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Managing risks and system performance in supply network: a conceptual framework

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  • Huy Truong Quang
  • Yoshinori Hara

Abstract

Examining a certain risk will provide an insight into a single dimension, but a picture of different risks in the supply chain (SC) is still lacking, as risks do not take place independently, but typically simultaneously. This research aims to propose and validate a conceptual framework for linking various dimensions of risk to system performance in the SC by applying SC mapping - a new approach in the SC risk body of literature. In the model, risks were classified into three categories with regard to their level of impact on performance: 1) core risks, e.g., supply risk, investor-related operational risks, contractor-related operational risks and demand risks; 2) infrastructure risks, e.g., finance risk, information risk and time risk; 3) external risks, e.g., human-made risks; 4) natural risks. Using the framework, companies will have a systematic view of risks in the whole SC network whereby they can define risks in their own context and ascertain critical SC risks that cause negative effects on SC performance.

Suggested Citation

  • Huy Truong Quang & Yoshinori Hara, 2019. "Managing risks and system performance in supply network: a conceptual framework," International Journal of Logistics Systems and Management, Inderscience Enterprises Ltd, vol. 32(2), pages 245-271.
    • Handle: RePEc:ids:ijlsma:v:32:y:2019:i:2:p:245-271
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    1. Z. Jemai & F. Karaesmen, 2005. "The influence of demand variability on the performance of a make-to-stock queue," Post-Print hal-00126137, HAL.
    2. Thun, Jörn-Henrik & Hoenig, Daniel, 2011. "An empirical analysis of supply chain risk management in the German automotive industry," International Journal of Production Economics, Elsevier, vol. 131(1), pages 242-249, May.
    3. Lee, Yongwoong & Rösch, Daniel & Scheule, Harald, 2016. "Accuracy of mortgage portfolio risk forecasts during financial crises," European Journal of Operational Research, Elsevier, vol. 249(2), pages 440-456.
    4. Kandil, Magda & Mirazaie, Ida, 2004. "The Effects of Exchange Rate Fluctuations on Output and Prices: Evidence from Developing Countries," Journal of Developing Areas, Tennessee State University, College of Business, vol. 38(2), pages 189-219, January-M.
    5. Ajay Kumar Pandey & Rajiv Kumar Sharma, 2017. "FMEA-based interpretive structural modelling approach to model automotive supply chain risk," International Journal of Logistics Systems and Management, Inderscience Enterprises Ltd, vol. 27(4), pages 395-419.
    6. Fleischhacker, Adam J. & Fok, Pak-Wing, 2015. "On the relationship between entropy, demand uncertainty, and expected loss," European Journal of Operational Research, Elsevier, vol. 245(2), pages 623-628.
    7. Bernanke, Ben S, 1981. "Bankruptcy, Liquidity, and Recession," American Economic Review, American Economic Association, vol. 71(2), pages 155-159, May.
    8. Girish K. Nair & Nidhi Choudhary, 2016. "Influence of critical success factors of total quality management on financial and non-financial performance of hospitality industry: an empirical study," International Journal of Productivity and Quality Management, Inderscience Enterprises Ltd, vol. 17(4), pages 409-436.
    9. Xu, Minghui & Chen, Youhua (Frank) & Xu, Xiaolin, 2010. "The effect of demand uncertainty in a price-setting newsvendor model," European Journal of Operational Research, Elsevier, vol. 207(2), pages 946-957, December.
    10. Axel Dreher & Martin Gassebner, 2013. "Greasing the wheels? The impact of regulations and corruption on firm entry," Public Choice, Springer, vol. 155(3), pages 413-432, June.
    11. Jemai, Zied & Karaesmen, Fikri, 2005. "The influence of demand variability on the performance of a make-to-stock queue," European Journal of Operational Research, Elsevier, vol. 164(1), pages 195-205, July.
    12. DonHee Lee & Byeonghwa Park, 2016. "Impact of manufacturing systems on quality management practices, competitive advantages, and operational performance," International Journal of Productivity and Quality Management, Inderscience Enterprises Ltd, vol. 19(3), pages 301-318.
    13. Ackermann, Fran & Howick, Susan & Quigley, John & Walls, Lesley & Houghton, Tom, 2014. "Systemic risk elicitation: Using causal maps to engage stakeholders and build a comprehensive view of risks," European Journal of Operational Research, Elsevier, vol. 238(1), pages 290-299.
    14. William Ho & Tian Zheng & Hakan Yildiz & Srinivas Talluri, 2015. "Supply chain risk management: a literature review," International Journal of Production Research, Taylor & Francis Journals, vol. 53(16), pages 5031-5069, August.
    15. AfDB AfDB, . "Annual Report 2012," Annual Report, African Development Bank, number 461.
    16. Mitra, Sovan & Date, Paresh & Mamon, Rogemar & Wang, I-Chieh, 2013. "Pricing and risk management of interest rate swaps," European Journal of Operational Research, Elsevier, vol. 228(1), pages 102-111.
    17. Parks, Richard W, 1978. "Inflation and Relative Price Variability," Journal of Political Economy, University of Chicago Press, vol. 86(1), pages 79-95, February.
    18. Ali Haj Aghapour & Govindan Marthandan & David Yong Gun Fie & Suhaiza Zailani, 2017. "Risk management process towards operation performance in supply chain management: a survey of manufacturing SMEs," International Journal of Logistics Systems and Management, Inderscience Enterprises Ltd, vol. 27(1), pages 78-114.
    19. Hau L. Lee & Corey Billington, 1993. "Material Management in Decentralized Supply Chains," Operations Research, INFORMS, vol. 41(5), pages 835-847, October.
    20. Kim, Kwansoo & Chavas, Jean-Paul, 2003. "Technological change and risk management: an application to the economics of corn production," Agricultural Economics, Blackwell, vol. 29(2), pages 125-142, October.
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