Data-driven decision-making will avoid operational disruptions

By developing data-driven decision-making processes, researchers at Luleå University of Technology and the company Predge hope to be able to predict errors and prevent unwanted stops in the mining and railway industry.

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The purpose of the work is to develop a system that supports and guides organizations in their data-driven decision-making processes. By collecting and analyzing large amounts of data, they can minimize unwanted interference, and propose direct measures for increased availability and increased longevity.

Increased availability and longevity in the mining and ore railway industry

The work, which is carried out within the framework of the AI project Applied AI DIH North at Luleå University of Technology, is based on the Luleåbased company Predge’s operations. The company works with predictive maintenance, which involves analyzing data from sensors on machines and equipment from, among others, the mining and ore railway industry to predict maintenance needs, errors and prevent unwanted stops. By col- lecting and analyzing large amounts of data, they can minimize unwanted interference, and propose direct measures for increased availability and increased longevity.

Develop guidelines for decision-making

By collecting and analyzing operation and maintenance data from customers, researchers will now investigate how current analysis and decisionmaking tools are used in practice to, based on this, develop new decision scenarios to be able to provide customers with feedback and guidelines for decision-making.

Joakim Lindström, developer at Predge, says:

- The project will investigate how our decision support systems are used in practice to make better decisions in workflows. Hopefully, it can help us understand how our analytical decision support products are used by our customers in their decision-making processes.