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?
At that time, Starbucks’ CTO wrote: “The tech is currently live across thousands of coffeehouses, and will be in use across the chain’s entire North American system by the end of September. At cafes using the AI systems inventory is now counted 8 times more frequently, giving us real-time visibility and enabling faster, more precise replenishment.”
The business impact is measurable: reduced downtime, lower mobilization costs, reduced safety risk and faster response to problem detection. In the energy and utilities sector, drone-based inspection has been estimated to reduce inspection costs by 70% and downtime by 90%.
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.
Professor Misty Blessley, at Temple U., cohosts many of our podcasts, as well as sharing her insights with our readers monthly.
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.
Professor Misty Blessley at Temple U. looks at the “No- Buy” movement.
At its core the movement is a consumer mindset focused on refraining from non-essential purchases for a set period, for some an entire year. Trending on online communities are people sharing their No-Buy challenges and success stories. Some are motivated to cut debt or save for long-term goals, while others are concerned with sustainability, minimalism, or anti- consumption values. Participation is surging, especially among millennials and Gen Zs, who are juggling inflation, student debt, and climate anxiety.
For over a decade, self-checkout was the retail future, speed, convenience, and cost savings. 
Temple U. Professor Misty Blessley raises an interesting inventory issue–returns.
This is the latest shift in a logistics effort that has historically left companies scrambling to meet the retail giant’s demands.
Shrink is now one of the most frequently discussed topics among management at Home Depot, said the firm’s CFO, having moved onto its list of top priorities two years ago. That focus hasn’t changed even though some mitigation efforts, such as locking up certain items and using live-view parking lot cameras, are in place.
Sportswear giant Nike has cut lead times for orders from 60 days to just 10 days by installing over a thousand automated machines at supplier factories across manufacturing processes. The machines handle cutting, cementing, shoe assembly and solemaking, helping to increase efficiency and reduce labor times. Nike has also developed methods to produce footwear with 30% fewer production steps, and 50% less labor.