Occurring disruption in the supply chain and its recovery: Review of literature

Document Type : Research Paper

Authors

1 Department of Industrial Management, Faculty of Management and Economic, Science and Research Branch, Islamic Azad University, Tehran, Iran

2 Department of Industrial Engineering, Faculty of Industrial Engineering, Firoozkooh Branch, Islamic Azad University, Firoozkooh, Iran

Abstract

In this research, the concepts, theories and models on supply chain disruption and its recovery have been investigated. The purpose of this paper is to collect theoretical and applied research for researchers who will tend to research in the future in the field of supply chain disruption and its recovery. After reviewing theoretical studies, the applied research has been studied and the methods and techniques used in these papers have also been examined and its conclusions presented in a separate table comparatively in case.

Keywords

[1] N.A.Z. Abidin and B. Ingirige, Identification of the ”Pathogenic” effects of disruptions to supply chain resilience in construction, Proc. Eng. 212 (2018), 467–474.
[2] A. Azar, M. Shahbazi, H.R. Yazdani and O. Mahmoudian, Designing a resilience assessment model of the electricity industry supply chain using the theme analysis approach, Indust. Manag. J. 11 (2019), no. 1, 45–62.
[3] G. Behzadi, M.J. O’Sullivan, T.L. Olsen, F. Scrimgeour and A. Zhang, Robust and resilient strategies for managing supply disruptions in an agribusiness supply chain, Int. J. Product. Econ. 191 (2017), 207–220.
[4] I. Biswas, B. Avittathur and A.K. Chatterjee, Impact of structure, market share and information asymmetry on supply contracts for a single supplier multiple buyer network, European J. Oper. Res. 253 (2016), no. 3, 593–601.
[5] J.V. Blackhurst, K.P. Scheibe and D.J. Johnson, Supplier risk assessment and monitoring for the automotive industry, Int. J. Phys. Distribut. Logistics Manag. 38 (2008), no. 2, 143–165.
[6] C. Bode and S.M. Wagner, Structural drivers of upstream supply chain complexity and the frequency of supply chain disruptions, J. Oper. Manag. 36 (2015), 215–228.
[7] U. Buscher and A. Wels, Supply chain risk assessment with the functional quantification of lead time deviation, Int. J. Integrated Supply Manag. 5 (2010), no. 3, 197–213.
[8] Y. Canbolat, G. Gupta, S. Matera and K. Chelst, Analysing risk in sourcing design and manufacture of components and sub-systems to emerging markets, Int. J. Product. Res. 46 (2008), no. 18, 5145–5164.
[9] C. Cao, C. Li, Q. Yang, Y. Liu and T. Qu, A novel multi-objective programming model of relief distribution for sustainable disaster supply chain in large-scale natural disasters, J. Cleaner Product. 174 (2018), 1422–1435.
[10] V.M. Carvalho, M. Nirei, Y.U. Saito and A. Tahbaz-Salehi, Supply chain disruptions: Evidence from the great east Japan earthquake, Quart. J. Econ. 136 (2016), no. 2, 1255–1321.
[11] F. Chen, Z. Drezner, J.K. Ryan and D. Simchi-Levi, Quantifying the bullwhip effect in a simple supply chain: The impact of forecasting, lead times, and information, Manag. Sci. 46 (2000), no. 3, 436–443.
[12] D. Clarke, C. Murphy and I. Lorenzoni, Place attachment, disruption and transformative adaptation, J. Envir. Psycho. 55 (2017), 81–89.
[13] S. Dani and A. Deep, Fragile food supply chains: Reacting to risks, Int. J. Logistics: Res. Appl. 13 (2010), no. 5, 395–410.
[14] J.K. Deane, C.W. Craighead and C.T. Ragsdale, Mitigating environmental and density risk in global sourcing, Int. J. Phys. Distribut. Logistics Manag. 39 (2009), no. 10, 861–883.
[15] L. Dong, W. Shu, X. Li, Z. Zhou, F. Gong and X. Liu, Quantitative evaluation and case study of risk degree for underground goafs with multiple indexes considering uncertain factors in mines, Geofluids 2017 (2017).
[16] M.N. Faisal, D. Banwet and R. Shankar, Information risks management in supply chains: an assessment and mitigation framework, J. Enterprise Info. Manag. 20 (2007), no. 6, 677–699.
[17] E. Fernandez, V. Bogado, E. Salomone and O. Chiotti, Framework for modelling and simulating the supply process monitoring to detect and predict disruptive events, Comput. Ind. 80 (2016), 30–42.
[18] M. Ghiyasi, S. Naderi, Z. Ameri and G. Ghesmati Tabrizi, Cost efficiency analysis of network DEA models: the case of Mashhad hospitals, Int. J. Hospital Res. 8 (2019), no. 2.
[19] L. Glendon, Supply chain resilience 2011, 3rd Ann. Survey Bus. Continuity Institute (BCI), Caversham, UK, 2011.
[20] A. Guarnaschelli, O. Chiotti and H.E. Salomone, An approach based on constraint satisfaction problems to disruptive event management in supply chains, Int. J. Product. Econ. 144 (2013), no. 1, 223–242.
[21] A.A. Hasani, Resilience cloud-based global supply chain network design under uncertainty: Resource-based approach, Comput. Indust. Eng. 158 (2021), 107382.
[22] B. He, H. Huang and K. Yuan, The comparison of two procurement strategies in the presence of supply disruption, Comput. Indust. Eng. 85 (2015), 296–305.
[23] Y. Higuchi, T. Inui, T. Hosoi, I. Takabe and A. Kawakami, The impact of the great east Japan earthquake on the labor market—need to resolve the employment mismatch in the disaster-stricken areas, Japan Labor Rev. 9 (2012), no. 4, 4–21.
[24] H. Hishamuddin, R. Sarker and U.N.S.W. Sydney, A disruption recovery model for a single stage production-inventory system, Eur. J. Oper. Res. 222 (2012), no. 3, 464–473.
[25] S.M. Hosseini, D. Ivanov and A. Dolgui, Review of quantitative methods for supply chain resilience analysis, Transport. Res. Part E: Logistics Transport. Rev. 125 (2019), 285–307.
[26] H.-Y. Huang, Y.-C. Chou and S. Chang, A dynamic system model for proactive control of dynamic events in full-load states of manufacturing chains, Int. J. Product. Res. 47 (2009), no. 9, 2485–2506.
[27] H.G. Huntington, Measuring oil supply disruptions: A historical perspective, Energy Policy 115 (2018), 426–433.
[28] D. Ivanov, A. Dolgui, B. Sokolov and M. Ivanova, Optimal control representation of the mathematical programming model for supply chain dynamic reconfiguration, IFAC-PapersOnLine 50 (2017), no. 1, 4994–4999.
[29] D. Ivanov and B. Sokolov, Adaptive Supply Chain Management, Springer, 2010.
[30] M. Jannessari, M. Karbasian, O. Yousefi and B. Khayambashi, Identifying and prioritizing supply chain disruptions using PROMETHEEIII and Fuzzy ANP combined method in the steel alloy plant of Isfahan, Second Nat. Conf. Ind. Engin. Sustain. Manag., 2014.
[31] Z. Jiao, L. Ran, Y. Zhang, Z. Li and W. Zhang, Data-driven approaches to integrated closed-loop sustainable supply chain design under multi-uncertainties, J. Cleaner Product. 185 (2018), 105–127.
[32] M. Kamalahmadi and M. Mellatparast, An assessment of supply chain disruption mitigation strategies, Int. J. Product. Econ. 184 (2017), 210–230.
[33] O. Kırılmaz and S. Erol, A proactive approach to supply chain risk management: Shifting orders among suppliers to mitigate the supply side risks, J. Purch. Supply Manag. 23 (2017), no. 1, 54–65.
[34] A.M. Knemeyer, W. Zinn and C. Eroglu, Proactive planning for catastrophic events in supply chains, J. Oper. Manag. 27 (2009), no. 2, 141–153.
[35] A. Kondo, The effects of supply chain disruptions caused by the great east Japan earthquake on workers, Japan World Econ. 47 (2018), 40–50.
[36] T.J. Kull and S. Talluri, A supply risk reduction model using integrated multicriteria decision making, IEEE Trans. Engin. Manag. 55 (2008), no. 3, 409–419.
[37] M. Kumar, P. Basu and B. Avittathur, Pricing and sourcing strategies for competing retailers in supply chains under disruption risk, Eur. J. Oper. Res. 265 (2018), no. 2, 533–543.
[38] S.K. Kumar, M. Tiwari and R.F. Babiceanu, Minimisation of supply chain cost with embedded risk using computational intelligence approaches, Int. J. Product. Res. 48 (2010), no. 13, 3717–3739.
[39] R.R. Levary, Using the analytic hierarchy process to rank foreign suppliers based on supply risks, Comput. Indust. Eng. 55 (2008), no. 2, 535–542.
[40] C. Li, X. Qi and D. Song, Real-time schedule recovery in liner shipping service with regular uncertainties and disruption events, Transport. Res. Part B: Methodol. 93 (2016), 762–788.
[41] H.S. Loh and V. Van Thai, Cost consequences of a port-related supply chain disruption, Asian J. Shipp. Logistics 31 (2015), no. 3, 319–340.
[42] H.S. Loh, Q. Zhou, V. Van Thai, Y.D. Wong and K.F. Yuen, Fuzzy comprehensive evaluation of port-centric supply chain disruption threats, Ocean Coastal Manag. 148 (2017), 53–62.
[43] I. Manuj and J.T. Mentzer, Global supply chain risk management strategies, Int. J. Phys. Distribut. Logistics Manag. 38 (2008), no. 3, 192223.
[44] S. Matook, R. Lasch and R. Tamaschke, Supplier development with benchmarking as part of a comprehensive supplier risk management framework, Int. J. Operat. Product. Manag. 29 (2009), no. 3, 241–267.
[45] S.A. Melnyk, A. Rodrigues and G.L. Ragatz, Using Simulation to Investigate Supply Chain Disruptions, Supply chain risk: A handbook of assessment, management, and performance, 2009.
[46] F. Mohammaddust, S. Rezapour, R. Zanjirani Farahani, M. Mofidfar and A. Hill, Developing lean and responsive supply chains: A robust model for alternative risk mitigation strategies in supply chain designs, Int. J. Product. Econ. 183 (2017), Part C, 632–653.
[47] N. Mori, T. Takahashi, T. Yasuda and H. Yanagisawa, Survey of 2011 Tohoku earthquake tsunami inundation and run-up, Geophys. Res. Lett. 38 (2016), no. 7.
[48] M. Mousavi, G. Jamali and A. Ghorbanpour, A green-resilient supply chain network optimization model in cement industries, Indust. Manag. J. 13 (2021), no. 2, 222–245.
[49] J. Oehmen, A. Ziegenbein, R. Alard and P. Schonsleben, System-oriented supply chain risk management, Prod. Plann. Control 20 (2009), no. 4, 343–361.
[50] A. Oke and M. Gopalakrishnan, Managing disruptions in supply chains: a case study of a retail supply chain, Int. J. Product. Econ. 118 (2009), no. 1, 168–174.
[51] A. Parajuli, O. Kuzgunkaya and N. Vidyarthi, Responsive contingency planning of capacitated supply networks under disruption risks, Transport. Res. Part E: Logistics Transport. Rev. 102 (2017), 13–37.
[52] M. Pariazar and M.Y. Sir, A multi-objective approach for supply chain design considering disruptions impacting supply availability and quality, Comput. Industr. Eng. 121 (2018), 113–130.
[53] Y.B. Park and H.S. Kim, Simulation-based evolutionary algorithm approach for deriving the operational planning of global supply chains from the systematic risk management, Comput. Ind. 83 (2016), 68–77.
[54] K. Park, H. Min and S. Min, Inter-relationship among risk taking propensity, supply chain security practices, and supply chain disruption occurrence, J. Purch. Supply Manag. 22 (2016), no. 2, 120–130.
[55] D. Parmar, T. Wu, T. Callarman, J. Fowler and P. Wolfe, A clustering algorithm for supplier base management, Int. J. Product. Res. 48 (2010), no. 13, p. 38033821.
[56] S.K. Paul, R. Sarker and D. Essam, Real time disruption management for a two-stage batch production–inventory system with reliability considerations, Eur. J. Oper. Res. 237 (2014), no. 1, 113–128.
[57] S.K. Paul, R. Sarker and D. Essam, A quantitative model for disruption mitigation in a supply chain, Eur. J. Oper. Res. 257 (2017), no. 3, 881–895.
[58] R. Pellegrino, N. Costantino and D. Tauro, Supply chain finance: A supply chain-oriented perspective to mitigate commodity risk and pricing volatility, J. Purch. Supply Manag. 25 (2018), no. 2.
[59] T.J. Pettit, K.L. Croxton and J. Fiksel, The evolution of resilience in supply chain management: A retrospective on ensuring supply chain resilience, J. Bus. Logistics 40 (2019), no. 1, 56-65.
[60] D. Pyke and C.S. Tang, How to mitigate product safety risks proactively? Process, challenges and opportunities, Int. J. Logistics: Res. Appl. 13 (2010), no. 4, 243–256.
[61] M. Rabbani, S. Elahi, H. Rafiei and A. Farshbaf Geranmayeh, A genetic algorithm approach for a dynamic cell formation problem considering machine breakdown and buffer, Journal Qual. Engin. Prod. Optim. 1 (2015), no. 2, 1–18.
[62] P. Rajagopal, R. Kurniawan, S.H. Zailani and M. Iranmanesh, The effects of vulnerability mitigation strategies on supply chain effectiveness: risk culture as moderator, Supply Chain Manag.: Int. J. 22 (2017), no. 1, 1–15.
[63] S. Ratick, B. Meacham and Y. Aoyama, Locating backup facilities to enhance supply chain disaster resilience, Growth Change 39 (2008), no. 4, 642–666.
[64] A.R. Ravindran, R. Ufuk Bilsel, V. Wadhwa and T. Yang, Risk adjusted multicriteria supplier selection models with applications, Int. J. Product. Res. 48 (2010), no. 2, 405–424.
[65] A.V. Roth, A.A. Tsay, M.E. Pullman and J.V. Gray, Unraveling the food supply chain: strategic insights from China and the 2007 recalls, J. Supply Chain Manag. 44 (2008), no. 1, 22–39.
[66] V. Sanchez-Rodrigues, A. Potter and M.M. Naim, The impact of logistics uncertainty on sustainable transport operations, Int. J. Phy. Distribut. Logistics Manag. 40 (2010), no. 1/2, 61–83.
[67] T. Sawik, Disruption mitigation and recovery in supply chains using portfolio approach, Omega 84 (2019), 232–248.
[68] T. Schoenherr, V. Rao Tummala and T.P. Harrison, Assessing supply chain risks with the analytic hierarchy process: Providing decision support for the offshoring decision, J. Purchas. Supply Manag. 14 (2008), no. 2, 100–111.
[69] J.-B. Sheu and C. Pan, A method for designing centralized emergency supply network to respond to large-scale natural disasters, Transport. Res. Part B: Methodol. 67 (2014), 284–305.
[70] M. Sodhi and S. Lee, An analysis of sources of risk in the consumer electronics industry, J. Oper. Res. Soc. 58 (2007), no. 11, 1430–1439.
[71] K.E. Stecke and S. Kumar, Sources of supply chain disruptions, factors that breed vulnerability, and mitigating strategies, J. Market. Chan. 16 (2009), no. 3, 193–226.
[72] C.S. Tang, Robust strategies for mitigating supply chain disruptions, Int. J. Logistics: Res. Appl. 9 (2006), no. 1, 33–45.
[73] C. Tang and B. Tomlin, The power of flexibility for mitigating supply chain risks, Int. J. Product. Econ. 116 (2008), no. 1, 12–27.
[74] J. Tokui, K. Kawasaki and T. Miyagawa, The economic impact of supply chain disruptions from the Great East-Japan earthquake, Japan World Econ. 41 (2017), 59–70.
[75] S. Tomasiello and Z. Alijani, Fuzzy-based approaches for agri-food supply chains: A mini-review, Soft Comput. 25 (2021), 7479–7492.
[76] A. Wieland and C.F. Durah, Two perspectives on supply chain resilience, J. Bus. Logistics 42 (2021), no. 3, 315–322.
[77] D. Wu and D.L. Olson, Supply chain risk, simulation, and vendor selection, Int. J. Product. Econ. 114 (2008), no. 2, 646–655.
[78] Y.-C. Yang, Impact of the container security initiative on Taiwan’s shipping industry, Maritime Policy Manag. 37 (2010), no. 7, 699–722.
[79] S.H. Zegordi and H. Davarzani, Developing a supply chain disruption analysis model application of colored Petri-Nets, Expert Syst. Appl. 39 (2012), no. 2, 2102–2111.
Volume 15, Issue 7
July 2024
Pages 51-67
  • Receive Date: 08 November 2022
  • Revise Date: 24 April 2023
  • Accept Date: 29 April 2023