Digital twin-driven smart supply chain

Lu WANG , Tianhu DENG , Zuo-Jun Max SHEN , Hao HU , Yongzhi QI

Front. Eng ›› 2022, Vol. 9 ›› Issue (1) : 56 -70.

PDF (538KB)
Front. Eng ›› 2022, Vol. 9 ›› Issue (1) : 56 -70. DOI: 10.1007/s42524-021-0186-9
REVIEW ARTICLE
REVIEW ARTICLE

Digital twin-driven smart supply chain

Author information +
History +
PDF (538KB)

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.

Graphical abstract

Keywords

digital twin / supply chain management

Cite this article

Download citation ▾
Lu WANG, Tianhu DENG, Zuo-Jun Max SHEN, Hao HU, Yongzhi QI. Digital twin-driven smart supply chain. Front. Eng, 2022, 9(1): 56-70 DOI:10.1007/s42524-021-0186-9

登录浏览全文

4963

注册一个新账户 忘记密码

References

[1]

AlMulhim A F (2021). Smart supply chain and firm performance: The role of digital technologies. Business Process Management Journal, 27(5): 1353–1372

[2]

Anasoft (2019). Digital twin: Smart industry and intelligent enterprise. Available at:

[3]

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

[4]

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

[5]

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

[6]

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

[7]

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)

[8]

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

[9]

Beltrami M, Orzes G, Sarkis J, Sartor M (2021). Industry 4.0 and sustainability: Towards conceptualization and theory. Journal of Cleaner Production, 312: 127733

[10]

Bertsimas D, Thiele A (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]

Boschert S, Rosen R (2016). Digital twin—the simulation aspect. In: Hehenberger P, Bradley D, eds. Mechatronic Futures. Cham: Springer, 59–74

[12]

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

[13]

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

[14]

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

[15]

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

[16]

Butner K (2010). The smarter supply chain of the future. Strategy and Leadership, 38(1): 22–31

[17]

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

[18]

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

[19]

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

[20]

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

[21]

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

[22]

Chen Z, Huang L (2021). Digital twins for information-sharing in remanufacturing supply chain: A review. Energy, 220: 119712

[23]

Christopher M (2011). Logistics and Supply Chain Management, 4th ed. London: Pearson

[24]

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

[25]

Colicchia C, Dallari F, Melacini M (2010). Increasing supply chain resilience in a global sourcing context. Production Planning and Control, 21(7): 680–694

[26]

Cozmiuc D, Petrisor I (2018). Industrie 4.0 by Siemens: Steps made today. Journal of Cases on Information Technology, 20(2): 30–48

[27]

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

[28]

Daugherty P, Carrel-Billiard M, Biltz M (2021). Accenture technology vision 2021. Available at:

[29]

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

[30]

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

[31]

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

[32]

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

[33]

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

[34]

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

[35]

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

[36]

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

[37]

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

[38]

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

[39]

European Union (2018). The General Data Protection Regulation (GDPR). Available at:

[40]

Feng Q, Shanthikumar J G (2018). Supply and demand functions in inventory models. Operations Research, 66(1): 77–91

[41]

Frazzon E M, Agostino I R S, Broda E, Freitag M (2020). Manufactur-ing networks in the era of digital production and operations: A socio–cyber–physical perspective. Annual Reviews in Control, 49: 288–294

[42]

Fuller A, Fan Z, Day C, Barlow C (2020). Digital twin: Enabling technologies, challenges and open research. IEEE Access, 8: 108952–108971

[43]

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

[44]

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

[45]

Ghobakhloo M (2018). The future of manufacturing industry: A strategic roadmap toward Industry 4.0. Journal of Manufacturing Technology Management, 29(6): 910–936

[46]

Glaessgen E H, Stargel D S (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]

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

[48]

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

[49]

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

[50]

Greif T, Stein N, Flath C M (2020). Peeking into the void: Digital twins for construction site logistics. Computers in Industry, 121: 103264

[51]

Grieves M (2005). Product lifecycle management: The new paradigm for enterprises. International Journal of Product Development, 2(1/2): 71–84

[52]

Grieves M (2006). Product Lifecycle Management: Driving the Next Generation of Lean Thinking. New York: McGraw Hill

[53]

Grieves M (2011). Virtually Perfect: Driving Innovative and Lean Products through Product Lifecycle Management. Brevard County: Space Coast Press

[54]

Grieves M (2015). Digital twin: Manufacturing excellence through virtual factory replication. Whitepaper

[55]

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

[56]

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

[57]

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

[58]

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

[59]

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

[60]

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

[61]

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

[62]

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

[63]

Hippold S (2020). Coronavirus: How to secure your supply chain. Available at:

[64]

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

[65]

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

[66]

Internet of Business (2017). Uncertainty persists around ownership and value of IoT data. Available at:

[67]

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

[68]

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

[69]

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

[70]

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: Ivanov D, Dolgui A, Sokolov B, eds. Handbook of Ripple Effects in the Supply Chain. Cham: Springer, 309–332

[71]

Jiang G X, Hong L J, Nelson B L (2020). Online risk monitoring using offline simulation. INFORMS Journal on Computing, 32(2): 356–375

[72]

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

[73]

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

[74]

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

[75]

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

[76]

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

[77]

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

[78]

Klappich D (2019). Hype cycle for supply chain execution technologies. Available at:

[79]

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

[80]

Lee D, Lee S (2021). Digital twin for supply chain coordination in modular construction. Applied Sciences, 11(13): 5909

[81]

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

[82]

Levi R, Perakis G, Uichanco J (2015). The data-driven newsvendor problem: New bounds and insights. Operations Research, 63(6): 1294–1306

[83]

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

[84]

Liyanage L H, Shanthikumar J G (2005). A practical inventory control policy using operational statistics. Operations Research Letters, 33(4): 341–348

[85]

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

[86]

Lucas A (2020). Apple warns on revenue guidance due to production delays, weak demand in China because of coronavirus. Available at:

[87]

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

[88]

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

[89]

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

[90]

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

[91]

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

[92]

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

[93]

Marr B (2017). What is digital twin technology and why is it so important? Available at:

[94]

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

[95]

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

[96]

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

[97]

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

[98]

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

[99]

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

[100]

Olcott S, Mullen C (2020). Digital twin consortium defines digital twin. Available at:

[101]

Olsen T L, Tomlin B (2020). Industry 4.0: Opportunities and challenges for operations management. Manufacturing & Service Operations Management, 22(1): 113–122

[102]

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): 1–31

[103]

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

[104]

Panetta K (2017). Gartner’s top 10 strategic technology trends for 2017. Available at:

[105]

Panetta K (2018). Gartner’s top 10 strategic technology trends for 2018. Available at:

[106]

Panetta K (2019). Gartner’s top 10 strategic technology trends for 2019. Available at:

[107]

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

[108]

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

[109]

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

[110]

Pettey C (2017). Prepare for the impact of digital twins. Available at:

[111]

Pilati F, Tronconi R, Nollo G, Heragu S S, Zerzer F (2021). Digital twin of COVID-19 mass vaccination centers. Sustainability, 13(13): 7396

[112]

Power D J (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]

Preut A, Kopka J P, Clausen U (2021). Digital twins for the circular economy. Sustainability, 13(18): 10467

[114]

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

[115]

Rajagopal V, Venkatesan S P, Goh M (2017). Decision-making models for supply chain risk mitigation: A review. Computers & Industrial Engineering, 113: 646–682

[116]

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

[117]

Rehana S (2018). Making a digital twin supply chain a reality. Available at:

[118]

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

[119]

Sarkar S, Kumar S (2015). A behavioral experiment on inventory management with supply chain disruption. International Journal of Production Economics, 169: 169–178

[120]

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

[121]

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:

[122]

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

[123]

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

[124]

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

[125]

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)

[126]

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

[127]

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

[128]

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

[129]

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

[130]

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

[131]

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

[132]

Stanford-Clark A, Frank-Schultz E, Harris M (2019). What are digital twins? Available at:

[133]

Stark R, Damerau T (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]

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

[135]

Tohamy N (2019). Hype cycle for supply chain strategy. Available at:

[136]

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

[137]

Ulmer M W (2019). Anticipation versus reactive reoptimization for dynamic vehicle routing with stochastic requests. Networks, 73(3): 277–291

[138]

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

[139]

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

[140]

Wang S (2021). Users intend to have the right to choose to close the algorithm recommendation service. Available at: in Chinese)

[141]

Wayland M (2020). Coronavirus impact spreads to European auto plant and could hit GM truck production. Available at:

[142]

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

[143]

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

[144]

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

[145]

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

[146]

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

RIGHTS & PERMISSIONS

The Author(s) 2022. This article is published with open access at link.springer.com and journal.hep.com.cn

AI Summary AI Mindmap
PDF (538KB)

14295

Accesses

0

Citation

Detail

Sections
Recommended

AI思维导图

/