Overall equipment effectiveness (OEE) provides a means to measure the percentage of planned manufacturing time that is productive. By measuring OEE, you gain insight into the challenges affecting optimal productivity and how to improve your manufacturing operations.
In manufacturing, OEE is the most widely used standard for measuring manufacturing productivity. This is because it measures the extent at which a manufacturing operation is being utilized by comparing current usage measurements to measurements taken when the operation was run at its optimal capacity. OEE can also be applied to measuring the performance of individual machines on the shop floor.
In situations where the benchmark data for a manufacturing facility are not available, OEE calculations can be compared to available industry standards or to the data collected from similar shop floors using the same equipment.
OEE combines machine or equipment data and multiple operational issues that can affect production to calculate OEE percentages or scores. To calculate OEE, three important metrics that define every manufacturing process is required. These metrics include:
With these metrics in mind, calculating the OEE = Availability x Performance x Quality.
A simple case study will help put OEE calculations in perspective. In this scenario, the OEE of a CNC machine is being calculated. The duration for an optimal shift on the machine is 500 minutes but the operator is expected to go for a 30 minute lunch. The machine is given oil breaks for 40 minutes and an operator switch takes 30 minutes to be completed. Aside from these breaks, the machine runs for the remaining duration.
To calculate availability, the planned downtime must be removed from the optimal running time of the CNC machine. The total downtime = 30 + 30 + 40 = 100 minutes.
This leaves the machine availability at 500 – 100 = 400 minutes. This means the machine ran for 400 minutes against the optimal 500 minutes it is capable of. Thus, the availability percentage = 80%
The CNC machine is capable of producing 5 tool bits every minute. This means within the availability period of 400 minutes, the machine should produce = 400 x 5 = 2,000 tool bits. Here, 2000 bits is the optimal production capacity for each machine running at 100%. But due to the operator in charge, the production cycle was slowed-down and it took 1.5 minutes to produce 5 tool bits.
The additional seconds will slow down production speed by two-thirds using this calculation, 1/1.5 = 0.67. Multiplying this slowed-down rate to the maximum tool bits the machine is capable of producing in the allotted time frame we get, 0.67 x 2,000 = 1340 tool bits. This shows that the machine was running at 66.7% of its optimal capacity and 33.3% was lost to inefficiencies. Converting that 33.3% to minutes, 400 x 33.3% = 133 minutes.
In total, from the optimal 500-minute running time, 100 minutes was lost to planned downtimes and a further 133 minutes were lost to a slower operating process. This brings the total running time to 500 -100 -133 = 267 minutes while the performance is 66.7%.
In this scenario, 200 tool bits were defective and did not meet the quality required from the customer. This leaves only 1140 functional tool bits for delivery. To calculate the quality rate, the usable products will be divided by the 1,340 bits produced which is (1340-200)/1340 = 0.85 or 85%. This means 85% quality was achieved.
To convert the defective 200 tool bits to time which shows how many minutes were lost making these products, the 200 bits will be divided by the 5 bits which can be made per minute. This gives 200/5 = 40 minutes.
The total minutes lost during the entire manufacturing process takes into account the planned downtime, the slowed down process, and the time spent making defective tool bits. This gives 100+133+40 = 273minutes. This also means that the CNC machine ran optimally for (500 – 273) 227 minutes.
Using the OEE calculations you get Availability (80%) * Performance (66.7%) * Quality (85%) = 45%.
This OEE calculation shows that the overall equipment effectiveness was 45%. The optimal production time achieved was 227minutes as against 500minutes and 1,140 tool bits were manufactured against a possible 2,500. You can also divide the achievable production time by the optimal time or the total tools produces divided by the optimal production capacity to get the OEE.
As you can see, this example takes into consideration a single machine working in a day but this isn’t ideal for real-time calculations. The general expectation is to collect operational data from a fleet of machines or equipment over the duration of a month or two. This provides more than enough data for OEE calculations and to receive actionable insights into optimizing equipment efficiency levels. To achieve this, an Industrial IoT platform that collects data round-the-clock from multiple machines and processes them is required.
As the case study showed, OEE calculations are dependent on the ability to collect usable data from machines such as time, output, and operational speed. This is where the MachineMetrics Platform comes into play. The MachineMetrics Platform offers a plug and play solution to capturing data from both digitized and analog machines. If your target is calculating the OEE of a fleet of manufacturing machines, MachineMetrics will capture the specific data and transform them into standard structures that simplify OEE calculations.
The example of National Oilwell Varco (NOV) is a case study that showcases the use of MachineMetrics in calculating OEE. In this scenario, NOV plugged 60 CNC machines across two facilities to the MachineMetrics Platform within two weeks. The MachineMetrics Platform captured operational data on a daily basis for three months. Access to shop floor data made OEE calculations possible and provided insight into the systematic problems NOV faced. With these insights, the manufacturer was able to increase operational efficiency by 20%.
You can learn more about MachineMetrics Platform’s data capture process and how it helps optimize manufacturing equipment and systems by visiting the MachineMetrics IoT Platform page.
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