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Above: from ​Maheshwaran Gopalakrishnan's PhD thesis

Maintenance management is central to digitalised manufacturing

​Digitalisation in manufacturing industry has rapidly increased the expectations on production systems to be highly productive and resource efficient. Fulfilling these expectations depends on how well the machines in the production system function.

So, successful maintenance management is central to the realisation of digitalised manufacturing. However, the practices of maintenance organisations in manufacturing companies are found to be well behind the needs of digitalised manufacturing. Mainly, industrial practices show non-factual maintenance decision-making, which is dangerous to the smooth functioning of production systems.  This is because maintenance is approached on a component-level by focusing only on maximising the availability of machines, instead of approaching from a system perspective which can maximise the productivity of the entire production system. Therefore, the purpose of this research work is to successfully transform the maintenance organisations from having a narrow focus to achieve a system perspective.
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PhD student Maheshwaran​ on the importance of maintenance for  effective digitalised manufacturing.

From a narrow focus to a system perspective

A data-driven maintenance decision support framework was developed in the thesis, which provides guidelines for the maintenance organisations to transform from having a narrow focus to a system perspective. Currently, manufacturing companies are classifying the machines based on its criticality with the motive of prioritising their maintenance efforts. But, the criticality assessment tools are not used for maintenance prioritisation. Instead, experience-driven or operator-influenced prioritisation decisions are made. The criticality assessment is problematic as they are not data-driven, rarely updated and lack a clear goal. In order to solve this, a data-driven machine criticality assessment was developed as a solution. It uses real-time machine data and data analytics to provide factual and dynamical updating decision support with a clear goal of increasing productivity. By using the data-driven approach for maintenance, the real-time data and data analytics helps in enlarging maintenance scope to having a system perspective. Prioritising maintenance based on data-driven criticality assessment leads to better performance of the production system and to increase its productivity.
 

About Maheshwaran Gopalakrishnan

Maheshwaran Gopalakrishnan presents his Ph.D. thesis, Data-Driven Decision Support for Maintenance Prioritisation - Connecting Maintenance to Productivity, in VDL on the 31 of August. He has a background in Production engineering, which he obtained from his M.Sc. degree at KTH, Stockholm.

Maheshwaran feels that the field of maintenance management offers plenty of research opportunities, especially as it has got low attention in the global research community compared to other fields. As industries are still using traditional approaches for maintenance, the theory on maintenance is disjoint for industrial practice. This drives him to work in close collaboration with industries in order to close this gap. He currently conducts empirical research within smart maintenance for digitalised manufacturing. In addition to his focus on discrete manufacturing, he plans to expand the maintenance research to include other types of manufacturing and inter-disciplinary research.


On his spare time Maheshwaran loves to travel and is an ardent fan of cricket.
He has also been playing curling since 2016.


 

Published: Fri 24 Aug 2018. Modified: Thu 30 Aug 2018