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Application of a hybrid dynamic MCDM to explore the key factors for the internal control of procurement circulation

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  • Fu-Hsiang Chen

Abstract

Decisions related to the internal control measurement of procurement circulation are inherently multiple criteria decision-making problems and are strategically important to enterprises. Although efforts have been made to discover systematic methods for enhancing purchasing performance, the criteria have been assumed to be independent, which is not actually the case. Most conventional decision models cannot capture the complex interrelationships the criteria. Here, we propose a workable hybrid dynamic multiple criteria decision-making (HDMCDM) method to solve the problems arising from the complex dynamics in the real world. The relationship between the criteria and the criteria weights for the HDMCDM are developed by combining the decision-making trial and evaluation laboratory (DEMATEL) method with the fundamental concept of the analytic network process (ANP), the DEMATEL-based ANP (DANP). VlseKriterijumska Optimizacija I Kompromisno Resenje is then used to aggregate the performance gaps with the weights obtained from the DANP. The results obtained for a specific company show that the four dimensions of internal control of procurement circulation have a mutual influence relationship, with the dimension of ‘receiving and storage’ demonstrating the greatest influence and ‘recorded liabilities’ the least. Empirical analysis helps to improve the scores for achieving the aspiration levels for each factor.

Suggested Citation

  • Fu-Hsiang Chen, 2015. "Application of a hybrid dynamic MCDM to explore the key factors for the internal control of procurement circulation," International Journal of Production Research, Taylor & Francis Journals, vol. 53(10), pages 2951-2969, May.
  • Handle: RePEc:taf:tprsxx:v:53:y:2015:i:10:p:2951-2969
    DOI: 10.1080/00207543.2014.961210
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    Cited by:

    1. Kuang-Hua Hu & Wei Jianguo & Gwo-Hshiung Tzeng, 2017. "Risk Factor Assessment Improvement for China’s Cloud Computing Auditing Using a New Hybrid MADM Model," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 16(03), pages 737-777, May.
    2. Chi-Yo Huang & Pei-Han Chung & Joseph Z. Shyu & Yao-Hua Ho & Chao-Hsin Wu & Ming-Che Lee & Ming-Jenn Wu, 2018. "Evaluation and Selection of Materials for Particulate Matter MEMS Sensors by Using Hybrid MCDM Methods," Sustainability, MDPI, vol. 10(10), pages 1-35, September.
    3. Kuang-Hua Hu & Fu-Hsiang Chen & Gwo-Hshiung Tzeng, 2016. "Evaluating the Improvement of Sustainability of Sports Industry Policy Based on MADM," Sustainability, MDPI, vol. 8(7), pages 1-21, June.
    4. Kheybari, Siamak & Rezaie, Fariba Mahdi & Farazmand, Hadis, 2020. "Analytic network process: An overview of applications," Applied Mathematics and Computation, Elsevier, vol. 367(C).

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