Uncovering hidden capacity in overall equipment effectiveness management

Yick Hin Hung, Leon Y.O. Li, T. C.E. Cheng

Research output: Journal article publicationJournal articleAcademic researchpeer-review


An ongoing challenge in manufacturing management is to maximize the output volume from limited production capacity. Total productive maintenance (TPM) is a promising strategy to enhance machinery efficiency in factories by reducing downtime, speed, and quality losses. The associated metric of TPM is overall equipment effectiveness (OEE), which comprises three components, namely availability, performance, and quality, that measure the various aspects of production losses. However, output is restricted by the design cycle time in OEE, so purely improving OEE could only yield limited output increase. We argue that excess motion and processing in the ideal design cycle lead to design cycle losses. If these non-value-added activities as hidden capacity could be eliminated, production output would have considerable increment without extra investment. Adopting the input distance function approach, we show that the improvements gained from enhancing overall equipment availability and performance are different from the capacity unleashed by eliminating the design cycle losses. Accordingly, we propose value-added overall equipment effectiveness (VAOEE) as a novel metric to measure all the identified losses in search of hidden capacity. We provide three examples to demonstrate application of our novel productivity improvement approach in both semi-automatic and manual production in a non-continuous based plant.

Original languageEnglish
Article number108494
JournalInternational Journal of Production Economics
Publication statusPublished - Jun 2022


  • Design losses
  • Hidden capacity
  • Non-value-added activity
  • Overall equipment effectiveness
  • Value-added overall equipment effectiveness

ASJC Scopus subject areas

  • Business, Management and Accounting(all)
  • Economics and Econometrics
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering

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