Re-Engineering Supply Chain Education: A Theoretical Framework for Integrating Financial Asymmetry and Systems Thinking

Jiří Strouhal; Gabriel Xiao-Guang Yue; Vaiva Kiaupaite-Grušniene; Josef Horák

doi:10.3991/ijep.v16i5.62782

Authors

Jiří Strouhal TalTech, Tallinn, Estonia https://orcid.org/0000-0001-9109-7087
Gabriel Xiao-Guang Yue Sohar University, Sohar, Oman; Turon University, Karshi, Uzbekistan
Vaiva Kiaupaite-Grušniene TalTech, Tallinn, Estonia
Josef Horák Pan-European University, Prague, Czech Republic

DOI:

https://doi.org/10.3991/ijep.v16i5.62782

Keywords:

engineering education, system thinking, problem-based learning, financial bullwhip effect, supply chain finance

Abstract

Modern industrial and systems engineering curricula excel at teaching students to mitigate physical supply-side disruptions, yet frequently fail to address the intra-supply-chain allocation of financial distress. This theoretical paper proposes a novel, problem-based learning curriculum framework designed to bridge the gap between operational logistics and financial risk-shifting. It critiques the pedagogical reliance on traditional bankruptcy prediction models, demonstrating how they systematically misclassify capital-intensive original equipment manufacturers (OEMs) into distress zones due to the structural liabilities of captive financial divisions. Grounded in systems thinking and Kolb’s experiential learning theory, the proposed framework introduces dynamic industry benchmarks into the classroom, specifically utilising the Strouhal credibility index. By structuring a pedagogical module around the 2020-2022 automotive crisis, and utilising descriptive statistical comparisons rather than complex econometrics, this framework equips engineering educators with an accessible blueprint to teach the financial bullwhip effect. This ensures future engineering managers understand the structural vulnerabilities embedded within hierarchical supply chains and the limits of OEM-driven supply chain finance.

References

1. ABET (2022). Criteria for Accrediting Engineering Programs (2022-2023). Baltimore, MD: ABET. Retrieved from: https://www.abet.org/wp-content/uploads/2022/01/2022-23-EAC-Criteria.pdf

2. Altman, E. I. (1968). Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy. The Journal of Finance, Vol. 23, No. 4, pp. 589-609. doi: https://doi.org/10.2307/2978933

3. Barboza, F., Kimura, H., and Altman, E. (2017). Machine Learning Models and Bankruptcy Prediction. Expert Systems with Applications, Vol. 83, pp. 405-417. doi: https://doi.org/10.1016/j.eswa.2017.04.006

4. Bharath, S. T., and Shumway, T. (2008). Forecasting Default with the Merton Distance to Default Model. The Review of Financial Studies, Vol. 21, No. 3, pp. 1339-1369. doi: https://doi.org/10.1093/rfs/hhn044

5. Crawley, E. F., Malmqvist, J., Östlund, S., and Brodeur, D. R. (2014). Rethinking Engineering Education: The CDIO Approach. New York, NY: Springer.

6. Dym, C. L., Agogino, A. M., Eris, O., Frey, D. D., and Leifer, L. J. (2005). Engineering Design Thinking, Teaching and Learning. Journal of Engineering Education, No. 94, pp. 103-120. doi: https://doi.org/10.1002/j.2168-9830.2005.tb00832.x

7. European Network for Accreditation of Engineering Education (2021). EUR-ACE Framework Standards and Guidelines. Brussels: ENAEE. Retrieved from: https://www.enaee.eu/wp-content/uploads/2022/03/EAFSG-04112021-English-1-1.pdf

8. Kolb, D. A. (1984). Experiential Learning: Experience as the Source of Learning and Development. Englewood Cliffs, NJ: Prentice Hall.

9. Lee, H. L., Padmanabhan, V., and Whang, S. (1997). The Bullwhip Effect in Supply Chains. MIT Sloan Management Review, Vol. 38, No. 3, pp. 93-102.

10. Mai, F., Tian, S., Lee, C., and Ma, L. (2019). Deep Learning Models for Bankruptcy Prediction Using Textual Disclosures. European Journal of Operational Research, Vol. 274, No. 2, pp. 743-758. doi: https://doi.org/10.1016/j.ejor.2018.10.024

11. Merton, R. C. (1974). On the Pricing of Corporate Debt: The Risk Structure of Interest Rates. The Journal of Finance, Vol. 29, pp. 449-470. doi: https://doi.org/10.2307/29788140

12. Piaget, J. (1976). To Understand Is to Invent: The Future of Education. New York, NY: Penguin Books.

13. Rudin, C. (2019). Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead. Nature Machine Intelligence, Vol. 1, No. 5, pp. 206-215. doi: https://doi.org/10.1038/s42256-019-0048-x

14. Senge, P. (1990). The Fifth Discipline: The Art and Practice of the Learning Organization. New York, NY: Doubleday/Currency.