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Analyzing upstream and downstream risk propagation in supply networks by combining Agent-based Modeling and Bayesian networks

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Abstract

When serious material flow disruptions occur in supply networks, complex interdependencies can lead to unpredictable effects which propagate through the entire network and endanger the existence of participating enterprises. Practitioners seek risk analysis methods that quantify risk propagation in the network as realistically and extensively as possible and present the results with sufficiently clear key figures and diagrams. Existing approaches, however, reveal their weaknesses precisely in these areas. While existing Bayesian network approaches only model the results of forward propagative effects through subjectively estimated and assumed conditional probabilities, current Agent-based Models provide insight into the dynamic interactions for specific research purposes, but fail to present the results of propagations concisely and to consider the network in total. This paper highlights the synergy effects of a combination of Agent-based Modeling and Bayesian networks not previously considered in the literature. A risk analysis methodology is presented that captures the complex interrelationships of various risk scenarios in disrupted supply networks. It offers both forward and backward risk propagations and direct and indirect risk consequences in a way that is easily digestible by management. For this purpose, three novel risk metrics for Bayesian networks are presented that quantify the exact risk propagation pattern in the network and provide a more accurate planning basis for mitigation strategies. Our methodology is applied to an excerpt of a supply network in the consumer goods industry and illustrates the approach and its benefits.

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Data availability

The dataset generated during and analysed during the current study are available from the corresponding author on reasonable request.

Notes

  1. The asterisk in the search string serves as the truncation operator allowing different endings / forms of the word to be searched.

Abbreviations

n :

Node n of the network

v :

Node v of the network

j :

Node j of the network

cr(n):

Criticality of a node n

\(c_{s_{i}}\) :

Expected disruption costs

\(D_{s_{i}}\) :

Disruption location of scenario i

\({\overline{D}}_{s_{i}}\) :

Location which does not experience a direct disruption in scenario \({s_{i}}\)

dr(n):

Direct risk of a node n

\(\gamma _{prop}\) :

Propagation ratio

dpr(n, j):

Direct propagation ratio between node n and node j

exr(n):

Experienced propagated risk

\(P(X_{i})\) :

Probability of variable \(X_{i}\)

\(N_{\textbf{T}}^{-}(v)\) :

Set of predecessor nodes of a node v in the directed acyclic graph \(\textbf{T}\)

\(N_{\textbf{T}}^{+}(v)\) :

Set of successor nodes of a node v in the directed acyclic graph \(\textbf{T}\)

pr(n):

Propagation contribution of a node n

N :

Set of nodes / locations

\(s_{i}\) :

Scenario i

S :

Set of risk scenarios

S1:

Supplier 1

S2:

Supplier 2

S3:

Supplier 3

P1:

Producer 1

P2:

Producer 2

P3:

Producer 3

W1:

Wholesaler 1

W2:

Wholesaler 2

R1:

Retailer 1

R2:

Retailer 2

R3:

Retailer 3

tr(j):

Total risk

\(\textbf{T}\) :

Directed acyclic graph

vu(n):

Vulnerabilty of a node n

\(X_{i}\) :

Random variable

References

  • Allwood JM, Lee J-H (2005) The design of an agent for modelling supply chain network dynamics. Int J Prod Res 43:4875–4898

    Article  Google Scholar 

  • Badurdeen F, Shuaib M, Wijekoon K, Brown A, Faulkner W, Amundson J, Jawahir I, Goldsby T, Iyengar D, Boden B (2014) Quantitative modeling and analysis of supply chain risks using bayesian theory. J Manuf Technol Manag 25:631–654

    Article  Google Scholar 

  • Basole RC, Bellamy MA (2014) Supply network structure, visibility, and risk diffusion: a computational approach. Decis Sci 45:753–789

    Article  Google Scholar 

  • Borshchev A, Filippov A (2004). From system dynamics and discrete event to practical agent based modeling: reasons, techniques, tools. The 22nd International Conference of the System Dynamics Society. Oxford, England

  • Bugert N (2019) Risk budget planning through assessing the criticality and vulnerability of supply network entities facing disruption risks. IFAC-Papers-OnLine 52:1295–1300

    Article  Google Scholar 

  • Bugert N, Lasch R (2018) Supply chain disruption models: a critical review. Logist Res 11(5):1–35

    Google Scholar 

  • Capgemini Research Institute (2020). Fast forward: rethinking supply chain resilience for a post-COVID-19 world. https://www.capgemini.com/wp-content/uploads/2020/11/Fast-forward_Report.pdf. Accessed \({15}^{\text{th}}\) January 2021

  • Chopra S, Sodhi M (2004) Mitigating supply chain risk through improved confidence. Int J Phys Distrib Logist Manag 34:388–396

    Article  Google Scholar 

  • Cimpanu C (2019). Ransomware halts production for days at major airplane parts manufacturer. https://www.zdnet.com/article/ransomware-halts-production-for-days-at-major-airplane-parts-manufacturer/. Accessed \({16}^{\text{ th }}\) January 2021

  • Craighead C, Blackhurst J, Rungtusanathma M, Handfield R (2007) The severity of supply chain disruptions: design characteristics and mitigation capabilities. Decis Sci 38:131–156

    Article  Google Scholar 

  • Diabat A, Govindan K, Panicker VV (2012) Supply chain risk management and its mitigation in a food industry. Int J Prod Res 50:3039–3050

    Article  Google Scholar 

  • Dolgui A, Ivanov D, Sokolov B (2018) Ripple effect in the supply chain: an analysis and recent literature. Int J Prod Res 56:414–430

    Article  Google Scholar 

  • Elliott R, Thomas C, Muhammad K (2019). Supply Chain Resilience Report 2019. https://insider.zurich.co.uk/app/uploads/2019/11/BCISupplyChainResilienceReportOctober2019SingleLow1.pdf. Accessed \({16}^{\text{ th }}\) January 2021

  • Fink A (2014) Conducting research literature reviews: from the internet to paper. SAGE Publications, Los Angeles

    Google Scholar 

  • Fox CR, Clemen RT (2005) Subjective probability assessment in decision analysis: partition dependence and bias toward the ignorance prior. Manage Sci 51:1417–1432

    Article  Google Scholar 

  • Garvey MD, Carnovale S, Yeniyurt S (2015) An analytical framework for supply network risk propagation: a Bayesian network approach. Eur J Oper Res 243:618–627

    Article  Google Scholar 

  • Hemmerdinger J (2019) Spirit to close Asco deal by October after delays. https://www.flightglobal.com/airframers/spirit-to-close-asco-deal-by-october-after-delays/133743.article. Accessed \({16}^{\text{ th }}\) January 2021

  • Ho W, Zheng T, Yildiz H (2015) Supply chain risk management: a literature review. Int J Prod Res 53:5031–5069

    Article  Google Scholar 

  • Hosseini S, Ivanov D (2020) Bayesian networks for supply chain risk, resilience and ripple effect analysis: a literature review. Expert Syst Appl 161:113649

    Article  Google Scholar 

  • Hosseini S, Ivanov D, Blackhurst J (2020). Conceptualization and measurement of supply chain resilience in an open-system context. IEEE Transactions on Engineering Management. pp 1-16

  • Hu H, Li Z (2009) Modeling and scheduling for manufacturing grid workflows using timed Petri nets. Int J Adv Manuf Technol 42:553–568

    Article  Google Scholar 

  • Hung W, Kucherenko Y, Samsatli N, Shah N (2004) A Flexible and Generic Approach to Dynamic Modelling of Supply Chains. J Oper Res Soc 55(8):801–813

    Article  Google Scholar 

  • Huang Z, Rosowsky D, Sparks P (2001) Long-Term Hurricane Risk Assessment and Expected Damage to Residential Structures. Reliab Eng Syst Saf 74(3):239–249

    Article  Google Scholar 

  • Ivanov D (2017) Simulation-based ripple effect modelling in the supply chain. Int J Prod Res 55:2083–2101

    Article  Google Scholar 

  • Jeng MD, DiCesare F (1993) A review of synthesis techniques for Petri nets with applications to automated manufacturing systems. IEEE Trans Syst, Man, and Cybern 23:301–312

    Article  Google Scholar 

  • Jensen F (2001) Bayesian networks and decision graphs. Statistics for engineering and information science. Springer, New York

    Book  Google Scholar 

  • Jüttner U, Peck H, Christopher M (2003) Supply chain risk management: outlining an agenda for future research. Int J Logist 6:197–210

    Article  Google Scholar 

  • Kaihara T (2003) Multi-agent based supply chain modelling with dynamic environment. Int J Prod Econ 85:263–269

    Article  Google Scholar 

  • Käki A, Salo A, Talluri S (2015) Disruptions in supply networks: a probabilistic risk assessment approach. J Bus Logist 36:273–287

    Article  Google Scholar 

  • International propagation of economic shocks through global supply chains. WINPEC Working Paper Series No. E1810: 1-16

  • Kemblowski M, Grzyl B, Kristowski A, Siemaszko A (2017) Risk modelling with Bayesian networks—case study: construction of tunnel under the Dead Vistula River in Gdansk. Proc Eng 196:585–591

    Article  Google Scholar 

  • Keow Cheng S, Hon Kam B (2008) A conceptual framework for analysing risk in supply networks. J Enterp Inf Manag 21:345–360

    Article  Google Scholar 

  • Lawrence J-M, Hossain NU, Jaradat R, Hamilton M (2020) Leveraging a Bayesian network approach to model and analyze supplier vulnerability to severe weather risk: a case study of the U.S. pharmaceutical supply chain following Hurricane Maria. Int J Disaster Risk Redcution 49:101607

    Article  Google Scholar 

  • Ledwoch A, Yasarcan H, Brintrup A (2018) The moderating impact of supply network topology on the effectiveness of risk management. Int J Prod Econ 197:13–26

    Article  Google Scholar 

  • Li H, Pedrielli G, Lee L, Chew E (2017) Enhancement of Supply Chain Resilience through Inter-Echelon Information Sharing. Flex Serv Manuf J 29(2):260–285

    Article  Google Scholar 

  • Li Y, Zobel CW (2020) Exploring supply chain network resilience in the presence of the ripple effect. Int J Prod Econ 228:107693

    Article  Google Scholar 

  • Li Y, Chen K, Collignon S, Ivanov D (2021) Ripple effect in the supply chain network: forward and backward disruption propagation, network health and firm vulnerability. Eur J Oper Res 291(3):1117–1131

    Article  Google Scholar 

  • Liu M, Liu Z, Chu F, Zheng F, Chu C (2020) A new robust dynamic Bayesian network approach for disruption risk assessment under the supply chain ripple effect. Int J Prod Res 59:265–285

    Article  Google Scholar 

  • Long Q (2014) Distributed supply chain network modelling and simulation: integration of Agent-based distributed simulation and improved SCOR model. Int J Prod Res 52:6899–6917

    Article  Google Scholar 

  • Manuj I, Mentzer JT (2008) Global supply chain risk management. J Bus Logist 29:133–155

    Article  Google Scholar 

  • March J, Shapira Z (1987) Managerial Perspectives on Risk and Risk Taking. Manage Sci 33(11):1404–1418

    Article  Google Scholar 

  • Min S, Zacharia Z, Smith C (2019) Defining supply chain management: In the past, present, and future. J Bus Logist 40:44–55

    Article  Google Scholar 

  • Montibeller G, von Winterfeldt D (2018) Individual and group biases in value and uncertaint judgments. In: Dieas L, Morton A, Quigley J (eds) Elicititation International Series in Operations Research & Management Science, vol 261. Springer, Cham

    Google Scholar 

  • Ojha R, Ghadge A, Tiwari MK, Bititci US (2018) Bayesian network modelling for supply chain risk propagation. Int J Prod Res 56:5795–5819

    Article  Google Scholar 

  • Otto C, Willner SN, Wenz L, Frieler K, Levermann A (2017) Modeling loss propagation in the global supply network: the dynamic agent-based model acclimate. J Econ Dyn Control 83:232–269

    Article  Google Scholar 

  • Park K (2014) Effect of destruction and disruption of an enterprise network using agent-based simulation and system dynamics. Adv Sci Technol Lett 62:13–16

    Article  Google Scholar 

  • Qazi A, Dickson A, Quickley J, Gaudenzi B (2018) Supply chain risk network management: a Bayesian belief network and expected utility based approach for managing supply chain risks. Int J Prod Econ 196:24–42

    Article  Google Scholar 

  • Rajesh R, Ravi V, Venkata Rao R (2014) Selection of risk mitigation strategy in electronic supply chains using grey theory and digraph-matrix approaches. Int J Prod Res 53:238–257

    Article  Google Scholar 

  • Ribeiro JP, Barbosa-Povoa A (2018) Supply chain resilience: definitions and quantitative modelling approaches—a literature review. Comput & Ind Eng 115:109–122

    Article  Google Scholar 

  • Šalamon T (2011) Design of Agent-based Models: developing computer simulations for a better understanding of social processes. Eva & Tomáš Bruckner Publishing, Řepín

    Google Scholar 

  • Seck M, Rabadi G, Koestler C (2015) A simulation-based approach to risk assessment and mitigation in supply chain networks. Proc Comput Sci 61:98–104

    Article  Google Scholar 

  • Sharma SK, Sharma S (2015) Developing a Bayesian network model for supply chain risk management. Supply Chain Forum Int J 16:50–72

    Article  Google Scholar 

  • Shi X, Long W, Li Y, Deng D, Wei Y, Liu H (2019) Research on supply network resilience considering random and targeted disruptions simulataneously. Int J Prod Res 58:6670–6688

    Article  Google Scholar 

  • Swierczek A (2018) Supply chain risk management in the transmission and amplification of disruptions. In: Khojasteh Y (ed) Supply chain risk management. Springer, Singapore, pp 155–178

    Chapter  Google Scholar 

  • Talluri S, Kull T, Yildiz H, Yoon J (2013) Assessing the efficiency of risk mitigation strategies in supply chains. J Bus Logist 34:253–269

    Article  Google Scholar 

  • Tang CS (2006) Perspectives in supply chain risk management. Int J Prod Econ 103:451–488

    Article  Google Scholar 

  • Taylor S (2014) Introducing agent-based modeling and simulation. In: Taylor S (ed) Agent-based modeling and simulation. Palgrave Macmillan, London, pp 1–10

    Chapter  Google Scholar 

  • Tchankova L (2002) Risk identification-basic stage in risk management. Environ Manag Health 13:290–297

    Article  Google Scholar 

  • Wagner S, Bode C (2006) An empirical investigation into supply chain vulnerability. J Purch & Supply Manag 12:301–312

    Article  Google Scholar 

  • Wagner S, Bode C (2008) An empirical examination of supply chain performance along several dimensions of risk. J Bus Logist 29:307–325

    Article  Google Scholar 

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The authors did not receive support from any organization for the submitted work. No funding was received to assist with the preparation of this manuscript. All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.

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  1. TU Dresden, Münchner Platz 1/3, Dresden, Germany

    Niels Bugert & Rainer Lasch

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  1. Niels Bugert
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Correspondence to Niels Bugert.

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Bugert, N., Lasch, R. Analyzing upstream and downstream risk propagation in supply networks by combining Agent-based Modeling and Bayesian networks. J Bus Econ 93, 859–889 (2023). https://doi.org/10.1007/s11573-022-01128-2

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