Author: John Buglino
Closing the Gap Between Planning and Execution in Manufacturing
In manufacturing, execution rarely aligns perfectly with the original plan. A machine goes into an unplanned maintenance state. A supplier delivers late. A quality hold pulls a batch off the line. An urgent customer order changes the demand picture mid-shift. Within hours of a production day starting, the schedule that looked optimal at the beginning …
Why Purpose-Built Software Still Matters in an AI-First World
There is a growing assumption in software that AI-assisted development is rapidly closing the gap between general-purpose tools and specialized applications. If a capable model can generate code quickly and iterate on it in real time, the argument goes, purpose-built software becomes less necessary. Domain expertise gets absorbed into the model, and the barriers to …
SLMs vs. LLMs: The Edge Advantage for Factory Operations
As manufacturers evaluate AI strategies, one of the most important architectural decisions is where AI inference should run and what type of model is best suited for shop floor operations. For many complex manufacturing environments, Small Language Models (SLMs) deployed at the edge offer meaningful advantages over large language models running in the cloud. Understanding those advantages …
Why General-Purpose LLMs Fall Short in Complex Manufacturing
There is a lot of discussion right now about the role of large language models (LLMs) in manufacturing. The excitement is understandable. LLMs like those powering ChatGPT or Gemini can reason through complex, multi-step problems, generate code, summarize documents, and respond to natural language queries across a wide range of topics. For many business functions, …
Insight “autonomy” on the shop floor
From flying elephants to aliens landing UFOs at Hyde Park, there are certain things we’re never likely to see. Extend that list into the world of manufacturing however, and soon more practical concerns emerge.
