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Introduction of the company

Vanderlande is the global market leader for value-added logistic process automation at airports, and in the parcel market. Vanderlande’s baggage handling systems move 3.7 billion pieces of luggage around the world per year and its systems are active in 600 airports worldwide and at all major parcel companies.


The challenge they faced

The challenge for Vanderlande is to continously increase the output of their machines, in terms of handled packages per hour and uptime. Therefore, Vanderlande wants to move their service approach from a reactive model to a more proactive model. Their intention is to use technology to reduce unplanned downtime and optimise and scale their service department in order to realise a higher OEE.


The solutions we came up with

In order to move from a reactive maintenance model to a more proactive model, we need to determine when a machine or system needs maintenance. One way to do so is by implementing condition-based maintenance. In this scenario, the assets will be continuously monitored on wear and tear. Data about these conditions will be send to ALFA for analysis. Predictive Maintenance Algorithms can then predict when a machine will need maintenance based on historical and real-time data. Accordingly, algorithms can also make service orders with accurate information when, where and why maintenance is needed. This not only leads to proactive maintenance, but also to a better understanding of the machines and systems, which helps improving the OEE of these machines. This means that Vanderlande will be able to guarantee a certain service level and performance, quality and speed of their machines.

Industries: Industrial equipment

Regions: Europe

Analyse historical log data

  • Identify maintenance types and unscheduled maintenance events
  • Prioritize specific event combinations
  • Derive Calculated Maintenance Categories

Model maintenance

  • Analyse historical sensor data
  • Refine Calculated Maintenance Categories
  • Define maintenance/breakdown indicators

Predictive maintenance

  • Alert in real time
  • Trigger proactive maintenance
  • Predict remaining useful life

Want to know more?

Contact Maarten de Boo