Modeling Supply Chain Resilience

This chapter is devoted to supply chain resilience modeling. The chapter starts with the classification of the existing modeling methods and explanation of their applications and managerial insights. Subsequently, this chapter introduces some modeling examples and presents some optimization and simulation techniques to identify the impacts of disruptions on supply chain performance (i.e., to answer the question “what happened?”) and explain them (i.e., to answer the question “why it happened?”). Moreover, the chapter also considers supply chain optimization to answer the question “how to recover?”

[1]  Alexandre Dolgui,et al.  A control approach to scheduling flexibly configurable jobs with dynamic structural-logical constraints , 2020, IISE Trans..

[2]  Tadeusz Sawik,et al.  Two-period vs. multi-period model for supply chain disruption management , 2018, Int. J. Prod. Res..

[3]  Dmitry Ivanov,et al.  Structure dynamics control approach to supply chain planning and adaptation , 2012 .

[4]  Alexandre Dolgui,et al.  The impact of digital technology and Industry 4.0 on the ripple effect and supply chain risk analytics , 2018, Int. J. Prod. Res..

[5]  Dmitry Ivanov,et al.  Coronavirus (COVID-19/SARS-CoV-2) and supply chain resilience: a research note , 2020 .

[6]  Dmitry A. Ivanov,et al.  Disruption tails and revival policies: A simulation analysis of supply chain design and production-ordering systems in the recovery and post-disruption periods , 2019, Comput. Ind. Eng..

[7]  S. Bonilla,et al.  Blockchain and supply chain management integration: a systematic review of the literature , 2019, Supply Chain Management: An International Journal.

[8]  Maria Paola Scaparra,et al.  Hedging against disruptions with ripple effects in location analysis , 2012 .

[9]  Abroon Qazi,et al.  Supply chain risk network management : a Bayesian belief network and expected utility based approach for managing supply chain risks , 2018 .

[10]  Tadeusz Sawik,et al.  Selection of supply portfolio under disruption risks , 2011 .

[11]  T. Sawik.  Selection of resilient supply portfolio under disruption risks , 2013 .

[12]  Sarah Root,et al.  Supply chain design considering correlated failures and inspection in pharmaceutical and food supply chains , 2017, Comput. Ind. Eng..

[13]  Abhijeet Ghadge,et al.  A Systems Approach for Modelling Supply Chain Risks , 2012 .

[14]  Dmitry Ivanov,et al.  Dual problem formulation and its application to optimal redesign of an integrated production–distribution network with structure dynamics and ripple effect considerations , 2013 .

[15]  Alexandre Dolgui,et al.  Integration of aggregate distribution and dynamic transportation planning in a supply chain with capacity disruptions and the ripple effect consideration , 2015 .

[16]  Boris Sokolov,et al.  Minimization of disruption-related return flows in the supply chain , 2017 .

[17]  Alexandre Dolgui,et al.  Ripple effect in the supply chain: an analysis and recent literature , 2018, Int. J. Prod. Res..

[18]  Nickolas K. Freeman,et al.  Robust Sourcing from Suppliers under Ambiguously Correlated Major Disruption Risks , 2018, Production and Operations Management.

[19]  A. Gunasekaran,et al.  The role of Big Data in explaining disaster resilience in supply chains for sustainability , 2017 .

[20]  Ilaria Giannoccaro,et al.  The Impact of Control and Complexity on Supply Network Performance: An Empirically Informed Investigation Using NK Simulation Analysis , 2018, Decis. Sci..

[21]  Ruhul A. Sarker,et al.  A quantitative model for disruption mitigation in a supply chain , 2017, Eur. J. Oper. Res..

[22]  Henrik S. Sternberg,et al.  Distributed ledger technology in supply chains: a transaction cost perspective , 2020, Int. J. Prod. Res..

[23]  Dmitry Ivanov,et al.  Coordination of production and ordering policies under capacity disruption and product write-off risk: an analytical study with real-data based simulations of a fast moving consumer goods company , 2020, Ann. Oper. Res..

[24]  Alexandre Dolgui,et al.  Review of quantitative methods for supply chain resilience analysis , 2019, Transportation Research Part E: Logistics and Transportation Review.

[25]  Alexandre Dolgui,et al.  Disruption-driven supply chain (re)-planning and performance impact assessment with consideration of pro-active and recovery policies , 2016 .

[26]  Alexandre Dolgui,et al.  Ripple effect modelling of supplier disruption: integrated Markov chain and dynamic Bayesian network approach , 2020, Int. J. Prod. Res..

[27]  Benoît Iung,et al.  Challenges for the cyber-physical manufacturing enterprises of the future , 2019, Annu. Rev. Control..

[28]  Dmitry Ivanov,et al.  Simulation-based ripple effect modelling in the supply chain , 2017, Int. J. Prod. Res..

[29]  Dmitry Ivanov,et al.  Ripple effect in the supply chain network: Forward and backward disruption propagation, network health and firm vulnerability , 2020, European Journal of Operational Research.

[30]  Ruhul A. Sarker,et al.  A mathematical modelling approach for managing sudden disturbances in a three-tier manufacturing supply chain , 2019, Ann. Oper. Res..

[31]  Myles D. Garvey,et al.  An analytical framework for supply network risk propagation: A Bayesian network approach , 2015, Eur. J. Oper. Res..

[32]  Enzo Morosini Frazzon,et al.  A supervised machine learning approach to data-driven simulation of resilient supplier selection in digital manufacturing , 2019, Int. J. Inf. Manag..

[33]  Rahul C. Basole,et al.  Supply Network Structure, Visibility, and Risk Diffusion: A Computational Approach , 2014, Decis. Sci..

[34]  Manoj Kumar Tiwari,et al.  Bayesian network modelling for supply chain risk propagation , 2018, Int. J. Prod. Res..

[35]  Dmitry Ivanov,et al.  Adaptive Supply Chain Management , 2009 .

[36]  Yuhong Li,et al.  Network characteristics and supply chain resilience under conditions of risk propagation , 2020 .