--- title: "OEE Tracking Software That Measures Availability and Performance Automatically | IoTFlows" description: "IoTFlows tracks OEE factor by factor: availability from running and stopped time and performance from parts counted against the ideal cycle time, both measured automatically by a sensor on every machine, and quality from the scrap operators log with a reason. Each factor is reported on its own. Customers see 35% less unplanned downtime in the first six months (median across IoTFlows customers, internal data)." source_url: "https://www.iotflows.com/solutions/oee-tracking" --- OEE tracking # OEE tracking that shows which factor is costing you A sensor on every machine measures availability and performance on its own, second by second. Quality comes from the scrap your operators log. Each factor gets its own number, so you know whether to fix the stops, the speed or the scrap. [How it works](https://www.iotflows.com/solutions/oee-tracking#availability) ## One OEE number says something is wrong. Three say what. Availability, performance and quality fall for different reasons and get fixed by different people. IoTFlows measures each one from the machine, so the morning meeting starts with the lever to pull, not an argument about the number. Availability ## Availability, measured by the machine [SenseAi](https://www.iotflows.com/products/senseai), [SenseAi Embedded](https://www.iotflows.com/products/senseai-embedded) or [BeamTracker](https://www.iotflows.com/products/beamtracker) decides every second whether the machine is running. **Availability is running time over running plus stopped**, inside the shifts you set. No one types a start or a stop. Every stop then carries a reason, so you see [where the lost hours went](https://www.iotflows.com/solutions/downtime-tracking), not just how many there were. [How uptime is calculated](https://www.iotflows.com/docs/monitoring/metrics-reference#uptime) Performance ## Performance, against the ideal cycle time Set an ideal cycle time on each operation and the parts gauge compares **what the machine made with what it could have made** in the time it ran. A press can run all shift and still fall short. A green uptime gauge beside a red parts gauge says it’s running slow, not stopped, and it shows up while the shift can still be saved. [Set cycle times](https://www.iotflows.com/docs/production/cycle-times-and-filters) Quality ## Quality, from the scrap your operators log Operators log scrap from the job runner in a few taps, and **every entry carries a reason** from one list the whole plant shares. [Shift Production](https://www.iotflows.com/docs/production/shift-production) shows good parts as a share of everything counted, with the bad count beneath it, for every operation and every machine. [Log scrap with a reason](https://www.iotflows.com/docs/production/log-scrap) Goals ## One goal for the plant. Your own where it differs. Set one OEE goal and **every gauge colors itself against it**: green at or above, red below, on uptime and parts alike. Give an operator-loaded mill its own goal beside a lights-out cell, and each machine is held to a number it can actually reach. [Set OEE goals](https://www.iotflows.com/docs/monitoring/oee-goals) The definitions ## Every number explains itself. One formula each. When someone asks where a number came from, the answer is one line long. The [metrics reference](https://www.iotflows.com/docs/monitoring/metrics-reference) defines each one. Availability: Uptime on every machine card (Running time) ÷ (Running time + Stopped time) Measured by the sensor, inside your shifts. Time it could not see is reported as unknown and counts on neither side. Performance: The parts gauge, on cards and Shift Production (Total units × Ideal cycle time) ÷ (Run time) Counted by the sensor, against the ideal cycle time set on each operation. Quality: The Quality column on Shift Production (Good units) ÷ (Total units) The scrap your operators log, each entry with a reason, shown as the bad count. Reports ## By shift, machine and operator. Today or last quarter. Every report exports to CSV or PDF, and can [email itself on a schedule](https://www.iotflows.com/docs/monitoring/share-and-schedule-reports). - [### Shift Production This shift, operation by operation: goal, good count, pace and quality, one row per machine. Read the guide](https://www.iotflows.com/docs/production/shift-production) - [### Advanced Report Uptime, downtime and unknown hours for any period, by machine and department. Read the guide](https://www.iotflows.com/docs/monitoring/advanced-report) - [### Historical Good parts and scrap side by side, week over week or year over year. Read the guide](https://www.iotflows.com/docs/production/historical-production) - [### Operator Performance Each operator with the machine they ran, session by session. Read the guide](https://www.iotflows.com/docs/monitoring/operator-performance) Customer story ## Jesel made OEE the daily driver of its CNC shop Jesel builds valvetrain components on precision CNC machines in Lakewood, New Jersey, and reads its dashboard every morning. Daily OEE and utilization on every machine 100% cycle time accuracy, actual against the bill of material Minutes to respond to an issue, not hours or days ### Challenge Jesel ran capable CNC machines but couldn’t see how well each one was performing. The cycle times in its bills of material were taken on faith, and setup or tooling problems surfaced only after they had caused scrap or rework. ### Solution SenseAi on every CNC machine puts OEE and utilization on one dashboard, shift by shift. The team compares actual cycle times with the bills of material every day and catches deviations while they can still be fixed. ### Products [SenseAi](https://www.iotflows.com/products/senseai), with ideal cycle times set per operation. [Read the Jesel story](https://www.iotflows.com/customers/jesel) > “The IoTFlows Machine Sensors continue to provide us with valuable real-time data that we rely on to measure machine utilization and overall equipment effectiveness (OEE). The dashboard has become an essential tool in our daily operations.” Pete Gonella, Director of Manufacturing, Jesel Datwyler raised efficiency 20% across 20+ injection molding machines by acting on its downtime reports. [Read the Datwyler story](https://www.iotflows.com/customers/datwyler). Criollos Foods has 92% of its production lines hitting their target rate. [Read the Criollos Foods story](https://www.iotflows.com/customers/criollos-foods). ## Questions people ask ### What does IoTFlows OEE tracking include? Each OEE factor, measured and reported on its own. Availability is uptime: running time over running plus stopped time inside your shifts, decided second by second by a SenseAi, SenseAi Embedded or BeamTracker on the machine, with time the sensor could not see reported separately as unknown. Performance is the parts gauge: parts made against what the machine could have made at its ideal cycle time in the same running time. Quality is the Quality column on Shift Production: good parts as a share of everything counted, with the scrap operators log, each entry with a reason. One percentage goal colors every gauge green or red, with per-machine overrides. Shift Production, the Advanced Report, Historical and Operator Performance cover the shift, any period and each operator, and every report exports to CSV or PDF. SenseAi and SenseAi Embedded attach magnetically in seconds and calibrate in under five minutes; BeamTracker bolts on with the supplied mount in 10 to 20 minutes. ### How does IoTFlows measure OEE? Factor by factor. Availability comes from running and stopped time and performance from parts counted against the ideal cycle time, both measured automatically by the sensor. Quality comes from the scrap your operators log with a reason. Each factor is reported on its own, so you see which one is costing you. ### What is OEE? Overall equipment effectiveness: availability × performance × quality. Availability is how much of the scheduled time a machine ran, performance how fast it ran against its ideal cycle time, and quality how many of the parts it made were good. A machine at 90% availability, 95% performance and 99% quality has an OEE of 84.6%. ### How does it get availability without a PLC? The sensor decides, second by second, whether the machine is running or stopped: SenseAi from vibration and sound, SenseAi Embedded from vibration, BeamTracker from parts crossing its laser beam. Availability is running time over running plus stopped time inside the shifts you define. Nothing connects to the controls. ### Where does the performance number come from? From parts counted against the ideal cycle time. Each operation in the Parts List carries an ideal cycle time and a quantity per cycle, and the parts gauge divides the parts made by what that cycle time allows in the machine’s running time. A press running all shift at 40 strokes a minute against an ideal of 60 shows a green uptime gauge and a red parts gauge. ### Do operators have to log scrap? For quality, yes. An operator presses Scrap in the job runner, sets the quantity and picks a reason from one organization-wide list, and Shift Production shows the bad count in its Quality column. Availability and performance need nothing from operators: the sensor measures both. ### Can I set a different OEE goal per machine? Yes. One organization goal colors every gauge; you override it on the machines whose realistic ceiling genuinely differs by turning on Set Custom Utilization / OEE Goal from that machine’s Uptime gauge. The same goal colors the machine’s parts gauge. ### Can I see OEE by shift, machine or operator? Yes. Shift Production lists every operation that ran this shift with its calculated goal, good count, pace and quality, one row per machine. The Advanced Report totals uptime, downtime and unknown hours for any period by machine and department, Historical compares good parts and scrap across weeks, months and years, and Operator Performance pairs each operator with the machine they worked. ### Why report each factor on its own? Because each one has a different fix. Availability drops when machines stop, performance when they run slower than their ideal cycle, quality when parts are scrapped. With each factor beside its own definition, you know whether the next fix is a changeover, a cycle time or a tool, and who should make it. ## Documentation Want the detail? These guides walk through each feature step by step. - [How each metric is calculatedUptime, the parts gauge, FPY and cycle time, defined once.](https://www.iotflows.com/docs/monitoring/metrics-reference) - [Set OEE and utilization goalsThe one percentage that colors every gauge, and where to override it.](https://www.iotflows.com/docs/monitoring/oee-goals) - [Track production against shift goalsEvery operation this shift against its calculated goal, with quality.](https://www.iotflows.com/docs/production/shift-production) - [Set cycle times and downtime filtersThe numbers on an operation that the parts gauge runs on.](https://www.iotflows.com/docs/production/cycle-times-and-filters) - [Log scrap and manage scrap reasonsScrap per work session, with a reason from one list.](https://www.iotflows.com/docs/production/log-scrap) - [Build a utilization reportUptime, downtime and unknown hours for any period.](https://www.iotflows.com/docs/monitoring/advanced-report) - [Compare production over timeGood parts and scrap by week, month, quarter or year.](https://www.iotflows.com/docs/production/historical-production) - [Troubleshoot wrong utilization or OEETimezone, shifts, window, switches: the order to check in.](https://www.iotflows.com/docs/monitoring/troubleshoot-monitoring) ## See your three factors this month Start with 10 to 25 machines for four weeks, with no commitment to buy. Availability is there after the first shift; performance follows once each operation has its ideal cycle time.