How can you boost machine efficiency with OEE?

The formula looks simple but the challenge is to get reliable and precise data from the shopfloor. In this day and age with the advances in i4.0/IIoT, the right machine data source is the machine's PLC. Additionally, default cycle times (potentially by product) are required to determine the performance rate. Additionally you'd want to capture the loss reasons separated by availability, performance, and quality, as OEE is only a ratio and not disclosing much.

There is no doubt that OEE is a valuable metric for measuring manufacturing productivity, but it is not perfect. It’s a simple formula, but the numbers can be misleading depending on how you define various constituent parameters - uptime, throughput, and quality. Should you count rework as downtime or quality? Should you count planned downtime as downtime? Should you count setup time as downtime? In many cases, the data used to calculate OEE is inaccurate or incomplete - misleading results and making it difficult for you as the manufacturing executive or the plant manager to identify areas for improvement.

Fully agreed with the article. If you funnel the problems you can prioritise the problems and convert to 80:20 ratio. If you concentrate on 20% remaining 80% problems will be solved and OEE of the machine will be increased and enhance the productivity which is nothing but Lean process.

To take full advantage of the OEE concept, additionally to the OEE measurement you'd want to capture the availability, performance, and quality loss reasons selected from a catalogue. This allows for a loss waterfall for a respective time and that's where the improvement process starts transparently.

6M based OEE can boost . 1 . Man - if manpower operating machine, training and education need , even one wrong operating method can lead Machine DT 2. Method - Process spec standards based on products,methods of operation sequence to be linked 3. Machine - PM,Spare replacement, Daily cleaning, Operation divided by worker, Engineer, service . 4. Measurements. - Quality of product and spec verification.Negative simulation for every spec.identify each spec related spare.measure their usage. 5.Monitoring, - Xbar, Rchart, SPC,variation identify ,daily OEE calculations , review, SGA, PokeYoke ,Interlocks 6.Material - Input material package standards , Material variation with in Spec.Those standards will boost Machine OEE.

An example I have seen is , the operator of machin don't want to stand for small things like size cutting, fusing pressing etc., so it's give more efficiency to man as well as machine.

One of the most critical steps in terms of using OEE effectively is data consistency. And a great tool to establish data consistency is using data models. The second tool is visualization. Seeing the contributing factors in real-time and being able to correlate them for meaningful insights is a key capability.

The AI has summarized is very well. Additionally, fluctuations are a good source to identify archivable goals. An operations organization in good shape would deliver stable performance each time a process is executed, e.g. a change over process. If you measure the distribution of change over times for specific products/ product groups and plot it as a bell curve, you'll see the improvement potential in the distribution. A narrower distribution (with a lower median) can be defined as a measurable improvement target to also optimize the OEE.

As IT leaders, think about implementing a UNS as in parallel the OT teams use OEE as an essential metric and tool for improving operational efficiency. Drive full visibility into plant operations led by OEE. This can allow you to create a data model that works for individual plants and the enterprise in its entirety.