Structural-aware simulation analysis of supply chain resilience

Supply chain resilience (SCRES) refers to the ability of a supply chain (SC) to both resist disruptions and recover its operational capability after disruptions. This paper presents a simulation model that includes network structural properties in the analysis of SCRES. This simulation model extends an existing graph model to consider operational behaviours in order to capture disruption-recovery dynamics. Through structural analysis of a supply chain network (SCN), mitigation strategies are designed to build redundancy, while contingency strategies are developed to prioritise recovery of the affected SCN. SCRES indexes are proposed by sampling SC performance measures of disruption for each plant and aggregating the measures based on the criticality of the plants in the SCN. The applicability of this simulation model is demonstrated in a real-world case study of different disruption scenarios. The application of mitigation and contingency strategies is shown to both improve recovery and reduce the total costs associated with disruptions. Through such simulation-based analysis, firms can gain insight into the SCRES of their existing SCNs and identify suitable strategies to improve SCRES by considering recovery time and costs.

[1]  Mario Ventresca,et al.  Modeling topologically resilient supply chain networks , 2018, Applied Network Science.

[2]  David Simchi-Levi,et al.  Identifying Risks and Mitigating Disruptions in the Automotive Supply Chain , 2015, Interfaces.

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

[4]  Alexandre Dolgui,et al.  Low-Certainty-Need (LCN) supply chains: a new perspective in managing disruption risks and resilience , 2018, Int. J. Prod. Res..

[5]  Janny Leung,et al.  Inventory lot-sizing with supplier selection , 2005, Comput. Oper. Res..

[6]  Benoît Montreuil,et al.  Modeling client profiles for order promising and delivery , 2013, Simul. Model. Pract. Theory.

[7]  Jean-Claude Hennet,et al.  Inventory control in a multi-supplier system , 2004 .

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

[9]  Hirofumi Matsuo,et al.  Implications of the Tohoku earthquake for Toyota׳s coordination mechanism: Supply chain disruption of automotive semiconductors ☆ , 2015 .

[10]  Brian Tomlin Disruption‐management strategies for short life‐cycle products , 2009 .

[11]  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 .

[12]  Virgilio Cruz-Machado,et al.  Supply chain redesign for resilience using simulation , 2012, Comput. Ind. Eng..

[13]  A. P. Barroso,et al.  Quantifying the Supply Chain Resilience , 2015 .

[14]  Stephan M. Wagner,et al.  DO PERCEPTIONS BECOME REALITY? THE MODERATING ROLE OF SUPPLY CHAIN RESILIENCY ON DISRUPTION OCCURRENCE , 2010 .

[15]  Barış Tan Design of balanced energy savings performance contracts , 2020, Int. J. Prod. Res..

[16]  Jennifer Blackhurst,et al.  The Severity of Supply Chain Disruptions: Design Characteristics and Mitigation Capabilities , 2007, Decis. Sci..

[17]  Maqbool Dada,et al.  A Newsvendor's Procurement Problem when Suppliers Are Unreliable , 2007, Manuf. Serv. Oper. Manag..

[18]  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..

[19]  Kaitlin S. Dunn,et al.  An Empirically Derived Framework of Global Supply Resiliency , 2011 .

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

[21]  Alain Martel,et al.  The design of robust value-creating supply chain networks , 2010, Eur. J. Oper. Res..

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

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

[24]  W. Leontief,et al.  The structure of American economy, 1919-1929 : an empirical application of equilibrium analysis , 1942 .

[25]  Brian Tomlin,et al.  Operational Strategies for Managing Supply Chain Disruption Risk , 2011 .

[26]  Keely L. Croxton,et al.  ENSURING SUPPLY CHAIN RESILIENCE: DEVELOPMENT OF A CONCEPTUAL FRAMEWORK , 2010 .

[27]  Alexandra Brintrup,et al.  The moderating impact of supply network topology on the effectiveness of risk management , 2018 .

[28]  M. Christopher,et al.  Building the Resilient Supply Chain , 2004 .

[29]  Rajive Bagrodia,et al.  Parallel languages for discrete-event simulation models , 1998 .

[30]  Wallace J. Hopp,et al.  Mitigating the Impact of Disruptions in Supply Chains , 2011 .

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

[32]  Muhammad Saad Memon,et al.  Sustainable and Resilient Supply Chain Network Design under Disruption Risks , 2014 .

[33]  Ruiying Li,et al.  A New Resilience Measure for Supply Chain Networks , 2017 .

[34]  F. Caniato,et al.  Building a Secure and Resilient Supply Chain , 2003 .

[35]  Dmitry Ivanov,et al.  ‘A blessing in disguise’ or ‘as if it wasn’t hard enough already’: reciprocal and aggravate vulnerabilities in the supply chain , 2020, Int. J. Prod. Res..

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

[37]  Richard E. Nance A history of discrete event simulation programming languages , 1996 .

[38]  D. Ivanov Revealing interfaces of supply chain resilience and sustainability: a simulation study , 2018, Int. J. Prod. Res..

[39]  Mahender Singh,et al.  Quantifying supply chain disruption risk using Monte Carlo and discrete-event simulation , 2009, Proceedings of the 2009 Winter Simulation Conference (WSC).

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

[41]  Brian Tomlin,et al.  On the Value of Mitigation and Contingency Strategies for Managing Supply Chain Disruption Risks , 2006, Manag. Sci..

[42]  Sean P. Willems,et al.  Data Set - Real-World Multiechelon Supply Chains Used for Inventory Optimization , 2008, Manuf. Serv. Oper. Manag..

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