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Measurement of overall equipment effectiveness as a basis for TPM activities

Ljungberg, Õrjan·1998·International Journal of Operations & Production Management

Abstract

Assessment of disturbance data from production equipment provides valuable information for improving productivity. To implement total productive maintenance (TPM) it is necessary to assess the magnitude of different types of production losses, in order to direct activities and allocate resources in an optimal way. In many enterprises, the focus is directed towards major time losses due to break‐downs rather than minor losses in speed and time. A method for implementing data collection is to start gradually by a simple model and develop this to a combined model with computerised systems and manual recording. This gives both an exact assessment of the magnitude of the disturbance and a deeper understanding of the reason for losses. According to about 20 cases, the overall equipment effectiveness was only around 55 per cent. Further, it is quite clear that performance losses are the dominating ones. If the production process is new to the company it is hypothesised that overall equipment effectiveness will be lower than if the company is already familiar with production process.

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Metadata

Authors
Ljungberg, Õ.
Venue
International Journal of Operations & Production Management
Publisher
Emerald
Published
1998-05-01
Volume
18
Issue
5
Pages
495-507
ISSN
0144-3577 (ISSN-L)
OpenAlex
W2082851933
Cited by
289
References
0
FWCI
7.43
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Measurement of overall equipment effectiveness as a basis for TPM activities. https://app.cochat.ai/papers/10.1108/01443579810206334
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Ljungberg, Õ. (1998). Measurement of overall equipment effectiveness as a basis for TPM activities. International Journal of Operations & Production Management. https://doi.org/10.1108/01443579810206334

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.