Logistics competitiveness of OECD countries using an improved TODIM method

Abstract

The Organization for Economic Cooperation and Development (OECD) provides a forum where governments can work together to increase the global welfare and to seek solutions to common problems through economic growth, where logistics plays an important role and contributes to financial stability. Evaluation of the logistics competitiveness of countries is a technical decision-making issue involving a variety of criteria. Most importantly, these criteria usually conflict with each other and they often act and react upon one another. As in logistics competitiveness as well as in many decision-making problems, the relationships among criteria are interdependent. Moreover, different dimensions and criteria weights also affect the evaluation results. By considering these situations, in order to handle these criteria interactions, Mahalanobis distance (MD) based TODIM (an acronym in Portuguese for Interactive and Multicriteria Decision Making) method has been developed and it has been applied to evaluate the logistics competitiveness of the OECD countries. Evaluation of the correlation between criteria develops the consideration outcomes (regarding sorting) to a certain degree with the traditional TODIM method.

Hybrid MCDM Solutions for Evaluation of the Logistics Performance Index of the Western Balkan Countries

“…The authors have concluded that the logistics impact largely depends on revenue and geographical area. Ozmen (2019) believes that assessing the logistics competitiveness of countries is a technical issue of decisionmaking that includes a number of criteria, and that the criteria conflict with each other and often act and react to each other, i.e. they are interdependent.…”

MCDM applications in logistics performance evaluation: A literature review

“…Other researchers (Ulutas ¸& Karaköy, 2019;Ozmen, 2019 andÇakır, 2017) also have used LPI data in their logistics performance evaluation research.…”

“…Technique for order Preference by Similarity to an Ideal Solution (TOPSIS) Kumar & Singh, 2012;Alkhatib et al, 2015;Senthil et al, 2014;Yayla et al, 2015;Yazdani et al, 2017;Petrovi'c et al, 2018;Senthil et al, 2018;Singh et al, 2018;Han & Trimi, 2018;Miao et al, 2018;Bai & Sarkis, 2019;Jovcic et al, 2019 Gray Relational Analysis (GRA) Khodaverdi & Hashemi, 2015 VIsekriterijumska optimizacija i Kompromisno Resenje (VIKOR) Vahabzadeh et al, 2015;Yien et al, 2017;Petrovi'c et al, 2018;Paul et al, 2019;Bai & Sarkis, 2019;Tian et al, 2019 TODIM (an acronym in Portuguese for Interactive and Multicriteria Decision Making) Wang et al, 2016;Ozmen, 2019 Stochastic Multicriteria Acceptability Analysis (SMAA) Govindan et al, 2019 Data Envelopment Analysis (DEA) Azadi and Sean, 2011;Falsini et al, 2012;Wanke, 2012;Joo et al, 2013;Min et al, 2013;Venkatesh et al, 2015;Joo & Yun, 2017;Raut et al, 2017Raut et al, & 2018Rodrigues et al, 2018;Rodrigues et al, 2018;Fathi & Saen, 2018…”

“…14 (11%) of the articles have used some or other form of sensitivity to evaluate the stability of their results. Some researchers indicated that sensitivity analysis could be done on their results (Ho et al, 2012;Kayakutlu & Buyukozkan, 2011;Raut et al, 2018 and2019;Singh et al, 2018). Four common sensitivity analyses observed are: altering criteria/decision-maker weight values, model parameter values, methods, or implementation of robust engineering methods.…”

“…12 papers considered both LSP as well as customer's performance evaluation, for example, Rashidi and Cullinane (2019), use LPI data published by World Bank and other published data to rank the Organization for Economic Cooperation and Development (OECD) countries on sustainable operational logistics performance using DEA. Other researchers (Ulutaş & Karaköy, 2019; Ozmen, 2019 and Çakır, 2017) also have used LPI data in their logistics performance evaluation research.…”