New Licentiate of Engineering: Maheshwaran Gopalakrishnan!


Existing industrial criticality classifications have not been satisfactory in enabling effective maintenance planning.

Congratulations to your degree of licentiate of engineering! Your licentiate seminar was held at Chalmers University of Technology on the 29th of January 2016 with the title Towards Effective Maintenance Planning. Can you describe your topic?

Thanks very much!

Resources within production systems could not be utilized to their full capacities. The main reasons are machine failures and the effects of one machine’s failure on others. The purpose of my research is to improve the efficiency of the production system as a whole by making the effective maintenance planning for these machines. Particularly, my research work aims at analyzing and identifying the critical resource of the production system and plan its maintenance accordingly. The main outcome of my thesis was the identification of maintenance planning principles such as critical machine identification, maintenance activity prioritization, and allocation of maintenance technicians.

What do you see as the main contribution of your work?
The main contributions of my work so far have been the mapping of industrial practices with respect to maintenance planning. The results show that industries want to work with prioritizing maintenance to the critical resources. However, the major problem affecting it are the lack of robust criticality classifications and the ad-hoc way of planning and scheduling maintenance. Results from my other studies show the possibility of increasing throughput just by prioritizing the maintenance activities of the throughput-critical (bottleneck) machines over others. The achieved results can be decision support tools for maintenance engineers in the manufacturing factories in order to plan their maintenance effectively.

What will your future work focus on from now up to your PhD dissertation?
My main focus so far has been on the prioritization of the maintenance aspect, which has shown positive results. However, the main industrial problems still lie in identifying the “right” critical machines for prioritization as the criticality tends to move from one machine to another over time. Existing industrial criticality classifications have not been satisfactory in enabling effective maintenance planning. Therefore, my focus for the future will be on achieving a robust criticality classification which can provide the platform for implementing the maintenance planning principles achieved thus far.


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