Lying Dashboards: How to Avoid the Traps of Visualization
"The dashboard says everything is fine — but on the shop floor, we know it’s not."
Sound familiar? Dashboards should be trusted allies in operational management, but too often they become distorted mirrors — showing a partial, polished, or even misleading version of reality.
In manufacturing, where every second counts and every mistake has a cost, relying on dashboards that “look good but lie” can be just as dangerous as flying blind.
The Paradox of the Perfect Dashboard
With the rise of MES (Manufacturing Execution Systems), factories are now filled with charts, indicators, digital traffic lights, and colorful trends. And that’s great — but only if what you see actually reflects what’s happening.
The problem? Many dashboards are designed to "look clear" — not to be useful. That’s where the deception begins.
The 5 Most Common MES Visualization Traps
1. KPIs That Are Too Generic (and Therefore, Useless)
Showing only OEE, scrap rate, and cycle time is like reviewing a movie based on the title alone. Without context, you don’t know why performance is good or bad, or what to do next.
🛠 Solution: Introduce custom KPIs that reflect your real value drivers — like setup time variability, material complexity, or operator load.
2. Beautiful, But Static Dashboards
Dashboards that always show the same data, in the same way, become background noise. Worse: they can hide significant changes or anomalies.
🛠 Solution: Use dynamic dashboards that adapt to shifts, departments, or production types — and highlight real-time anomalies.
3. Over-Reliance on Traffic Lights
Green is comforting. But sometimes it's a false sense of security. A machine may be running and "green," but producing with the wrong setup. Result? Scrap coming soon — even though the dashboard looks fine.
🛠 Solution: Combine qualitative and quantitative indicators, and implement smart alerts that flag inconsistencies even when KPIs appear normal.
4. No Cause-Effect Clarity
"Production below target" is a number — but what's behind it? Without connecting KPIs to process variables, we can’t act effectively.
🛠 Solution: Build cause-effect chains in your reports. Go beyond the "what" and surface the "why" — and who needs to respond.
5. Designed for Management, Not for People Who Operate
A dashboard with 20 KPIs might work for a director — but it's useless for a line operator. Likewise, a hyper-technical view won’t help executives.
🛠 Solution: Create role-based dashboards: operational for operators and supervisors, tactical for production managers, strategic for leadership.
A Good Dashboard Doesn’t Show Everything — Just What Matters
The truth is: an effective dashboard isn’t the one with more data, but the one that drives better actions. Think of it like a car dashboard — quick to read, relevant, and alerting you only when necessary.
3 Questions to Spot a Lying Dashboard
❓ Does this dashboard help someone make a concrete decision?
❓ Am I seeing just a symptom, or also its causes?
❓ If a metric changes, do I know who should act and how?
If the answer is “no,” you're likely looking at a dashboard that’s beautiful... but dishonest.
Conclusion: Visualization Is Power — But Only When Done Right
In today’s fast, complex manufacturing world, seeing clearly is the first form of control. But seeing poorly is worse than seeing nothing at all.
A modern MES must provide smart, contextual, and customizable dashboards. It’s not just about “showing data,” but about building real operational guidance tools.