Above: from Maheshwaran Gopalakrishnan's PhD thesis
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.
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
On his spare time Maheshwaran loves
to travel and is an ardent fan of cricket.He has also been playing curling since