A Power BI OEE dashboard that shows where machine time goes
A Power BI OEE dashboard measures overall equipment effectiveness, which is availability multiplied by performance multiplied by quality. It tells a plant how much of its planned production time turns into good parts. The page then breaks the lost time into causes. Our consultants build the model in your own Microsoft tenant and keep every formula visible.
- Availability, performance, quality
- Losses ranked by time
- Machine and shift views
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What does a Power BI OEE dashboard measure?
A machine can lose output in three ways. It can stand still when it should run. It can run slower than it should. Or it can make parts that fail inspection. Overall equipment effectiveness puts all three into one figure.
The single figure is only a start, however. Two machines with the same score may have very different problems. One machine stops often, while the other runs slowly. So the page always shows the three factors next to the total.
Below that, a loss view ranks the reasons. For example, changeovers, breakdowns and material shortages each get their own bar in minutes. Then a production manager sees which loss to attack first instead of chasing the score. For plant views beyond machine time, such as output, stock and service levels, see operations reporting in Power BI.
The three OEE factors and the inputs behind them
These are the standard formulas, and each one needs specific inputs from the shop floor.
Planned production time
Take the shift time and remove planned stops such as breaks and scheduled maintenance. Every factor builds on this base.
Availability
Divide run time by planned production time. Run time equals planned production time less the stops.
Performance
Multiply the ideal cycle time by the total count, then divide by run time. It shows speed loss and small stops.
Quality
Divide the good count by the total count. Reworked parts do not count as good in the strict definition.
OEE
Multiply availability, performance and quality. So a weakness in any one factor pulls the whole score down.
Loss minutes by reason
Sum the stop time for each reason code, because minutes are easier to act on than ratios.
Which data does OEE reporting in Power BI depend on?
Three inputs feed the model. The schedule gives planned production time per machine and shift. Stop records give each downtime event with a start, an end and a reason. Production counts give total and good units per order or per hour.
These may come from a manufacturing execution system, from machine signals through a historian, or from operator entries. Any of them can work. The quality of the reason codes matters most, since a large bucket named other teaches nobody anything.
Totals need one rule that teams often miss. The score for a line or a week must come from summed times and counts, not from an average of machine scores. Therefore each factor is a measure that divides totals at whatever level the visual shows. Ideal cycle time also differs by product, so it sits in a product table.
Mistakes that distort a Power BI OEE dashboard
Six errors make the score look better or worse than the plant really runs.
- Averaging machine scores to get a plant score
- Using one ideal cycle time for every product
- Logging breakdowns as planned stops to protect the figure
- Leaving short stops unrecorded, so they hide in performance
- Counting reworked parts as good first time
- Comparing machines that make very different products on score alone
How we build a Power BI OEE dashboard
We check the inputs on one machine first, then widen to the plant.
Confirm the definitions
First operations agrees what counts as planned time, which stops are planned and how rework counts.
Connect schedule, stops and counts
We read the three inputs from your systems by a supported route and relate them to machine, product and shift tables.
Validate on a pilot machine
Next we calculate one machine by hand for a week and compare it with the model.
Roll out and hand over
Finally we extend the model to all machines, design the pages and train your team.
OEE reporting in Power BI: questions from plant managers
Can a Power BI OEE dashboard run in real time?
It depends on the data path. An imported model refreshes on a schedule, which suits shift and daily reviews. For shorter delays, options such as DirectQuery against a suitable database exist, and Microsoft documents their limits. Still, screens at the machine usually come from the floor system, while Power BI serves analysis.
What is a good score on an OEE dashboard in Power BI?
There is no single right number, and we do not quote targets. The score depends on the process, the product mix and how strictly you define planned time. So the useful comparison is a machine against its own past. A steady rise after a change tells you more than any outside benchmark.
We record downtime on paper. Can we still start?
Yes. Many plants begin with operator sheets typed into a simple structured list. The data is coarser, but it already ranks the big losses. Then the model stays the same when automatic machine signals arrive later. We help design the entry template, so reason codes stay consistent.
How do you handle products with different speeds?
Each product carries its own ideal cycle time in the product table. The performance measure multiplies that time by the count for each run and then sums the results. As a result, a slow product does not punish the machine's score. Your engineers supply the ideal times, and we store them with a valid-from date.
How do we get started?
Book a free 30-minute assessment, and we reply within 2 hours. Power BI Agency is a division of Sataware Technologies and is independent of Microsoft. Our consultants work online in your tenant, with a written fixed scope and weekly demos. You can also call (480) 485-7373.
Let's build your first dashboard together.
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