Business Intelligence, when plugged into manufacturing processes, allows executives to track metrics and KPIs. By tracking these metrics, you will derive insights on the processes, and by making slight improvements to the process, you can potentially save millions of dollars.
Among many metrics, OEE is one of the important metrics that highlights the manufacturing productivity of an organization. By measuring OEE, management gains important insights on how to systematically improve manufacturing process. OEE is the single good metric for identifying losses, benchmarking progress, and improving the productivity of manufacturing equipment (i.e., eliminating waste).
Wyn Enterprise provides modern visualizations to help presenting OEE data in a way to assist the executives to come up with actionable insights. Our new sample dashboard highlights the Overall Equipment Effectiveness (OEE) and its associated KPIs that identifies the percentage of productive manufacturing time for an organization operating in multiple shifts.
In any OEE dashboard, the spotlight is almost always on three important KPIs represented by gauges on this sample.
The following are the KPIs and their usage:
This KPI takes into account the time for which the equipment was available for production after leaving out the unplanned stops and planned stops.
The ratio of actual run time to the planned production time gives the idea on the potential of the equipment.
|Available Time||1440 mins|
|Planned Stops||50 mins|
|Net Run Time||1440-50 = 1390 mins|
|Unplanned Stops||400 mins|
|Actual Run Time||1390-400 = 990 mins|
Availability = 990/1390 = 0.71 or 71%
The above example shows a good ratio that means the equipment is not very prone to frequent breakdowns or does not demand high maintenance.
It is a measure of net process or production cycle run time against its full capability. It is calculated as (Production Cycle time * Total pieces)/Run Time.
The production cycle is the time equipment should take to produce one unit.
|Planned Production Cycle||15 units/min|
|Run Time||900 mins|
|Planned Units||15*990 = 14850|
Performance = 12000/14850 = 0.8 or 80%
The resultant figure shows an efficient manufacturing process where the capacity of a plant, equipment & personnel, is effectively being used with a scope of improvement.
This highlights the count of manufactured parts that meet quality standards. This is because not all units can meet the quality specifications.
The quality factor is calculated as a ratio of Quality Units to the Total Units produced. If 10000 units are good units out of 12000 total units produced, the Quality factor would be:
Quality = 10000/12000 = 0.87 or 87%
As you may noticed that all the three factors can be valid independently though to get a complete picture, OEE is calculated by multiplying the 3 above factors. For example,taking the values from the above factors:
OEE = 0.710.80.87 = 0.49 or 49%
Please note that collating the data is one thing, but making sense of all that noise and retrieving the insights/analysis is a challenge. One way organizations can overcome this is by making use of data visualizations. e.g. considering the Performance and Quality metrics in a single frame throws an interesting analysis here. The Performance KPI seems to be a high percentage and does look good on the reports/dashboard, however, the manager should be making sure that the quality is not being compromised in attempt to improve the performance percentage.
A sample dashboard by our newly launched Wyn Enterprise highlights the data visualizations to capture OEE and other factors for a manufacturing process. One can see the KPIs and charts displaying the utilization and production capacity of multiple shifts.
The important visualizations in the dashboard are the gauges that are used to indicate the OEE and other factors over a time frame for multiple shifts.
The four Gauges display individual achievements for the factors respectively spanning multiple shifts. The Gauges chart helps to display the single measure effectively on a scale and shows the progress of the KPI clearly. The information can be broken down on a single shift also.
Simply speaking, TEEP is the overall production effectiveness of the organization, the true capacity. It is calculated by multiplying OEE by Utilization. E.g.
|Planned Production Time||108 hrs (6 hrs*3 shifts*6 days)|
|Total Time available||168 hrs (24 hrs*7 days)|
|Utilization||108/168 = 64%|
TEEP = 0.64*0.49 = 0.31 or 31%
One may notice the use of KPI chart that is helpful to quickly inform the user about overall performance of and organization or operation. Here, a manager can quickly refer to the potential of increasing throughput with the current equipment.
Usually the KPIs are accompanied by another chart or KPI to give context to overall KPI data. As you may notice the other KPI in this sample dashboard displaying the quantity of good quality units produced.
Observing the above two KPIs independently pretty much hand out useful information in their own context. The organization is producing excellent quantity of good units and thereby less wastage of raw material and there is room for increasing the output too. It seems to be a very straightforward position to be in as you tell your plant manager to increase the working hours or number of shifts to increase the utilization or running time of the machines.
Now let’s put both the KPIs together and you may notice that you are already achieving a high throughput with the current production time. Would you like to run a risk of disturbing the quality of the units or longevity of the equipment by increasing their run time? Probably not.
Alongside the KPI charts, we can notice the stacked area chart that is used to display the comparison between the unplanned and planned stoppage of the equipment. These stoppages can arise due to scheduled maintenance or unexpected equipment failure. Stacked area charts are useful to plot multiple variables changing over an interval if an user is looking for any patterns.
Another important metric for any successful manufacturing process is the quality of production. Quality units are a vital factor that can up the revenue for a company.
The use of a simple stacked bar graph helps a user to compare the number of quality units against the defective units produced. The visualization is helpful in cases where it is required to display proportions.
OEE dashboards equally assist the upper management and floor managers/supervisors in keeping a tab on processes and productivity of the organization. A user can keep track of Availability, Performance and the Quality of the units produced in a single shift or multiple shifts. Managers can analyze the reasons behind KPI figures of one shift over other and act accordingly.
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