The keys to productivity with assembly line images
In manufacturing, it’s true that a picture is worth a thousand words — and often hours or days. At Instrumental, we’re building an intelligent factory quality system to modernize manufacturing. We learned from speaking with engineers and leaders at both product companies and manufacturers that simply looking at dashboards of numerical data hasn’t lived up to its promise of enabling more efficient development and production. We believe it’s because the data that’s typically collected and stored is too low-resolution to be actionable — manufacturing needs better data sources. Our first product incorporates multiple data types, but is designed around images, one of the few types of “measurements” you can take where you don’t need to know what you want to measure beforehand. Below, we’ll cover best practices to follow when using such images to keep manufacturing schedules on track.
Make them accessible from anywhere
Unfortunately, assembly line data is too often trapped on individual machines and simply overwritten over time (if it’s collected at all). However, when images are available and other best practices are followed, “using [assembly line images] to review issues is more efficient than being in the factory” according to John Brock, Director of Product Design at Pearl Automation. That’s because images can provide a complete historical record and can be compared easily, whereas an engineer on the assembly line can only see a subset of units in real time.
Make them searchable
Finding the right data quickly and easily is the key to problem-solving efficiency, and that means you’ll need a fast search system that supports unit and inspection metadata. Here’s an example of a search for unit data in Instrumental:
Similarly, the ability to see “more like this” makes comparison faster. In practice, this typically means seeing data from other units that went through the same station around the same time. Users do this in about 57% of Instrumental sessions.
We've also written about data management best practices as a whole. There’s much more to say about data derived from images — but we’ll save that for another post.