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Risk Assessment and Identification Methodology for the Defense Industry in Times of Crisis: Decision-Making

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Handbook for Management of Threats

Part of the book series: Springer Optimization and Its Applications ((SOIA,volume 205))

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Abstract

Annually, different corporations within the defense industry must analyze their needs and decide whether to renew existing contracts or sign new contracts with government suppliers and contractors. There are multiple risks in entering these agreements, especially in times of crisis, such as potential contractor delays, shutdowns, or even bankruptcy. This paper aimed to present a novel data-driven supplier risk identification and assessment framework that rigorously examines the stability of such potential firms and their respective plants, with a focus on financial risk factors. Supply chain risk and financial bankruptcy literature are analyzed, and different aspects are combined to create a novel supplier risk assessment methodology. This hybrid procedure combines a linear discriminant bankruptcy model with a multicriteria scoring procedure built using the DELPHI method. The methodology calculates individual plant-level supplier risk indices, which can be used to aid the defense purchasers in choosing more stable suppliers and contractors. This framework is then applied to a case study of an aviation supply chain purchaser within the US government, specifically in the context of plant closures during the COVID-19 pandemic. The model was tested and validated using historical supplier data provided by the purchaser. The purchaser was able to implement the framework and use it for risk management, demonstrating its importance to manufacturers and purchasers within the defense industry. We show that the model performs well both in crisis situations (COVID-19) and in non-crisis situations. This hybrid data-driven risk analysis methodology is practical to implement and can be used proactively by firms to improve the stability of their supplier base through risk assessment and reduction.

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References

  1. Altman, E.I.: Discriminant analysis and the prediction of corporate bankruptcy. J. Financ. 23(4), 589–609 (1968)

    Article  Google Scholar 

  2. Altman, E., Hotchkiss, E.: Corporate Financial Distress and Bankruptcy: Predict and Avoid Bankruptcy, Analyze and Invest in Distressed Debt, 3rd edn. Wiley, New York (2005). https://doi.org/10.1002/9781118267806

    Book  Google Scholar 

  3. Baryannis, G., Dani, S., Antoniou, G.: Predicting supply chain risks using machine learning: the trade-off between performance and interpretability. Futur. Gener. Comput. Syst. 101, 993–1004 (2019a). https://doi.org/10.1016/j.future.2019.07.059

    Article  Google Scholar 

  4. Baryannis, G., Validi, S., Dani, S., Antoniou, G.: Supply chain risk management and artificial intelligence: state of the art and future research directions. Int. J. Prod. Res. 57(7), 2179–2202 (2019b). https://doi.org/10.1080/00207543.2018.1530476

    Article  Google Scholar 

  5. Beaver, W.H.: Financial ratios as predictors of failure. J. Account. Res. 4, 77–111 (1966)

    Article  Google Scholar 

  6. Bellovary, J.L., Giacomino, D.E., Akers, M.D.: A review of bankruptcy prediction studies: 1930 to present. J. Financ. Educ. 33, 1–42 (2007)

    Google Scholar 

  7. Blackhurst, J.V., Scheibe, K.P., Johnson, D.J.: Supplier risk assessment and monitoring for the automotive industry. Int. J. Phys. Distrib. Logist. Manag. 38, 143–165 (2008)

    Article  Google Scholar 

  8. Bower, A.G., Garber, S.: Statistical Forecasting of Bankruptcy of Defense Contractors: Problems and Prospects. RAND – Project Air force (1994)

    Google Scholar 

  9. Cambell, J., Hilscher, J., Szilagyi, J.: In search of distress risk. J. Financ. 63, 2899–2939 (2008)

    Article  Google Scholar 

  10. Columbia University: US Natural Hazards Index. National Center for Disaster Preparedness, Earth Institute (2020)

    Google Scholar 

  11. Cranfield University: Supply Chain Vulnerability: Executive Report. School of Management (2002)

    Google Scholar 

  12. Dagel, H.W., Pepper, R.M.: A financial distress model for DoD hardware contractors. J. Parametrics. 10(1), 5–40 (1990). https://doi.org/10.1080/10157891.1990.10462471

    Article  Google Scholar 

  13. Dalkey, N., Helmer, O.: An experimental application of the DELPHI method to the use of experts. Manag. Sci. 9(3), 458–467 (1963). https://doi.org/10.1287/mnsc.9.3.458

    Article  Google Scholar 

  14. Dickerson, D., Boswell, B., Sobeski, A., Antony, I., Artolozaga, R., Sanders, I.: Forecasting bankruptcy within department of defense suppliers using linear discriminant analysis. In: Proceedings of the Annual General Donald R. Keith Memorial Conference (2022).

    Google Scholar 

  15. Eberle, A.O.: Risikomanagement in der Beschaffungslogistik – Gestaltungs-empfehlungen fur ein System, dissertation. University of Bamberg, Bamberg (2005)

    Google Scholar 

  16. Eckstein, D., Kunzel, V., Shafer, L., Winges, M.: Global Climate Risk Index 2020: Who Suffers the Most from Extreme Weather Events? Weather-Related Loss Events in 2018 & 1999–2018. Germanwatch (2020)

    Google Scholar 

  17. Employment and Social Development Canada: Work Stoppages by Jurisdiction and Year. Government of Canada. Date Accessed 04 August 2020. *Referred to in paper as ESDC (2020)

    Google Scholar 

  18. Fund for Peace: Fragile States Index Annual Report 2019. Fragile States Index. Fund for Peace. *Referred to in paper as FFP (2019)

    Google Scholar 

  19. General Litigation Branch: Contracting Officer’s Guide to Bankruptcy, Army Litigation Division (2012)

    Google Scholar 

  20. Godfrey, I.C.: Predicting Bankruptcy in the Air Force. (Master’s thesis). Air Force Institute of Technology (1990)

    Google Scholar 

  21. Green, K., Armstrong, J., & Graefe, A. Methods to elicit forecasts from groups: Delphi and prediction markets compared. Foresight: The International Journal of Applied Forecasting (2007)

    Google Scholar 

  22. Institute for Economics & Peace: Global Terrorism Index 2019: Measuring the Impact of Terrorism, Sydney, November 2019. Available from: http://visionofhumanity.org/reports (accessed 01 June 2020). *referred to in the paper as IFEP (2019)

  23. Jung, K., Lim, Y., Oh, J.: A model for measuring supplier risk: do operational capability indicators enhance the prediction accuracy of supplier risk? Br. J. Manag. 22, 609–627 (2011)

    Article  Google Scholar 

  24. Kersten, W., Boger, M., Hohrath, P., Spath, H.: Supply chain risk management: development of a theoretical and empirical framework. In: Kersten, W., Blecker, T. (eds.) Managing Risks in Supply Chains. Springer (2006)

    Google Scholar 

  25. MacCarthy, B.L., Atthirawong, W.: Factors affecting location decision in international operations – a Delphi study. Int. J. Oper. Prod. Manag. 23(7), 794–818 (2003)

    Article  Google Scholar 

  26. Marsh: Political Risk Map 2020: Trade Tensions Threaten Political Stability. Marsh JLT Specialty, Marsh (2020)

    Google Scholar 

  27. Mason, R.: Coping with complexity and turbulence – an entrepreneurial solution. J. Enterprising Cult. 14(4), 241–266 (2006)

    Article  Google Scholar 

  28. Mason-Jones, R., Towill, D.R.: Shrinking the supply chain uncertainty cycle. In: Control, pp. 17–22. Logistics Systems Dynamics Group, Cardiff (1998)

    Google Scholar 

  29. Matook, S., Lasch, R., Tamaschke, R.: Supplier development with benchmarking as part of a comprehensive supplier risk management framework. Int. J. Oper. Prod. Manag. 29 (2009)

    Google Scholar 

  30. MH&L Staff: Supply Chain Experiencing High Rate of Terrorist Attacks. Material Handling and Logistics. Global Supply Chain (2017)

    Google Scholar 

  31. Moses, D., Liao, S.: On developing models for failure prediction. J. Commercial Bank Lending. 69, 27–38 (1987)

    Google Scholar 

  32. National Right to Work Committee:. Message form the National Right to Work Committee Chairman (2020)

    Google Scholar 

  33. Ohlson, J.A.: Financial ratios and the probabilistic prediction of bankruptcy. J. Account. Res. 18(1), 109–131 (1980)

    Article  MathSciNet  Google Scholar 

  34. Organization of American States, Department of Regional Development; Natural Hazards Project; United States Agency for International Development, Office of Foreign Disaster Assistance: Disaster, planning and development: managing natural hazards to reduce loss (PDF). Organization of American States. Washington DC (1990)

    Google Scholar 

  35. Peck, H., Christopher, M.: The Five Principles of Supply Chain Resilience. Logistics Europe, February (2004)

    Google Scholar 

  36. Pettit, T., Fiksel, J., Croxton, K.: Ensuring supply chain resilience: development of a conceptual framework. J. Bus. Logist. 34, 1–21 (2010)

    Article  Google Scholar 

  37. Pettit, T., Croxton, K., Fiskel, J.: Ensuring supply chain resilience: development and implementation of an assessment tool. J. Bus. Logist. 34, 46–76 (2013)

    Article  Google Scholar 

  38. Rice, J.B.: Prepare Your Supply Chain for Coronavirus. Harvard Business Review. Harvard University. (2020)

    Google Scholar 

  39. SEC (2017) Fact sheet: disclosing the use of conflict minerals. U.S. Securities and Exchange Commission. https://www.sec.gov/opa/Article/2012-2012-163htm%2D%2D-related-materials.html. Accessed 5 Aug 2020

  40. Shumway, T.: Forecasting bankruptcy more accurately: a simple hazard model. J. Bus. 74, 101–124 (2001)

    Article  Google Scholar 

  41. Sinha, P., Whitman, L.E., Malzahn, D.: Methodology to mitigate supplier risk in an aerospace supply chain. Supply Chain Manag. Int. J. (2004). https://doi.org/10.1108/13598540410527051

  42. Sleeper, S., Starns, J., Warner, E.: Identifying and mitigating industrial base risk for the DoD: results of a pilot study. In: AFCEA Acquisition Research Symposium (2014)

    Google Scholar 

  43. Sodhi, M.S., Son, B.-G., Tang, C.S.: Researchers’ perspectives on supply chain risk management. Prod. Oper. Manag. 21(1), 1–13 (2012). https://doi.org/10.1111/j.1937-5956.2011.01251.x

    Article  Google Scholar 

  44. Stanford, J.: Briefing Note: Historical Data on the Decline in Australian Industrial Disputes, p. 3. The Australia Institute, Centre for Future Work (2018)

    Google Scholar 

  45. Statista: Unemployment Canada https://www.statista.com/statistics/578362/unemployment-rate-canada/, https://www.statista.com/statistics/464156/number-of-full-time-workers-in-canada/ (2020)

  46. Theiler, J.: Matched-pair machine learning. Technometrics. 55(4), 536–547 (2013). https://doi.org/10.1080/00401706.2013.838191

    Article  MathSciNet  Google Scholar 

  47. Tinoco, M.H., Wilson, N.: Financial distress and bankruptcy prediction among listed companies using accounting, market and macroeconomic variables. Int. Rev. Financ. Anal. 20 (2013). https://doi.org/10.1016/j.irfa.2013.02.013

  48. U.S. Bureau of Labor Statistics: U.S. Work Stoppages. U.S. Bureau of Labor Statistics. Retrieved 20 July 2020. *Referred to as USBLS in paper (2020)

    Google Scholar 

  49. U.S. Courts: Caseload Statistics Data Tables. Table F-2: U.S. Bankruptcy Courts—Business and Nonbusiness Cases Filed, by Chapter of the Bankruptcy Code—During the 12-Month Period Ending March 31, 2019. U. S. Courts. Administrative Office of the U.S. Courts. Retrieved 20 July 2020 (2019)

    Google Scholar 

  50. United Nations: Human Rights. Global Issues. United Nations (2020). https://www.un.org/en/sections/issues-depth/human-rights/. Accessed 14 Aug 2020

    Google Scholar 

  51. Vandaele, K.: Strikes’ Map of Europe. European Trade Union Institute (2019). Retrieved 20 July 2020

    Google Scholar 

  52. Wentholt, M.T.A., Rowe, G., Konig, A., Marvin, H.J.P., Frewer, L.J.: The views of key stakeholders on an evolving food risk governance framework: results from a Delphi study. Food Policy. 34, 539–548 (2009)

    Article  Google Scholar 

  53. Zsidisin, G.A.: Managerial perceptions of supply risk. J. Supply Chain Manag., 14–26 (2003). https://doi.org/10.1111/j.1745-493X.2003.tb00146.x

  54. Zsidisin, G.A., Ellram, L.M., Carter, J.R., Cavinato, J.L.: An analysis of supply risk assessment techniques. Int. J. Phys. Distrib. Logist. Manag. 34(5), 397–414 (2004)

    Article  Google Scholar 

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Acknowledgements

The author thanks the cadets who helped shape the financial model used in this paper, Isaac J. Antony, Rodrigo R. Artolozaga, Mary E. Bell, Brett R. Boswell, William D. Dickerson, Joshua N. Kim, Benjamin T. Berry, Angel J. Espinoza, and Alexander P. Sobeski. The author acknowledges the assistance provided by several US government organizations in data collection. The author also thanks the co-organizers of the 2022 INFORMS Business Analytics Conference and attendees for providing feedback and anonymous reviewers for helpful suggestions on an earlier version of this paper.

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Authors and Affiliations

  1. Department of Systems Engineering, United States Military Academy, West Point, NY, USA

    Isabella T. Sanders

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  1. Isabella T. Sanders
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Correspondence to Isabella T. Sanders .

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Editors and Affiliations

  1. Director General of General Directorate of National Defence Policy, International Relations – Hellenic Ministry of National Defense, Athens, Greece

    Konstantinos P. Balomenos

  2. Department of Chemical Engineering, KU Leuven, Leuven, Belgium

    Antonios Fytopoulos

  3. Department of Industrial & Systems Engineering, University of Florida, Gainesville, FL, USA

    Panos M. Pardalos

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Sanders, I.T. (2023). Risk Assessment and Identification Methodology for the Defense Industry in Times of Crisis: Decision-Making. In: Balomenos, K.P., Fytopoulos, A., Pardalos, P.M. (eds) Handbook for Management of Threats. Springer Optimization and Its Applications, vol 205. Springer, Cham. https://doi.org/10.1007/978-3-031-39542-0_6

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