As your data starts pouring in, it’s time to do something smart with it. One smart thing to do is optimising your machine’s Overall Equipment Effectiveness (OEE). The OEE is defined by three components: namely availability, performance, and quality. Improving your OEE can be done by applying different methods, which affect the different OEE components. Those who want to maximise machine availability can use predictive maintenance to minimise unplanned downtime and reduce unnecessary maintenance costs. Performance analytics and optimisation can be used to improve the performance of your machines in terms of input versus output, while guaranteeing consistent or higher quality output. Ultimately, it helps OEMs and operators achieve higher KPIs like units/hour and yield (%), which begs the next question: how does performance analytics and optimisation work?
Like with predictive maintenance, performance analytics and optimisation entails continuous monitoring of condition information to get insights on how machine components are working. For predictive maintenance, it’s determined which components could lead to failure and why they do that, whereas performance analytics and optimisation focuses on which components could lead to higher performance and quality and what their highest efficiency is. In both cases, this information allows OEMs and operators to get insights on the future condition of their machines and how to act accordingly.
The highest efficiency and output is determined by analysing historical data. Algorithms go through this data set to determine which condition or setting the machine worked best. Through continuous monitoring of the performance in real time, the algorithms automatically detect when a certain change in settings is causing inefficiency. Performance analytics help facilitate the reflection and determine the root cause behind inefficiencies. Optimisation happens when these algorithms describe what actions should be taken to achieve the highest efficiency. For instance, an algorithm may recommend the following: ‘this setting is currently on X, which causes inefficiency. Put this setting on Y and it will perform Z, which we know is the highest efficiency for this setting.’
Applying performance analytics and optimisation allows OEMS and machine operators to improve the performance and quality of their machines. They can optimise the number of units produced per hour, or the quality yield (%) by applying the conditions that are historically known to optimise the efficiency of those two. By applying performance analytics and optimisation, OEMs and operators not only optimise their efficiency, they also get a better understanding of how their machines work and can predict the performance and quality of their output. This can even be extended by alerting the appropriate service members to take appropriate actions and categorising service orders based on future OEE.
Widget Brain can help OEMs and machine operators to analyse and optimise their machine performance. We provide the platform The Algorithm Factory and the algorithms to make machines smarter and OEE higher. Want to know more about the performance analytics and optimisation possibilities for your business and our intelligent algorithms? Contact us today at www.widgetbrain.com/demo/.
Originally published at widgetbrain.com on July 12, 2018.