Ranking risk attitudes using an integrated AHP-TOPSIS approach

Obaid, Lubna; Abuzaid, Haneen; Dalalah, Dorid; Alhassani, Salhah; Albastaki, Fouzeya; Khalil, Tania El

doi:10.1504/ijads.2022.120279

Cited by 1 publication

(1 citation statement)

References 86 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The latest work refers to Sharma and Tripathy (2023), who developed an integrated Quality Function Deployment (QFD) and Fuzzy TOPSIS approach for supplier evaluation and selection. In a recent study by Obaid et al (2022), the researchers used both TOPSIS and "Analytical Hierarchy Process" (AHP) methods to rank the different risk attitudes in the backdrop of the factors that affect the behavior of the DMs to segregate the risk attitude with the highest impact. Kushwaha et al (2020) presented an application of IF-TOPSIS over IF-FMEA to evaluate the risks of cutting systems in the sugar mill industry.…”

Section: Literature Reviewmentioning

confidence: 99%

Supply chain risk prioritization: a multi-criteria based Intuitionistic Fuzzy TOPSIS approach

Mukherjee,

De,

Roy

2024

IJQRM

View full text Add to dashboard Cite

Purpose Identifying and prioritizing supply chain risk is significant from any product’s quality and reliability perspective. Under an input-process-output workflow, conventional risk prioritization uses a risk priority number (RPN) aligned to the risk analysis. Imprecise information coupled with a lack of dealing with hesitancy margins enlarges the scope, leading to improper assessment of risks. This significantly affects monitoring quality and performance. Against the backdrop, a methodology that identifies and prioritizes the operational supply chain risk factors signifies better risk assessment.Design/methodology/approach The study proposes a multi-criteria model for risk prioritization involving multiple decision-makers (DMs). The methodology offers a robust, hybrid system based on the Intuitionistic Fuzzy (IF) Set merged with the “Technique for Order Performance by Similarity to Ideal Solution.” The nature of the model is robust. The same is shown by applying fuzzy concepts under multi-criteria decision-making (MCDM) to prioritize the identified business risks for better assessment.Findings The proposed IF Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) for risk prioritization model can improve the decisions within organizations that make up the chains, thus guaranteeing a “better quality in risk management.” Establishing an efficient representation of uncertain information related to traditional failure mode and effects analysis (FMEA) treatment involving multiple DMs means identifying potential risks in advance and providing better supply chain control.Research limitations/implications In a company’s supply chain, blockchain allows data storage and transparent transmission of flows with traceability, privacy, security and transparency (Roy et al., 2022). They asserted that blockchain technology has great potential for traceability. Since risk assessment in supply chain operations can be treated as a traceability problem, further research is needed to use blockchain technologies. Lastly, issues like risk will be better assessed if predicted well; further research demands the suitability of applying predictive analysis on risk.Practical implications The study proposes a hybrid framework based on the generic risk assessment and MCDM methodologies under a fuzzy environment system. By this, the authors try to address the supply chain risk assessment and mitigation framework better than the conventional one. To the best of their knowledge, no study is found in existing literature attempting to explore the efficacy of the proposed hybrid approach over the traditional RPN system in prime sectors like steel (with production planning data). The validation experiment indicates the effectiveness of the results obtained from the proposed IF TOPSIS Approach to Risk Prioritization methodology is more practical and resembles the actual scenario compared to those obtained using the traditional RPN system (Kim et al., 2018; Kumar et al., 2018).Originality/value This study provides mathematical models to simulate the supply chain risk assessment, thus helping the manufacturer rank the risk level. In the end, the authors apply this model in a big-sized organization to validate its accuracy. The authors validate the proposed approach to an integrated steel plant impacting the production planning process. The model’s outcome substantially adds value to the current risk assessment and prioritization, significantly affecting better risk management quality.

show abstract

Section: Literature Reviewmentioning

confidence: 99%