The Seven Metrics Worth Putting on a Factory Dashboard

The seven metrics worth putting on a factory dashboard are live machine status, production count against target, downtime by reason, first-pass yield, changeover time, schedule adherence, and OEE as a trend. Each one passes the same test: someone on the floor can look at it and change what they do in the next hour, or a manager can look at it and change what they fix this month. Everything else on a factory dashboard is decoration, and decoration trains people to stop looking.

That test matters more than the specific list. Dashboards fail in a predictable way: they launch with eighteen tiles, nobody can say what any tile should make them do, and within a quarter the screen is wallpaper. Seven numbers that provoke action beat thirty that provoke nothing. We've written a broader piece on building production dashboards that covers displays, refresh rates, and rollout; this one is about what actually belongs on the screen.

What Makes a Metric Worth Space on a Factory Dashboard?

Three filters, applied ruthlessly. First: is it automatic and trustworthy? A metric fed by end-of-shift memory will be optimistic, and one manual correction discovered by the floor destroys the whole screen's credibility. Numbers should come from sensors, PLCs, or single-keystroke operator input at the moment things happen. Second: is it timely enough to act on? A live status needs to be seconds old; a yield number can be shift-old; a monthly figure belongs in a meeting, not on a wall. Third: does someone own it? Every tile needs a person who is expected to react when it goes the wrong color. If you can't name that person, delete the tile.

One more design note before the list: the audience decides the metric's form. Operators need "now and today." Supervisors need "today versus plan, and why." Owners need trends. Cramming all three views onto one screen is how eighteen-tile dashboards happen.

The Seven Metrics

1. Live machine status

The simplest and most-watched tile: every major machine, colored running, idle, down, or in changeover, with a timer showing how long it's been in that state. The behavioral effect is blunt and useful. A red tile with "47 min" on it gets a supervisor walking toward the machine in a way a paper log never did. Status should come from the machine itself, via a PLC bit, a stack-light tap, or a current transformer on the motor, so nobody can argue with it.

2. Production count vs. target

Actual pieces against where the shift should be right now, not against the end-of-shift total. A run-rate line ("you are 62 pieces behind pace") converts a vague sense of "busy" into a specific gap while there's still time to close it. Counts should come from a sensor or PLC register; targets from the schedule, adjusted for planned downtime so the target is honest.

3. Downtime by reason

Total downtime is a mood; downtime by reason is a to-do list. The tile that matters is a rolling Pareto: this week's top five reasons by minutes lost. Capturing reasons is the hard part, and the workable pattern in small plants is automatic detection of the stop (from the same signal as the status tile) plus a two-tap operator prompt for the why. Keep the reason list under a dozen options or the data degrades into "other."

4. First-pass yield

Good parts straight through, divided by total parts, with scrap and rework counted separately. Yield is the quality metric that belongs on the wall because it moves shift to shift and operators influence it directly. Pair the number with its top scrap reason, captured the same two-tap way as downtime. A plant that watches yield daily finds process drift in days; a plant that reviews quality monthly finds it in the customer-complaint queue.

5. Changeover time

Last changeover duration against the standard, per machine or cell. In high-mix shops, changeover is frequently the largest single availability loss, and it's peculiarly responsive to being measured: posting the number where the crew can see it typically shaves minutes before anyone runs a formal SMED event. Measure from last good piece of job A to first good piece of job B, automatically where the machine signals allow it.

6. Schedule adherence

Jobs completed on time as a percentage, plus the current at-risk list. This is the tile that connects the floor to the customer. It's also the one most dependent on your scheduling data being real; if the schedule is a fiction maintained for the morning meeting, fix that first. For supervisors, the at-risk list is the actionable half: three jobs, each with the reason it slipped.

7. OEE, as a trend

OEE earns its slot only in trend form: this machine, this month, against its own history, ideally split into its availability, performance, and quality components so a move is diagnosable. As a big live number it's nearly useless on the floor, because no operator can act on "68 percent" without knowing which component moved. The full math and its traps are covered in our OEE guide for small manufacturers.

Where Does the Data Come From?

Every metric above reduces to four data feeds: machine state, piece counts, operator reasons, and the schedule.

Machine state and counts are machine monitoring territory. Modern PLCs expose both over Ethernet, typically via Modbus TCP, OPC UA, or EtherNet/IP, and a small gateway can poll a whole floor. Older equipment gets instrumented from the outside: a split-core current transformer on the motor leads to infer run state, a retroreflective photoelectric sensor or proximity switch on the ejection point to count parts, a relay tap on the stack light where one exists. Hardware per machine commonly lands between $200 and $1,500 depending on how cooperative the machine is, before integration labor.

Reasons come from people, and the design rule is that capture must cost the operator less than five seconds: a wall-mounted tablet, two taps, done. The schedule feed comes from wherever scheduling truly lives, which in many small plants means the dashboard project forces an honest answer about whether that's the ERP or a spreadsheet. Under it all sits a time-series database and a display layer; the software side is a modest build, and the integration with your specific machines is invariably where the real engineering hours go.

Which Metrics Should You Put Up First?

Start with two: live machine status and downtime by reason. They share the same sensor infrastructure, they're the fastest to trust because the machine itself is the witness, and they produce the quickest behavioral change on the floor. Hypothetically, a shop instrumenting eight machines might spend $5,000 to $12,000 on sensors and gateway hardware plus integration time, and have both tiles live in a few weeks.

Add production count versus target once counting is reliable, then first-pass yield once reason capture is habitual. Schedule adherence and OEE come last, not because they matter less, but because they depend on everything underneath being honest first. A dashboard rolled out in this order builds credibility with each tile. One launched all at once, with half the feeds still manual, spends its credibility on day one.

What to Leave Off

A few numbers show up on factory dashboards constantly and almost never survive the three-filter test. Total units produced this year: impressive, unactionable, and invisible within a week. Utilization as a headline percentage: it quietly rewards running machines to build inventory nobody ordered. Anything measured in dollars on a floor-facing screen: cost data invites arguments about the accounting instead of action on the process. Safety-incident day counters: track safety seriously, but a big number that resets on a bad day punishes reporting, which is the opposite of what you want. And any metric updated by hand on Fridays: if it's manual and weekly, it's a report, and it should live in one.

FAQ

How many metrics should a factory dashboard have?

Five to eight per screen is a defensible ceiling, and fewer is fine. The limit isn't screen space; it's attention. If a supervisor can't glance at the board and know within five seconds whether to act, the board has too much on it.

Should the dashboard show individual operator performance?

Publicly, no. Operator-level output on a wall screen reliably produces gaming, resentment, and misleading comparisons across jobs of different difficulty. Team- and machine-level numbers on the wall, individual coaching in private, is the pattern that holds up.

How often should factory dashboard data refresh?

Match the refresh to the decision. Machine status and counts should update within seconds to a minute; yield and downtime Paretos each shift; OEE and adherence trends daily. Refreshing a monthly-decision metric every second adds motion, not information.

Can I build this with the systems I already have?

Frequently, yes, at least for the first tiles. If your machines have networked PLCs, the data path may already be 80 percent there, and the work is extraction and display rather than new sensors. Mixed-age floors need some outside-in instrumentation, which is inexpensive per machine but benefits from someone who has done it before.

If you're ready to put honest numbers on the wall and want the instrumentation done right the first time, this sits squarely in our industrial automation work. Reach out here and we'll talk through your machines, your data, and which two tiles to light up first.