Novel approach for manufacturing supply chain risk analysis using fuzzy supply inoperability input-output model. (April 2017)

• Record Type: Journal Article
• Authors: Brosas, Mary Eloise; Kilantang, Michelle Abigail; Li, Noreen Bless; Ocampo, Lanndon; Promentilla, Michael Angelo; Yu, Krista Danielle
• Is Part Of: Manufacturing letters. Volume 12(2017)
• Page Start: 1
• Page End: 5
• Publication Date: 2017-04
• Subjects: Supply chain -- Risk analysis -- Fuzzy set theory -- Input-output model -- Inoperability; Manufacturing industries -- Periodicals; Production engineering -- Periodicals; Manufacturing industries; Periodicals; 670
• DOI: 10.1016/j.mfglet.2017.03.001
• Languages: English
• ISSNs: 2213-8463

Abstract

Abstract: Aside from becoming more complex and dynamic, manufacturing supply chains must be capable in adapting to disruptive events caused by natural and man-made disasters. Risk analysis aids in developing mitigation policies to achieve a resilient manufacturing supply chain. However, the uncertainty and vagueness of information along the supply chain pose a challenge to risk analysis. Previous approaches on the analysis of supply chain risks have been proposed but have drawbacks that may provide counterintuitive results. Thus, this study attempts to develop a methodological approach based on supply-driven input-output analysis with fuzzy parameters in order to address supply chain risk analysis. The motivation behind the adoption of such approach lies in the strength of I-O analysis in addressing interdependent systems and its ability to address uncertainty of information shared among members. The proposed approach was applied to an herbal manufacturing supply chain to illustrate the methodology.