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Identification and Characterization of Supply Chain Operational Risk Profiles in Manufacturing Companies

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  • Hai Thanh Pham

    (Department of Management and Engineering, University of Padova, Stradella San Nicola 3, 36100 Vicenza, Italy)

  • Chiara Verbano

    (Department of Management and Engineering, University of Padova, Stradella San Nicola 3, 36100 Vicenza, Italy)

Abstract

Research on the interactions between risk, integration, and performance in supply chains (SCs) is increasingly attracting attention of researchers in recent years. Although risk usually has negative effects on performance, limited evidence has been provided to show whether companies differently exposed to operational risk (i.e., high, moderate, or low exposure) also have different levels of integration and operational performance. Therefore, this study aims to identify and characterize different profiles of operational risk (i.e., supply, manufacturing, and demand risks) between manufacturing companies along with considering contextual factors such as company size and industry type. Data are collected from the fourth round of the High Performance Manufacturing Project and subsequently analyzed by cluster analysis and analysis of variance (ANOVA). Three different clusters have been identified: Two clusters are moderately and highly impacted by operational risk, respectively, while the other cluster is almost not impacted by manufacturing risk but highly impacted by supply risk and demand risk. The results also indicate that companies with different profiles of operational risk have different levels of integration and operational performance. An important contribution of the current study is the development of a hypothesized framework of interactions between operational risk, integration, and operational performance to provide opportunities for further research.

Suggested Citation

  • Hai Thanh Pham & Chiara Verbano, 2022. "Identification and Characterization of Supply Chain Operational Risk Profiles in Manufacturing Companies," Sustainability, MDPI, vol. 14(4), pages 1-17, February.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:4:p:1996-:d:746124
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    References listed on IDEAS

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