You know that feeling when something just fits? Not just a good fit, but a perfect, made-for-you kind of fit. That's what we often mean when we say something is 'tailored.' But in the world of complex information, especially in fields like engineering and manufacturing, 'tailored' can sometimes feel a bit… well, too simple.
I was digging into some material about managing technical knowledge – the kind that spans from initial research all the way through to production. It’s fascinating how much information gets generated, and how it’s passed from one team to another. Think about it: researchers, modelers, developers, manufacturers – they all have their piece of the puzzle. But the way this knowledge is handed over can be a real bottleneck. It’s often a formal process, involving reinterpretation, which, as you can imagine, can lead to errors and inconsistencies. And then there's the sheer volume of it all; it’s not something you can just shove into a standard database.
What struck me most was the distinction between explicit knowledge (the stuff you can write down, like experimental data or models) and tacit knowledge (the 'know-how' that lives in people's heads). Decisions, especially in manufacturing, are a blend of both. You take what’s documented and combine it with that intuitive understanding that comes from experience.
To really use knowledge effectively, though, you need more than just the data itself. You need the context – the circumstances under which it was created. This is what they call 'provenance.' It’s like knowing the story behind a recipe, not just the ingredients. This context is crucial for understanding the quality of the knowledge, for reproducing it, and for learning from it. And it needs to be accessible, structured in a way that makes sense.
The approach discussed involves a 'workflow-based framework.' The idea is that for every piece of information, there's a well-defined procedure, a workflow, that captures its context. These workflows can be thought of as networks of tasks, performed in a specific order, with information flowing between them. They’ve categorized these into business workflows, scientific workflows, experimental procedures, and manufacturing recipes. Each serves a distinct purpose, but they often connect and influence each other, creating a hierarchy.
For instance, a scientific workflow might calculate a production schedule, which then feeds into a manufacturing recipe that dictates how a product is actually made. These hierarchies can be quite deep, spanning different departments and involving multiple types of workflows. It’s about creating a system that not only helps generate knowledge but also makes it accessible to everyone involved throughout its entire life cycle.
So, when we talk about making knowledge fit, it’s not just about tailoring. It’s about creating a system that’s customized, bespoke, purpose-built, and context-aware. It’s about ensuring that the right information, with all its nuances and history, is available to the right people at the right time, allowing for more informed decisions and smoother operations. It’s a much deeper, more integrated kind of fit.
