Revolution in facility management: The future with predictive maintenance

Everyone wishes they could look into a crystal ball to see what the future holds. Even if we at POGO don't have a crystal ball - thanks to predictive maintenance, we can at least dare to look into the future in facility management.  

In today's era where technology is advancing relentlessly, facility management faces the constant challenge of minimizing unplanned downtime and costly repairs. A key concept that addresses these challenges is predictive maintenance, which is ushering in a revolution in technical facility management by intelligently combining the Internet of Things (IoT) and artificial intelligence (AI).  

IoT and AI: the foundations of the revolution  

The Internet of Things (IoT) defines a world of networked devices that can collect, share, process and analyze data.  

Artificial intelligence plays a crucial role in predictive maintenance by detecting potential system failures at an early stage and enabling the planning of maintenance or repair measures. Machine learning and AI help to analyze data from various sensors and execute actions based on the results.  

The basics of predictive maintenance  

Predictive maintenance is defined as the proactive maintenance of all technical components of a building or system. Preventive maintenance and predictive maintenance are similar, but they have a significant difference: predictive maintenance uses real-time data to predict maintenance needs, while preventive maintenance follows a predetermined plan.  

Predictive maintenance, an innovative approach from the Internet of Things (IoT), uses sensors and other connected devices to continuously monitor performance data. As soon as an anomaly is detected, a maintenance notification is automatically sent.  

This means that with predictive maintenance, you only intervene when it is necessary, unlike preventive maintenance, where maintenance work is carried out according to a fixed schedule. This foresight enables more efficient use of resources and avoids unnecessary downtime.  

Use cases: Successful implementations of predictive maintenance  

The successful implementation of predictive maintenance has proven itself in various real estate sectors. Companies such as Apleona, PRiOT AG and Hundt Consult have shown that predictive maintenance in areas such as technical building systems, ventilation systems, elevators and pipe networks can lead to significant improvements in efficiency and cost reduction.  

One outstanding example is the application of predictive maintenance in ventilation systems by PRiOT AG in Switzerland. Traditional maintenance of such systems faces challenges such as unplanned downtime, manual inspections and high energy costs. PRiOT AG has developed a solution that addresses these problems. The system monitors the condition of filters as well as room temperature and humidity using a battery-powered device that transmits the data wirelessly via the LoRa network (=Long Range Network) available throughout Switzerland. This data is then analyzed, and customers can view values such as the current filter contamination or the suggested filter change date at any time via the dashboard. Interfaces to facility management providers enable automated maintenance interventions and integration into the customer's ERP system. Multiple server locations in Switzerland guarantee data security and protection.  

Continuous monitoring of the system statuses enables failures to be detected and avoided at an early stage. This not only reduces operational interruptions, but also significantly lowers maintenance costs. In addition, the system improves the customer experience by ensuring a more reliable operating system and providing users with transparent information on the status of the system. 

Challenges of predictive maintenance  

Some of the main problems that companies face when implementing predictive maintenance strategies are the lack of resources, the lack of visibility into asset data and the complexity of maintenance schedules, especially in large facilities. To address such challenges, there are smart software solutions that not only minimize the risk of technical equipment failure, but also ensure smooth operation. As an example of this, we would like to introduce you to the “Tango Maintenance” module from Tango Analytics (USA):  

Tango prioritizes maintenance work according to the risk of failure to use resources efficiently and reduce downtime. Tango also consolidates all plant data in one place and makes it transparently accessible. This gives employees the information they need to make informed decisions and view individual maintenance plans. In addition, Tango automatically creates preventive maintenance plans based on warranties, asset data and more, and automatically assigns tasks to the right people to simplify management. Pogo-Consulting has partnered with Tango Analytics to provide our customers with improved maintenance solutions and more efficient management of their assets.  

Outlook: The future of predictive maintenance  

The future of predictive maintenance lies in further integration of sensors, digital twins, and AI models. A comprehensive data flow and a comprehensive approach are crucial to fully realize the benefits.  

Predictive maintenance can not only lead to more efficient plant operation but can also play a key role in creating smart, connected and sustainable buildings. However, realizing the full potential of this technology requires close collaboration and strategic adjustments.  

Do you want to optimize your facility management but don't know how?  

We - the Pogo-Consulting team - advise and support companies in the introduction of new software solutions for efficient real estate management. We are your strategic partner and reliable companion on the path to digital transformation. 

Make an appointment now  

Our experienced team at Pogo-Consulting is here to help you optimize your business. Contact us today and be inspired by the possibilities of the latest innovations to take your business to the next level.  

References 

Tango Analytics - Link 

Apleona - Link 

Arrow - Link 

Bundesamt für Sicherheit in der Informationstechnik - Link  

Chemanager - Link

Compamind - Link 

Die Möglichmacher - Facility Management - Link 

GIT SICHERHEIT - Link 

Hundt-Consult - Link 

PRiOT AG - Link 

SAP - Link