Easing knowledge management in the power sector by means of a neuro-genetic system

Sáiz-Bárcena, Lourdes; Herrero, Álvaro; Martínez, Ricardo del Olmo

doi:10.1504/ijbic.2015.069556

Cited by 3 publications

(1 citation statement)

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“…Authors of present paper have previously combined intelligent techniques to address KM in the automotive industry versus wall-painting companies (Herrero et al 2010) and power sector (Sáiz-Bárcena et al 2015). Apart from analysing KM in companies from different sectors and from a cross-sectorial perspective, present paper differentiates from previous work in the target tasks and applied methods, as described in Sect.…”

Section: Introduction and Previous Workmentioning

confidence: 99%

A hybrid proposal for cross-sectoral analysis of knowledge management

et al. 2016

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At present time, although many theoretical formulations have been successfully proposed, there is a lack of ICT-based tools to support practical deployment of knowledge management (KM) in real settings. To bridge this gap, a hybrid artificial intelligence system is proposed in present study, aimed at gaining deeper knowledge about KM practices in four different economic sectors. By means of soft computing, companies are diagnosed according to their status regarding KM and subsequent explanations about crucial KM practices and perspectives are generated. Interesting conclusions are then derived from these explanations, allowing KM managers to optimise their decisions and obtain better results. Experimental results of real-life data from Spanish companies associated with different economic sectors validate the proposed combination of techniques. Keywords Knowledge management • Hybrid artificial intelligence system • Unsupervised neural networks • Exploratory projection pursuit • Classification trees Communicated by A. Herrero.

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Section: Introduction and Previous Workmentioning

confidence: 99%