Login | Register

Resilient Digital Supply Chain Twins Modelling: Simulation-based Analysis on the COVID-19 Pandemic Outbreak


Resilient Digital Supply Chain Twins Modelling: Simulation-based Analysis on the COVID-19 Pandemic Outbreak

Moazzeni, Faridoddin (2021) Resilient Digital Supply Chain Twins Modelling: Simulation-based Analysis on the COVID-19 Pandemic Outbreak. Masters thesis, Concordia University.

[thumbnail of Moazzeni_MASc_S2021.pdf]
Text (application/pdf)
Moazzeni_MASc_S2021.pdf - Accepted Version


Over the past few years, Supply Chains (SC) have expanded rapidly in terms of dimensions and complexity (e.g., globalization, outsourcing, etc.). Besides, numerous practitioners and researchers proposed models mainly focused on minimizing SC’s total cost. Consequently, the potential financial advantages of reduced stock levels and inventory buffers have made SCs more vulnerable to local and global Low-Frequency High-Impact disruption risks which have long-term destructive effects. For instance, the COVID-19 pandemic outbreak has severely disturbed SCs, especially for essential products, by a sharp increase in demand and raw material supply failure. During this challenging situation, the focus should be shifted from cost minimization to SC’s survival, maximizing demand satisfaction, and minimizing delivery time. Consequently, these emerging issues have put forth the need for greater emphasis to develop resilient supply chains.
This study presents a methodological SC simulation modelling framework that enables visualizing the SC and making quick decisions by SC managers in near real-time to ensure resiliency during the disruption. The solution approach is applied as a case study in Luxxeen Co., a Canadian manufacturer of green disposable products, i.e. Toilet Tissues, which is considered an essential product.
First, we develop SC’s structural and behavioral conceptual model by customizing the SCOR reference model. Afterwards, we translate it to Discrete Event Simulation formalist and implement it using the “Arena simulation software” platform. Next, we design three COVID-19 pandemic outbreak disruption scenarios in suppliers, transportation networks, and retailers. Finally, three risk mitigation strategies (i.e., Multiple Sourcing, Changing Inventory Control Policy and Buffering) are suggested to ensure SC resiliency in terms of reliability and responsiveness performance metrics. Moreover, by conducting a comparison analysis using “Process Analyzer” and “Optquest” between these scenarios, the best set of actions are proposed for each disruption scenario.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Mechanical, Industrial and Aerospace Engineering
Item Type:Thesis (Masters)
Authors:Moazzeni, Faridoddin
Institution:Concordia University
Degree Name:M.A. Sc.
Program:Industrial Engineering
Date:20 March 2021
Thesis Supervisor(s):Awasthi, Anjali
Keywords:Supply Chain, Supply Chain Management, Supply Chain Risk Management, Supply Chain Digital Twin, COVID-19 Pandemic Disruption Risk, Discrete Event Simulation, Scenario Analysis, Essential Product Supply Chain, SCOR Model, Case Study
ID Code:988381
Deposited By: Faridoddin Moazzeni
Deposited On:27 Oct 2022 13:51
Last Modified:27 Oct 2022 13:51


1. Bandaly, D., Satir, A., Kahyaoglu, Y., & Shanker, L. (2012). Supply chain risk management I: Conceptualization, framework and planning process. Risk Management, 14(4), 249–271. https://doi.org/10.1057/rm.2012.7
2. Banks, J., Carson, J. S., Nelson, B. L., & Nicol, D. (2010). Discrete-Event System Simulation.
3. Battarra, M., Balcik, B., & Xu, H. (2018). Disaster preparedness using risk-assessment methods from earthquake engineering. European Journal of Operational Research, 269(2), 423–435. https://doi.org/10.1016/j.ejor.2018.02.014
4. Beamon, B. M. (1998). Supply chain design and analysis: Models and methods. International Journal of Production Economics, 55(3), 281–294. https://doi.org/10.1016/S0925-5273(98)00079-6
5. Bekker, J., & Guittet-Remaud, S. (2012). Simulation in Supply Chains: An Arena basis. The South African Journal of Industrial Engineering, 11(2). https://doi.org/10.7166/11-2-360
6. Ben-Haim, Y. (2012). Doing Our Best: Optimization and the Management of Risk. Risk Analysis, 32(8), 1326–1332. https://doi.org/10.1111/j.1539-6924.2012.01818.x
7. Bolstorff, P., & Rosenbaum, R. (2003). Supply chain excellence : a handbook for dramatic improvement using the SCOR model. Undefined.
8. Borshchev, A. (2014). Multi-method modelling: AnyLogic. In Discrete-Event Simulation and System Dynamics for Management Decision Making (Vol. 9781118349, Issue January). https://doi.org/10.1002/9781118762745.ch12
9. Bühler, A., Wallenburg, C. M., & Wieland, A. (2016). Accounting for external turbulence of logistics organizations via performance measurement systems. Supply Chain Management, 21(6), 694–708. https://doi.org/10.1108/SCM-02-2016-0040
10. Büyüközkan, G., & Göçer, F. (2018). Computers in Industry Digital Supply Chain : Literature review and a proposed framework for future research. 97, 157–177.
11. Carvalho, H., Barroso, A. P., Machado, V. H., Azevedo, S., & Cruz-machado, V. (2012). Computers & Industrial Engineering Supply chain redesign for resilience using simulation q. Computers & Industrial Engineering, 62(1), 329–341. https://doi.org/10.1016/j.cie.2011.10.003
12. Cavinato, J. L. (2004). Supply chain logistics risks: From the backroom to the board room. International Journal of Physical Distribution & Logistics Management, 34(5), 383–387. https://doi.org/10.1108/09600030410545427
13. Chattopadhyay, P., Glick, W. H., & Huber, G. P. (2001). Organizational actions in response to threats and opportunities. Academy of Management Journal, 44(5), 937–955.
14. Chellanthara, A. (2013). Evaluating car-sharing fleet management strategies using Discrete Event Simulation.
15. Choi, T. M., Wen, X., Sun, X., & Chung, S. H. (2019). The mean-variance approach for global supply chain risk analysis with air logistics in the blockchain technology era. Transportation Research Part E: Logistics and Transportation Review, 127(March), 178–191. https://doi.org/10.1016/j.tre.2019.05.007
16. Chopra, S., Sodhi. M. S. (2004). Supply-Chain Breakdown.
17. Chopra, S., & Sodhi, M. S. (2014). Reducing the Risk of Supply Chain Disruptions.
18. Christopher, M., & Lee, H. (2004). Mitigating supply chain risk through improved confidence. International Journal of Physical Distribution and Logistics Management, 34(5), 388–396. https://doi.org/10.1108/09600030410545436
19. Christopher, M., & Peck, H. (2004). International Journal of Logistics Management, Vol. 15, No. 2, pp1-13, 2004. 15(2), 1–13.
20. Cimino, A., Longo, F., & Mirabelli, G. (2010). A General Simulation Framework for Supply Chain Modeling: State of the Art and Case Study. 7(2). http://arxiv.org/abs/1004.3271
21. Cochran, L. (1997). Career counselling: A narrative approach. Sage publications.
22. Cohen, M. A., & Kunreuther, H. (2009). Operations Risk Management: Overview of Paul Kleindorfer’s Contributions. Production and Operations Management, 16(5), 525–541. https://doi.org/10.1111/j.1937-5956.2007.tb00278.x
23. CTV News. (2020). Paper towel shortage? Major Canadian manufacturer warns inventory “very tight.” CTV News. https://www.ctvnews.ca/health/coronavirus/is-a-paper-towel-shortage-nigh-major-canadian-manufacturer-warns-inventory-very-tight-1.5113891
24. Dehkhoda, K. (2016). Developing a framework on supply chain risk mapping, prioritization and engagement.
25. Dolgui, A., Ivanov, D., & Rozhkov, M. (2020). Does the ripple effect influence the bullwhip effect? An integrated analysis of structural and operational dynamics in the supply chain†. International Journal of Production Research, 58(5), 1285–1301. https://doi.org/10.1080/00207543.2019.1627438
26. Drath, R., & Horch, A. (2014). Industrie 4.0: Hit or Hype? [Industry Forum]. June, 56–58.
27. Ebrahimi, D. S., David, P., & Alpan, G. (2012). A model-based specification for a decision support tool for supply chain risk management. Proceedings of International Conference on Computers and Industrial Engineering, CIE, 1(July), 251–265.
28. Ellram, L. M., & Cooper, M. C. (2014). Supply chain management: It’s all about the journey, not the destination. Journal of Supply Chain Management, 50(1), 8–20. https://doi.org/10.1111/jscm.12043
29. Fahimnia, B., Jabbarzadeh, A., & Sarkis, J. (2018). Greening versus resilience: A supply chain design perspective. Transportation Research Part E: Logistics and Transportation Review, 119(August 2017), 129–148. https://doi.org/10.1016/j.tre.2018.09.005
30. Fan, Y., & Stevenson, M. (2018). A review of supply chain risk management: definition, theory, and research agenda. International Journal of Physical Distribution and Logistics Management, 48(3), 205–230. https://doi.org/10.1108/IJPDLM-01-2017-0043
31. Fragapane, G., Ivanov, D., Peron, M., Sgarbossa, F., & Strandhagen, J. O. (2020). Increasing flexibility and productivity in Industry 4.0 production networks with autonomous mobile robots and smart intralogistics. Annals of Operations Research. https://doi.org/10.1007/s10479-020-03526-7
32. Gao, S. Y., Simchi-Levi, D., Teo, C. P., & Yan, Z. (2019). Disruption risk mitigation in supply chains: The risk exposure index revisited. Operations Research, 67(3), 831–852. https://doi.org/10.1287/opre.2018.1776
33. Gaudenzi, B., & Borghesi, A. (2006). Managing risks in the supply chain using the AHP method. The International Journal of Logistics Management, 17(1), 114–136. https://doi.org/10.1108/09574090610663464
34. Ghadge, A., Er Kara, M., Moradlou, H., & Goswami, M. (2020). The impact of Industry 4.0 implementation on supply chains. Journal of Manufacturing Technology Management, 31(4), 669–686. https://doi.org/10.1108/JMTM-10-2019-0368
35. Golan, M. S., Jernegan, L. H., & Linkov, I. (2020). Trends and applications of resilience analytics in supply chain modelling: systematic literature review in the context of the COVID-19 pandemic. Environment Systems and Decisions, 40(2), 222–243. https://doi.org/10.1007/s10669-020-09777-w
36. Govindan, K., Fattahi, M., & Keyvanshokooh, E. (2017). Supply chain network design under uncertainty: A comprehensive review and future research directions. European Journal of Operational Research, 263(1), 108–141. https://doi.org/10.1016/j.ejor.2017.04.009
37. Guan, D., Wang, D., Hallegatte, S., Davis, S. J., Huo, J., Li, S., Bai, Y., Lei, T., Xue, Q., Coffman, D. M., Cheng, D., Chen, P., Liang, X., Xu, B., Lu, X., Wang, S., Hubacek, K., & Gong, P. (2020). Global supply-chain effects of COVID-19 control measures. Nature Human Behaviour, 4(6), 577–587. https://doi.org/10.1038/s41562-020-0896-8
38. Gümüş, M., Ray, S., & Gurnani, H. (2012). Supply-side story: Risks, guarantees, competition, and information asymmetry. Management Science, 58(9), 1694–1714. https://doi.org/10.1287/mnsc.1110.1511
39. Haimes, Y. Y., Kaplan, S., & Lambert, J. H. (2002). Risk Filtering, Ranking, and Management Framework Using Hierarchical Holographic Modeling. Risk Analysis, 22(2), 383–397. https://doi.org/10.1111/0272-4332.00020
40. Hanus, M. (2015). Customer order cycle of a production company, its bottlenecks and potential for improvements. July.
41. Harland, C., Brenchley, R., & Walker, H. (2003). Risk in supply networks. Journal of Purchasing and Supply Management, 9(2), 51–62. https://doi.org/10.1016/S1478-4092(03)00004-9
42. Harvard Business Review. (2020). Coronavirus + business. Harvard Business Review.
43. Heath, S. K., Brailsford, S. C., Buss, A., & Macal, C. M. (2011). Cross-paradigm simulation modelling: Challenges and successes. Proceedings - Winter Simulation Conference, 2783–2797. https://doi.org/10.1109/WSC.2011.6147983
44. Ho, W., Zheng, T., Yildiz, H., & Talluri, S. (2015). Supply chain risk management: A literature review. International Journal of Production Research, 53(16), 5031–5069. https://doi.org/10.1080/00207543.2015.1030467
45. Hobbs, J. E. (2020). Food supply chains during the COVID-19 pandemic. Canadian Journal of Agricultural Economics, 68(2), 171–176. https://doi.org/10.1111/cjag.12237
46. Hopp, W. J., Spearman, M. L., & Zhang, R. Q. (1997). Easily implementable inventory control policies. Operations Research, 45(3), 327–340. https://doi.org/10.1287/opre.45.3.327
47. Hunt, D. v. (1996). Process Mapping: How to Reengineer your Business Processes. undefined-undefined. https://www.mendeley.com/catalogue/8340d439-0327-3bd7-ad65-e8d28f6db84d/?utm_source=desktop&utm_medium=1.19.8&utm_campaign=open_catalog&userDocumentId=%7Bb46d99ec-8d83-38ec-9df1-96a6c8bde5f2%7D
48. Ivanov, D. (2017). Simulation-based ripple effect modelling in the supply chain. International Journal of Production Research, 7543, 0. https://doi.org/10.1080/00207543.2016.1275873
49. Ivanov, D. (2018). Revealing interfaces of supply chain resilience and sustainability: a simulation study. International Journal of Production Research, 56(10), 3507–3523. https://doi.org/10.1080/00207543.2017.1343507
50. Ivanov, D. (2020a). Predicting the impacts of epidemic outbreaks on global supply chains: A simulation-based analysis on the coronavirus outbreak (COVID-19/SARS-CoV-2) case. Transportation Research Part E: Logistics and Transportation Review, 136, 101922.
51. Ivanov, D. (2020b). Viable supply chain model: integrating agility, resilience and sustainability perspectives—lessons from and thinking beyond the COVID-19 pandemic. Annals of Operations Research. https://doi.org/10.1007/s10479-020-03640-6
52. Ivanov, D., & Das, A. (2020). Coronavirus (COVID-19/SARS-CoV-2) and supply chain resilience: a research note. International Journal of Integrated Supply Management, 13(1), 90. https://doi.org/10.1504/ijism.2020.107780
53. Ivanov, D., & Dolgui, A. (2020a). The Management of Operations A digital supply chain twin for managing the disruption risks and resilience in the era of Industry 4. 0. Production Planning & Control, 0(0), 1–14. https://doi.org/10.1080/09537287.2020.1768450
54. Ivanov, D., & Dolgui, A. (2020b). Viability of intertwined supply networks: extending the supply chain resilience angles towards survivability. A position paper motivated by the COVID-19 outbreak. International Journal of Production Research, 58(10), 1–12. https://doi.org/10.1080/00207543.2020.1750727
55. Ivanov, D., Dolgui, A., Das, A., & Sokolov, B. (2019). Handbook of Ripple Effects in the Supply Chain (Vol. 276). Springer International Publishing. https://doi.org/10.1007/978-3-030-14302-2
56. Ivanov, D., Dolgui, A., & Sokolov, B. (2019). The impact of digital technology and Industry 4.0 on the ripple effect and supply chain risk analytics. International Journal of Production Research, 57(3), 829–846. https://doi.org/10.1080/00207543.2018.1488086
57. Ivanov, D., Dolgui, A., Sokolov, B., & Ivanova, M. (2017). Literature review on disruption recovery in the supply chain*. International Journal of Production Research, 55(20), 6158–6174. https://doi.org/10.1080/00207543.2017.1330572
58. Jahangirian, M., Eldabi, T., Naseer, A., Stergioulas, L. K., & Young, T. (2010). Simulation in manufacturing and business: A review. European Journal of Operational Research, 203(1), 1–13. https://doi.org/10.1016/j.ejor.2009.06.004
59. Jbara, N. ben. (2018). Risk management in supply chains : a simulation and model-driven engineering approach. https://tel.archives-ouvertes.fr/tel-01730805
60. Jüttner, U., Peck, H., & Christopher, M. (2003). Supply chain risk management: outlining an agenda for future research. International Journal of Logistics Research and Applications, 6(4), 197–210. https://doi.org/10.1080/13675560310001627016
61. Karl, A. A., Micheluzzi, J., Leite, L. R., & Pereira, C. R. (2018). Supply chain resilience and key performance indicators: A systematic literature review. Producao, 28. https://doi.org/10.1590/0103-6513.20180020
62. Kelton, W. D. (2002). Simulation with ARENA. McGraw-hill.
63. Kenné, J. P., Dejax, P., & Gharbi, A. (2012). Production planning of a hybrid manufacturing system under uncertainty within a closed-loop supply chain. International Journal of Production Economics, 135(1), 81–93. https://doi.org/10.1016/j.ijpe.2010.10.026
64. Kinra, A., Ivanov, D., Das, A., & Dolgui, A. (2019). Ripple effect quantification by supplier risk exposure assessment. International Journal of Production Research, 0(0), 1–20. https://doi.org/10.1080/00207543.2019.1675919
65. Kleindorfer, P. R., & Saad, G. H. (2005). Managing disruption risks in supply chains. Production and Operations Management, 14(1), 53–68. https://doi.org/10.1111/j.1937-5956.2005.tb00009.x
66. Kliment, M., Popovič, R., & Janek, J. (2014). Analysis of the production process in the selected company and proposal a possible model optimization through PLM software module Tecnomatix Plant Simulation. Procedia Engineering, 96, 221–226. https://doi.org/10.1016/j.proeng.2014.12.147
67. Knemeyer, A. M., Zinn, W., & Eroglu, C. (2009). Proactive planning for catastrophic events in supply chains. Journal of Operations Management, 27(2), 141–153. https://doi.org/10.1016/j.jom.2008.06.002
68. Kr Sarmah, H., Sarmah, H. K., Bora Hazarika, B., & Choudhury, G. (2013). An investigation on the effect of bias on the determination of sample size on the basis of data related to the students of the school of Guwahati. In researchgate.net. https://www.researchgate.net/publication/303014899
69. Larue, B. (2020). Labour issues and COVID-19. Canadian Journal of Agricultural Economics, 68(2), 231–237. https://doi.org/10.1111/cjag.12233
70. Lavastre, O., Gunasekaran, A., & Spalanzani, A. (2012). Supply chain risk management in French companies. Decision Support Systems, 52(4), 828–838. https://doi.org/10.1016/j.dss.2011.11.017
71. Law, A. M., Kelton, W. D., & Kelton, W. D. (2000). Simulation modelling and analysis (Vol. 3). McGraw-Hill New York.
72. Liao, Y., Deschamps, F., Loures, E. de F. R., & Ramos, L. F. P. (2017). Past, present and future of Industry 4.0 - a systematic literature review and research agenda proposal. International Journal of Production Research, 55(12), 3609–3629. https://doi.org/10.1080/00207543.2017.1308576
73. Long, Q. (2014). Distributed supply chain network modelling and simulation: Integration of agent-based distributed simulation and improved SCOR model. International Journal of Production Research, 52(23), 6899–6917. https://doi.org/10.1080/00207543.2014.910623
74. Luxxeen Production. (2021). http://luxxeen.com/
75. Manuj, I., & Mentzer, J. T. (2008). Global supply chain risk management strategies. International Journal of Physical Distribution and Logistics Management, 38(3), 192–223. https://doi.org/10.1108/09600030810866986
76. Marhavilas, P. K., Koulouriotis, D., & Gemeni, V. (2011). Risk analysis and assessment methodologies in the work sites: On a review, classification and comparative study of the scientific literature of the period 2000-2009. In Journal of Loss Prevention in the Process Industries (Vol. 24, Issue 5, pp. 477–523). Elsevier. https://doi.org/10.1016/j.jlp.2011.03.004
77. Marmolejo, J. A., & Hurtado, M. (2020). Digital Twins in Supply Chain Management : A Brief Literature Review. January. https://doi.org/10.1007/978-3-030-33585-4
78. Millet, P. A., Schmitt, P., & Botta-Genoulaz, V. (2009). The SCOR model for the alignment of business processes and information systems. Enterprise Information Systems, 3(4), 393–407. https://doi.org/10.1080/17517570903030833
79. Mishra, D., Sharma, R. R. K., Kumar, S., & Dubey, R. (2016). Bridging and buffering: Strategies for mitigating supply risk and improving supply chain performance. International Journal of Production Economics, 180, 183–197. https://doi.org/10.1016/j.ijpe.2016.08.005
80. Mollenkopf, D. A., Ozanne, L. K., & Stolze, H. J. (2020). A transformative supply chain response to COVID-19. Journal of Service Management. https://doi.org/10.1108/JOSM-05-2020-0143
81. Mrabet, W. el. (2012). Analysis of Supply Chain Models in a System of Systems Context.
82. Naing, N. N. (2003). Determination of sample size. Malaysian Journal of Medical Sciences, 10(2), 84–86.
83. Namdar, J., Li, X., Sawhney, R., & Pradhan, N. (2018). Supply chain resilience for single and multiple sourcing in the presence of disruption risks. International Journal of Production Research, 56(6), 2339–2360. https://doi.org/10.1080/00207543.2017.1370149
84. Norrman, A., & Jansson, U. (2004). Ericsson’s proactive supply chain risk management approach after a serious sub-supplier accident. International Journal of Physical Distribution and Logistics Management, 34(5), 434–456. https://doi.org/10.1108/09600030410545463
85. Oehmen, J., Ziegenbein, A., Alard, R., & Schönsleben, P. (2009). System-oriented supply chain risk management. Production Planning & Control, 20(4), 343–361. https://doi.org/10.1080/09537280902843789
86. Oliver, R. K., & Webber, M. D. (1982). Supply-chain management: logistics catches up with a strategy. Outlook, 5(1), 42–47.
87. Palma-Mendoza, J. A. (2014). Analytical hierarchy process and SCOR model to support supply chain re-design. International Journal of Information Management, 34(5), 634–638. https://doi.org/10.1016/j.ijinfomgt.2014.06.002
88. Paul, S. K., & Chowdhury, P. (2020a). A production recovery plan in manufacturing supply chains for a high-demand item during COVID-19. International Journal of Physical Distribution and Logistics Management. https://doi.org/10.1108/IJPDLM-04-2020-0127
89. Paul, S. K., & Chowdhury, P. (2020b). Strategies for Managing the Impacts of Disruptions During COVID-19: an Example of Toilet Paper. Global Journal of Flexible Systems Management, 21(3), 283–293. https://doi.org/10.1007/s40171-020-00248-4
90. Pettit, T. J., Croxton, K. L., & Fiksel, J. (2013). Ensuring supply chain resilience: Development and implementation of an assessment tool. Journal of Business Logistics, 34(1), 46–76. https://doi.org/10.1111/jbl.12009
91. Poluha, R. (2007). Application of the SCOR Model in Supply Chain Management. Cambria Press.
92. Popovic, V. M., Vasic, B. M., Rakicevic, B. B., & Vorotovic, G. S. (2012). Optimization of maintenance concept choice using risk-decision factor – a case study. International Journal of Systems Science, 43(10), 1913–1926. https://doi.org/10.1080/00207721.2011.563868
93. Quick reference guide. (1999). Nursing Standard, 13(42), 29–29. https://doi.org/10.7748/ns.13.42.29.s50
94. Rizou, M., Galanakis, I. M., Aldawoud, T. M. S., & Galanakis, C. M. (2020). Safety of foods, food supply chain and environment within the COVID-19 pandemic. Trends in Food Science and Technology, 102(June), 293–299. https://doi.org/10.1016/j.tifs.2020.06.008
95. Rogers, R. L., Broeckmann, B., & Maddison, N. (2000). Risk assessment standard for equipment for use in potentially explosive atmospheres: The RASE project. Institution of Chemical Engineers Symposium Series, 147, 337–350.
96. Sachdeva, A., Sharma, V., Arvind Bhardwaj, D., Kayis, B., & Dana Karningsih, P. (2012). SCRIS: A knowledge-based system tool for assisting manufacturing organizations in identifying supply chain risks. Journal of Manufacturing Technology Management, 23(7), 834–852. https://doi.org/10.1108/17410381211267682
97. Salman, F. S., & Yücel, E. (2015). Emergency facility location under random network damage: Insights from the Istanbul case. Computers and Operations Research, 62, 266–281. https://doi.org/10.1016/j.cor.2014.07.015
98. SCC, S. C. C. (2010). Supply chain operations reference model SCOR version 10.0. The Supply Chain Council, Inc. SCOR: The Supply Chain Reference ISBN 0-615-20259-4 (Binder).
99. Schmidt, J. W., & Taylor, R. E. (1970). Simulation and analysis of industrial systems. RD Irwin.
100. Schoenherr, T., Rao Tummala, V. M., & Harrison, T. P. (2008). Assessing supply chain risks with the analytic hierarchy process: Providing decision support for the offshoring decision by a US manufacturing company. Journal of Purchasing and Supply Management, 14(2), 100–111. https://doi.org/10.1016/j.pursup.2008.01.008
101. Spekman, R. E., & Davis, E. W. (2004). Risky business: Expanding the discussion on risk and the extended enterprise. International Journal of Physical Distribution and Logistics Management, 34(5), 414–433. https://doi.org/10.1108/09600030410545454
102. Spiegler, V. L. M., Naim, M. M., & Wikner, J. (2012). A control engineering approach to the assessment of supply chain resilience. International Journal of Production Research, 50(21), 6162–6187. https://doi.org/10.1080/00207543.2012.710764
103. Srai, J. S., Settanni, E., Tsolakis, N., & Aulakh, P. K. (2019). Supply Chain Digital Twins : Opportunities and Challenges Beyond the Hype. September 2019, 26–27.
104. Strozzi, F., Colicchia, C., Creazza, A., & Noè, C. (2017). Literature review on the ‘smart factory’ concept using bibliometric tools. International Journal of Production Research, 55(22), 1–20. https://doi.org/10.1080/00207543.2017.1326643
105. Tang, C. S., & Veelenturf, L. P. (2019). The strategic role of logistics in the industry 4.0 era. Transportation Research Part E: Logistics and Transportation Review, 129(July), 1–11. https://doi.org/10.1016/j.tre.2019.06.004
106. Thilakarathna, R. H., Dharmawardana, M. N., & Rupasinghe, T. (2015). The Supply Chain Operations Reference (SCOR) Model: A Systematic Review of Literature from the Apparel Industry. SSRN Electronic Journal, January. https://doi.org/10.2139/ssrn.2699886
107. Thomas Willemain, Ph. D. (2019). Top 3 Most Common Inventory Control Policies. https://smartcorp.com/b-policy/inventory-control-policies-software/
108. Trkman, P., & McCormack, K. (2009). Supply chain risk in a turbulent environments-A conceptual model for managing supply chain network risk. International Journal of Production Economics, 119(2), 247–258. https://doi.org/10.1016/j.ijpe.2009.03.002
109. Tsai, M. C., Liao, C. H., & Han, C. S. (2008). Risk perception on logistics outsourcing of retail chains: Model development and empirical verification in Taiwan. Supply Chain Management, 13(6), 415–424. https://doi.org/10.1108/13598540810905679
110. Tuncel, G. (2010). Computers in Industry Risk assessment and management for supply chain networks : A case study. 61, 250–259. https://doi.org/10.1016/j.compind.2009.09.008
111. Uhlemann, T. H. J., Schock, C., Lehmann, C., Freiberger, S., & Steinhilper, R. (2017). The Digital Twin: Demonstrating the Potential of Real-Time Data Acquisition in Production Systems. Procedia Manufacturing, 9, 113–120. https://doi.org/10.1016/j.promfg.2017.04.043
112. Vaidya, S., Ambad, P., & Bhosle, S. (2018). Industry 4.0 - A Glimpse. Procedia Manufacturing, 20, 233–238. https://doi.org/10.1016/j.promfg.2018.02.034
113. Wagner, S. M., & Neshat, N. (2010). Assessing the vulnerability of supply chains using graph theory. International Journal of Production Economics, 126(1), 121–129. https://doi.org/10.1016/j.ijpe.2009.10.007
114. Windelberg, M. (2016). Objectives for managing cyber supply chain risk. International Journal of Critical Infrastructure Protection, 12, 4–11. https://doi.org/10.1016/j.ijcip.2015.11.003
115. Worldometers. (2021). COVID-19 Coronavirus Pandemic. https://www.worldometers.info/coronavirus/
116. Wu, T., Blackhurst, J., & O’Grady, P. (2007). Methodology for supply chain disruption analysis. International Journal of Production Research, 45(7), 1665–1682. https://doi.org/10.1080/00207540500362138
117. Wu, Y. (2006). Robust optimization applied to uncertain production loading problems with import quota limits under the global supply chain management environment. International Journal of Production Research, 44(5), 849–882. https://doi.org/10.1080/00207540500285040
118. Xu, M., Wang, X., & Zhao, L. (2014). Predicted supply chain resilience based on structural evolution against random supply disruptions. 2674. https://doi.org/10.1080/23302674.2014.934748
119. Zsidisin, G. A., Ellram, L. M., Carter, J. R., & Cavinato, J. L. (2004). An analysis of supply risk assessment techniques. International Journal of Physical Distribution and Logistics Management, 34(5), 397–413. https://doi.org/10.1108/09600030410545445
All items in Spectrum are protected by copyright, with all rights reserved. The use of items is governed by Spectrum's terms of access.

Repository Staff Only: item control page

Downloads per month over past year

Research related to the current document (at the CORE website)
- Research related to the current document (at the CORE website)
Back to top Back to top