Details

Autor(en) / Beteiligte

• Anh, Duy Tran Le
• Pham, Hiep Cong
• Hoang, Nhu YNgoc
• Pham, Hai Thanh
• Sampaio, Paulo
• My, Hang Nguyen Thi
• Quang, Huy Truong
• Quyền, Nguyễn T.
• Halibas, Alrence
• Nguyen, Hung
• Thuan, Nguyen Hoang

Titel

External Supply Chain Risk Assessment in the Covid 19 Pandemic

Ist Teil von

• Business Innovation for the Post-Pandemic Era in Vietnam, 2023, p.191-203

Ort / Verlag

Singapore: Springer

Erscheinungsjahr

2023

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• The aim of this paper is to identify and evaluate global risks in supply chain performance (SCP). Firstly, three criteria that are content, probability and context, are applied to identify and categorize global risks. Next, the theory of Resource-based view and Balanced Scorecard is applied to establish a series of SCP quantifiable measures. With the purpose of assessing the external risks in the supply chain, the Structural Equation Modeling (SEM) method is employed. This article is unique in the supply-chain risk management literature catalogue in that it presents an in-depth operationalization of external supply chain risk constructs, e.g. natural disasters, war & terrorism, fire accidents, political and economic fluctuation, social and cultural related issues, and disease. According to the empirical results, the supply chain can be widely considered to be vulnerable as the developed risk model can explain up to 12.6% variance of Supplier performance, 25.2% Learning and Innovation, 23% Internal business, 40.6% Customer service, and 32.4% Finance. The implications of this study proposed risks, being contextual variables, should be considered when making strategic supply chain decisions. To minimize damage from major risks to the company, supply chain managers should recognize cost/benefit tradeoffs as viable options. Such trade-offs, suggested by the study, can be resource reallocation, preemptive speculating and assembly postponement. Moreover, the topicality of this paper is demonstrated by the data used which were collected during the COVID-19 pandemic to validate the research models and from domains greatly affected by it.