A Fuzzy-Based Holistic Approach for Supply Chain Risk Assessment and Aggregation Considering Risk Interdependencies
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Departamento de Ingeniería Química y Tecnología del Medio Ambiente, Universidad de Valladolid, 47011 Valladolid, Spain
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Departamento de Organización de Empresas y Comercialización e Investigación de Mercado, Universidad de Valladolid, 47011 Valladolid, Spain
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Department of Industrial Engineering, Pennsylvania State University, Erie, PA 16802-4400, USA
*
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(24), 5329; https://doi.org/10.3390/app9245329
Received: 23 October 2019 / Revised: 30 November 2019 / Accepted: 2 December 2019 / Published: 6 December 2019
(This article belongs to the Special Issue Industrial Engineering and Management: Current Issues and Trends)
Supply chain risk management requires dealing with uncertainty, interrelations, and subjectivity inherent in the risk assessment process. This paper proposes a holistic approach for risk management that considers the impact on multiple performance objectives, the relation between risk agents, and the risk event interdependencies. An aggregated risk score is proposed to capture the cascading effects of common risk triggers and quantify the aggregated score by risk agent and objective. The approach also uses fuzzy logic to allow for the treatment of vague and ambiguity data as input parameters to the model from different domains and scales, according to knowledge and criteria nature. The integration of the balanced scorecard tool improves the analysis and prioritization of mitigation strategies in decision-making, both by risk agent and by strategic objective. A case study of a telecommunication company is presented to illustrate the applicability of the proposed approach.
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Keywords:
risk assessment; risk management; supply chain; uncertainty; epistemic; fuzzy logic; telecommunication industry
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MDPI and ACS Style
Díaz-Curbelo, A.; Gento, Á.M.; Redondo, A.; Aqlan, F. A Fuzzy-Based Holistic Approach for Supply Chain Risk Assessment and Aggregation Considering Risk Interdependencies. Appl. Sci. 2019, 9, 5329.
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