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Revised overall labour effectiveness

delete2020-07-23
delete11
PRE
AI
M
Marcello Braglia
D
Davide Castellano
M
Marco Frosolini *
M
Mosè Gallo
L
Leonardo Marrazzini
DOI:10.1108/IJPPM-08-2019-0368delete
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Abstract

Abstract

En 中文
Purpose The paper proposes a new workforce effectiveness metric that is a sophisticated evolution of a pre-existing overall labour effectiveness (OLE) indicator. The KPI, named revised OLE (ROLE), provides a structured methodology to measure in a holistic way the losses relating to labour, maintaining some formal similitude to the overall equipment effectiveness (OEE) Design/methodology/approach A new structure of losses is proposed to overcome the drawbacks and the difficulties that usually affect the data collection stage, referring to directly measurable quantities or, when this is not the case, suggesting a viable method to quantify the loss. Besides, this approach facilitates the comprehension of labour-related issues, suggesting potential countermeasures. The novel ROLE indicator has been defined, based on this new structure, to evaluate the labour effectiveness in batch process industries. A real case study is provided, which explains the methodology and illustrates the capability of the corresponding KPI. Findings The present work analyses the labour performance indexes available in literature, with the aim of evidencing those aspects that can be properly observed and quantified and, at the same time, categorizing them to identify their possible drawbacks. A new structure of losses is derived, with respect to four different categories, which may help to measure the losses themselves more effectively Originality/value The paper investigates some important KPIs dealing with labour performance and individuates some significant drawbacks. Then it suggests a new, inclusive structure of losses and a modified KPI that not only measures effectiveness but also allows to identify viable countermeasures.
Keywords:
Labour performance
Workforce efficiency
Overall equipment effectiveness
Human resource management
Lean manufacturing
AI Summary

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Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.

Journal

International Journal of Productivity and Performance Management cover
International Journal of Productivity and Performance Management
IF:
3.5
Papers:
778
Citations:
4.4K

Organization

U
University of Naples Federico II
Scholars:
4.7W
Papers: 3.6W
Citations: 51
U
University of Pisa
Scholars:
3.1W
Papers: 2.4W
Citations: 2.4W
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