Please wait a minute...
Shopping Cart Email List Chinese
Advanced Search Fig/Tab
Frontiers of Engineering Management

ISSN 2095-7513

ISSN 2096-0255(Online)

CN 10-1205/N

Postal Subscription Code 80-905

Featured Articles  |  Most Downloaded  |  Most Reading
Online Submission Subscribe to Our RSS Content Feed
Front. Eng    2022, Vol. 9 Issue (1) : 56-70    https://doi.org/10.1007/s42524-021-0186-9
REVIEW ARTICLE
Digital twin-driven smart supply chain
Lu WANG1, Tianhu DENG1(), Zuo-Jun Max SHEN2, Hao HU3, Yongzhi QI3
1. Department of Industrial Engineering, Tsinghua University, Beijing 100084, China
2. Faculty of Engineering and Faculty of Business and Economics, University of Hong Kong, Hong Kong 999077, China; Department of Industrial Engineering and Operations Research and Department of Civil and Environmental Engineering, University of California, Berkeley, Berkeley, CA 94720, USA
3. JD.COM, Beijing 100101, China
 Download: PDF(538 KB)   HTML
 Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks
Abstract

Today’s supply chain is becoming complex and fragile. Hence, supply chain managers need to create and unlock the value of the smart supply chain. A smart supply chain requires connectivity, visibility, and agility, and it needs be integrated and intelligent. The digital twin (DT) concept satisfies these requirements. Therefore, we propose creating a DT-driven supply chain (DTSC) as an innovative and integrated solution for the smart supply chain. We provide background information to explain the DT concept and to demonstrate the method for building a DTSC by using the DT concept. We discuss three research opportunities in building a DTSC, including supply chain modeling, real-time supply chain optimization, and data usage in supply chain collaboration. Finally, we highlight a motivating case from JD.COM, China’s largest retailer by revenue, in applying the DTSC platform to address supply chain network reconfiguration challenges during the COVID-19 pandemic.
Keywords digital twin      supply chain management     
Corresponding Author(s): Tianhu DENG   
Just Accepted Date: 29 December 2021   Online First Date: 19 January 2022    Issue Date: 14 February 2022
 Cite this article:   
Lu WANG,Tianhu DENG,Zuo-Jun Max SHEN, et al. Digital twin-driven smart supply chain[J]. Front. Eng, 2022, 9(1): 56-70.
 URL:  
https://academic.hep.com.cn/fem/EN/10.1007/s42524-021-0186-9
https://academic.hep.com.cn/fem/EN/Y2022/V9/I1/56
Year Event
2002 Conceptual ideal for product lifecycle management (PLM) (a presentation by Dr. Grieves)
2005 Mirrored Spaces Model (Grieves, 2005)
2006 Information Mirroring Model (Grieves, 2006)
2011 Digital twin (Grieves, 2011)
2012 A DT is an integrated multiphysics, multiscale, probabilistic simulation of an as-built vehicle or system that uses the best available physical models, sensor updates, and fleet history to mirror the life of its corresponding flying twin. The DT is ultra-realistic and may consider one or more important and interdependent vehicle systems, including airframe, propulsion and energy storage, life support, avionics, and thermal protection. The concept is proposed by NASA and the US Air Force (Glaessgen and Stargel, 2012; Shafto et al., 2012)
2015 A virtual representation of what has been produced (Grieves, 2015)
2016 A set of virtual information constructs (Grieves and Vickers, 2017) (first online in 2016)
2016 The linked collection of the relevant digital artifacts including engineering data, operation data, and behavior descriptions via several simulation models (Boschert and Rosen, 2016)
2019 A DT is a digital representation of an active unique product (real device, object, machine, service, or intangible asset) or unique product–service system (a system consisting of a product and a related service) that is composed of selected characteristics, properties, conditions, and behaviors by means of models, information, and data within a single or even across multiple life cycle phases. The concept is proposed by the International Academy for Production Engineering Encyclopedia of Production Engineering (Stark and Damerau, 2019)
2019 A DT is a dynamic virtual representation of a physical object or system, usually across multiple stages of its lifecycle. It uses real-world data, simulation, or machine learning models, combined with data analysis, to enable understanding, learning, and reasoning. DTs can be used to answer what-if questions and present insights in an intuitive way. The concept is proposed by IBM (Stanford-Clark et al., 2019)
2020 A DT is a virtual representation of real-world entities and processes, synchronized at a specified frequency and fidelity. The concept is proposed by the Digital Twin Consortium (Stanford-Clark et al., 2019)
Tab.1  History of the digital twin (DT) concept
Fig.1  Basic real-time decision scheme in a DTSC.
Fig.2  Framework of JD.COM’s DTSC platform.
1 A F AlMulhim (2021). Smart supply chain and firm performance: The role of digital technologies. Business Process Management Journal, 27(5): 1353–1372
https://doi.org/10.1108/BPMJ-12-2020-0573
2 Anasoft (2019). Digital twin: Smart industry and intelligent enterprise. Available at:
3 M Andronie, G Lazaroiu, R Stefanescu, C Uta, I Dijmarescu (2021). Sustainable, smart, and sensing technologies for cyber–physical manufacturing systems: A systematic literature review. Sustainability, 13(10): 5495
https://doi.org/10.3390/su13105495
4 J Autiosalo, R Ala-Laurinaho, J Mattila, M Valtonen, V Peltoranta, K Tammi (2021). Towards integrated digital twins for industrial products: Case study on an overhead crane. Applied Sciences, 11(2): 683
https://doi.org/10.3390/app11020683
5 G Avventuroso, M Silvestri, P Pedrazzoli (2017). A networked production system to implement virtual enterprise and product lifecycle information loops. In: 20th IFAC World Congress. Toulouse: Elsevier, 7964–7969
6 G Baruffaldi, R Accorsi, R Manzini (2019). Warehouse management system customization and information availability in 3PL companies: A decision-support tool. Industrial Management & Data Systems, 119(2): 251–273
https://doi.org/10.1108/IMDS-01-2018-0033
7 S Y Barykin, A A Bochkarev, E Dobronravin, S M Sergeev (2021). The place and role of digital twin in supply chain management. Academy of Strategic Management Journal, 20(2S)
8 S Y Barykin, A A Bochkarev, O V Kalinina, V K Yadykin (2020). Concept for a supply chain digital twin. International Journal of Mathematical, Engineering and Management Sciences, 5(6): 1498–1515
https://doi.org/10.33889/IJMEMS.2020.5.6.111
9 M Beltrami, G Orzes, J Sarkis, M Sartor (2021). Industry 4.0 and sustainability: Towards conceptualization and theory. Journal of Cleaner Production, 312: 127733
https://doi.org/10.1016/j.jclepro.2021.127733
10 D Bertsimas, A Thiele (2006). Robust and data-driven optimization: Modern decision making under uncertainty. In: INFORMS Tutorials in Operations Research: Models, Methods, and Applications for Innovative Decision Making, 95–122
11 S Boschert, R Rosen (2016). Digital twin—the simulation aspect. In: Hehenberger P, Bradley D, eds. Mechatronic Futures. Cham: Springer, 59–74
12 E Bottani, M Bertolini, A Rizzi, G Romagnoli (2017). Monitoring on-shelf availability, out-of-stock and product freshness through RFID in the fresh food supply chain. International Journal of RF Technologies: Research and Applications, 8(1–2): 33–55
https://doi.org/10.3233/RFT-171780
13 A Bueno-Solano, M G Cedillo-Campos (2014). Dynamic impact on global supply chains performance of disruptions propagation produced by terrorist acts. Transportation Research Part E: Logistics and Transportation Review, 61: 1–12
https://doi.org/10.1016/j.tre.2013.09.005
14 D Burgos, D Ivanov (2021). Food retail supply chain resilience and the COVID-19 pandemic: A digital twin-based impact analysis and improvement directions. Transportation Research Part E: Logistics and Transportation Review, 152: 102412
https://doi.org/10.1016/j.tre.2021.102412
15 A Busse, B Gerlach, J C Lengeling, P Poschmann, J Werner, S Zarnitz (2021). Towards digital twins of multimodal supply chains. Logistics, 5(2): 25
https://doi.org/10.3390/logistics5020025
16 K Butner (2010). The smarter supply chain of the future. Strategy and Leadership, 38(1): 22–31
https://doi.org/10.1108/10878571011009859
17 P Cao, N G Zhao, J Wu (2019). Dynamic pricing with Bayesian demand learning and reference price effect. European Journal of Operational Research, 279(2): 540–556
https://doi.org/10.1016/j.ejor.2019.06.033
18 I M Cavalcante, E M Frazzon, F A Forcellini, D Ivanov (2019). A supervised machine learning approach to data-driven simulation of resilient supplier selection in digital manufacturing. International Journal of Information Management, 49: 86–97
https://doi.org/10.1016/j.ijinfomgt.2019.03.004
19 C Chandra, S Kumar (2000). Supply chain management in theory and practice: A passing fad or a fundamental change? Industrial Management & Data Systems, 100(3): 100–114
https://doi.org/10.1108/02635570010286168
20 J L Chen, X B Zhao, Z J Shen (2015). Risk mitigation benefit from backup suppliers in the presence of the horizontal fairness concern. Decision Sciences, 46(4): 663–696
https://doi.org/10.1111/deci.12157
21 X Chen, P Hu, Z Y Hu (2017). Efficient algorithms for the dynamic pricing problem with reference price effect. Management Science, 63(12): 4389–4408
https://doi.org/10.1287/mnsc.2016.2554
22 Z Chen, L Huang (2021). Digital twins for information-sharing in remanufacturing supply chain: A review. Energy, 220: 119712
https://doi.org/10.1016/j.energy.2020.119712
23 M Christopher (2011). Logistics and Supply Chain Management, 4th ed. London: Pearson
24 T Clark, B Barn, V Kulkarni, S Barat (2020). Language support for multi agent reinforcement learning. In: 13th Innovations in Software Engineering Conference (ISEC). Jabalpur: ACM, 7
25 C Colicchia, F Dallari, M Melacini (2010). Increasing supply chain resilience in a global sourcing context. Production Planning and Control, 21(7): 680–694
https://doi.org/10.1080/09537280903551969
26 D Cozmiuc, I Petrisor (2018). Industrie 4.0 by Siemens: Steps made today. Journal of Cases on Information Technology, 20(2): 30–48
https://doi.org/10.4018/JCIT.2018040103
27 A D’Angelo, E K P Chong (2018). A systems engineering approach to incorporating the Internet of Things to reliability-risk modeling for ranking conceptual designs. In: ASME International Mechanical Engineering Congress and Exposition—Design, Reliability, Safety, and Risk. Pittsburgh, PA, V013T05A027
28 P Daugherty, M Carrel-Billiard, M Biltz (2021). Accenture technology vision 2021. Available at:
29 T Defraeye, C Shrivastava, T Berry, P Verboven, D Onwude, S Schudel, A Buehlmann, P Cronje, R M Rossi (2021). Digital twins are coming: Will we need them in supply chains of fresh horticultural produce? Trends in Food Science & Technology, 109: 245–258
https://doi.org/10.1016/j.tifs.2021.01.025
30 T Defraeye, G Tagliavini, W Wu, K Prawiranto, S Schudel, M A Kerisima, P Verboven, A Buhlmann (2019). Digital twins probe into food cooling and biochemical quality changes for reducing losses in refrigerated supply chains. Resources, Conservation and Recycling, 149: 778–794
https://doi.org/10.1016/j.resconrec.2019.06.002
31 T de Kok, C Grob, M Laumanns, S Minner, J Rambau, K Schade (2018). A typology and literature review on stochastic multi-echelon inventory models. European Journal of Operational Research, 269(3): 955–983
https://doi.org/10.1016/j.ejor.2018.02.047
32 T H Deng, Z J M Shen, J G Shanthikumar (2014). Statistical learning of service-dependent demand in a multiperiod newsvendor setting. Operations Research, 62(5): 1064–1076
https://doi.org/10.1287/opre.2014.1303
33 T H Deng, K R Zhang, Z J M Shen (2021). A systematic review of a digital twin city: A new pattern of urban governance toward smart cities. Journal of Management Science and Engineering, 6(2): 125–134
https://doi.org/10.1016/j.jmse.2021.03.003
34 W de Paula Ferreira, F Armellini, L A de Santa-Eulalia (2020). Simulation in Industry 4.0: A state-of-the-art review. Computers & Industrial Engineering, 149: 106868
https://doi.org/10.1016/j.cie.2020.106868
35 M Dobler, P Busel, C Hartmann, J Schumacher (2020). Supporting SMEs in the Lake Constance region in the implementation of cyber–physical-systems: Framework and demonstrator. In: 2020 IEEE International Conference on Engineering, Technology and Innovation. Cardiff, 1–8
36 J Ducree, M Gravitt, R Walshe, S Bartling, M Etzrodt, T Harrington (2020). Open platform concept for blockchain-enabled crowdsourcing of technology development and supply chains. Frontiers in Blockchain, 3: 586525
https://doi.org/10.3389/fbloc.2020.586525
37 G Dutta, R Kumar, R Sindhwani, R K Singh (2021). Adopting shop floor digitalization in Indian manufacturing SMEs: A transformational study. In: Phanden R K, Mathiyazhagan K, Kumar R, Paulo Davim J, eds. Advances in Industrial and Production Engineering. Singapore: Springer, 599–611
38 H Ehm, N Ramzy, P Moder, C Summerer, S Fetz, C Neau (2019). Digital reference: A semantic web for semiconductor manufacturing and supply chains containing semiconductors. In: Winter Simulation Conference (WSC). National Harbor, MD: IEEE, 2409–2418
39 European Union (2018). The General Data Protection Regulation (GDPR). Available at:
40 Q Feng, J G Shanthikumar (2018). Supply and demand functions in inventory models. Operations Research, 66(1): 77–91
https://doi.org/10.1287/opre.2017.1648
41 E M Frazzon, I R S Agostino, E Broda, M Freitag (2020). Manufactur-ing networks in the era of digital production and operations: A socio–cyber–physical perspective. Annual Reviews in Control, 49: 288–294
https://doi.org/10.1016/j.arcontrol.2020.04.008
42 A Fuller, Z Fan, C Day, C Barlow (2020). Digital twin: Enabling technologies, challenges and open research. IEEE Access, 8: 108952–108971
https://doi.org/10.1109/ACCESS.2020.2998358
43 M D Garvey, S Carnovale, S Yeniyurt (2015). An analytical framework for supply network risk propagation: A Bayesian network approach. European Journal of Operational Research, 243(2): 618–627
https://doi.org/10.1016/j.ejor.2014.10.034
44 A Ghate (2015). Optimal minimum bids and inventory scrapping in sequential, single-unit, Vickrey auctions with demand learning. European Journal of Operational Research, 245(2): 555–570
https://doi.org/10.1016/j.ejor.2015.03.015
45 M Ghobakhloo (2018). The future of manufacturing industry: A strategic roadmap toward Industry 4.0. Journal of Manufacturing Technology Management, 29(6): 910–936
https://doi.org/10.1108/JMTM-02-2018-0057
46 E H Glaessgen, D S Stargel (2012). The digital twin paradigm for future NASA and US Air Force vehicles. In: 53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference. Honolulu, HI, 1818
47 D Gligor, N Gligor, M Holcomb, S Bozkurt (2019). Distinguishing between the concepts of supply chain agility and resilience: A multidisciplinary literature review. International Journal of Logistics Management, 30(2): 467–487
https://doi.org/10.1108/IJLM-10-2017-0259
48 M S Golan, B D Trump, J C Cegan, I Linkov (2021). Supply chain resilience for vaccines: Review of modeling approaches in the context of the COVID-19 pandemic. Industrial Management & Data Systems, 121(7): 1723–1748
https://doi.org/10.1108/IMDS-01-2021-0022
49 V I Gorodetsky, S S Kozhevnikov, D Novichkov, P O Skobelev (2019). The framework for designing autonomous cyber–physical multi-agent systems for adaptive resource management. In: 9th International Conference on Industrial Applications of Holonic and Multi-Agent Systems (HoloMAS). Linz: Springer, 52–64
50 T Greif, N Stein, C M Flath (2020). Peeking into the void: Digital twins for construction site logistics. Computers in Industry, 121: 103264
https://doi.org/10.1016/j.compind.2020.103264
51 M Grieves (2005). Product lifecycle management: The new paradigm for enterprises. International Journal of Product Development, 2(1/2): 71–84
https://doi.org/10.1504/IJPD.2005.006669
52 M Grieves (2006). Product Lifecycle Management: Driving the Next Generation of Lean Thinking. New York: McGraw Hill
53 M Grieves (2011). Virtually Perfect: Driving Innovative and Lean Products through Product Lifecycle Management. Brevard County: Space Coast Press
54 M Grieves (2015). Digital twin: Manufacturing excellence through virtual factory replication. Whitepaper
55 M Grieves, J Vickers (2017). Digital twin: Mitigating unpredictable, undesirable emergent behavior in complex system. In: Kahlen F J, Flumerfelt S, Alves A, eds. Transdisciplinary Perspectives on Complex Systems. Cham: Springer, 85–113
56 X Y Guo, G Trimponias, X X Wang, Z T Chen, Y H Geng, X Liu (2017). Cellular network configuration via online learning and joint optimization. In: IEEE International Conference on Big Data. Boston, MA, 1295–1300
57 N Gupta, A Tiwari, S T S Bukkapatnam, R Karri (2020). Additive manufacturing cyber–physical system: Supply chain cybersecurity and risks. IEEE Access, 8: 47322–47333
https://doi.org/10.1109/ACCESS.2020.2978815
58 S Haag, C Simon (2019). Simulation of horizontal and vertical integration in digital twins. In: 33rd International ECMS Conference on Modelling and Simulation. Caserta, 284–289
59 J M Harrison, N B Keskin, A Zeevi (2012). Bayesian dynamic pricing policies: Learning and earning under a binary prior distribution. Management Science, 58(3): 570–586
https://doi.org/10.1287/mnsc.1110.1426
60 W P Heemels, K H Johansson, P Tabuada (2012). An introduction to event-triggered and self-triggered control. In: 51st IEEE Conference on Decision and Control (CDC). Maui, HI, 3270–3285
61 C Hegedus, A Franko, P Varga (2019). Asset and production tracking through value chains for Industry 4.0 using the arrowhead framework. In: IEEE International Conference on Industrial Cyber Physical Systems (ICPS). Taipei, 655–660
62 S Heim, J Clemens, J E Steck, C Basic, D Timmons, K Zwiener (2020). Predictive maintenance on aircraft and applications with digital twin. In: 8th IEEE International Conference on Big Data. Atlanta, GA, 4122–4127
63 S Hippold (2020). Coronavirus: How to secure your supply chain. Available at:
64 G T S Ho, Y M Tang, K Y Tsang, V Tang, K Y Chau (2021). A blockchain-based system to enhance aircraft parts traceability and trackability for inventory management. Expert Systems with Applications, 179: 115101
https://doi.org/10.1016/j.eswa.2021.115101
65 L J Hong, G X Jiang (2019). Offline simulation online application: A new framework of simulation-based decision making. Asia-Pacific Journal of Operational Research, 36(6): 1940015
https://doi.org/10.1142/S0217595919400153
66 Internet of Business (2017). Uncertainty persists around ownership and value of IoT data. Available at:
67 D Ivanov (2020). 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
https://doi.org/10.1016/j.tre.2020.101922 pmid: 32288597
68 D Ivanov, A Dolgui (2019). New disruption risk management perspectives in supply chains: Digital twins, the ripple effect, and resileanness. In: 9th IFAC Conference on Manufacturing Modelling, Management and Control. Berlin: Elsevier, 337–342
69 D Ivanov, A Dolgui (2021). A digital supply chain twin for managing the disruption risks and resilience in the era of industry 4.0. Production Planning and Control, 32(9): 775–788
https://doi.org/10.1080/09537287.2020.1768450
70 D Ivanov, A Dolgui, A Das, B Sokolov (2019). Digital supply chain twins: Managing the ripple effect, resilience, and disruption risks by data-driven optimization, simulation, and visibility. In: Ivanov D, Dolgui A, Sokolov B, eds. Handbook of Ripple Effects in the Supply Chain. Cham: Springer, 309–332
71 G X Jiang, L J Hong, B L Nelson (2020). Online risk monitoring using offline simulation. INFORMS Journal on Computing, 32(2): 356–375
https://doi.org/10.1287/ijoc.2019.0892
72 D Joannou, R Kalawsky, M Martinez-Garcia, C Fowler, K Fowler (2020). Realizing the role of permissioned blockchains in a systems engineering lifecycle. Systems, 8(4): 41
https://doi.org/10.3390/systems8040041
73 K Kalaboukas, J Rozanec, A Kosmerlj, D Kiritsis, G Arampatzis (2021). Implementation of cognitive digital twins in connected and agile supply networks: An operational model. Applied Sciences, 11(9): 4103
https://doi.org/10.3390/app11094103
74 A Kanak, N Ugur, S Ergun (2019). A visionary model on blockchain-based accountability for secure and collaborative digital twin environments. In: IEEE International Conference on Systems, Man and Cybernetics (SMC). Bari, 3512–3517
75 A Kanak, N Ugur, S Ergun (2020). Diamond accountability model for blockchain-enabled cyber–physical systems. In: IEEE 1st International Conference on Human–Machine Systems. Rome, 1–5
76 N Kang, H Shen, Y Xu (2021). JD.Com improves delivery networks by a multi-period facility location model. INFORMS Journal on Applied Analytics, in press, doi: 10.1287/inte.2021.1077
77 R S Kenett, J Bortman (2021). The digital twin in Industry 4.0: A wide-angle perspective. Quality and Reliability Engineering International, in press, doi: 10.1002/qre.2948
78 D Klappich (2019). Hype cycle for supply chain execution technologies. Available at:
79 G Landolfi, S Menato, M Sorlini, A Valdata, D Rovere, R Fornasiero, P Pedrazzoli (2017). Intelligent value chain management framework for customized assistive healthcare devices. In: 11th CIRP Conference on Intelligent Computation in Manufacturing Engineering (CIRP ICME). Naples: Elsevier, 583–588
80 D Lee, S Lee (2021). Digital twin for supply chain coordination in modular construction. Applied Sciences, 11(13): 5909
https://doi.org/10.3390/app11135909
81 J Leng, G Ruan, P Jiang, K Xu, Q Liu, X Zhou, C Liu (2020). Blockchain-empowered sustainable manufacturing and product lifecycle management in Industry 4.0: A survey. Renewable & Sustainable Energy Reviews, 132: 110112
https://doi.org/10.1016/j.rser.2020.110112
82 R Levi, G Perakis, J Uichanco (2015). The data-driven newsvendor problem: New bounds and insights. Operations Research, 63(6): 1294–1306
https://doi.org/10.1287/opre.2015.1422
83 X Li, J Cao, Z Liu, X Luo (2020). Sustainable business model based on digital twin platform network: The inspiration from Haier’s case study in China. Sustainability, 12(3): 936
https://doi.org/10.3390/su12030936
84 L H Liyanage, J G Shanthikumar (2005). A practical inventory control policy using operational statistics. Operations Research Letters, 33(4): 341–348
https://doi.org/10.1016/j.orl.2004.08.003
85 K Lowrey, A Rajeswaran, S Kakade, E Todorov, I Mordatch (2018). Plan online, learn offline: Efficient learning and exploration via model-based control. arXiv preprint, arXiv:1811.01848
86 A Lucas (2020). Apple warns on revenue guidance due to production delays, weak demand in China because of coronavirus. Available at:
87 R R Lummus, D W Krumwiede, R J Vokurka (2001). The relationship of logistics to supply chain management: Developing a common industry definition. Industrial Management & Data Systems, 101(8): 426–432
https://doi.org/10.1108/02635570110406730
88 S Ma, Y Zhang, Y Liu, H Yang, J Lv, S Ren (2020). Data-driven sustainable intelligent manufacturing based on demand response for energy-intensive industries. Journal of Cleaner Production, 274: 123155
https://doi.org/10.1016/j.jclepro.2020.123155
89 V L Makarov, A R Bakhtizin, G L Beklaryan, A S Akopov (2021). Digital plant: Methods of discrete-event modeling and optimization of production characteristics. Business Informatics, 15(2): 7–20
https://doi.org/10.17323/2587-814X.2021.2.7.20
90 C Mandolla, A M Petruzzelli, G Percoco, A Urbinati (2019). Building a digital twin for additive manufacturing through the exploitation of blockchain: A case analysis of the aircraft industry. Computers in Industry, 109: 134–152
https://doi.org/10.1016/j.compind.2019.04.011
91 J A Marmolejo-Saucedo (2020). Design and development of digital twins: A case study in supply chains. Mobile Networks and Applications, 25(6): 2141–2160
https://doi.org/10.1007/s11036-020-01557-9
92 J A Marmolejo-Saucedo, M Hurtado-Hernandez, R Suarez-Valdes (2019). Digital twins in supply chain management: A brief literature review. In: International Conference on Intelligent Computing & Optimization. Koh Samui: Springer, 653–661
93 B Marr (2017). What is digital twin technology and why is it so important? Available at:
94 S Min, J T Mentzer (2000). The role of marketing in supply chain management. International Journal of Physical Distribution & Logistics Management, 30(9): 765–787
https://doi.org/10.1108/09600030010351462
95 R Minerva, G M Lee, N Crespi (2020). Digital twin in the IoT context: A survey on technical features, scenarios, and architectural models. Proceedings of the IEEE, 108(10): 1785–1824
https://doi.org/10.1109/JPROC.2020.2998530
96 P Moder, H Ehm, E Jofer (2020a). A holistic digital twin based on semantic web technologies to accelerate digitalization. In: International Conference on Digital Transformation in Semiconductor Manufacturing. Milan: Springer, 3–13
97 P Moder, H Ehm, N Ramzy (2020b). Digital twin for plan and make using semantic web technologies: Extending the JESSI/SEMATECH MIMAC Standard to the digital reference. In: International Conference on Digital Transformation in Semiconductor Manufacturing. Milan: Springer, 24–32
98 T D Moshood, G Nawanir, S Sorooshian, O Okfalisa (2021). Digital twins driven supply chain visibility within logistics: A new paradigm for future logistics. Applied System Innovation, 4(2): 29
https://doi.org/10.3390/asi4020029
99 S B Nasir, T Ahmed, C L Karmaker, S M Ali, S K Paul, A Majumdar (2021). Supply chain viability in the context of COVID-19 pandemic in small- and medium-sized enterprises: Implications for sustainable development goals. Journal of Enterprise Information Management, in press, doi: 10.1108/JEIM-02-2021-0091
100 S Olcott, C Mullen (2020). Digital twin consortium defines digital twin. Available at:
101 T L Olsen, B Tomlin (2020). Industry 4.0: Opportunities and challenges for operations management. Manufacturing & Service Operations Management, 22(1): 113–122
https://doi.org/10.1287/msom.2019.0796
102 D I Onwude, G Chen, N Eke-Emezie, A Kabutey, A Y Khaled, B Sturm (2020). Recent advances in reducing food losses in the supply chain of fresh agricultural produce. Processes, 8(11): 1–31
https://doi.org/10.3390/pr8111431
103 A Orozco-Romero, C Y Arias-Portela, J A Marmolejo-Saucedo (2020). The use of agent-based models boosted by digital twins in the supply chain: A literature review. In: International Conference on Intelligent Computing and Optimization. Koh Samui: Springer, 642–652
104 K Panetta (2017). Gartner’s top 10 strategic technology trends for 2017. Available at:
105 K Panetta (2018). Gartner’s top 10 strategic technology trends for 2018. Available at:
106 K Panetta (2019). Gartner’s top 10 strategic technology trends for 2019. Available at:
107 K T Park, Y H Son, S D Noh (2021). The architectural framework of a cyber physical logistics system for digital-twin-based supply chain control. International Journal of Production Research, 59(19): 5721–5742
https://doi.org/10.1080/00207543.2020.1788738
108 A Pehlken, S Baumann (2020). Urban mining: Applying digital twins for sustainable product cascade use. In: IEEE International Conference on Engineering, Technology and Innovation. Cardiff, 1–7
109 M M Pereira, E M Frazzon (2021). A data-driven approach to adaptive synchronization of demand and supply in omni-channel retail supply chains. International Journal of Information Management, 57: 102165
https://doi.org/10.1016/j.ijinfomgt.2020.102165
110 C Pettey (2017). Prepare for the impact of digital twins. Available at:
111 F Pilati, R Tronconi, G Nollo, S S Heragu, F Zerzer (2021). Digital twin of COVID-19 mass vaccination centers. Sustainability, 13(13): 7396
https://doi.org/10.3390/su13137396
112 D J Power (2011). Challenges of real-time decision support. In: Burstein F, Brézillon P, Zaslavsky A, eds. Supporting Real Time Decision-Making. Boston, MA: Springer, 3–11
113 A Preut, J P Kopka, U Clausen (2021). Digital twins for the circular economy. Sustainability, 13(18): 10467
https://doi.org/10.3390/su131810467
114 Q Qi, F Tao, T Hu, N Anwer, A Liu, Y Wei, L Wang, A Y C Nee (2021). Enabling technologies and tools for digital twin. Journal of Manufacturing Systems, 58: 3–21
https://doi.org/10.1016/j.jmsy.2019.10.001
115 V Rajagopal, S P Venkatesan, M Goh (2017). Decision-making models for supply chain risk mitigation: A review. Computers & Industrial Engineering, 113: 646–682
https://doi.org/10.1016/j.cie.2017.09.043
116 K Reeves, C Maple (2019). Realising the vision of digital twins: Challenges in trustworthiness. In: Living in the Internet of Things (IoT 2019). London, 33
117 S Rehana (2018). Making a digital twin supply chain a reality. Available at:
118 J A M Santos, M R Lopes, J L Viegas, S M Vieira, J M C Sousa (2020). Internal supply chain digital twin of a pharmaceutical company. In: 21st IFAC World Congress on Automatic Control. Berlin: Elsevier, 10797–10802
119 S Sarkar, S Kumar (2015). A behavioral experiment on inventory management with supply chain disruption. International Journal of Production Economics, 169: 169–178
https://doi.org/10.1016/j.ijpe.2015.07.032
120 A J Schmitt, M Singh (2012). A quantitative analysis of disruption risk in a multi-echelon supply chain. International Journal of Production Economics, 139(1): 22–32
https://doi.org/10.1016/j.ijpe.2012.01.004
121 G Schuh, R Anderl, J Gausemeier, M ten Hompel, W Wahlster (2017). Industrie 4.0 maturity index: Managing the digital transformation of companies. Available at:
122 A Seif, C Toro, H Akhtar (2019). Implementing Industry 4.0 asset administrative shells in mini factories. In: 23rd KES International Conference on Knowledge-Based and Intelligent Information and Engineering Systems. Budapest: Elsevier, 495–504
123 Y Semenov, O Semenova, I Kuvataev (2020). Solutions for digitalization of the coal industry implemented in UC Kuzbassrazrezugol. In: 5th International Innovative Mining Symposium (IIMS). Kemerovo, 01042
124 Z Seyedghorban, H Tahernejad, R Meriton, G Graham (2020). Supply chain digitalization: Past, present and future. Production Planning and Control, 31(2–3): 96–114
https://doi.org/10.1080/09537287.2019.1631461
125 M Shafto, M Conroy, R Doyle, E Glaessgen, C Kemp, J LeMoigne, L Wang (2012). Modeling, simulation, information technology & processing roadmap. National Aeronautics and Space Administration (NASA)
126 M Sharma, M K Singla, P Nijhawan, A Dhingra (2021). Sensor-based optimization of energy efficiency in Internet of Things: A review. In: Singh H, Singh Cheema P P, Garg P, eds. Sustainable Development through Engineering Innovations. Singapore: Springer, 153–161
127 W Shen, C Yang, L Gao (2020). Address business crisis caused by COVID-19 with collaborative intelligent manufacturing technologies. IET Collaborative Intelligent Manufacturing, 2(2): 96–99
https://doi.org/10.1049/iet-cim.2020.0041
128 X Shen, Y Zhang, Y Tang, Y Qin, N Liu, Z Yi (2021). A study on the impact of digital tobacco logistics on tobacco supply chain performance: Taking the tobacco industry in Guangxi as an example. Industrial Management & Data Systems, in press, doi: 10.1108/IMDS-05-2021-0270
129 Z M Shen, Y Sun (2021). Strengthening supply chain resilience during COVID-19: A case study of JD.COM. Journal of Operations Management, in press, doi: 10.1002/joom.1161
130 K Shoji, S Schudel, D Onwude, C Shrivastava, T Defraeye (2022). Mapping the postharvest life of imported fruits from packhouse to retail stores using physics-based digital twins. Resources, Conservation and Recycling, 176: 105914
https://doi.org/10.1016/j.resconrec.2021.105914
131 S Smetana, K Aganovic, V Heinz (2021). Food supply chains as cyber–physical systems: A path for more sustainable personalized nutrition. Food Engineering Reviews, 13(1): 92–103
https://doi.org/10.1007/s12393-020-09243-y
132 A Stanford-Clark, E Frank-Schultz, M Harris (2019). What are digital twins? Available at:
133 R Stark, T Damerau (2019). Digital twin. In: The International Academy for Production Engineering, Chatti S, Tolio T, eds. CIRP Encyclopedia of Production Engineering. Berlin, Heidelberg: Springer, 5
134 I Sung, B Choi, P Nielsen (2021). On the training of a neural network for online path planning with offline path planning algorithms. International Journal of Information Management, 57: 102142
https://doi.org/10.1016/j.ijinfomgt.2020.102142
135 N Tohamy (2019). Hype cycle for supply chain strategy. Available at:
136 O Tozanlı, E Kongar, S M Gupta (2020). Evaluation of waste electronic product trade-in strategies in predictive twin disassembly systems in the era of blockchain. Sustainability, 12(13): 5416
https://doi.org/10.3390/su12135416
137 M W Ulmer (2019). Anticipation versus reactive reoptimization for dynamic vehicle routing with stochastic requests. Networks, 73(3): 277–291
https://doi.org/10.1002/net.21861
138 K Wang, Q Hu, M Zhou, Z Zun, X Qian (2021). Multi-aspect applications and development challenges of digital twin-driven management in global smart ports. Case Studies on Transport Policy, 9(3): 1298–1312
https://doi.org/10.1016/j.cstp.2021.06.014
139 K Wang, W Xie, B Wang, J Pei, W Wu, M Baker, Q Zhou (2020). Simulation-based digital twin development for blockchain enabled end-to-end industrial hemp supply chain risk management. In: Winter Simulation Conference. Orlando, FL: IEEE, 3200–3211
140 S Wang (2021). Users intend to have the right to choose to close the algorithm recommendation service. Available at: (in Chinese)
141 M Wayland (2020). Coronavirus impact spreads to European auto plant and could hit GM truck production. Available at:
142 R Wilson, P H J Mercier, B Patarachao, A Navarra (2021). Partial least squares regression of oil sands processing variables within discrete event simulation digital twin. Minerals, 11(7): 689
https://doi.org/10.3390/min11070689
143 L Wu, X Yue, A Jin, D C Yen (2016). Smart supply chain management: A review and implications for future research. International Journal of Logistics Management, 27(2): 395–417
https://doi.org/10.1108/IJLM-02-2014-0035
144 T Wu, S M Huang, J Blackhurst, X L Zhang, S S Wang (2013). Supply chain risk management: An agent-based simulation to study the impact of retail stockouts. IEEE Transactions on Engineering Management, 60(4): 676–686
https://doi.org/10.1109/TEM.2012.2190986
145 J Yang, S Lee, Y S Kang, S D Noh, S S Choi, B R Jung, S H Lee, J T Kang, D Y Lee, H S Kim (2020). Integrated platform and digital twin application for global automotive part suppliers. In: IFIP International Conference on Advances in Production Management Systems (APMS). Novi Sad: Springer, 230–237
146 M Zafarzadeh, M Wiktorsson, J Baalsrud Hauge (2021). A systematic review on technologies for data-driven production logistics: Their role from a holistic and value creation perspective. Logistics, 5(2): 24
https://doi.org/10.3390/logistics5020024
[1] Algan TEZEL, Eleni PAPADONIKOLAKI, Ibrahim YITMEN, Per HILLETOFTH. Preparing construction supply chains for blockchain technology: An investigation of its potential and future directions[J]. Front. Eng, 2020, 7(4): 547-563.
[2] Jun WANG, Peng WU, Xiangyu WANG, Wenchi SHOU. The outlook of blockchain technology for construction engineering management[J]. Front. Eng, 2017, 4(1): 67-75.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed