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IIoT and OEE in car manufacturing

 

Industry Week defines OEE (Overall Equipment Effectiveness) as “How much right-first-time product did this machine produce compared to what it should have produced in the allocated time?”

 

OEE is typically calculated as Availability x Performance x Quality = OEE. If a manufacturer wants to track their factory’s OEE using this equation, then all the data they need can be extracted from their IIoT (Industrial Internet of Things) and ERP (enterprise resource planning) systems.

 

The factors in OEE are:

Availability Rate

running time / scheduled time

 

e.g. 10 hours out of 12 = 83%

Downtime, changeovers, and adjustments take away from scheduled time. Scheduled time is used instead of elapsed time, as no machine is scheduled to run 100% of the time.

Performance Rate

actual output / max theoretical output 

 

e.g. theoretical output = 60 minutes *

- 1.2 per minute = 72 

- actual output = 60 

- 60 / 72 = 83%

 Theoretical output is velocity times running time. So if a machine stamps out one device per minute under optimal conditions then it should product 60 devices in 1 hour.  To set a baseline, this threshold can be the maximum observed value over some period of time.

Quality Rate

(actual output - rejected output) / actual output 

 

e.g. total output = 60 

scrap = 1 

rework = 1 

 

(60 - 1 - 1 ) / 60 = 97%

 Rejected output includes scrap and rework.

 

OEE

 

67% = 83% * 83% * 97%

 

  

So, using these three variables we can then determine the OEE to be 67% (83% * 83% * 97% = 67%).

 

This simple math equation can be tracked continuously, in real time, using ERP and IIoT sensor data.  The initial data can be further analyzed to narrow down and solve problems.  Most commonly, issues known as the “6 Big Losses” will be identified.

 

Availability results in downtime, most commonly due to:

  1. Equipment failure (breakdowns)
  2. Setup and adjustment

Performance and the speed of production is affected by:

  1. Idling and minor stoppages
  2. Reduced speed of operation

Quality and the presence of product defects is usually caused by:

  1. Process defects (scrap, repairs)

6. Reduced yield (from startup to stable production)

 

Items 1 - 6 are calculated by looking at transactions in the ERP system and sensor data.  For example, the preventive maintenance module of the ERP system should have maintenance work orders for each equipment failure. For those manufacturers using SAP, your quality numbers come from the ERP production planning, quality management, and material management modules.  For manufacturers using other software the defects, yield, scrap, etc. data can come from whatever those modules are called in that software. 

 

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IIoT and Quality

Improving car manufacturing with IoT

 

In a factory, IoT sensors can measure operating time, starts and stoppage, harmonics (vibrations on the line), breakage, and quality.  For example, harmonics can be used to determine whether a component needs rework or should be scrapped.  The sensor pings a device’s acoustic waves, listens for the resonance/vibrations, and acts accordingly - whether it’s do nothing, because the harmonics are obeying the set parameters, or send an alert along to the dashboard if they’re unbalanced/off.  Manually, it could be thought of like tapping on a finished item, such as a side panel on a vehicle, and listening to see if anything is unfinished and rattling around.

 

 

IIoT and Process

IIoT sensors can monitor power usage, temperature, ambient light, humidity, etc.  Some of these variables require a simple retrofitting and can be added to the manufacturing device without requiring any kind of large, disruptive changes to the equipment or severe time delays to resuming production.

 

For example, to know whether a press is operating, the plant can monitor electrical usage.  The collected data is analyzed, comparing the power usage over a certain time to the usage predicted for its scheduled time.  This difference is the availability, our first factor in determining the overall OEE.

 

Another example of how IIoT can improve process is in performance (OEE equation variable 2!) The RPM gauges on electric motors shows how fast a machine is running. The data about a machine’s speed can be analyzed to pinpoint what could be causing the machine to run too slow, and affect productivity, or run too fast, and risk potential equipment damage and quality issues.

 

 

What do I need, to start harnessing the power of the IIoT and improve my OEE?

To draw all of this data together and have an automated way to calculate OEE, the plant needs to have IoT sensors, an IoT analytics cloud, a database large enough to store all the information, and programs to pair that data up with ERP transactions.  Any manufacturer following the principles of Industry 4.0 should already have all of these initial requirements, so contact relayr to find out where to go from there!

 

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