI autonomously adjusts physical processes, and how should managers design controls to mitigate them?
Jersey Mike’s claimed first place, with an ACSI score of 84 out of 100, edging Chick-fil-A’s 83. The shift marks the first time in over a decade that a new chain has led the ACSI’s quick-service restaurant (QSR) category. ASCI credited Jersey Mike’s with maintaining high customer satisfaction while rapidly expanding its restaurant footprint, writing :”Jersey Mike’s success is consistent with their business performance, including rapid unit growth, strong customer demand, and a model designed around throughput and off-premise convenience from high digital pickup usage.”
The solution, developed by the company in collaboration with Google Cloud, uses computer vision and the Gemini platform to support quality inspectors in distribution centers.
In this chapter, we have suggested that building quality into a process and its people is difficult. In the old days, inspection was the main form of quality control. But inspection may not catch all the errors, and it may be expensive. To indicate just how difficult inspections can be, ask your students to turn to the OM in Action box on page 234, called “Inspecting the Boeing 787”.
Prof. Howard Weiss, retired from Temple U., shares his thoughts monthly.
Ford just recalled 850,000 pickup trucks and SUVs because of a potential fuel-pump failure. A bad fuel pump could result in the engine stalling while a driver is operating the vehicle.
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?
A shadow factory is what Boeing executives call a production line where engineers and mechanics work on fixing, maintaining or updating aircraft instead of building new ones. They exist for the company’s two-bestselling models, the 737 MAX and 787 Dreamliner.
