A Comparative Analysis Selecting the Transport Routes of Electronics Components from China to Vietnam

Vietnam has successfully integrated itself into the global value chains (GVCs) as a base for the assembly and production of electronics goods and mobile phones beyond China. Therefore, adequate transport routes from China to Vietnam are essential factors for a seamless supply chain. This study aimed to evaluate the competing transport routes for door-to-door transportation from Shenzhen (China) to Hai Phong (Vietnam) from the logistics service providers and shippers’ perspective. The Delphi method and the Consistent Fuzzy Preference Relations (CFPR) method were employed, using both qualitative and quantitative factors. The results illustrate that, among the principal factors, reliability is prioritized, followed by transportation costs, transportation mode capacity, and transportation time. Meanwhile, of the sub-factors, risk of freight damage and loss is the most important. The route using airway and truck is preferred over the two alternatives. Furthermore, a sensitivity analysis was conducted to examine the possibility of rank reversal. Thus, the study offers crucial academic and practical implications.

[1]  Thomas L. Saaty,et al.  Decision-making with the AHP: Why is the principal eigenvector necessary , 2003, Eur. J. Oper. Res..

[2]  Shinya Hanaoka,et al.  Assessment of intermodal transport corridors: Cases from North-East and Central Asia , 2012 .

[3]  Vincent S. Lai,et al.  Managing international data communications , 2002, CACM.

[4]  Chengxuan Cao,et al.  A generalized interval fuzzy mixed integer programming model for a multimodal transportation problem under uncertainty , 2017 .

[5]  Gavin Fisher,et al.  Transport cost analysis: a case study of the total costs of private and public transport in Auckland , 2006 .

[6]  Hande Yaman,et al.  An intermodal multicommodity routing problem with scheduled services , 2012, Comput. Optim. Appl..

[7]  D. Moon,et al.  A Study on Competitiveness of Sea Transport by Comparing International Transport Routes between Korea and EU , 2015 .

[8]  A. Samimi,et al.  A behavioral analysis of freight mode choice decisions , 2011 .

[9]  A. Marucheck,et al.  Product safety and security in the global supply chain: Issues, challenges and research opportunities , 2011 .

[10]  Hui-Yin Tsai,et al.  Fuzzy hierarchy sensitive with Delphi method to evaluate hospital organization performance , 2010, Expert Syst. Appl..

[11]  Tadashi Imaizumi,et al.  A fuzzy statistical test of fuzzy hypotheses , 1993 .

[12]  Ying Wang,et al.  Evaluating the competitiveness of the aerotropolises in East Asia , 2013 .

[13]  Loon Ching Tang,et al.  Analysis of intermodal freight from China to Indian Ocean: A goal programming approach , 2011 .

[14]  Jae Hyung Cho,et al.  An intermodal transport network planning algorithm using dynamic programming—A case study: from Busan to Rotterdam in intermodal freight routing , 2010, Applied Intelligence.

[15]  Monica R Geist,et al.  Using the Delphi method to engage stakeholders: a comparison of two studies. , 2010, Evaluation and program planning.

[16]  Emilio Esposito,et al.  How to deal with knowledge management misalignment: a taxonomy based on a 3D fuzzy methodology , 2018, J. Knowl. Manag..

[17]  Francisco Herrera,et al.  Some issues on consistency of fuzzy preference relations , 2004, Eur. J. Oper. Res..

[18]  Riccardo Manzini,et al.  Designing Sustainable Cold Chains for Long-Range Food Distribution: Energy-Effective Corridors on the Silk Road Belt , 2017 .

[19]  Wei Deng Solvang,et al.  A Stochastic Programming Approach with Improved Multi-Criteria Scenario-Based Solution Method for Sustainable Reverse Logistics Design of Waste Electrical and Electronic Equipment (WEEE) , 2016 .

[20]  Wei Lu,et al.  A multimodal location and routing model for hazardous materials transportation. , 2012, Journal of hazardous materials.

[21]  A. Ishikawa,et al.  The Max-Min Delphi method and fuzzy Delphi method via fuzzy integration , 1993 .

[22]  L. Nguyen,et al.  The valuation of shipment time variability in Greater Mekong Subregion , 2014 .

[23]  Rinaldo A. Cavalcante,et al.  A conceptual framework for agent-based modelling of logistics services , 2010 .

[24]  Ru-Jen Chao,et al.  Supplier selection using consistent fuzzy preference relations , 2012, Expert Syst. Appl..

[25]  Gökay Akkaya,et al.  An integrated fuzzy AHP and fuzzy MOORA approach to the problem of industrial engineering sector choosing , 2015, Expert Syst. Appl..

[26]  Photis M. Panayides,et al.  Marketing in Asia‐Pacific logistics companies: a discriminant analysis between marketing orientation and performance , 2004 .

[27]  Huseyin Selcuk Kilic,et al.  Reverse logistics system design for the waste of electrical and electronic equipment (WEEE) in Turkey , 2015 .

[28]  George Wright,et al.  The Delphi technique as a forecasting tool: issues and analysis , 1999 .

[29]  Gwo-Hshiung Tzeng,et al.  Using Fuzzy Integral Approach to Enhance Site Selection Assessment - A Case Study of the Optoelectronics Industry , 2013, ITQM.

[30]  Gi-Tae Yeo,et al.  Application of Fuzzy Delphi TOPSIS to Locate Logistics Centers in Vietnam: The Logisticians’ Perspective , 2017 .

[31]  Robert Loo,et al.  The Delphi method: a powerful tool for strategic management , 2002 .

[32]  Ru-Jen Chao,et al.  Evaluation of the criteria and effectiveness of distance e-learning with consistent fuzzy preference relations , 2009, Expert Syst. Appl..

[33]  Mary J. Meixell,et al.  A review of the transportation mode choice and carrier selection literature , 2008 .

[34]  Stephen C. Hayne,et al.  A comparative analysis of critical issues facing Canadian information systems personnel: a national and global perspective , 2000, Inf. Manag..

[35]  R. Banomyong,et al.  Multimodal transport: the case of Laotian garment exporters , 2001 .

[36]  Youngin Seo,et al.  Multimodal Transportation: The Case of Laptop from Chongqing in China to Rotterdam in Europe☆ , 2017 .

[37]  S. Pettit,et al.  Multimodal route choice in maritime transportation: the case of Korean auto-parts exporters , 2018 .

[38]  Tien-Chin Wang,et al.  A proposed model for measuring the aggregative risk degree of implementing an RFID digital campus system with the consistent fuzzy preference relations , 2013 .

[39]  Gi-Tae Yeo,et al.  Intermodal route selection for cargo transportation from Korea to Central Asia by adopting Fuzzy Delphi and Fuzzy ELECTRE I methods , 2018 .

[40]  James C. Wetherbe,et al.  Key Issues in Information Systems Management: 1994-95 SIM Delphi Results , 1996, MIS Q..

[41]  Gi-Tae Yeo,et al.  A Study on International Multimodal Transport Networks from Korea to Central Asia: Focus on Secondhand Vehicles☆ , 2016 .

[42]  Hsin-Yu Chiang,et al.  Optimal selection of international exhibition agency by using the delphi method and AHP , 2011 .

[43]  Shinya Hanaoka,et al.  Evaluating the logistics performance of intermodal transportation in Thailand , 2008 .

[44]  Okan Duru,et al.  A fuzzy extended DELPHI method for adjustment of statistical time series prediction: An empirical study on dry bulk freight market case , 2012, Expert Syst. Appl..

[45]  L. Ustinovichius,et al.  Sensitivity Analysis for Multiple Criteria Decision Making Methods: TOPSIS and SAW , 2010 .

[46]  Chui-Yu Chiu,et al.  A Consistent Fuzzy Preference Relations Based ANP Model for R&D Project Selection , 2017 .

[47]  Anthony Kenneth Charles Beresford,et al.  Multimodal supply chains: iron ore from Australia to China , 2011 .

[48]  Tien-Chin Wang,et al.  Applying the consistent fuzzy preference relations to select merger strategy for commercial banks in new financial environments , 2009, Expert Syst. Appl..

[49]  Zeng Ye,et al.  Constructing road safety performance indicators using Fuzzy Delphi Method and Grey Delphi Method , 2011, Expert Syst. Appl..