Perumal, Puvanasvaran A. Universiti Teknikal Malaysia Melaka
Teoh, Yong Siang Universiti Teknikal Malaysia Melaka
Yoong, Sai Sieng Universiti Teknikal Malaysia Melaka
Overall Equipment Efficiency (OEE)
Maynard’s Operation Sequence Technique (MOST)
Study of Work
It is a common practice to quantify any process or entire production line in manufacturing industry especially to measure three main losses named time losses, performance losses and quality defect exist in production. Overall Equipment Effectiveness (OEE) fulfils the requirement by providing the measure of equipment via single measure which is monitored from time to time by responsible personnel so that corresponding optimization or Kaizen could be done. However, there are many lean wastes which could be 'invisible' or tolerated under the conventional definition of OEE. The hidden waste includes unnecessary production which was classified as operating time and the underestimated effect of excessive transportation or setup time. These could be minimized and sometimes avoidable via work measurement, method study and study of the work, which are under the study of Maynard's Operation Sequence Technique (MOST). This paper intends to examine and quantify the hidden lean waste in OEE from the perspective of method and work of an operation with the integration of MOST study. Operations are analyzed in every single step and broken down into details of activities, which are then re-designed for minimal non-value added activity in operation based on the standard allowable. The OEE data after the study of work is computed and compared with the OEE before the MOST study. The comparison shows the improvement in term of OEE after the MOST study and this implies that the hidden waste inside OEE definition could be tracked out for a better effectiveness. Any reduction in the non-value added activities or downtime ensure larger room for more value added activities or uptime and therefore the availability of production. It is expected to provide a new insight in implementing OEE at a different way and stay beware of the assumptions in OEE to avoid any hidden waste.
American Journal of Applied Sciences
© 2016 Puvanasvaran A. Perumal, Ito Teruaki, Teoh Yong Siang and Yoong Sai Sieng. This open access article is distributed under a Creative Commons Attribution (CC-BY) 3.0 license(https://creativecommons.org/licenses/by/3.0/).
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