Malvern-based Lantek Systems Ltd (www.lanteksms.com) says it has sufficient resources on its MES (manufacturing execution system) software platform to provide systematic and reliable OEE (overall equipment effectiveness) indicators related to the data generated from the production of sheet-metal parts, metal tubes or profiles.

This, says the company, makes it possible for manufacturers to evaluate their operation and make decisions at the right time.

Furthermore, Lantek can give its clients business intelligence systems that can be applied to the various operational areas of a company for the analysis of data.

When a company decides to implement an MES system, it mainly looks to provide visibility for its manufacturing operations and ensure that the system is developed on all levels — for operators, supervisors and management.

This visibility makes it possible to react to any type of event that may occur during day-to-day operations. Ultimately, the company will be capable of analysing how its resources and processes are optimised, and so discover how to improve 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 reliable. Such a task will involve generating vast quantities of information ready for review and analysis.

One of the most powerful and direct tools for measuring and analysing whether resources and processes are achieving the desired level of optimisation is to define the key performance indicators (KPIs).

Here, the ‘OEE metric’ is one of the most relevant KPIs in production environments, as it reflects the productivity of industrial equipment by using the information collected by validation systems in the workshop. Calculations are performed using a formula that comprises three factors — availability, performance and quality.

Combining the results of these factors provides a quick overview of workshop productivity. However, data should also be analysed separately — and from different perspectives — for each machine technology, for specific time periods, work shifts and so on.

This will help to identify training problems, malfunctioning equipment or the need to redesign certain processes that have a negative impact.