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International Conference on Intelligent Networking and Collaborative Systems

INCoS 2017: Advances in Intelligent Networking and Collaborative Systems pp 3–15Cite as

Fine Tuning a Bayesian Network and Fairly Allocating Resources to Improve Procurement Performance

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Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 8))

Abstract

Procurement is one of the most important activities in any organization. Hence it is vital for an organization to track procurement practices. Through performance measurement, the organization will have a clear understanding on how it’s performing as well as the effect of any action that it makes towards improvement. In our previous work, we proposed a Bayesian Network (BN) model to measure the level of procurement performance in an organization. This paper extends that model in two ways. First it uses the Best-Worst Method (BWM) to adjust the impact of each KPI on the procurement performance according to its importance to the overall business strategy. Second is by using the relative importance of the KPIs, it demonstrates how procurement can be improved by re-allocating the available resources among the KPIs in a fair way.

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Notes

  1. 1.

    The organization’s reputation in the community against competitors.

  2. 2.

    Conserving the environment and aligning business strategies with environmental regulatory schemes.

  3. 3.

    The satisfactory level of end users.

  4. 4.

    The level of the organization’s undependability to suppliers and business partners.

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Author information

Authors and Affiliations

  1. School of Business, University of New South Wales, Canberra, BC, 2610, Australia

    Mohammad Hassan Abolbashari, Omar Khadeer Hussain, Morteza Saberi & Elizabeth Chang

Authors
  1. Mohammad Hassan Abolbashari
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  2. Omar Khadeer Hussain
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  3. Morteza Saberi
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  4. Elizabeth Chang
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Corresponding author

Correspondence to Mohammad Hassan Abolbashari .

Editor information

Editors and Affiliations

  1. Faculty of Information Engineering, Fukuoka Institute of Technology, Fukuoka, Japan

    Leonard Barolli

  2. Department of Computer Science, Ryerson University, Toronto, Ontario, Canada

    Isaac Woungang

  3. School of Business, UNSW Australia, Canberra, Aust Capital Terr, Australia

    Omar Khadeer Hussain

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Abolbashari, M.H., Hussain, O.K., Saberi, M., Chang, E. (2018). Fine Tuning a Bayesian Network and Fairly Allocating Resources to Improve Procurement Performance. In: Barolli, L., Woungang, I., Hussain, O. (eds) Advances in Intelligent Networking and Collaborative Systems. INCoS 2017. Lecture Notes on Data Engineering and Communications Technologies, vol 8. Springer, Cham. https://doi.org/10.1007/978-3-319-65636-6_1

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  • DOI: https://doi.org/10.1007/978-3-319-65636-6_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-65635-9

  • Online ISBN: 978-3-319-65636-6

  • eBook Packages: EngineeringEngineering (R0)

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