Getting to grips with the OEE of your workshop

Ultimately, it will be capable of analysing how its resources and processes are optimised, so it can discover how its organisation can improve Overall Equipment Effectiveness (OEE).

There are two key objectives when implementing a solution of this type: defining what information will be recorded during each phase of the production process and ensuring that this information is accurate and completely reliable. This task will involve various types of specialists and systems that will generate vast quantities of information ready for review and analysis. We should not succumb to the false belief that by merely collecting data that this, in itself, will result in an improvement. This is illustrated by the saying: “It's just data. Until you use it”.

One of the most powerful and direct tools for measuring and analysing whether resources and processes are achieving a desired level of optimisation is to define the Key Performance Indicators (KPIs) that are to be calculated from all the data collected.

In order to define appropriate KPIs, they should be defined to measure the performance of those critical factors that demonstrate whether our business is operating as we expect. ‘More’ is not always synonymous with ‘better.’ Therefore, one must select very carefully which operations are to be measured in order to obtain a clear idea of whether we are getting the expected results.

The Overall Equipment Effectiveness (OEE) metric is one of the most relevant KPIs in production environments where there is intensive use of industrial equipment. This metric reflects the productivity of industrial equipment by using the information collected by the validation systems in the workshop. Calculations are performed using a formula that comprises three factors:

• Availability: this measures the time for which machines have actually been producing compared to their theoretical availability. This is usually defined by their schedule, first taking into account the time required for scheduled maintenance. Losses for this factor may be caused by breakdowns or unscheduled downtime.
• Performance: this measures the production volume obtained by a machine compared to its theoretical nominal capacity. Losses for this factor may be caused by decreases in production speeds due to the incorrect configuration of machines.
• Quality: this measures the quality of production, comparing the faulty units to the total number of units produced. It is important to carry out a more thorough investigation of this factor in order to find repeating patterns that help managers identify the origin of these faults.

Combining the results of all of these factors provides a quick overview of the productivity of a workshop. However, data should also be analysed separately and from different perspectives (for each different machine technology, for specific time periods, work shifts, etc.) in order to identify training problems, malfunctioning equipment or the need to redesign certain processes that have a negative impact.

With resources on a software platform, Lantek are able to provide systematic and reliable OEE indicators or any other type of defined indicator related to the data generated from the production of sheet metal parts, metal tubes or profiles.

Business Intelligence (BI) systems are also a factor that can be applied to the various operational areas of a sheet metal company, available from Lantek. These offer the power to analyse cross-sectional data that would be difficult to combine using any other method.

Lantek provides these software tools that can be used to learn the state of production and monitor KPIs and behaviour patterns. These have an effect on particular aspects of the production process leading to efficiency and productivity in the production of sheet metal parts, metal tubes and profiles.