An Expert Based Human Resource Management System in Dealing With Selection, Training and Performance Evaluation Measures the Case of Ministry of Capacity Building
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Date
2006-03
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Addis Ababa University
Abstract
In practice, management problems require a high skill of managing with inter disciplinary
background combining social science, business and behavioral sciences. Effective human
resource management system represents the essence of production efficiency, growth
processes, transparency and predictability of production, getting additional profit due to
proper and quicker decision-making, keeping equal employment opportunities, and so on.
Today's human resource workers run most of the activities in the HRM system
increasingly by consulting top executives and experts. With regard to Ethiopia, the
process of human resource management is done manually under the control of committee
members organized for the process, which is not an efficient method in measuring and
evaluating an applicant's performance and skill. And consequently a number of problems
are faced, like irregular evaluation mechanism, rater errors and personal bias, external
influence of liking, and so on.
Literature's suggest that the success of an organization lies down on experts engaged in
joining the production with its management process. And to solve such and related
human resource problem, using an appropriate technology based system is paramount. A
knowledge based (Expert) systems is the one, which is appropriate in solving problems
regarding human resource management.
In an attempt to address such problems, the present study, specifically, explores the
potentiality of applying expert systems technology in Human Resource Management
problems specific to the Selection, Training and Performance evaluation measures for the
Ministry of Capacity Building in Addis Ababa. The knowledge acquired from manuals and domain experts is represented using
production rules. These rules are then implemented in the Knowledge Pro Gold expert
system shell uSll1g back ward chaining inference mechanism.
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Information Science