Operational supply chain risk identification and prioritization using the SCOR model

被引:0
|
作者
Ríos, Jenifer Ramos [1 ]
Duque, Diego Fernando Manotas [2 ]
Gómez, Juan Carlos Osorio [3 ]
机构
[1] Industrial Engineering Universidad del Valle, Universidad del Valle, Adjunct Faculty, Universidad del Valle, School of Industrial Engineering, Research Group Logistics & Production, Cali, Colombia
[2] Industrial Engineering Universidad del Valle, Universidad del Valle, School of Industrial Engineering, Research group Quantitative Finance, Cali, Colombia
[3] Industrial Engineering Universidad del Valle, School of Industrial Engineering, Research Group Logistics & Production, Cali, Colombia
来源
Ingenieria y Universidad | 2019年 / 23卷 / 01期
关键词
Benchmarking - Risk assessment - Risk perception - Factor analysis;
D O I
10.11144/Javeriana.iyu23-1.oscr
中图分类号
学科分类号
摘要
Objective: This study aims to propose a methodology that identifies and prioritizes the operational risk factors in a supply chain (SC) to provide a tool according to the process-based SC approach that is useful for risk assessment throughout the SC. Materials and methods: Risk identification was conducted by a scenario analysis, which linked the risk factors with the standard key performance indicators (KPIs) of the processes and logistics activities proposed by the supply chain operational reference model (SCORM o SCOR). These influence relationships were quantified using a proposed scale, and then, the risk factors were prioritized by the definition of their influence levels. This approach was applied to a real SC. Results and discussion: Twenty risk factors were clearly and effectively identified, analyzed and prioritized, and priority was given to those with the highest influence level, which can be understood as the risk factors that have a larger capacity to negatively affect SC performance. Conclusions: The methodology allows the identification of the most influential risk factors in a SC, and as it is based on a standard model, it fosters a collaborative analysis among its echelons. The main contributions of this paper are the risk identification by means of the KPIs of the SCOR model and the measurement of their influence levels, which is a new and useful feature for risk prioritization.
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