Tech, ExplainedIndustrial tech and mobility
Product

OEE and machine monitoring

Running, idle or stopped, and why.

BEFORE ITAFTER ITOEE andmachine monitoringCAI SoftwareVegam SolutionsApplixCOVERSRun or stopStop reasonsOEELive screensProgrammable logiccontroller (PLC)reportsmachinestatusIndustrial edge computingsuppliesmachine datasendsOEE figuresManufacturing executionsystem (MES)sendsrunning hoursAsset performancemanagement (APM)↗ PREDICTIVEMAINTENANCE SOFTWARECONCEPTS IT USESIndustrial IoT (IIoT)BEFORE ITAFTER ITOEE andmachine monitoringCAI SoftwareVegam SolutionsApplixCOVERSRun or stopStop reasonsOEELive screensProgrammablelogiccontroller (PLC)reportsmachinestatusIndustrialedge computingsuppliesmachine datasendsOEE figuresManufacturingexecutionsystem (MES)sendsrunning hoursAsset performancemanagement (APM)↗ PREDICTIVEMAINTENANCESOFTWARECONCEPTS IT USESIndustrial IoT (IIoT)BEFORE ITProgrammable logic controller (PLC)↓ reports machine statusIndustrial edge computing↓ supplies machine dataOEE and machine monitoringCAI SoftwareVegam SolutionsApplixRun or stopStop reasonsOEELive screensAFTER IT↓ sends OEE figuresManufacturing execution system (MES)↓ sends running hoursAsset performance management (APM)↗ PREDICTIVE MAINTENANCE SOFTWARECONCEPTS IT USESIndustrial IoT (IIoT)
OEE and machine monitoring collect the status of each machine from PLCs and edge computers, and calculate availability, performance and quality for the MES and asset performance software.
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OEE and machine monitoring is software that shows whether each machine in a factory is running, idle or stopped, and why. OEE stands for overall equipment effectiveness. It is one percentage that sums up the good output a machine made, compared with what it could have made.

OEE is the product of three numbers. Availability is the share of the planned time in which the machine actually ran. Performance is how fast it ran, compared with its ideal speed. Quality is the share of the pieces that were good. Multiplying the three gives the OEE.

Take, for example, a filling machine that runs an 8-hour shift, which is 480 minutes. It stops for 60 minutes because of a jam and a change of product. So it ran for 420 minutes, and its availability is 420 out of 480, or 87.5%. Its ideal speed is 100 bottles a minute, but it made 33,600 bottles in 420 minutes, which is 80 a minute. Its performance is 80%. Of those bottles, 32,256 were good, so its quality is 96%. The OEE is 0.875 × 0.80 × 0.96, which is 67.2%.

The number alone does not fix anything. The software also records a reason for every stop, such as no material or a broken tool, so that the plant can see what loses the most time. Machines send their status through PLCs, or through edge computers that connect to machines of any age. This is a basic use of industrial IoT. Machine monitoring differs from the MES: it watches the machine, whereas the MES follows the customer's order.

The stop reasons are captured in two ways. A PLC can detect some of them by itself, such as a jam sensor that trips. For the others, the operator picks the reason from a short list on a screen at the machine. A common rule of thumb is that an OEE near 85% is excellent for a plant that makes separate items, and many plants find that theirs is below 60% when they first measure it. The plant head and the shift supervisors use the live screens, and the maintenance team uses the stop reasons.

The data goes to the MES for the production records. It goes also to asset performance management, which uses it to plan maintenance.

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