Evaluation of the innovative value proposition for the rail freight transport: an integrated DEMATEL–ANP approach
Abstract
Freight transport represents a very dynamic and competitive market with high requirements for reliability, lead time, cost, flexibility and visibility of transport service. Rationalization of transport service, reduced travelling time and reliable delivery times represent the main prerequisites for lowering the costs and increasing the efficiency of entire transport chain. These performance indicators actually represent the main factors affecting the shipper’s mode choice. Improvement of these factors could be achieved by improved coordination between rail and non-rail-related stakeholders involved in freight transport service planning and realization. Since this solution requires a multi-stakeholder collaboration, it is needed to evaluate the interests of each of them in order to derive a preferred set of indicators, which will facilitate a collectively accepted solution and value alignment of all involved actors. In this paper, the preferred set of indicators was selected by using the DEcision MAking Trial and Evaluation Laboratory (DEMATEL) model technique integrated with the Analytic Network Process (ANP). DEMATEL is applied to analyse the causal relationships among the relevant dimensions and among the criteria within each dimension. The causal relationships are then used in ANP for determining the weights of the criteria. An empirical case study based on implementation of information sharing platform in rail intermodal transport chain is presented to demonstrate the effectiveness of the proposed approach. Based on this study the attributes that belong to reliability (“departing / arriving on-time”, “cancelled services”), lead time (“idle time”) and investment cost (“organizational culture”, “business process redesign”) dimensions represent five the most critical factors for obtaining a collectively accepted solution. This effective evaluation model enables policy makers and stakeholders in transportation / logistics to understand and conduct appropriate actions towards fulfilling the objectives for greener transportation.
First published online 2 April 2021
Keyword : DEMATEL, ANP, rail intermodal transport chain, information sharing platform
How to Cite
Milenković, M., Val, S., Lutovac, D., Bojović, N., & Knežević, N. (2021). Evaluation of the innovative value proposition for the rail freight transport: an integrated DEMATEL–ANP approach. Transport, 36(5), 406-432. https://doi.org/10.3846/transport.2021.14519
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Hsu, C.-C.; Liou, J. J. H.; Lo, H.-W.; Wang, Y.-C. 2018. Using a hybrid method for evaluating and improving the service quality of public bike-sharing systems, Journal of Cleaner Production 202: 1131–1144. https://doi.org/10.1016/j.jclepro.2018.08.193
Ishfaq, R. 2012. Resilience through flexibility in transportation operations, International Journal of Logistics Research and Applications: a Leading Journal of Supply Chain Management 15(4): 215–229. https://doi.org/10.1080/13675567.2012.709835
Kaplinsky, R.; Morris, M. 2001. A Handbook for Value Chain Research. International Development Research Centre, Ottawa, Canada. 113 p.
Karaşan, A.; Kahraman, C. 2019. A novel intuitionistic fuzzy DEMATEL–ANP–TOPSIS integrated methodology for freight village location selection, Journal of Intelligent & Fuzzy Systems 36(2): 1335–1352. https://doi.org/10.3233/JIFS-17169
Kheybari, S.; Rezaie, F. M.; Farazmand, H. 2020. Analytic network process: an overview of applications, Applied Mathematics and Computation 367: 124780. https://doi.org/10.1016/j.amc.2019.124780
Kijewska, K.; Torbacki, W.; Iwan, S. 2018. Application of AHP and DEMATEL methods in choosing and analysing the measures for the distribution of goods in Szczecin region, Sustainability 10(7): 2365. https://doi.org/10.3390/su10072365
Kim, H.-C.; Nicholson, A.; Kusumastuti, D. 2017. Analysing freight shippers’ mode choice preference heterogeneity using latent class modelling, Transportation Research Procedia 25: 1109–1125. https://doi.org/10.1016/j.trpro.2017.05.123
Kumar, A.; Anbanandam, R. 2020. Analyzing interrelationships and prioritising the factors influencing sustainable intermodal freight transport system: a grey-DANP approach, Journal of Cleaner Production 252: 119769. https://doi.org/10.1016/j.jclepro.2019.119769
Kundakcı, N.; Adalı, E. A.; Işık, A. T. 2014. Combination of DEMATEL and ANP for the cargo shipping company selection problem, International Journal of Engineering Management and Economics 4(2): 99–116. https://doi.org/10.1504/IJEME.2014.066574
Lee, K.-C.; Tsai, W.-H.; Yang, C.-H.; Lin, Y.-Z. 2018. An MCDM approach for selecting green aviation fleet program management strategies under multi-resource limitations, Journal of Air Transport Management 68: 76–85. https://doi.org/10.1016/j.jairtraman.2017.06.011
Lin, C.-L.; Hsieh, M.-S.; Tzeng, G.-H. 2010. Evaluating vehicle telematics system by using a novel MCDM techniques with dependence and feedback, Expert Systems with Applications 37(10): 6723–6736. https://doi.org/10.1016/j.eswa.2010.01.014
Liou, J. J. H.; Hsu, C.-C.; Chen, Y.-S. 2014. Improving transportation service quality based on information fusion, Transportation Research Part A: Policy and Practice 67: 225–239. https://doi.org/10.1016/j.tra.2014.07.007
Liou, J. J. H.; Tzeng, G.-H.; Chang, H.-C. 2007. Airline safety measurement using a hybrid model, Journal of Air Transport Management 13(4): 243–249. https://doi.org/10.1016/j.jairtraman.2007.04.008
Liu, C.-H.; Tzeng, G.-H.; Lee, M.-H.; Lee, P.-Y. 2013. Improving metro–airport connection service for tourism development: using hybrid MCDM models, Tourism Management Perspectives 6: 95–107. https://doi.org/10.1016/j.tmp.2012.09.004
Lu, M.-T.; Hsu, C.-C.; Liou, J. J. H.; Lo, H.-W. 2018. A hybrid MCDM and sustainability-balanced scorecard model to establish sustainable performance evaluation for international airports, Journal of Air Transport Management 71: 9–19. https://doi.org/10.1016/j.jairtraman.2018.05.008
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Ossadnik, W.; Schinke, S.; Kaspar, R. H. 2016. Group aggregation techniques for analytic hierarchy process and analytic network process: a comparative analysis, Group Decision and Negotiation 25(2): 421–457. https://doi.org/10.1007/s10726-015-9448-4
Pamučar, D.; Ćirović, G. 2015. The selection of transport and handling resources in logistics centers using multi-attributive border approximation area comparison (MABAC), Expert Systems with Applications 42(6): 3016–3028. https://doi.org/10.1016/j.eswa.2014.11.057
Pamučar, D.; Đorović, B.; Božanić, D.; Ćirović, G. 2012. Modification of the dynamic scale of marks in analytic hierarchy process (AHP) and analytic network approach (ANP) through application of fuzzy approach, Scientific Research and Essays 7(1): 24–37.
Patterson, Z.; Ewing, G. O.; Haider, M. 2007. Shipper preferences suggest strong mistrust of rail: results from stated preference carrier choice survey for Quebec City–Windsor Corridor in Canada, Transportation Research Record: Journal of the Transportation Research Board 2008: 67–74. https://doi.org/10.3141/2008-09
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