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Coompetency-Based Mapping Tool in Personnel Management System using Analytical Hierarchy Process

Published:29 December 2021Publication History

ABSTRACT

Organizations that want to have a highly efficient and productive workforce should develop a skill mapping approach. However, research shows that most organizations fail to detect and effectively use their employees' competencies, keeping them from working at their best. If these organizations understand that employees are their most important assets, then one of their functions is to help them navigate their careers. Competence mapping in this context is a valuable tool. It is an extension to information management and other organizational initiatives–the process structures to regularly quantify and evaluate individual and group success related to its and its clients' expectations–as key attributes (knowledge, competencies, and attitudes) needed to work effectively in the job classification or process identified. Competency Mapping interweaves two data sets build on the techniques and organizational workflow, starts with the consistent articulation of workflow and procedures, including all specifications for quality and quantity, inputs and outputs, decision criteria, and most significantly, internal and external client needs. It defines specific performance criteria for each phase in each process, along with all related metrics and expectations. The other collection of data is on the ability to work individually by gathering the use of a range of assessment instruments and procedures to determine the degree to which individuals can reliably demonstrate the competencies needed to meet standards over time. The output from the competency maps matches the individual performance capacities. The generated aggregated trend line determines where and with what particular population unique growth opportunities occur in the process.

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  • Published in

    cover image ACM Other conferences
    MLMI '21: Proceedings of the 2021 4th International Conference on Machine Learning and Machine Intelligence
    September 2021
    189 pages
    ISBN:9781450384247
    DOI:10.1145/3490725

    Copyright © 2021 ACM

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    Publication History

    • Published: 29 December 2021

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