6 Metrics Every Shop Floor Dashboard Needs
Your shop floor dashboard is probably showing you 47 metrics. And you're ignoring 45 of them.
That's not a criticism. It's human nature. When everything is important, nothing is important. The dashboard becomes wallpaper.
The question isn't what you can track. It's what you should track. What numbers actually change behavior on your floor? What metrics, if they moved, would make someone do something different?
After working with manufacturers of all sizes, we've found that most effective shop floor dashboards focus on six core metrics. Not because other data doesn't matter, but because these six give you the clearest picture of what's happening right now and what needs attention.
The Problem With Most Manufacturing Dashboards
Walk into any modern shop and you'll see screens everywhere. Wall monitors. Tablets at machines. TVs in the break room showing colorful charts.
But ask the supervisor what those numbers mean and you'll often get a shrug. "IT set that up. I just look at the schedule."
This happens because most dashboard projects start from the wrong question. They ask "what data do we have?" instead of "what decisions do we need to make?"
The result is a dashboard that displays everything the system can measure. Cycle times. Machine states. Part counts. Temperatures. Vibration readings. It looks impressive. It impresses visitors. But it doesn't help anyone run the floor better.
Effective dashboards start with a different question: if this number changed, what would you do about it?
If the answer is "nothing," the metric doesn't belong on your main dashboard. Maybe it belongs in a detailed report. Maybe it's useful for troubleshooting. But it shouldn't compete for attention with the numbers that drive daily decisions.
Metric 1: OEE (Overall Equipment Effectiveness)
OEE is the single most useful number for understanding how well your equipment is performing. It combines three factors into one percentage: availability, performance, and quality.
Availability measures how much of the scheduled time your machine was actually running. If you planned to run eight hours but the machine was down for two, availability is 75%.
Performance compares actual output to theoretical maximum. If your machine should produce 100 parts per hour but actually made 80, performance is 80%.
Quality tracks first-pass yield. If you made 100 parts and 5 were scrap, quality is 95%.
Multiply these three together and you get OEE. A machine with 75% availability, 80% performance, and 95% quality has an OEE of 57%.
World-class manufacturing targets 85% OEE. Most shops run between 40% and 60%. That gap represents massive untapped capacity. You don't need more machines. You need to get more from the ones you have.
The power of OEE is that it tells you where to focus. Low availability? You have a downtime problem. Low performance? Something is slowing production. Low quality? You're making parts you can't ship.
Without OEE, you might chase the wrong problem. You might buy another machine when the real issue is excessive changeovers. OEE keeps you honest about where the losses actually are.
Metric 2: Machine Utilization
Machine utilization answers a simpler question than OEE: what percentage of available time is the machine actually cutting?
Some shops confuse utilization with OEE. They're related but different. Utilization only cares about whether the machine is running. OEE cares about whether it's running well.
A machine can have high utilization and low OEE if it's running slowly or making scrap. And you can have decent OEE on a machine that sits idle half the day because when it runs, it runs well.
Utilization matters because idle machines are expensive. The building is heated whether the machine runs or not. The operator is paid whether the machine runs or not. Every hour of idle time is capacity you can't recover.
Target utilization depends on your operation. Job shops with high-mix production might target 60-70%. Repeat production environments can push toward 85%.
If utilization is low, the questions to ask are: why isn't the machine running? Is it waiting for material? Waiting for setup? Waiting for an operator? Waiting for the previous operation to finish?
Low utilization usually points to a scheduling or flow problem, not a machine problem.
Metric 3: On-Time Delivery
On-time delivery measures the percentage of orders shipped by the promised date. It's the metric your customers care about most.
You can have the best OEE in the industry. You can run your machines 24/7. None of it matters if parts don't arrive when customers expect them.
Calculate on-time delivery by dividing orders shipped on time by total orders due in that period. If 85 out of 100 orders shipped when promised, you're at 85% on-time delivery.
Most manufacturers target 95% or higher. World-class operations hit 98%+. But be honest about how you calculate it. Some shops measure against the "revised" promise date after delays were communicated. That's not on-time delivery. That's on-time-to-the-new-date.
On-time delivery is a lagging indicator. By the time it drops, the damage is done. But watching the trend helps you catch problems before they become patterns. If on-time delivery starts slipping from 96% to 93% to 89%, something is breaking down in your process.
The fix usually isn't working faster. It's better visibility into what's due, what's behind, and what's at risk.
Metric 4: Work in Progress (WIP)
WIP counts the number of jobs currently on the floor that haven't shipped yet. It's one of the most misunderstood metrics in manufacturing.
Intuition says more WIP is better. More jobs in process means more options, right? More flexibility?
Actually, the opposite is true. High WIP increases lead times. It clutters the floor. It makes it harder to find urgent jobs. It ties up capital in inventory that isn't earning revenue.
Little's Law proves this mathematically: lead time equals WIP divided by throughput. If you have 50 jobs in progress and complete 10 per day, average lead time is 5 days. Cut WIP to 30 and lead time drops to 3 days, even though throughput stays the same.
Most shops carry far more WIP than they need. They release jobs early "just in case." They start jobs before materials are ready. They let jobs sit between operations instead of pulling them through.
Track WIP daily. Set a target based on your capacity and desired lead times. When WIP creeps above target, stop releasing new jobs until the floor clears.
It feels counterintuitive to slow down job releases when customers want their parts. But controlling WIP is one of the fastest ways to reduce lead times and improve delivery.
Metric 5: Downtime by Reason
Tracking total downtime is useful. Tracking downtime by reason is powerful.
When a machine stops, why did it stop? Was it planned maintenance? Changeover? Breakdown? Waiting for material? Waiting for an operator?
Without reason codes, all downtime looks the same. You know machines aren't running as much as they should, but you don't know what to fix.
With reason codes, patterns emerge. Maybe 40% of your downtime is changeovers. Now you know to focus on setup reduction. Maybe breakdowns are concentrated on one machine. Now you know where to invest in maintenance.
The categories don't need to be complicated. Most shops do well with 8-12 reason codes:
- Planned maintenance
- Unplanned breakdown
- Changeover/setup
- Waiting for material
- Waiting for operator
- Waiting for quality approval
- Tooling issue
- Programming issue
The key is consistency. Operators need to log the actual reason every time, not just pick the first option. This takes training and follow-up, but the data is worth it.
Run a weekly Pareto chart of downtime reasons. Focus your improvement efforts on the top three. When those shrink, the next three become the priority.
Metric 6: First Pass Yield
First pass yield (FPY) measures the percentage of parts that pass inspection without rework or scrap. It's your clearest indicator of process quality.
If you make 100 parts and 95 pass inspection on the first attempt, FPY is 95%. The other 5 parts might be scrapped, reworked, or sent back for additional operations.
FPY matters more than total defect rate because rework is expensive. A part that gets reworked might eventually ship, but it consumed extra labor, machine time, and floor space. Those hidden costs add up.
Track FPY by operation, not just by job. You might have 92% final yield but discover that one operation is running at 80% while others are at 99%. That one operation is where your quality problem lives.
When FPY drops, investigate immediately. Don't wait for the end of the month. Quality problems tend to get worse, not better, if you ignore them.
The goal is to catch problems at the source. A part rejected at final inspection already consumed all the value-add operations. A part caught at the first operation only wasted one step.
Building Your Dashboard
Start with these six metrics. Display them prominently. Update them in real time if possible, or at minimum once per shift.
Resist the temptation to add more. Every metric you add dilutes attention from the metrics that matter. If someone asks to add a metric, ask them: what decision will you make differently based on this number?
Color coding helps. Green for on-target, yellow for warning, red for action required. People process colors faster than numbers.
Position matters too. Put the most important metrics where eyes naturally land. That's usually top-left on a landscape display.
Consider different views for different roles. Operators care about the machine they're running. Supervisors care about the whole cell or line. Managers care about the whole floor. One dashboard doesn't fit everyone.
Finally, review your dashboard monthly. Are people actually looking at it? Are the thresholds still right? Has the operation changed in ways that make some metrics more or less relevant?
A dashboard is never finished. It evolves with your operation.
From Numbers to Action
The best dashboard in the world is useless if nobody acts on what it shows.
Every metric on your dashboard should connect to a response. When OEE drops below 65%, who gets notified? When on-time delivery falls under 90%, what meeting gets scheduled? When WIP exceeds target, who stops releasing new jobs?
Document these responses. Train people on them. Hold people accountable for following them.
The dashboard isn't the end goal. Improvement is the goal. The dashboard just makes the current state visible so you can see where to focus.
Ready to see your shop floor in real time? Book a demo and we'll show you how WorkCell's real-time dashboards give you the visibility to make better decisions, faster.
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