Digital

Connect Smart plants with the Industrial IoT technology

The Automation division of FIMIGroup has improved an own brand-new technology that adds value to electrical equipment by providing Industrial IoT solutions that transform plants into intelligent lines.

Plants have many sensors attached to the various machines, that collect great amounts of data. All that data is a potential source of information that can have a real impact on the productive efficiency and cost reduction in a company. But collection of data is not enough per se; it is necessary to set a method to extract information from it. This is why FIMI Digital, a newborn division of FIMI Automation, developed a way to delve into the true value of data by obtaining insights and valuable information, that can be used to enhance the productivity and the efficiency of our customers’ plants.

Following a customer-centric approach, our Application Engineers develops the design and the logic of the platform with our clients, to provide them with a tailor-made solution. This interaction continues even after the development of the solution, thanks to the possibility of obtaining insightful analyses to resolve practical problems arising from the line or to improve its productivity.

The user-friendly platform designed by FIMI Digital allows customers to monitor practical and simple graphs summarising the main variables of the lines, enabling them to be up-to-date on the status of all the plants they manage worldwide.

It is organised in tabs, each with a different module, and among the various indicators the most interesting ones are:

• OEE (Overall Equipment Effectiveness): it is an index that summarises information regarding the availability, the performance, and the quality of production, highlighting the losses due to downtime, low production speed or scrap.
• Machine Supervision: it acts as a black box with all the inputs received by the PLC moment by moment, to allow a thorough analysis of any event happening to the line.
• Energy Saving & Efficiency: by aggregating data with parameters as the thickness and the type of material, it is possible to analyse peaks and lows in the usage of energy to optimize the production and consume energy in a more efficient and responsible way.
• Predictive Maintenance: a module dedicated to the cataloguing of components to create preventive maintenance routines, planning activities based on the real usage of the components in the different setups. In addition to this, along with the customer, it is possible to identify critical components of the line, train machine learning algorithms to detect anomalies in their behaviour over time, and develop a predictive maintenance system, that allows to reduce costs and breakdowns that cause losses of production.
• Coil Traceability
• Production Traceability.