Walk into almost any operations and supply chain meeting today and you’ll hear it:
“We should use AI for this.”
“Can we plug in an LLM?”
“Let’s add a copilot.”
Machine Learning Machine learning uses historical data to detect patterns, improve predictions and support decisions. In real-world operations, that includes:
- Demand forecasting
- Inventory optimization
- Predictive maintenance
- Quality and anomaly detection
LLM (Large Language Model) LLM refers to systems that can read and generate human-like text. It processes and generates language based on patterns learned from large datasets. It shows up:
- Summarizing supplier emails or RFQs
- Drafting customer responses
- Translating ERP data into plain language
LLMs don’t “know” a business unless connected to the firm’s data. Without that context, they can sound confident—but be wrong.
Copilots “Copilot” is one of the most overused—and misunderstood—terms. They are a layer that sits on top of a business system (ERP, CRM, email) to assist users in real time. It is useful for:
- Suggesting responses inside email
- Helping navigate ERP workflows
- Recommending next steps
A copilot doesn’t replace a system—it improves how people interact with it.
Agents Agents move from assisting to acting. They are systems that can take a goal and execute steps to achieve it.
Examples:
- Monitoring inventory
- Detecting shortages
- Reaching out to suppliers
- Proposing or initiating reorders
Most agent-based systems are still early. They require strong guardrails and tight integration to work reliably in production environments.
Embeddings (The Quiet Connectors) Embeddings convert a company’s data into a format AI systems can understand and search. That’s what allows AI to:
- Reference ERP data
- Search internal documents
- Provide context-aware responses
For operations students and faculty, the goal is not to become AI experts. It’s to understand the language well enough to ask better questions and identify where these tools can create real advantage.
The shipping giant, which already deploys artificial intelligence in software development and other areas, is now looking to drive AI agents further into operations, including network planning and business processes. By 2028, FedEx expects to have AI integrated into more than half of its core operational workflows. FedEx is currently focused on setting up the underlying data and management foundation to oversee its AI bots.
In our latest podcast, Barry Render interviews John Dyer, a well‑known speaker, consultant, and expert in continuous improvement, and the author of 
The global map of robotics is specialized. There is a multi-polar supply chain that is difficult to disrupt:
This forces a shift from one supply chain to a portfolio of capabilities designed around distinct value propositions including speed, reliability, customization, cost-to-serve, and compliance. Where commercial commitments are made in isolation from operations, the consequences surface later through margin erosion, excess inventory, and lost customers.
For two decades, manufacturing has been defined by a relentless pursuit of optimization. We automated assembly lines (Ch. 9), digitized records and built predictive maintenance models (Ch. 17), all in the service of marginal gains in efficiency.
By modeling factories and distribution centers digitally before making physical changes, PepsiCo hopes to cut down on costly mistakes while improving speed and capacity.
Manufacturing faces a dual disruption. AI, robotics and automation are reshaping production at unprecedented speed, while skilled labor shortages intensify when experienced workers retire, taking decades of knowledge with them. 
Scale drives efficiency—for almost a century, industrial planners have relied on this simple principle. In 1936 aeronautical engineer Theodore Wright discovered that costs fell in a predictable way every time production doubled. The more you produce, the cheaper things become, in part because the learning cost per unit declines. This is the topic of Module E in your text.
Several researchers compared the circumstances to war. “We’re basically trying to speedrun 20 years of scientific progress in two years,” said one Anthropic scientist. “Extraordinary advances in AI systems are happening every few months. It’s the most interesting scientific question in the world right now.”