AI in factories
Why factory dashboards go unused
Dashboards fail when they show everything and answer nothing. If shift leads cannot see what to do next, they go back to paper and phone calls.
Design the dashboard around the next action
Ask a shift lead which decisions are difficult today and what information would change those decisions. A dashboard with every available metric can still leave the user unsure what to do. Establish clear definitions for output, downtime, rejects and other key measures. Show the last update time and explain gaps. An old number presented as live can be more harmful than an obvious missing value.
What to check
- Observe how operators currently identify problems and who they contact to resolve them.
- Compare metric definitions across shifts and source systems.
- Test whether users can find the current issue and its owner without a walkthrough.
What to change
Start with a small set of measures linked to actions. Use readable labels, units and time ranges. Offer detail when needed while keeping the first screen focused on the present shift. Identify data-quality problems visibly. Agree who owns each issue and how the dashboard fits into existing handover and review routines.
How to evaluate the result
Evaluate the time needed to identify a problem, the actions taken and whether those actions improve the underlying process. Review usage in context: an occasional management report and an operator display have different purposes.
What the result does not establish
A dashboard cannot repair unclear responsibilities or inconsistent source data by itself. Resolve those process issues alongside interface work, and keep a fallback available when the feed is delayed.
Further reading
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