Buhrmann Pressure Systems partnered with MEUNGO to make their online vision a reality.

Bringing their mobile high pressure and vacuum machines online with the MEUNGO platform in June 2018 was the first big step for Buhrmann. Knowing the real-time performance, location and specific use profile is critical to delivering the availability for their time-sensitive customers. Once the first machine was online (that same day!), Buhrmann Pressure Systems were able to take their rental fleet online in less than one week, without outside help nor IT support.

 

But getting the machines online visible was only step 1, now Buhrmann wanted to see the sensor data in a more relevant context with other data sources such as outside temperature (critical to inform customers when below freezing). This was step 2: “turning raw data into useful information” and Buhrmann was able to configure their own dashboard.

In the last 7 months, these connected and online machines have generated a clearer picture of what is needed to perfectly match with customer timeline. It has become much easier to predict maintenance, plan spare parts, foresee failure modes and also to ensure customer billing is transparent. In this period enough information has been gathered to start to build the 3rd Step: “reason with the information and establish rules to trigger actions”. In recent weeks Buhrmann Pressure have created automated messaging from the MEUNGO platform to a specific user or user group when the mobile machines are started or stopped. This is only the beginning of this chapter to bring the real-time context of the machines into smarter tasks.

Bas Buhrmann adds, “I am not an IT or automation person, but the MEUNGO platform and customer support is exactly what I need. I can create the dashboards I need and now set up the rules to get real-time messages, without needing help from MEUNGO or external IT people. I feel in control and fully supported, which allows me to focus on my customers and keeping the rental fleet in optimal condition and availability. My next aim is to use the historical data to optimize fuel consumption which could save my customers up to 50% in fuel and help the environment“.

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