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Benjamin T Clegg
Researcher at Aston University
Publications - 10
Citations - 291
Benjamin T Clegg is an academic researcher from Aston University. The author has contributed to research in topics: Enterprise systems engineering & Enterprise planning system. The author has an hindex of 6, co-authored 10 publications receiving 252 citations.
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Strategic supplier performance evaluation: a case-based action research of a UK manufacturing organisation
TL;DR: In this paper, a real-life case-based action research utilizing an integrated analytical model that combines quality function deployment and the analytic hierarchy process method for suppliers' performance evaluation is presented.
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Measuring the operational performance of intensive care units using the analytic hierarchy process approach
TL;DR: A hierarchical quantitative model for service performance measurement is proposed, which considers both subjective and objective performance criteria and is applied to ICUs of hospitals in developing nations in order to demonstrate its effectiveness.
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Managing enterprise resource planning projects
TL;DR: The case study results reveal that the effect of other projects on on-going ERP project, management of overall IT architecture and non-availability of resources for organizational transformation are most critical from likelihood and impact perspectives.
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Managing resource dependencies in electric vehicle supply chains: a multi-tier case study
TL;DR: In this paper, the authors investigate dependencies that arise between companies during the ramp-up of production volume in the electric vehicle (EV) supply chain and use the resource dependence theory (RDT) to analyse and explain the changing dependencies throughout the planning and execution of production rampup.
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Perceptions of growth-impeding constraints acting upon SMEs’ operations and the identification and use of transitionary paths to elevate them
TL;DR: In this paper, an open-ended survey and a series of group workshops have gathered new empirical data about these perceptions, which were coded using the relational content analysis to identify a parsimonious set of perceptual growth-impeding constraint categories.