首页 | 本学科首页   官方微博 | 高级检索  
     检索      


Target training with soft computing tools
Authors:Jussi Kantola  Antti Piirto  Jarmo Toivonen  Yoon Chang  Hannu Vanharanta[Author vitae]
Institution:aDepartment of Knowledge Service Engineering, Korea Advanced Institute of Science and Technology, 335 Gwahangno, Yuseong-gu, Daejeon 305-701, Republic of Korea;bTeollisuuden Voima Oyj, Olkiluoto, FI-27160 Eurajoki, Finland;cInstitute of Signal Processing, Tampere University of Technology, P.O. Box 553, FIN-33101 Tampere, Finland;dUbiquitous Technology Application Research Center, School of Air Transport, Transportation and Logistics, Korea Aerospace University, 412-791 GoYang City, Republic of Korea;eDepartment of Industrial Management, Tampere University of Technology at Pori, Pohjoisranta 11 A, PL 300, 28101 Pori, Finland
Abstract:The personal capabilities and intentions of employees indicate their performance within their organization. It is important for the organization to capture this kind of tacit knowledge since the workforce are the true experts in perceiving the organization's current reality and evaluating which assets require development – including themselves as knowledge assets. The collective inner voice of the workforce helps the organization's management to steer the company and its assets in a sustainable direction.This article presents how the collective inner voice of the workforce can be captured and how it can be used for the benefit of the organization and its employees. The objective is to support individuals’ personal aspirations, as well as to save the money, time and resources that an organization spends on personnel training.The focus of this article is on demonstrating a possible soft-computing method used for competency simulation. The process starts with a linguistic self-evaluation conducted by employees, where individuals’ own perception of current and target competence levels is captured. The self-evaluation is conducted with the help of fuzzy logic. Clusters are formed from the result dataset using an unsupervised neural network clustering method: self-organizing maps. A demonstrator tool is then used to perform a “what-if” type of analysis/simulation on the clusters in the results. With the demonstrator tool, employees can roughly test the impact of alternative training scenarios for themselves. For individuals this may open up new directions for self-development, and for organizations this may allow the efficient use of training resources. We tested the approach with a dataset from a real human resource development project among nuclear power plant operators.The case study reveals the potential of soft-computing based collective competency simulation as one part of personnel development projects in the future. Yet the techniques and the demonstrator tool used in this experiment are far from being products that employees could easily use as part of their training project. Possible benefits of the proposed approach are demonstrated in this article.
Keywords:Work role  Competencies  SOM  Self-organizing map  Self-evaluation  SIMU_SOM
本文献已被 ScienceDirect 等数据库收录!
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号