Assessing supply chain risks for chip industry with LDA and multi-layer Bayesian network method

Abstract

In recent years, global geopolitical turmoil, including events like the US–China trade war and the Russia–Ukraine conflict, has significantly reshaped the panorama of the global supply chain (SC). Among these, the chip SC stands out as particularly impacted. Chips form the backbone of all electronic industries, therefore, there is an urgent need for a reassessment of SC security within the chip sector. In this study, we begin by conducting an LDA analysis on 320 relevant news reports to develop a thematic model for the Chinese chip supply chain (CCSC). This approach helps identify the key risk landscape, ultimately distilling 10 major risk factors and four mitigation strategies. Subsequently, we propose an improved multi-layer sequential Bayesian Network (BN) model to assess and quantify risks within CCSC. Lastly, we utilize sensitivity analysis and propagation analysis to examine the impact of risk factors on the ultimate risk of SC disruption and define the resilience and importance of the risk nodes. Our research offers fresh theoretical insight into utilizing BN and LDA methods for modeling SC disruption risk. Furthermore, the study reveals that talent shortage, patent infringement, and insufficient Research and Development (R&D) investment are the three most significant factors contributing to the risk of disruptions in the CCSC. These factors are not only the most critical but also the least resilient, underscoring that enhancing innovation capabilities should be the foremost priority for strengthening the CCSC. Increasing government subsidies is the most effective mitigation measure, providing greater financial support for enterprises, boosting their innovation capabilities and competitiveness, and attracting more investors to the industry.

Graphical abstract

Keywords

supply chain risk / supply chain resilience / ripple effect / Bayesian network / chip supply chain

References

[1] Abdel-Basset M, Mohamed R, (2020). A novel plithogenic TOPSIS-CRITIC model for sustainable supply chain risk management. Journal of Cleaner Production, 247: 119586

[2] Mithun Ali S, Moktadir M A, Kabir G, Chakma J, Rumi M J U, Islam M T, (2019). Framework for evaluating risks in food supply chain: Implications in food wastage reduction. Journal of Cleaner Production, 228: 786–800

[3] BeyondConsulting (2023). Integrated circuit talent supply and demand report: Talent shortage of 35,000. Available at the website of Baidu

[4] Blei D M, Ng A Y, Jordan M I, (2003). Latent Dirichlet allocation. Journal of Machine Learning Research, 3: 993–1022

[5] Bourgeois C M, Soltanisehat L, Barker K, González A D, (2023). Risk-based inventory scheduling framework to fulfill multi-product orders within a production network. Computers & Industrial Engineering, 182: 109343

[6] Chen P S, Wu M T, (2013). A modified failure mode and effects analysis method for supplier selection problems in the supply chain risk environment: A case study. Computers & Industrial Engineering, 66( 4): 634–642

[7] Chen T, Wang Y C, Jiang P H, (2023). A selectively calibrated derivation technique and generalized fuzzy TOPSIS for semiconductor supply chain localization assessment. Decision Analytics Journal, 8: 100275

[8] Cheng Y, Elsayed E A, Huang Z, (2022). Systems resilience assessments: a review, framework and metrics. International Journal of Production Research, 60( 2): 595–622

[9] ConradySJouffeL (2015). Bayesian Networks and BayesiaLab: A Practical Introduction for Researchers. Franklin: Bayesia USA

[10] Corsini R R, Costa A, Fichera S, Framinan J M, (2024). Digital twin model with machine learning and optimization for resilient production–distribution systems under disruptions. Computers & Industrial Engineering, 191: 110145

[11] Cox L A Jr, (2008). What’s wrong with risk matrices. Risk Analysis, 28( 2): 497–512

[12] Dey P K, (2010). Managing project risk using combined analytic hierarchy process and risk map. Applied Soft Computing, 10( 4): 990–1000

[13] Dong L, Kouvelis P, (2020). Impact of tariffs on global supply chain network configuration: Models, predictions, and future research. Manufacturing & Service Operations Management, 22( 1): 25–35

[14] Faghih-Roohi S, Akcay A, Zhang Y, Shekarian E, de Jong E, (2020). A group risk assessment approach for the selection of pharmaceutical product shipping lanes. International Journal of Production Economics, 229: 107774

[15] Fan D, Yeung A C, Tang C S, Lo C K, Zhou Y, (2022a). Global operations and supply-chain management under the political economy. Journal of Operations Management, 68( 8): 816–823

[16] Fan D, Zhou Y, Yeung A C, Lo C K, Tang C, (2022b). Impact of the U.S.–China trade war on the operating performance of U.S. firms: The role of outsourcing and supply base complexity. Journal of Operations Management, 68( 8): 928–962

[17] FentonNNeilM (2018). Risk Assessment and Decision Analysis with Bayesian Networks. Boca Raton: CRC Press

[18] FusionWorldwide (2021). The global chip shortage: A timeline of unfortunate events. Available at the website of fusionww.com

[19] Ghadir A H, Vandchali H R, Fallah M, Tirkolaee E B, (2022). Evaluating the impacts of COVID-19 outbreak on supply chain risks by modified failure mode and effects analysis: A case study in an automotive company. Annals of Operations Research, 31: 1–31

[20] Handfield R B, Graham G, Burns L, (2020). Corona virus, tariffs, trade wars, and supply chain evolutionary design. International Journal of Operations & Production Management, 40( 10): 1649–1660

[21] Hansen C, Mena C, Aktas E, (2019). The role of political risk in service offshoring entry mode decisions. International Journal of Production Research, 57( 13): 4244–4260

[22] Hosseini S, Ivanov D, (2020). Bayesian networks for supply chain risk, resilience and ripple effect analysis: A literature review. Expert Systems with Applications, 161: 113649

[23] Hosseini S, Ivanov D, (2022). A multi-layer Bayesian network method for supply chain disruption modelling in the wake of the COVID-19 pandemic. International Journal of Production Research, 60( 17): 5258–5276

[24] Hosseini S, Ivanov D, Dolgui A, (2020). Ripple effect modelling of supplier disruption: Integrated Markov chain and dynamic Bayesian network approach. International Journal of Production Research, 58( 11): 3284–3303

[25] Ivanov D, (2021). Supply chain viability and the COVID-19 pandemic: A conceptual and formal generalisation of four major adaptation strategies. International Journal of Production Research, 59( 12): 3535–3552

[26] Ivanov D, (2022). Viable supply chain model: Integrating agility, resilience and sustainability perspectives—Lessons from and thinking beyond the COVID-19 pandemic. Annals of Operations Research, 319( 1): 1411–1431

[27] Ivanov D, Dolgui A, (2022). The shortage economy and its implications for supply chain and operations management. International Journal of Production Research, 60( 24): 7141–7154

[28] KollerDFriedmanN (2009). Probabilistic Graphical Models: Principles and Techniques. Cambridge: MIT Press

[29] Lee C, Yang Y, (2024). Design and analysis of government subsidies policy of capacity expansion under reselling and agency selling schemes. Computers & Industrial Engineering, 197: 110576

[30] Li M, Cai Y, Guo D, Qu T, Huang G Q, (2025). Data-driven diagnosis framework for platform product supply chains under disruptions. International Journal of Production Research, 63( 7): 2599–2621

[31] Librantz A F H, Costa I, Spinola M D M, de Oliveira Neto G C, Zerbinatti L, (2021). Risk assessment in software supply chains using the Bayesian method. International Journal of Production Research, 59( 22): 6758–6775

[32] Liu C, He T, Liu F, Liang S, Zhang C, (2024). Trade facilitation, market size, and supply chain efficiency of Taiwan semiconductor companies. PLoS One, 19( 10): e0299322

[33] Liu J, Xi Y, Wang J, (2023a). Resilience strategies for sustainable supply chains under budget constraints in the post COVID-19 era. Frontiers of Engineering Management, 10( 1): 143–157

[34] Liu M, Liu Z, Chu F, Dolgui A, Chu C, Zheng F, (2022). An optimization approach for multi-echelon supply chain viability with disruption risk minimization. Omega, 112: 102683

[35] Liu W, He Y, Dong J, Cao Y, (2023b). Disruptive technologies for advancing supply chain resilience. Frontiers of Engineering Management, 10( 2): 360–366

[36] Longauer D, Vasvári T, Hauck Z, (2024). Investigating make-or-buy decisions and the impact of learning-by-doing in the semiconductor industry. International Journal of Production Research, 62( 11): 3835–3852

[37] Magdy M, Grida M, Hussein G, (2024). Disruption mitigation in the semiconductors supply chain by using public blockchains. Journal of Supercomputing, 80( 2): 1852–1906

[38] Majumdar A, Sinha S K, Shaw M, Mathiyazhagan K, (2021). Analysing the vulnerability of green clothing supply chains in South and Southeast Asia using fuzzy analytic hierarchy process. International Journal of Production Research, 59( 3): 752–771

[39] Marcucci G, Ciarapica F E, Mazzuto G, Bevilacqua M, (2024). Analysis of ripple effect and its impact on supply chain resilience: A general framework and a case study on agri-food supply chain during the COVID-19 pandemic. Operations Management Research, 17( 1): 175–200

[40] MillerM (2022). US bars ‘advanced tech’ firms from building China factories for 10 years. Available at the website of BBC

[41] Mital M, Del Giudice M, Papa A, (2018). Comparing supply chain risks for multiple product categories with cognitive mapping and analytic hierarchy process. Technological Forecasting and Social Change, 131: 159–170

[42] Moktadir M A, Ren J, (2024). Global semiconductor supply chain resilience challenges and mitigation strategies: A novel integrated decomposed fuzzy set Delphi, WINGS and QFD model. International Journal of Production Economics, 273: 109280

[43] Nakandala D, Lau H, Zhao L, (2017). Development of a hybrid fresh food supply chain risk assessment model. International Journal of Production Research, 55( 14): 4180–4195

[44] NationalPatient Safety Agency (NPSA) (2008). A Risk Matrix for Risk Managers. London: National Patient Safety Agency

[45] Nikookar E, Yanadori Y, (2022). Preparing supply chain for the next disruption beyond COVID-19: Managerial antecedents of supply chain resilience. International Journal of Operations & Production Management, 42( 1): 59–90

[46] Ojha R, Ghadge A, Tiwari M K, Bititci U S, (2018). Bayesian network modelling for supply chain risk propagation. International Journal of Production Research, 56( 17): 5795–5819

[47] Pan L, Lei L, (2023). International trade friction and firm cash holdings. Finance Research Letters, 55: 103976

[48] Papadimitriou C H, Raghavan P, Tamaki H, Vempala S, (2000). Latent semantic indexing: A probabilistic analysis. Journal of Computer and System Sciences, 61( 2): 217–235

[49] Park Y W, Blackhurst J, Paul C, Scheibe K P, (2022). An analysis of the ripple effect for disruptions occurring in circular flows of a supply chain network. International Journal of Production Research, 60( 15): 4693–4711

[50] PorteousINewmanDIhlerAAsuncionASmythPWellingM (2008). Fast collapsed Gibbs sampling for latent Dirichlet allocation. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Las Vegas, NV: ACM, 569–577

[51] Pires Ribeiro J, Barbosa-Povoa A, (2018). Supply chain resilience: Definitions and quantitative modelling approaches—A literature review. Computers & Industrial Engineering, 115: 109–122

[52] Rodgers M, Singham D, (2020). A framework for assessing disruptions in a clinical supply chain using Bayesian belief networks. Journal of Pharmaceutical Innovation, 15( 3): 467–481

[53] Roscoe S, Aktas E, Petersen K J, Skipworth H D, Handfield R B, Habib F, (2022). Redesigning global supply chains during compounding geopolitical disruptions: The role of supply chain logics. International Journal of Operations & Production Management, 42( 9): 1407–1434

[54] Saisridhar P, Thürer M, Avittathur B, (2024). Assessing supply chain responsiveness, resilience and robustness (Triple-R) by computer simulation: A systematic review of the literature. International Journal of Production Research, 62( 4): 1458–1488

[55] Schaefer T, Udenio M, Quinn S, Fransoo J C, (2019). Water risk assessment in supply chains. Journal of Cleaner Production, 208: 636–648

[56] SemiInsights (2021). What is the solution to the talent shortage problem in the semiconductor industry. Available at the website of Tencent

[57] Sharma S K, Kumar V, (2015). Optimal selection of third-party logistics service providers using quality function deployment and Taguchi loss function. Benchmarking, 22( 7): 1281–1300

[58] Shi W, Mena C, (2023). Supply chain resilience assessment with financial considerations: A Bayesian network-based method. IEEE Transactions on Engineering Management, 70( 6): 2241–2256

[59] Sodhi M S, Tang C S, (2018). Corporate social sustainability in supply chains: A thematic analysis of the literature. International Journal of Production Research, 56( 1–2): 882–901

[60] Soltanisehat L, Ghorbani-Renani N, González A D, Barker K, (2023). Assessing production fulfillment time risk: Application to pandemic-related health equipment. International Journal of Production Research, 61( 24): 8401–8422

[61] TangC S (2022). Expect a new wave of supply chain headaches with Ukraine crisis, bevy of other issues. Available at the website of industrywee-k.com

[62] TewariSJosephsJ (2022). US–China chip war: How the technology dispute is playing out. Available at the website of BBC

[63] TewariSJosephsJ (2023). US–China chip war: America is winning. Available at the website of BBC

[64] TSMC (2019). TSMC details impact of Fab 14B photoresist material incident. Available at the website of TSMC

[65] Xue J, Li G, (2023). Balancing resilience and efficiency in supply chains: Roles of disruptive technologies under Industry 4.0. Frontiers of Engineering Management, 10( 1): 171–176

[66] YangZ (2022). Inside the software that will become the next battle front in US-China chip war. Available at the website of technologyreview.com

[67] Zhang M, Liu D, Shui X, Hu W, Zhan Y, (2025). Examining the impact of trade tariffs on semiconductor firms’ environmental performance. International Journal of Production Economics, 281: 109528

[68] Zhang Y, Zhu X, (2023). Analysis of the global trade network of the chip industry chain: Does the U.S–China tech war matter. Heliyon, 9( 6): e17092

RIGHTS & PERMISSIONS

Higher Education Press