It’s 4 a.m. at a large automotive parts plant. The night-shift supervisor freezes as the dashboard flashes an alert: a critical spindle is vibrating out of tolerance. In the old world, he’d wait for maintenance to evaluate and decide. But today, an AI agent has already paused the line, checked service records and called the right technician—before he even takes a step toward the control room.
That’s the new reality for many manufacturers facing a stubborn obstacle: the ever-widening gap between data and decisive action. Now, a new class of digital entities is changing that equation. AI agents powered by decision intelligence are beginning to sense, reason and act across the manufacturing ecosystem, cutting decision latency from minutes to milliseconds.
Think of AI agents as the digital nervous system of a modern factory. They continuously sense what’s happening across machines, people and systems, then respond intelligently without losing context. Across the manufacturing stack, they’re quietly reshaping work for every role:
On the shop floor: Agents merge operations and information technology (IT) data to give operators real-time context. They can recommend optimal machine parameters, trigger tool-change schedules, balance workloads across lines or alert technicians before deviations escalate. Maintenance teams can use agents to predict component wear and plan interventions that don’t interrupt production– a topic in Chapter 17.
In production and quality operations: Agents help supervisors and quality staff detect process drift early. They analyze sensor data, images and process variables, suggesting immediate corrections or automated parameter tuning. In continuous manufacturing, this can mean fewer rejects and less rework, which we discuss in Chapter 6.
In ERP and planning: Agents connect production, procurement and finance systems. A planning agent (see Chapter 14) can run simulations of “what if” scenarios, what happens if a supplier shipment is delayed or if energy costs spike and recommend production adjustments.
Across the supply chain: Agents can constantly monitor inventory, supplier performance and logistics signals. When a potential shortage or delay is detected, they are able to trigger contingency workflows such as redistributing available stock, recommending alternate suppliers or rescheduling deliveries–see Chapters 11 and 12.
To sum it up: “Tomorrow’s factories won’t just inform — they’ll decide,” writes Industry Week (June 12, 2026).
Classroom discussion questions:
- Summarize what AI agents can do in a factory setting.
- How does agentic AI have the potential to change the manufacturing operation?


In prior years consumers expressed dissatisfaction when Nutella reduced the amount of cocoa in its product. One reason for the change in the recipes for these two products is the high cost of cocoa. Clearly, a change in a recipe will affect inventory, material (ingredient) costs, and the supply chain.
Shoppers last year returned 17.6% of items they purchased online, valued at more than $247 billion and more than double the percentage of goods returned in 2019. Returns have become such an entrenched part of online commerce that companies have sprung up to handle the growing business. UPS acquired one of those specialized operators, Happy Returns, for $465 million.
Dr. Prince Vijai is Assistant Professor of Operations at IBS Hyderabad, India. This post is based on his recent presentation at the DSI meeting in Orlando.
The chain’s efforts in distribution operations that handle goods from general merchandise to pharmaceuticals are meant to” (1) help restock its stores faster and (2) free workers to help customers in stores and fill online orders for pickup and delivery.
GE Appliances, one of the largest home-appliances manufacturers in the U.S., says a $2 billion effort to remake its supply chain has helped it double revenue since 2017. The Louisville-based company, now a subsidiary of China’s Haier Smart Home, has added manufacturing capacity, opened seven new distribution centers and implemented digital tools to knit together operations from production through to delivery. It is an example of how companies are resetting their supply chains to be more flexible, moves that come after retailers and household goods companies navigated disruptions, shipping delays and dramatic shifts in consumer demand during a chaotic period marked by waves of stockouts and overstocking.
This is the latest shift in a logistics effort that has historically left companies scrambling to meet the retail giant’s demands.



