Hybrid Fuzzy-Probabilistic Approach to Supply Chain Resilience Assessment

In this paper, the existing models of supply chain resilience assessment are extended by incorporating ripple effect and structure reconfiguration. Ripple effect mitigation control is vital for supply chain risk management from positions of structural resilience and recoverability. The research approach is based on a hybrid fuzzy-probabilistic approach. The genome method is applied with the objective of including the structural properties of supply chain design into resilience assessment. A supply chain design resilience index is developed, and its computation and application are demonstrated. The results suggest a method of comparing different supply chain designs regarding the resilience both to disruption propagation and with recovery consideration. It also allows the identification of groups of critical suppliers whose failure interrupts supply chain operation.

[1]  Mark Stevenson,et al.  Supply chain resilience: definition, review and theoretical foundations for further study , 2015 .

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

[3]  Y. Sheffi,et al.  A supply chain view of the resilient enterprise , 2005 .

[4]  Angappa Gunasekaran,et al.  Supply chain resilience: role of complexities and strategies , 2015 .

[5]  Charles J. Colbourn,et al.  Edge-packing of graphs and network reliability , 1988, Discret. Math..

[6]  KwangSup Shin,et al.  Evaluation mechanism for structural robustness of supply chain considering disruption propagation , 2016 .

[7]  Alexandre Dolgui,et al.  The Ripple effect in supply chains: trade-off ‘efficiency-flexibility-resilience’ in disruption management , 2014 .

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

[9]  Manoj Kumar Tiwari,et al.  Measuring the Resilience of Supply Chain Systems Using a Survival Model , 2015, IEEE Systems Journal.

[10]  Mahour Mellat-Parast,et al.  Developing a resilient supply chain through supplier flexibility and reliability assessment , 2016 .

[11]  Keely L. Croxton,et al.  Ensuring Supply Chain Resilience: Development and Implementation of an Assessment Tool , 2013 .

[12]  デイビッド スミチレビ,et al.  From Superstorms to Factory Fires : Managing Unpredictable Supply-chain Disruptions , 2014 .

[13]  Scott J. Grawe,et al.  Firm's resilience to supply chain disruptions: Scale development and empirical examination , 2015 .

[14]  José M. Vidal,et al.  Supply network topology and robustness against disruptions – an investigation using multi-agent model , 2011 .

[15]  Yi-Kuei Lin,et al.  Network reliability with deteriorating product and production capacity through a multi-state delivery network , 2014 .

[16]  菅野 道夫,et al.  Theory of fuzzy integrals and its applications , 1975 .

[17]  Tadeusz Sawik,et al.  A portfolio approach to supply chain disruption management , 2017, Int. J. Prod. Res..

[18]  Joseph Sarkis,et al.  Quantitative models for managing supply chain risks: A review , 2015, Eur. J. Oper. Res..

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

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

[21]  Kathryn E. Stecke,et al.  Mitigating disruptions in a multi-echelon supply chain using adaptive ordering , 2017 .

[22]  Saurav Datta,et al.  Evaluation and selection of resilient suppliers in fuzzy environment: Exploration of fuzzy-VIKOR , 2016 .

[23]  Nitin Bakshi,et al.  Co-opetition and Investment for Supply-Chain Resilience , 2008 .

[24]  Lindu Zhao,et al.  Predicted supply chain resilience based on structural evolution against random supply disruptions , 2014 .

[25]  Jafar Rezaei,et al.  Production , Manufacturing and Logistics Multi-criteria supplier segmentation using a fuzzy preference relations based AHP , 2012 .

[26]  Boris V. Sokolov,et al.  A multi-structural framework for adaptive supply chain planning and operations control with structure dynamics considerations , 2010, Eur. J. Oper. Res..

[27]  Edward J.S. Hearnshaw,et al.  A complex network approach to supply chain network theory , 2013 .

[28]  Kanchan Das,et al.  Risk readiness and resiliency planning for a supply chain , 2015 .

[29]  J. Scott Provan,et al.  The Complexity of Counting Cuts and of Computing the Probability that a Graph is Connected , 1983, SIAM J. Comput..

[30]  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 .

[31]  Michelle Dunbar,et al.  On the quantification of operational supply chain resilience , 2015 .

[32]  Rodrigo Reyes Levalle,et al.  Resilience in supply networks: Definition, dimensions, and levels , 2017, Annu. Rev. Control..

[33]  Bernard Grabot,et al.  A fuzzy multi-criteria decision-making approach for managing performance and risk in integrated procurement–production planning , 2017, Int. J. Prod. Res..

[34]  D. Ivanov Structural Dynamics and Resilience in Supply Chain Risk Management , 2017 .

[35]  D. Singer A fuzzy set approach to fault tree and reliability analysis , 1990 .

[36]  Liang Tang,et al.  Complex interdependent supply chain networks: Cascading failure and robustness , 2016 .

[37]  H. Gurnani,et al.  Managing Risk of Supply Disruptions: Incentives for Capacity Restoration , 2013 .

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

[39]  Charles J. Colbourn,et al.  The Combinatorics of Network Reliability , 1987 .

[40]  Sang-Hyun Kim,et al.  Guilt by Association : Strategic Failure Prevention and Recovery Capacity Investments , 2011 .

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

[42]  Alexandre Dolgui,et al.  Structural quantification of the ripple effect in the supply chain , 2016 .

[43]  Kevin P. Scheibe,et al.  Supply chain disruption propagation: a systemic risk and normal accident theory perspective , 2018, Int. J. Prod. Res..

[44]  Artur Świerczek,et al.  The impact of supply chain integration on the “snowball effect” in the transmission of disruptions: An empirical evaluation of the model , 2014 .

[45]  Lara Khansa,et al.  Characterizing multi-event disaster resilience , 2014, Computers & Operations Research.

[46]  Yusoon Kim,et al.  Supply network disruption and resilience: A network structural perspective , 2015 .

[47]  Alexandre Dolgui,et al.  Literature review on disruption recovery in the supply chain* , 2017, Int. J. Prod. Res..

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

[49]  Boris V. Sokolov,et al.  Control and system-theoretic identification of the supply chain dynamics domain for planning, analysis and adaptation of performance under uncertainty , 2013, Eur. J. Oper. Res..

[50]  William Ho,et al.  Supply chain risk management: a literature review , 2015 .

[51]  Dmitry Ivanov,et al.  Exact and heuristic methods for integrated supply chain design reliability analysis , 2016 .

[52]  Amanda J. Schmitt,et al.  OR/MS models for supply chain disruptions: a review , 2014 .

[53]  M. Parast,et al.  A review of the literature on the principles of enterprise and supply chain resilience: Major findings and directions for future research , 2016 .

[54]  John Yen,et al.  Analyzing the Resilience of Complex Supply Network Topologies Against Random and Targeted Disruptions , 2011, IEEE Systems Journal.

[55]  Stephan M. Wagner,et al.  Structural drivers of upstream supply chain complexity and the frequency of supply chain disruptions , 2015 .

[56]  Jennifer Blackhurst,et al.  Methodology for supply chain disruption analysis , 2007 .

[57]  Yi-Kuei Lin,et al.  System reliability for a multistate intermodal logistics network with time windows , 2017, Int. J. Prod. Res..

[58]  Charu Chandra,et al.  Supply chain resilience: model development and empirical analysis , 2017, Int. J. Prod. Res..

[59]  K. K. Aggarwal,et al.  A new method for system reliability evaluation , 1973 .

[60]  Charles J. Colbourn,et al.  Computing Residual Connectedness Reliability for Restricted Networks , 1993, Discret. Appl. Math..

[61]  Eugene Levner,et al.  Entropy-based model for the ripple effect: managing environmental risks in supply chains , 2018, Int. J. Prod. Res..