数字孪生驱动的智慧供应链
作者：王璐1，邓天虎1，申作军2,3，胡浩4，戚永志4
单位：1. 清华大学；2. 香港大学；3. 加利福尼亚大学伯克利分校；4. 京东商务有限公司
来源：工程管理前沿
随着当今世界的供应链变得更为复杂和脆弱，智慧供应链的管理价值愈加凸显。智慧供应链具有连接性、可视性、敏捷性、集成化和智能化的特点。基于数字孪生（Digital Twin，DT）这一概念，数字孪生驱动的供应链（DT-driven supply chain，DTSC）提供一个创新的供应链解决方案。在本文中，我们首先对数字孪生的文献进行简要综述，论述了建立数字孪生驱动的供应链的基本方法，围绕供应链建模、实时优化和数据驱动的协作展开探讨并凝练关键研究问题。最后，通过京东的数字孪生供应链平台，介绍其如何在新冠肺炎疫情期间快速实现供应链网络重新配置，阐明数字孪生供应链的的优势。
引言
本文主要回答了如下研究问题：
文献综述
2.1 数字孪生概念
2.2 数字孪生供应链
建立数字孪生供应链
数字孪生驱动的智慧供应链新的研究问题
案例介绍
结论
参考文献可向上滑动阅览
AlMulhim A F (2021). Smart supply chain and firm performance: The role of digital technologies. Business Process Management Journal, 27(5): 1353–1372 doi:10.1108/BPMJ-12-2020-0573
Anasoft (2019). Digital twin: Smart industry and intelligent enterprise. Available at: anasoft.COM/emans/en/home/news-blog/blog/Digital-Twin-Smart-Industry-and-Intelligent-Enterprise
Andronie M, Lazaroiu G, Stefanescu R, Uta C, Dijmarescu I (2021). Sustainable, smart, and sensing technologies for cyber–physical manufacturing systems: A systematic literature review. Sustainability, 13(10): 5495 doi:10.3390/su13105495
Autiosalo J, Ala-Laurinaho R, Mattila J, Valtonen M, Peltoranta V, Tammi K (2021). Towards integrated digital twins for industrial products: Case study on an overhead crane. Applied Sciences, 11(2): 683 doi:10.3390/app11020683
Avventuroso G, Silvestri M, Pedrazzoli P (2017). A networked production system to implement virtual enterprise and product lifecycle information loops. In: 20th IFAC World Congress. Toulouse: Elsevier, 7964–7969
Baruffaldi G, Accorsi R, Manzini R (2019). Warehouse management system customization and information availability in 3PL companies: A decision-support tool. Industrial Management & Data Systems, 119(2): 251–273 doi:10.1108/IMDS-01-2018-0033
Barykin S Y, Bochkarev A A, Dobronravin E, Sergeev S M (2021). The place and role of digital twin in supply chain management. Academy of Strategic Management Journal, 20(2S)
Barykin S Y, Bochkarev A A, Kalinina O V, Yadykin V K (2020). Concept for a supply chain digital twin. International Journal of Mathematical, Engineering and Management Sciences, 5(6): 1498–1515 doi:10.33889/IJMEMS.2020.5.6.111
Beltrami M, Orzes G, Sarkis J, Sartor M (2021). Industry 4.0 and sustainability: Towards conceptualization and theory. Journal of Cleaner Production, 312: 127733 doi:10.1016/j.jclepro.2021.127733
Bertsimas D, Thiele A (2006). Robust and data-driven optimization: Modern decision making under uncertainty. In: Models, methods, and applications for innovative decision making. INFORMS, 95-122
Boschert S, Rosen R (2016). Digital twin—the simulation aspect. In: Mechatronic futures. 59-74
Bottani E, Bertolini M, Rizzi A, Romagnoli G (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 doi:10.3233/RFT-171780
Bueno-Solano A, Cedillo-Campos M G (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 doi:10.1016/j.tre.2013.09.005
Burgos D, Ivanov D (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 doi:10.1016/j.tre.2021.102412
Busse A, Gerlach B, Lengeling J C, Poschmann P, Werner J, Zarnitz S (2021). Towards digital twins of multimodal supply chains. Logistics, 5(2): 25 doi:10.3390/logistics5020025
Butner K (2010). The smarter supply chain of the future. Strategy and Leadership, 38(1): 22–31 doi:10.1108/10878571011009859
Cao P, Zhao N G, Wu J (2019). Dynamic pricing with Bayesian demand learning and reference price effect. European Journal of Operational Research, 279(2): 540–556 doi:10.1016/j.ejor.2019.06.033
Cavalcante I M, Frazzon E M, Forcellini F A, Ivanov D (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 doi:10.1016/j.ijinfomgt.2019.03.004
Chandra C, Kumar S (2000). Supply chain management in theory and practice: A passing fad or a fundamental change? Industrial Management & Data Systems, 100(3): 100–114 doi:10.1108/02635570010286168
Chen J L, Zhao X B, Shen Z J (2015). Risk mitigation benefit from backup suppliers in the presence of the horizontal fairness concern. Decision Sciences, 46(4): 663–696 doi:10.1111/deci.12157
Chen X, Hu P, Hu Z Y (2017). Efficient algorithms for the dynamic pricing problem with reference price effect. Management Science, 63(12): 4389–4408 doi:10.1287/mnsc.2016.2554
Chen Z, Huang L (2021). Digital twins for information-sharing in remanufacturing supply chain: A review. Energy, 220: 119712 doi:10.1016/j.energy.2020.119712
Christopher M (2011). Logistics and Supply Chain Management, 4th ed. London: Pearson
Clark T, Barn B, Kulkarni V, Barat S (2020). Language support for multi agent reinforcement learning. In: 13th Innovations in Software Engineering Conference (ISEC). Jabalpur: ACM, 7
Colicchia C, Dallari F, Melacini M (2010). Increasing supply chain resilience in a global sourcing context. Production Planning and Control, 21(7): 680–694 doi:10.1080/09537280903551969
Cozmiuc D, Petrisor I (2018). Industrie 4.0 by Siemens: Steps made today. Journal of Cases on Information Technology, 20(2): 30–48 doi:10.4018/JCIT.2018040103
D’Angelo A, Chong E K P (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
Daugherty P, Carrel-Billiard M, Biltz M (2021). Accenture technology vision 2021. Available at: accenture.COM/gb-en/insights/technology/technology-trends-2021
Defraeye T, Shrivastava C, Berry T, Verboven P, Onwude D, Schudel S, Buehlmann A, Cronje P, Rossi R M (2021). Digital twins are coming: Will we need them in supply chains of fresh horticultural produce? Trends in Food Science & Technology, 109: 245–258 doi:10.1016/j.tifs.2021.01.025
Defraeye T, Tagliavini G, Wu W, Prawiranto K, Schudel S, Kerisima M A, Verboven P, Buhlmann A (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 doi:10.1016/j.resconrec.2019.06.002
de Kok T, Grob C, Laumanns M, Minner S, Rambau J, Schade K (2018). A typology and literature review on stochastic multi-echelon inventory models. European Journal of Operational Research, 269(3): 955–983 doi:10.1016/j.ejor.2018.02.047
Deng T H, Shen Z J M, Shanthikumar J G (2014). Statistical learning of service-dependent demand in a multiperiod newsvendor setting. Operations Research, 62(5): 1064–1076 doi:10.1287/opre.2014.1303
Deng T H, Zhang K R, Shen Z J M (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
de Paula Ferreira W, Armellini F, de Santa-Eulalia L A (2020). Simulation in Industry 4.0: A state-of-the-art review. Computers & Industrial Engineering, 149: 106868 doi: 10.1016/j.cie.2020.106868
Dobler M, Busel P, Hartmann C, Schumacher J (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
Ducree J, Gravitt M, Walshe R, Bartling S, Etzrodt M, Harrington T (2020). Open platform concept for blockchain-enabled crowdsourcing of technology development and supply chains. Frontiers in Blockchain, 3: 586525 doi:10.3389/fbloc.2020.586525
Dutta G, Kumar R, Sindhwani R, Singh R K (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
Ehm H, Ramzy N, Moder P, Summerer C, Fetz S, Neau C (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
European Union (2018). The General Data Protection Regulation (GDPR). Available at: ec.europa.eu/info/law/law-topic/data-protection_en
Feng Q, Shanthikumar J G (2018). Supply and demand functions in inventory models. Operations Research, 66(1): 77–91 doi:10.1287/opre.2017.1648
Frazzon E M, Agostino I R S, Broda E, Freitag M (2020). Manufacturing networks in the era of digital production and operations: A socio–cyber–physical perspective. Annual Reviews in Control, 49: 288–294 doi:10.1016/j.arcontrol.2020.04.008
Fuller A, Fan Z, Day C, Barlow C (2020). Digital twin: Enabling technologies, challenges and open research. IEEE Access, 8: 108952–108971 doi:10.1109/ACCESS.2020.2998358
Garvey M D, Carnovale S, Yeniyurt S (2015). An analytical framework for supply network risk propagation: A Bayesian network approach. European Journal of Operational Research, 243(2): 618–627 doi:10.1016/j.ejor.2014.10.034
Ghate A (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 doi:10.1016/j.ejor.2015.03.015
Ghobakhloo M (2018). The future of manufacturing industry: A strategic roadmap toward Industry 4.0. Journal of Manufacturing Technology Management, 29(6): 910–936 doi:10.1108/JMTM-02-2018-0057
Glaessgen E, Stargel D (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 20th AIAA/ASME/AHS adaptive structures conference 14th AIAA, 1818
Gligor D, Gligor N, Holcomb M, Bozkurt S (2019). Distinguishing between the concepts of supply chain agility and resilience: A multidisciplinary literature review. International Journal of Logistics Management, 30(2): 467–487 doi:10.1108/IJLM-10-2017-0259
Golan M S, Trump B D, Cegan J C, Linkov I (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 doi:10.1108/IMDS-01-2021-0022
Gorodetsky V I, Kozhevnikov S S, Novichkov D, Skobelev P O (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
Greif T, Stein N, Flath C M (2020). Peeking into the void: Digital twins for construction site logistics. Computers in Industry, 121: 103264 doi:10.1016/j.compind.2020.103264
Grieves M (2005). Product lifecycle management: The new paradigm for enterprises. International Journal of Product Development, 2(1/2): 71–84 doi:10.1504/IJPD.2005.006669
Grieves M (2006). Product Lifecycle Management: Driving the Next Generation of Lean Thinking. New York: McGraw Hill
Grieves M (2011). Virtually Perfect: Driving Innovative and Lean Products through Product Lifecycle Management. Brevard County: Space Coast Press
Grieves M (2015). Digital twin: Manufacturing excellence through virtual factory replication. Whitepaper
Grieves M, Vickers J (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
Guo X Y, Trimponias G, Wang X X, Chen Z T, Geng Y H, Liu X (2017). Cellular network configuration via online learning and joint optimization. In: IEEE International Conference on Big Data. Boston, MA, 1295–1300
Gupta N, Tiwari A, Bukkapatnam S T S, Karri R (2020). Additive manufacturing cyber–physical system: Supply chain cybersecurity and risks. IEEE Access, 8: 47322–47333 doi:10.1109/ACCESS.2020.2978815
Haag S, Simon C (2019). Simulation of horizontal and vertical integration in digital twins. In: 33rd International ECMS Conference on Modelling and Simulation. Caserta, 284–289
Harrison J M, Keskin N B, Zeevi A (2012). Bayesian dynamic pricing policies: Learning and earning under a binary prior distribution. Management Science, 58(3): 570–586 doi:10.1287/mnsc.1110.1426
Heemels W P, Johansson K H, Tabuada P (2012). An introduction to event-triggered and self-triggered control. In: 51st IEEE Conference on Decision and Control (CDC). Maui, HI, 3270–3285
Hegedus C, Franko A, Varga P (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
Heim S, Clemens J, Steck J E, Basic C, Timmons D, Zwiener K (2020). Predictive maintenance on aircraft and applications with digital twin. In: 8th IEEE International Conference on Big Data. Atlanta, GA, 4122–4127
Hippold S (2020). Coronavirus: How to secure your supply chain. Available at: gartner.COM/smarterwithgartner/coronavirus-how-to-secure-your-supply-chain
Ho G T S, Tang Y M, Tsang K Y, Tang V, Chau K Y (2021). A blockchain-based system to enhance aircraft parts traceability and trackability for inventory management. Expert Systems with Applications, 179: 115101 doi:10.1016/j.eswa.2021.115101
Hong L J, Jiang G X (2019). Offline simulation online application: A new framework of simulation-based decision making. Asia-Pacific Journal of Operational Research, 36(6): 1940015 doi:10.1142/S0217595919400153
Internet of Business (2017). Uncertainty persists around ownership and value of IoT data. Available at: internetofbusiness.COM/uncertainty-ownership-value-iot-data-persists
Ivanov D (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 doi:10.1016/j.tre.2020.101922 PMID:32288597
Ivanov D, Dolgui A (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
Ivanov D, Dolgui A (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 doi:10.1080/09537287.2020.1768450
Ivanov D, Dolgui A, Das A, Sokolov B (2019). Digital supply chain twins: Managing the ripple effect, resilience, and disruption risks by data-driven optimization, simulation, and visibility. In: Handbook of ripple effects in the supply chain. 309-332
Jiang G X, Hong L J, Nelson B L (2020). Online risk monitoring using offline simulation. INFORMS Journal on Computing, 32(2): 356–375 doi:10.1287/ijoc.2019.0892
Joannou D, Kalawsky R, Martinez-Garcia M, Fowler C, Fowler K (2020). Realizing the role of permissioned blockchains in a systems engineering lifecycle. Systems, 8(4): 41 doi:10.3390/systems8040041
Kalaboukas K, Rozanec J, Kosmerlj A, Kiritsis D, Arampatzis G (2021). Implementation of cognitive digital twins in connected and agile supply networks: An operational model. Applied Sciences, 11(9): 4103 doi:10.3390/app11094103
Kanak A, Ugur N, Ergun S (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
Kanak A, Ugur N, Ergun S (2020). Diamond accountability model for blockchain-enabled cyber–physical systems. In: IEEE 1st International Conference on Human–Machine Systems. Rome, 1–5
Kang N, Shen H, Xu Y (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
Kenett R S, Bortman J (2021). The digital twin in Industry 4.0: A wide-angle perspective. Quality and Reliability Engineering International, in press, doi:10.1002/qre.2948
Klappich D (2019). Hype cycle for supply chain execution technologies. Available at: gartner.COM/en/documents/3947306/hype-cycle-for-supply-chain-execution-technologies-2019
Landolfi G, Menato S, Sorlini M, Valdata A, Rovere D, Fornasiero R, Pedrazzoli P (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
Lee D, Lee S (2021). Digital twin for supply chain coordination in modular construction. Applied Sciences, 11(13): 5909 doi:10.3390/app11135909
Leng J, Ruan G, Jiang P, Xu K, Liu Q, Zhou X, Liu C (2020). Blockchain-empowered sustainable manufacturing and product lifecycle management in Industry 4.0: A survey. Renewable & Sustainable Energy Reviews, 132: 110112 doi:10.1016/j.rser.2020.110112
Levi R, Perakis G, Uichanco J (2015). The data-driven newsvendor problem: New bounds and insights. Operations Research, 63(6): 1294–1306 doi:10.1287/opre.2015.1422
Li X, Cao J, Liu Z, Luo X (2020). Sustainable business model based on digital twin platform network: The inspiration from Haier’s case study in China. Sustainability, 12(3): 936 doi:10.3390/su12030936
Liyanage L H, Shanthikumar J G (2005). A practical inventory control policy using operational statistics. Operations Research Letters, 33(4): 341–348 doi:10.1016/j.orl.2004.08.003
Lowrey K, Rajeswaran A, Kakade S, Todorov E, Mordatch I (2018). Plan online, learn offline: Efficient learning and exploration via model-based control. arXiv preprint, arXiv:1811.01848
Lucas A (2020). Apple warns on revenue guidance due to production delays, weak demand in China because of coronavirus. Available at: cnbc.COM/2020/02/17/apple-warns-on-coronavirus-it-wont-meet-revenue-guidance-because-of-constrained-iphone-supply-and-suppressed-demand-in-china.html
Lummus R R, Krumwiede D W, Vokurka R J (2001). The relationship of logistics to supply chain management: Developing a common industry definition. Industrial Management & Data Systems, 101(8): 426–432 doi:10.1108/02635570110406730
Ma S, Zhang Y, Liu Y, Yang H, Lv J, Ren S (2020). Data-driven sustainable intelligent manufacturing based on demand response for energy-intensive industries. Journal of Cleaner Production, 274: 123155 doi:10.1016/j.jclepro.2020.123155
Makarov V L, Bakhtizin A R, Beklaryan G L, Akopov A S (2021). Digital plant: Methods of discrete-event modeling and optimization of production characteristics. Business Informatics, 15(2): 7–20 doi:10.17323/2587-814X.2021.2.7.20
Mandolla C, Petruzzelli A M, Percoco G, Urbinati A (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 doi:10.1016/j.compind.2019.04.011
Marmolejo-Saucedo J A (2020). Design and development of digital twins: A case study in supply chains. Mobile Networks and Applications, 25(6): 2141–2160 doi:10.1007/s11036-020-01557-9
Marmolejo-Saucedo J A, Hurtado-Hernandez M, Suarez-Valdes R (2019). Digital twins in supply chain management: A brief literature review. In: International Conference on Intelligent Computing & Optimization. Koh Samui: Springer, 653–661
Marr B (2017). What is digital twin technology and why is it so important? Available at: forbes.COM/sites/bernardmarr/2017/03/06/what-is-digital-twin-technology-and-why-is-it-so-important/
Min S, Mentzer J T (2000). The role of marketing in supply chain management. International Journal of Physical Distribution & Logistics Management, 30(9): 765–787 doi:10.1108/09600030010351462
Minerva R, Lee G M, Crespi N (2020). Digital twin in the IoT context: A survey on technical features, scenarios, and architectural models. Proceedings of the IEEE, 108(10): 1785–1824 doi:10.1109/JPROC.2020.2998530
Moder P, Ehm H, Jofer E (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
Moder P, Ehm H, Ramzy N (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
Moshood T D, Nawanir G, Sorooshian S, Okfalisa O (2021). Digital twins driven supply chain visibility within logistics: A new paradigm for future logistics. Applied System Innovation, 4(2): 29 doi:10.3390/asi4020029
Nasir S B, Ahmed T, Karmaker C L, Ali S M, Paul S K, Majumdar A (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
Olcott S, Mullen C (2020). Digital twin consortium defines digital twin. Available at: blog.digitaltwinconsortium.org/2020/12/digital-twin-consortium-defines-digital-twin.html
Olsen T L, Tomlin B (2020). Industry 4.0: Opportunities and challenges for operations management. Manufacturing & Service Operations Management, 22(1): 113–122 doi:10.1287/msom.2019.0796
Onwude D I, Chen G, Eke-Emezie N, Kabutey A, Khaled A Y, Sturm B (2020). Recent advances in reducing food losses in the supply chain of fresh agricultural produce. Processes, 8(11): 1431 doi:10.3390/pr8111431
Orozco-Romero A, Arias-Portela C Y, Marmolejo-Saucedo J A (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
Panetta K (2017). Gartner’s top 10 strategic technology trends for 2017. Available at: gartner.COM/smarterwithgartner/gartners-top-10-technology-trends-2017/
Panetta K (2018). Gartner’s top 10 strategic technology trends for 2018. Available at: gartner.COM/smarterwithgartner/gartner-top-10-strategic-technology-trends-for-2018/
Panetta K (2019). Gartner’s top 10 strategic technology trends for 2019. Available at: gartner.COM/smarterwithgartner/gartner-top-10-strategic-technology-trends-for-2019/
Park K T, Son Y H, Noh S D (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 doi:10.1080/00207543.2020.1788738
Pehlken A, Baumann S (2020). Urban mining: Applying digital twins for sustainable product cascade use. In: IEEE International Conference on Engineering, Technology and Innovation. Cardiff, 1–7
Pereira M M, Frazzon E M (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 doi:10.1016/j.ijinfomgt.2020.102165
Pettey C (2017). Prepare for the impact of digital twins. Available at: gartner.COM/smarterwithgartner/prepare-for-the-impact-of-digital-twins/
Pilati F, Tronconi R, Nollo G, Heragu S S, Zerzer F (2021). Digital twin of COVID-19 mass vaccination centers. Sustainability, 13(13): 7396 doi:10.3390/su13137396
Power D J (2011). Challenges of real-time decision support. In: Supporting real time decision-making. Springer, 3-11
Preut A, Kopka J P, Clausen U (2021). Digital twins for the circular economy. Sustainability, 13(18): 10467 doi:10.3390/su131810467
Qi Q, Tao F, Hu T, Anwer N, Liu A, Wei Y, Wang L, Nee A Y C (2021). Enabling technologies and tools for digital twin. Journal of Manufacturing Systems, 58: 3–21 doi:10.1016/j.jmsy.2019.10.001
Rajagopal V, Venkatesan S P, Goh M (2017). Decision-making models for supply chain risk mitigation: A review. Computers & Industrial Engineering, 113: 646–682 doi:10.1016/j.cie.2017.09.043
Reeves K, Maple C (2019). Realising the vision of digital twins: Challenges in trustworthiness. In: Living in the Internet of Things (IoT 2019). London, 33
Rehana S (2018). Making a digital twin supply chain a reality. Available at: asug.COM/news/making-a-digital-twin-supply-chain-a-reality
Santos J A M, Lopes M R, Viegas J L, Vieira S M, Sousa J M C (2020). Internal supply chain digital twin of a pharmaceutical company. In: 21st IFAC World Congress on Automatic Control. Berlin: Elsevier, 10797–10802
Sarkar S, Kumar S (2015). A behavioral experiment on inventory management with supply chain disruption. International Journal of Production Economics, 169: 169–178 doi:10.1016/j.ijpe.2015.07.032
Schmitt A J, Singh M (2012). A quantitative analysis of disruption risk in a multi-echelon supply chain. International Journal of Production Economics, 139(1): 22–32 doi:10.1016/j.ijpe.2012.01.004
Schuh G, Anderl R, Gausemeier J, ten Hompel M, Wahlster W (2017). Industrie 4.0 maturity index: Managing the digital transformation of companies. Available at: en.acatech.de/publication/industrie-4-0-maturity-index-managing-the-digital-transformation-of-companies/
Seif A, Toro C, Akhtar H (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
Semenov Y, Semenova O, Kuvataev I (2020). Solutions for digitalization of the coal industry implemented in UC Kuzbassrazrezugol. In: 5th International Innovative Mining Symposium (IIMS). Kemerovo, 01042
Seyedghorban Z, Tahernejad H, Meriton R, Graham G (2020). Supply chain digitalization: Past, present and future. Production Planning and Control, 31(2–3): 96–114 doi:10.1080/09537287.2019.1631461
Shafto M, Conroy M, Doyle R, Glaessgen E, Kemp C, LeMoigne J, Wang L (2012). Modeling, simulation, information technology & processing roadmap. National Aeronautics and Space Administration (NASA)
Sharma M, Singla M K, Nijhawan P, Dhingra A (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
Shen W, Yang C, Gao L (2020). Address business crisis caused by COVID-19 with collaborative intelligent manufacturing technologies. IET Collaborative Intelligent Manufacturing, 2(2): 96–99 doi:10.1049/iet-cim.2020.0041
Shen X, Zhang Y, Tang Y, Qin Y, Liu N, Yi Z (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
Shen Z M, Sun Y (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
Shoji K, Schudel S, Onwude D, Shrivastava C, Defraeye T (2022). Mapping the postharvest life of imported fruits from packhouse to retail stores using physics-based digital twins. Resources, Conservation and Recycling, 176: 105914 doi:10.1016/j.resconrec.2021.105914
Smetana S, Aganovic K, Heinz V (2021). Food supply chains as cyber–physical systems: A path for more sustainable personalized nutrition. Food Engineering Reviews, 13(1): 92–103 doi:10.1007/s12393-020-09243-y
Stanford-Clark A, Frank-Schultz E, Harris M (2019). What are digital twins? Available at: developer.ibm.COM/articles/what-are-digital-twins/
Stark R, Damerau T (2019). Digital twin. In: Cirp encyclopedia of production engineering. 1-8
Sung I, Choi B, Nielsen P (2021). On the training of a neural network for online path planning with offline path planning algorithms. International Journal of Information Management, 57: 102142 doi:10.1016/j.ijinfomgt.2020.102142
Tohamy N (2019). Hype cycle for supply chain strategy. Available at: gartner.COM/en/documents/3947438/hype-cycle-for-supply-chain-strategy-2019
Tozanlı O, Kongar E, Gupta S M (2020). Evaluation of waste electronic product trade-in strategies in predictive twin disassembly systems in the era of blockchain. Sustainability, 12(13): 5416 doi:10.3390/su12135416
Ulmer M W (2019). Anticipation versus reactive reoptimization for dynamic vehicle routing with stochastic requests. Networks, 73(3): 277–291 doi:10.1002/net.21861
Wang K, Hu Q, Zhou M, Zun Z, Qian X (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 doi:10.1016/j.cstp.2021.06.014
Wang K, Xie W, Wang B, Pei J, Wu W, Baker M, Zhou Q (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
Wang S (2021). Users intend to have the right to choose to close the algorithm recommendation service. Available at: news.cn/legal/2021-08/27/c_1127801496.htm (in Chinese)
Wayland M (2020). Coronavirus impact spreads to European auto plant and could hit GM truck production. Available at: cnbc.COM/2020/02/14/coronavirus-impact-to-potentially-disrupt-gm-truck-production.html
Wilson R, Mercier P H J, Patarachao B, Navarra A (2021). Partial least squares regression of oil sands processing variables within discrete event simulation digital twin. Minerals, 11(7): 689 doi:10.3390/min11070689
Wu L, Yue X, Jin A, Yen D C (2016). Smart supply chain management: A review and implications for future research. International Journal of Logistics Management, 27(2): 395–417 doi:10.1108/IJLM-02-2014-0035
Wu T, Huang S M, Blackhurst J, Zhang X L, Wang S S (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 doi:10.1109/TEM.2012.2190986
Yang J, Lee S, Kang Y S, Noh S D, Choi S S, Jung B R, Lee S H, Kang J T, Lee D Y, Kim H S (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
Zafarzadeh M, Wiktorsson M, Baalsrud Hauge J (2021). A systematic review on technologies for data-driven production logistics: Their role from a holistic and value creation perspective. Logistics, 5(2): 24 doi:10.3390/logistics5020024
