Knowledge Graphs for Factories: Connecting Work, Resources and Evidence
Some factory questions, like which jobs a late part will hold up, depend on how records connect across many systems. A knowledge graph stores those connections directly.
Records that connect
Say a part is going to arrive a week late. Which jobs use it, which jobs wait on those, and which crews and inspections does that touch? The answer is spread across several computer systems, and you have to follow it link by link. Factory information is connected by nature. A job uses tools, happens in a certain area, needs certain skills and uses up parts. It follows earlier steps and leaves records behind, like inspection results. Most companies store this in relational databases, which are sets of linked tables. They do that well. But questions that follow a long chain of links across many tables can get hard to write and understand.
A knowledge graph is more like a map of connections. It stores the things, like parts, jobs, tools and people. It also stores the named links between them, like “uses”, “needs” and “follows”, and treats those links as data too. It doesn’t have to replace a company’s existing systems. It can sit on top of them as a shared layer that says how everything fits together.
Asking questions about connections
With a graph, you ask questions by following links. Which later work gets hit by a late part? Which tool or crew ties together jobs that otherwise look separate? Which work instructions depend on a similar set of things? Embeddings can help too. An embedding turns an item into a list of numbers, so similar items get similar numbers. That helps you find look-alikes, on top of what the links show.
Keeping track of where each fact came from (its provenance) is a must. A graph that merges data from many company systems, but loses track of sources, gets hard to trust.
Where AI fits in
AI changes how people can ask these questions. A language model, the kind of AI behind chat tools, can turn a plain-English question into a graph search and sum up the results. But the graph should stay the source of the facts. The model explains, and fixed, repeatable searches pull up the facts. Keeping those jobs apart is a useful habit when building AI for engineering.