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Using a Novel Grey DANP Model to Identify Interactions between Manufacturing and Logistics Industries in China

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  • Peng Jiang

    (School of Economics and Management, Dalian Ocean University, Dalian 116023, China)

  • Yi-Chung Hu

    (College of Management & College of Tourism, Fujian Agriculture and Forestry University, Fuzhou 350002, China
    Department of Business Administration, Chung Yuan Christian University, Taoyuan City 32023, Taiwan)

  • Ghi-Feng Yen

    (College of Management & College of Tourism, Fujian Agriculture and Forestry University, Fuzhou 350002, China)

  • Hang Jiang

    (School of Business Administration, Jimei University, Xiamen 361021, China)

  • Yu-Jing Chiu

    (Department of Business Administration, Chung Yuan Christian University, Taoyuan City 32023, Taiwan)

Abstract

As a crucial part of producer services, the logistics industry is highly dependent on the manufacturing industry. In general, the interactive development of the logistics and manufacturing industries is essential. Due to the existence of a certain degree of interdependence between any two factors, interaction between the two industries has produced a basis for measurement; identifying the key factors affecting the interaction between the manufacturing and logistics industries is a kind of decision problem in the field of multiple criteria decision making (MCDM). A hybrid MCDM method, DEMATEL-based ANP (DANP) is appropriate to solve this problem. However, DANP uses a direct influence matrix, which involves pairwise comparisons that may be more or less influenced by the respondents. Therefore, we propose a decision model, Grey DANP, which can automatically generate the direct influence matrix. Statistical data for the logistics and manufacturing industries in the China Statistical Yearbook (2006–2015) were used to identify the key factors for interaction between these two industries. The results showed that the key logistics criteria for interaction development are the total number of employees in the transport business, the volume of goods, and the total length of routes. The key manufacturing criteria for interaction development are the gross domestic product and the value added. Therefore, stakeholders should increase the number of employees in the transport industry and freight volumes. Also, the investment in infrastructure should be increased.

Suggested Citation

  • Peng Jiang & Yi-Chung Hu & Ghi-Feng Yen & Hang Jiang & Yu-Jing Chiu, 2018. "Using a Novel Grey DANP Model to Identify Interactions between Manufacturing and Logistics Industries in China," Sustainability, MDPI, vol. 10(10), pages 1-20, September.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:10:p:3456-:d:172459
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    1. Gary S. Becker & Kevin M. Murphy, 1994. "The Division of Labor, Coordination Costs, and Knowledge," NBER Chapters, in: Human Capital: A Theoretical and Empirical Analysis with Special Reference to Education, Third Edition, pages 299-322, National Bureau of Economic Research, Inc.
    2. Peng Jiang & Yi-Chung Hu & Ghi-Feng Yen, 2017. "Applying Grey Relational Analysis to Find Interactions between Manufacturing and Logistics Industries in Taiwan," Advances in Management and Applied Economics, SCIENPRESS Ltd, vol. 7(3), pages 1-2.
    3. Jac C. Heckelman, 2003. "Probabilistic Borda rule voting," Social Choice and Welfare, Springer;The Society for Social Choice and Welfare, vol. 21(3), pages 455-468, December.
    4. Stosic, Darko & Stosic, Dusan & Ludermir, Teresa & de Oliveira, Wilson & Stosic, Tatijana, 2016. "Foreign exchange rate entropy evolution during financial crises," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 449(C), pages 233-239.
    5. Chia Sun, 2014. "Combining grey relation analysis and entropy model for evaluating the operational performance: an empirical study," Quality & Quantity: International Journal of Methodology, Springer, vol. 48(3), pages 1589-1600, May.
    6. Marasco, Alessandra, 2008. "Third-party logistics: A literature review," International Journal of Production Economics, Elsevier, vol. 113(1), pages 127-147, May.
    7. Fagerberg, Jan & Verspagen, Bart, 2002. "Technology-gaps, innovation-diffusion and transformation: an evolutionary interpretation," Research Policy, Elsevier, vol. 31(8-9), pages 1291-1304, December.
    8. Tsan-Ming Choi & Stein W Wallace & Yulan Wang, 2016. "Risk management and coordination in service supply chains: information, logistics and outsourcing," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 67(2), pages 159-164, February.
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