A digital supply chain twin for managing the disruption risks and resilience in the era of Industry 4.0

Abstract We theorize a notion of a digital supply chain (SC) twin – a computerized model that represents network states for any given moment in real time. We explore the conditions surrounding the design and implementation of the digital twins when managing disruption risks in SCs. The combination of model-based and data-driven approaches allows uncovering the interrelations of risk data, disruption modeling, and performance assessment. The SC shocks and adaptations amid the COVID-19 pandemic along with post-pandemic recoveries provide indisputable evidences for the urgent needs of digital twins for mapping supply networks and ensuring visibility. The results of this study contribute to the research and practice of SC risk management by enhancing predictive and reactive decisions to utilize the advantages of SC visualization, historical disruption data analysis, and real-time disruption data and ensure end-to-end visibility and business continuity in global companies.

[1]  Shams Rahman,et al.  A quantitative and simulation model for managing sudden supply delay with fuzzy demand and safety stock , 2018, Int. J. Prod. Res..

[2]  F. Sibel Salman,et al.  Emergency facility location under random network damage: Insights from the Istanbul case , 2015, Comput. Oper. Res..

[3]  Hing Kai Chan,et al.  Recent Development in Big Data Analytics for Business Operations and Risk Management , 2017, IEEE Transactions on Cybernetics.

[4]  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..

[5]  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..

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

[7]  Alexandre Dolgui,et al.  Viability of intertwined supply networks: extending the supply chain resilience angles towards survivability. A position paper motivated by COVID-19 outbreak , 2020, Int. J. Prod. Res..

[8]  David Präkel.  The impact of digital , 2021 .

[9]  Lucas P. Veelenturf,et al.  The strategic role of logistics in the industry 4.0 era , 2019, Transportation Research Part E: Logistics and Transportation Review.

[10]  Yasuhiko Takahara,et al.  General Systems Theory: Mathematical Foundations , 1975 .

[11]  Dmitry Ivanov.  Viable supply chain model: integrating agility, resilience and sustainability perspectives—lessons from and thinking beyond the COVID-19 pandemic , 2020, Annals of operations research.

[12]  Rahul C. Basole,et al.  Assimilation of tracking technology in the supply chain , 2016, Transportation Research Part E: Logistics and Transportation Review.

[13]  David Simchi-Levi,et al.  Disruption Risk Mitigation in Supply Chains - The Risk Exposure Index Revisited , 2016, Oper. Res..

[14]  Harpreet Kaur,et al.  Proactive and reactive models for disaster resilient supply chain , 2019, Ann. Oper. Res..

[15]  Yossi Sheffi,et al.  Preparing for disruptions through early detection , 2015 .

[16]  Christoph Bode,et al.  Stages of Supply Chain Disruption Response: Direct, Constraining, and Mediating Factors for Impact Mitigation , 2017, Decis. Sci..

[17]  F. Sibel Salman,et al.  Improving post-disaster road network accessibility by strengthening links against failures , 2018, Eur. J. Oper. Res..

[18]  Virginia L. M. Spiegler,et al.  Developing a resilient supply chain strategy during ‘boom’ and ‘bust’ , 2016 .

[19]  Hakan Yildiz,et al.  Reliable Supply Chain Network Design , 2016, Decis. Sci..

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

[21]  Abderrahim Ait-Alla,et al.  Simulation-based Analysis of the Interaction of a Physical and a Digital Twin in a Cyber-Physical Production System , 2019, IFAC-PapersOnLine.

[22]  Gerald J Kost,et al.  Disaster Preparedness , 2007 .

[23]  Tsan-Ming Choi,et al.  Data Analytics for Operational Risk Management , 2020, Decis. Sci..

[24]  HaiLin Wang.  Supply chain control model: A cybernetics-based approach , 2008, 2008 IEEE International Conference on Service Operations and Logistics, and Informatics.

[25]  Elizabeth Barber,et al.  Connectivity, Complexity, and Catastrophe in Large-Scale Systems , 1980 .

[26]  Gang Yu,et al.  Real-time disruption management in a two-stage production and inventory system , 2004 .

[27]  Fabio Sgarbossa,et al.  Increasing flexibility and productivity in Industry 4.0 production networks with autonomous mobile robots and smart intralogistics , 2020, Annals of Operations Research.

[28]  Tobias Schoenherr,et al.  A blockchain-based approach for a multi-echelon sustainable supply chain , 2020, Int. J. Prod. Res..

[29]  A. Gunasekaran,et al.  Big data analytics in logistics and supply chain management: Certain investigations for research and applications , 2016 .

[30]  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..

[31]  Paul Young,et al.  Design of a resilient shock absorber for disrupted supply chain networks: a shock-dampening fortification framework for mitigating excursion events , 2013 .

[32]  Samuel Fosso Wamba,et al.  Blockchain adoption challenges in supply chain: An empirical investigation of the main drivers in India and the USA , 2019, Int. J. Inf. Manag..

[33]  Tsan-Ming Choi,et al.  Advances in Risk Analysis with Big Data , 2017, Risk analysis : an official publication of the Society for Risk Analysis.

[34]  Ruhul A. Sarker,et al.  Real time disruption management for a two-stage batch production-inventory system with reliability considerations , 2014, Eur. J. Oper. Res..

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

[36]  Xu Chen,et al.  Communicating supply chain risks and mitigation strategies: a comprehensive framework , 2017 .

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

[38]  Kevin B. Hendricks,et al.  Association Between Supply Chain Glitches and Operating Performance , 2005, Manag. Sci..

[39]  Rameshwar Dubey,et al.  Bridging and buffering: Strategies for mitigating supply risk and improving supply chain performance , 2016 .

[40]  Fernando Deschamps,et al.  Past, present and future of Industry 4.0 - a systematic literature review and research agenda proposal , 2017, Int. J. Prod. Res..

[41]  Lucas Santos Dalenogare,et al.  Industry 4.0 technologies: Implementation patterns in manufacturing companies , 2019, International Journal of Production Economics.

[42]  F. Taylor.  Cybernetics (or control and communication in the animal and the machine). , 1949 .

[43]  Urs Magnus Strewe,et al.  Supply Chain Finance and Blockchain Technology: The Case of Reverse Securitisation , 2017 .

[44]  Oswaldo Morales-Matamoros,et al.  A systems science approach to enterprise resources planning systems , 2009 .

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

[46]  Barbara B. Flynn,et al.  On Theory in Supply Chain Uncertainty and its Implications for Supply Chain Integration , 2016 .

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

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

[49]  Angappa Gunasekaran,et al.  Empirical investigation of data analytics capability and organizational flexibility as complements to supply chain resilience , 2019, Int. J. Prod. Res..

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

[51]  Luk N. Van Wassenhove,et al.  Supply Chain Tsunamis: Research on Low‐Probability, High‐Impact Disruptions , 2018 .

[52]  T. Choi.  A System of Systems Approach for Global Supply Chain Management in the Big Data Era , 2018, IEEE Engineering Management Review.

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

[54]  Hans-J.  Dynamics in Logistics , 2016 .

[55]  S. Beer.  The Brain of the Firm , 1972 .

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

[57]  Huifu Xu,et al.  Disaster preparedness using risk-assessment methods from earthquake engineering , 2018, Eur. J. Oper. Res..

[58]  Michael Teucke,et al.  Effects of Sensor-Based Quality Data in Automotive Supply Chains - A Simulation Study , 2018, LDIC.

[59]  L. Bearzotti,et al.  An autonomous multi-agent approach to supply chain event management , 2008, 2008 IEEE International Conference on Service Operations and Logistics, and Informatics.

[60]  Ralf W. Seifert,et al.  Roles of inventory and reserve capacity in mitigating supply chain disruption risk , 2018, Int. J. Prod. Res..

[61]  Stephan M. Wagner,et al.  Modeling defaults of companies in multi-stage supply chain networks , 2012 .

[62]  M. Henke,et al.  A Simulation-Based Evaluation Approach for Digitalization Scenarios in Smart Supply Chain Risk Management , 2017 .

[63]  Chencheng Fang,et al.  Transmission of a supplier’s disruption risk along the supply chain: a further investigation of the Chinese automotive industry , 2018, Production Planning & Control.

[64]  A. Barabasi,et al.  Universal resilience patterns in complex networks , 2016, Nature.

[65]  Judith M. Whipple,et al.  Global supply chain design considerations: Mitigating product safety and security risks , 2011 .

[66]  Carlo Noe,et al.  Literature review on the ‘Smart Factory’ concept using bibliometric tools , 2017, Int. J. Prod. Res..

[67]  Petros Ieromonachou,et al.  Big data analytics in supply chain management: A state-of-the-art literature review , 2017, Comput. Oper. Res..

[68]  Manoj Kumar Tiwari,et al.  Digital Twin Driven Inclusive Manufacturing Using Emerging Technologies , 2019, IFAC-PapersOnLine.

[69]  Rahul C. Basole,et al.  Visual analytics for supply network management: System design and evaluation , 2016, Decis. Support Syst..

[70]  Luca Fumagalli,et al.  Flexible Automation and Intelligent Manufacturing , FAIM 2017 , 27-30 June 2017 , Modena , Italy A review of the roles of Digital Twin in CPS-based production systems , 2017 .

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

[72]  Oleg Yurievitch Gusikhin,et al.  JEDI: Just-in-Time Execution and Distribution Information Support System for Automotive Stamping Operations , 2012 .

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

[74]  Reza Zanjirani Farahani,et al.  Resilient supply chain network design under competition: A case study , 2017, Eur. J. Oper. Res..

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

[76]  Viktor Mikhaĭlovich Glushkov,et al.  An Introduction to Cybernetics , 1957, The Mathematical Gazette.

[77]  Nachiappan Subramanian,et al.  Logistics and cloud computing service providers’ cooperation: a resilience perspective , 2017 .

[78]  Magoroh Maruyama,et al.  THE SECOND CYBERNETICS Deviation-Amplifying Mutual Causal Processes , 1963 .

[79]  Amanda J. Schmitt,et al.  A Quantitative Analysis of Disruption Risk in a Multi-Echelon Supply Chain , 2011 .

[80]  A. Gunasekaran,et al.  Supply chain agility, adaptability and alignment: empirical evidence from the Indian auto components industry , 2018 .

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

[82]  Long Gao,et al.  Dynamic Supply Risk Management with Signal-Based Forecast, Multi-Sourcing, and Discretionary Selling , 2015 .

[83]  M. Henke,et al.  A Simulation-Based Evaluation Approachfor Digitalization Scenarios in Smart SupplyChain Risk Management , 2017 .

[84]  David L. Olson,et al.  Supply chain risk, simulation, and vendor selection , 2008 .

[85]  Norbert Wiener,et al.  Cybernetics. , 1948, Scientific American.

[86]  R. W. Revans,et al.  Decision and Control , 1968 .

[87]  Peter Marsh,et al.  Japan crisis impact on the supply chain: Global industries consider their options , 2011 .

[88]  Omar Chiotti,et al.  An autonomous multi-agent approach to supply chain event management , 2012 .

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

[90]  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..

[91]  Shahriar Akter,et al.  How ‘Big Data’ Can Make Big Impact: Findings from a Systematic Review and a Longitudinal Case Study , 2015 .

[92]  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..

[93]  G. Antoniou,et al.  Supply chain risk management and artificial intelligence: state of the art and future research directions , 2018, Int. J. Prod. Res..

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

[95]  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..

[96]  Shahriar Akter,et al.  Guest editorial: transforming operations and production management using big data and business analytics: future research directions , 2017 .

[97]  Alexandre Dolgui,et al.  Does the ripple effect influence the bullwhip effect? An integrated analysis of structural and operational dynamics in the supply chain† , 2019, Int. J. Prod. Res..

[98]  Mostafa Zandieh,et al.  A hybrid multi-stage predictive model for supply chain network collapse recovery analysis: a practical framework for effective supply chain network continuity management , 2011 .

[99]  Alexandre Dolgui,et al.  Blockchain-oriented dynamic modelling of smart contract design and execution in the supply chain , 2019, Int. J. Prod. Res..

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

[101]  Angappa Gunasekaran,et al.  Agility and resilience as antecedents of supply chain performance under moderating effects of organizational culture within the humanitarian setting: a dynamic capability view , 2018, Production Planning & Control.

[102]  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 , 2017, Annals of Operations Research.

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

[104]  Seyed Ali Torabi,et al.  Resilient supplier selection and order allocation under operational and disruption risks , 2015 .

[105]  Joseph Fiksel,et al.  The Evolution of Resilience in Supply Chain Management: A Retrospective on Ensuring Supply Chain Resilience , 2019, Journal of Business Logistics.

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

[107]  Steven A. Melnyk,et al.  Supply chain risk and resilience: theory building through structured experiments and simulation , 2018, Int. J. Prod. Res..

[108]  P. Schönsleben,et al.  System-oriented supply chain risk management , 2009 .

[109]  Angappa Gunasekaran,et al.  Antecedents of Resilient Supply Chains: An Empirical Study , 2019, IEEE Transactions on Engineering Management.

[110]  R. Handfield,et al.  An empirically derived agenda of critical research issues for managing supply-chain disruptions , 2005 .

[111]  Albert-László Barabási,et al.  Universal resilience patterns in complex networks , 2016, Nature.

[112]  Ozgur M. Araz,et al.  Simulation modeling for pandemic decision making: A case study with bi-criteria analysis on school closures , 2013, Decis. Support Syst..

[113]  Alexandre Dolgui,et al.  Hybrid Fuzzy-Probabilistic Approach to Supply Chain Resilience Assessment , 2018, IEEE Transactions on Engineering Management.

[114]  Tsan-Ming Choi,et al.  Optimal Bi-Objective Redundancy Allocation for Systems Reliability and Risk Management , 2016, IEEE Transactions on Cybernetics.

[115]  Christoph H. Glock,et al.  Methods for mitigating disruptions in complex supply chain structures: a systematic literature review , 2020, Int. J. Prod. Res..

[116]  Ray Y. Zhong,et al.  A big data approach for logistics trajectory discovery from RFID-enabled production data , 2015 .

[117]  Abhijeet Ghadge,et al.  The Impact of Industry 4.0 Implementation on Supply Chains , 2020, Journal of Manufacturing Technology Management.

[118]  Boris V. Sokolov,et al.  Simulation Vs. Optimization Approaches to Ripple Effect Modelling in the Supply Chain , 2018, LDIC.

[119]  Tsan-Ming Choi,et al.  Big Data Analytics in Operations Management , 2018 .

[120]  Sachchidanand Singh,et al.  Big Data analytics , 2012 .

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

[122]  K. B. Hendricks,et al.  The Effect of Operational Slack, Diversification, and Vertical Relatedness on the Stock Market Reaction to Supply Chain Disruptions , 2009 .

[123]  S. Fawcett,et al.  Data Science, Predictive Analytics, and Big Data: A Revolution that Will Transform Supply Chain Design and Management , 2013 .

[124]  Aruna Apte,et al.  Complexity and Self-Sustainment in Disaster Response Supply Chains , 2016, Decis. Sci..

[125]  D. Ivanov.  Predicting the impacts of epidemic outbreaks on global supply chains: A simulation-based analysis on the coronavirus outbreak (COVID-19/SARS-CoV-2) case , 2020, Transportation Research Part E: Logistics and Transportation Review.

[126]  Claudia Colicchia,et al.  Increasing supply chain resilience in a global sourcing context , 2010 .

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