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The Role of Fuzzy Logic to Dealing with Epistemic Uncertainty in Supply Chain Risk Assessment: Review Standpoints

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Abstract

The nature of supply chains presents a variety of issues related to uncertainties. Under various uncertainties, risk management plays a crucial role in an effective supply chain decision-making. The uncertainty involved in the risk assessment process can be divided into two types: random uncertainty and epistemic uncertainty. Fuzzy theory has been applied to deal with uncertainties. The purpose of this paper is to analyze the role and contribution of the fuzzy logic in the treatment of epistemic uncertainty into supply chain risk management approaches. A literature review process was performed, followed by analysis and discussions on the examined topic. The results revealed that the integration with multicriteria decision-making and disruptive analysis methods are the most common types adopted, with trend to petri nets and multicriteria decision-making approaches. Supply risks are the most studied type and identification and assessment are the most developed processes in supply chain risk management. Although the publications on the subject has been highlighted, they present some limitations related to the holistic complexity of risks in supply chains, the dynamic nature of the environment and the reliability of the background knowledge in the assessment. In that sense, these remarks reveal interesting future researches lines.

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Abbreviations

ABC:

Activity-based costing

AHP:

Analytic hierarchy process

ANP:

Analytic network process

ARP:

Aggregate risk potential

BN:

Bayesian network

CCDEA:

Chance constraint data envelopment analysis

DEA:

Data envelopment analysis

DEMATEL:

Decision-making trial and evaluation laboratory

FAHP:

Fuzzy analytic hierarchy process

FANP:

Fuzzy analytic network process

FDEA:

Fuzzy data envelopment analysis

F-DEMATEL:

Fuzzy DEMATEL

FDIIM:

Fuzzy dynamic inoperability input/output model

FEAHP:

Fuzzy extended AHP

F-MABAC:

Fuzzy multi-attributive border approximation area comparison

FMEA:

Failure mode and effects analysis

FTA:

Fault tree analysis

FUV:

Fuzzy utility value

GPN:

Global production networks

HOR:

House of risk

IIM:

Inoperability input/output model

OPC:

Operational process cycle

OPF:

Organizational performance factors

PLC:

Product life cycle

PN:

Petri nets

ROP:

Risk operational practices

SCOR:

Supply chain operations reference model

SCV:

Supply chain visibility

SFMOP:

Stochastic fuzzy multi-objective programming model

TBL:

Triple bottom line

TOPSIS:

Technique of order preference for similarity with the ideal solution

WFPN:

Weighted fuzzy Petri net

VIKOR:

Multicriteria optimization and compromise solution

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Correspondence to Alina Díaz-Curbelo.

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Díaz-Curbelo, A., Espin Andrade, R.A. & Gento Municio, Á.M. The Role of Fuzzy Logic to Dealing with Epistemic Uncertainty in Supply Chain Risk Assessment: Review Standpoints. Int. J. Fuzzy Syst. 22, 2769–2791 (2020). https://doi.org/10.1007/s40815-020-00846-5

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  • DOI: https://doi.org/10.1007/s40815-020-00846-5

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