A dashboard is usually where people want to start because it is visible. I get that. It feels like progress. But the dashboard is the last part of the system, not the first. Before the chart, I need the event.
The event is the thing that says what happened. The machine was running. It stopped. It was supposed to be stopped. It was waiting on material. It was down for maintenance. It was in changeover. It made parts. Some were good. Some were scrap. Without those definitions, the dashboard is just a nice way to display confusion.
The constraint is that events are not naturally clean. A machine can stop during changeover. A line can be waiting on upstream material but still have people working. A maintenance issue can start as an operator adjustment. Scrap can be found later. A production order can exist in SAP but the real machine timing lives somewhere else.
What I would check is whether every chart has a real event behind it. If I show utilization, what counts as available time? If I show downtime, what created the stop? If I show performance, where did ideal cycle time come from? If I show quality, when was scrap recorded and what was it tied to?
The surprise is that dashboard problems often look like visualization problems but are really definition problems. The chart is not wrong because the colors are bad. It is wrong because the underlying states are too vague.
Notes for next time: start with a timeline. Draw one shift by hand. Mark what the machine did, what the schedule expected, what the operator knew, what the PLC knew, and what SAP knew. The gaps will show up quickly. Then build the data model around those gaps. I do not need perfect. I need honest enough that the plant recognizes itself in the data.