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The current and future states of MCDM methods in sustainable supply chain risk assessment

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Abstract

In recent years, supply chain (SC) disruptions and their severe economic and social consequences have sparked a growing interest on the part of decision makers and researchers to adequately manage risk. The perception of risk is strongly related to the possibility of occurrence of disruptive events. In the supply chain risk management (SCRM) domain, disruptions such as the 2011 Japan earthquake and Hurricane Sandy have severely affected operations and put corporate finances at risk, becoming one of the most pressing concerns faced by companies competing in today's global marketplace. Without a doubt, the COVID-19 pandemic has exposed the fragility of SCs on a global scale as has never been seen in the past, impacting SCs from multiple sectors such as agriculture, manufacturing, transportation, leisure, to name a few and causing giants 32% drop in international trade in 2020 and an estimated 12% drop in the global economy. In order to mitigate and control the adverse effects caused by disruption risks, both in academia and in professional circles, important work is carried out in the area of SCRM. In recent times, scholars have utilized a various types of multi-criteria decision-making (MCDM) methods to evaluate sustainable supply chain risks in many contexts. Due to its importance, to date, there are no studies that can guide researchers and decision makers on what would be the most appropriate methods to face the multiple challenges posed by risk management in sustainable supply chains. In this study, we intend to cover this need, and for this, we carry out a careful review of 101 articles published since 2010. This review allows us to know the current state of MCDM applications in SCRM, and also to propose future research directions that allow us to properly manage the risks in sustainable SCs. We concluded that most of the studies used a single MCDM method or at most integrated two methods to assess sustainable supply chain risk. According to our findings, we propose a new future research agenda that considers, among others, the following: (a) use of MCDM methods to link risk with mitigation strategies, (b) integrate three or more MCDM methods to manage risks in the fields of cleaner and more sustainable production, and (c) development of methodologies that integrate MCDM with other operational research approaches such as optimization, simulation and mathematical modelling. We understand that these lines of research can contribute so that decision makers can better address the multiple consequences of increasingly frequent and intense disruptive events.

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Data availability

Data sharing not applicable to this article as no datasets were generated or analysed during the current study.

Abbreviations

ANP:

Analytical network process

AHP:

Analytical hierarchy process

BWM:

Best worst method

CRITIC:

Criteria importance through inter-criteria correlation

DIFHWA:

Dependent intuitionistic fuzzy hybrid weighed aggregation

DEMATEL:

Decision-Making Trial and Evaluation Laboratory

DST:

Dempster-shafer theory

ELECTRE:

Elimination and choice expressing reality

EDAS:

Evaluation based on distance from average solution

FIS:

Fuzzy inference system

FMEA:

Failure mode and effect analysis

FTA:

Fault-tree analysis

GRA:

Grey rational analysis

IFHWA:

Intuitionistic fuzzy hybrid weighed aggregation

IRP:

Interpretive ranking process

MOORA:

Multi-objective optimization on the basis of ratio analysis

PROMETHEE:

Preference ranking organization method for enrichment of evaluations

SWARA:

Step-wise weight assessment ratio analysis

SAW:

Simple additive weighting

TOPSIS:

Technique for order of preference by similarity to ideal solution

TODIM:

TOmada de decisao interativa multicriterio

VIKOR:

Vlse kriterijumska optimizacija kompromisno resenje

CoCoSo:

Combined compromise solution

WASPAS:

Weighted aggregated sum product assessment

MARCOS:

Measurement of alternatives and ranking according to COmpromise solution

DEA:

Data envelopment analysis

GRA:

Grey relational analysis

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Authors and Affiliations

  1. Institute of Leather Engineering and Technology, University of Dhaka, Dhaka, Bangladesh

    Md. Abdul Moktadir

  2. UTS Business School, University of Technology Sydney, Sydney, Australia

    Sanjoy Kumar Paul

  3. School of Management and Economics, University of Electronic Science and Technology of China, No. 2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, 611731, People’s Republic of China

    Chunguang Bai

  4. Department of Industrial Engineering, CES 4.0, Faculty of Engineering, Universidad de Talca, Los Niches Km. 1, Curicó, Chile

    Ernesto D. R. Santibanez Gonzalez

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Moktadir, M.A., Paul, S.K., Bai, C. et al. The current and future states of MCDM methods in sustainable supply chain risk assessment. Environ Dev Sustain (2024). https://doi.org/10.1007/s10668-023-04200-1

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