Digital Twins in Supply Chain Management: A Brief Literature Review

The rapid interest in the continuous improvement of supply chain management systems has motivated the development of digital tools in the automation of business problems. Currently, companies must continually adapt to changing conditions with respect to the management of their supply chain. However, the lack of real-time data available and responsive planning systems make this adaptation difficult. The current situation of the technology of digital twins is to migrate to the digital. More and more companies will develop and introduce their own digital twins in their business processes. This manuscript presents a literature review of the current context of digital twins. A total of 4884 searches combining keywords with respect to digital twins were analyzed. The years analyzed in the databases were 2017–2019.

[1]  Jian Zhang,et al.  Review of job shop scheduling research and its new perspectives under Industry 4.0 , 2017, Journal of Intelligent Manufacturing.

[2]  Fei Tao,et al.  Digital twin-driven product design, manufacturing and service with big data , 2017, The International Journal of Advanced Manufacturing Technology.

[3]  Rainer Stark,et al.  Development and operation of Digital Twins for technical systems and services , 2019, CIRP Annals.

[4]  Mohammad Abdullah Al Faruque,et al.  Manufacturing Supply Chain and Product Lifecycle Security in the Era of Industry 4.0 , 2018, J. Hardw. Syst. Secur..

[5]  Sang Do Noh,et al.  Smart manufacturing: Past research, present findings, and future directions , 2016, International Journal of Precision Engineering and Manufacturing-Green Technology.

[6]  Yu Zheng,et al.  An application framework of digital twin and its case study , 2018, Journal of Ambient Intelligence and Humanized Computing.

[7]  Lin Sun,et al.  Modular based flexible digital twin for factory design , 2018, Journal of Ambient Intelligence and Humanized Computing.

[8]  Khamdi Mubarok,et al.  Smart manufacturing systems for Industry 4.0: Conceptual framework, scenarios, and future perspectives , 2018, Frontiers of Mechanical Engineering.

[9]  Alexandre Dolgui,et al.  Review of quantitative methods for supply chain resilience analysis , 2019, Transportation Research Part E: Logistics and Transportation Review.

[10]  Luca Fumagalli,et al.  Flexible Automation and Intelligent Manufacturing , FAIM 2017 , 27-30 June 2017 , Modena , Italy A review of the roles of Digital Twin in CPS-based production systems , 2017 .

[11]  Wilfried Sihn,et al.  Digital Twin in manufacturing: A categorical literature review and classification , 2018 .

[12]  Jianhua Liu,et al.  Digital twin-based smart production management and control framework for the complex product assembly shop-floor , 2018, The International Journal of Advanced Manufacturing Technology.

[13]  Xiaojun Liu,et al.  Digital twin-based process reuse and evaluation approach for smart process planning , 2018, The International Journal of Advanced Manufacturing Technology.

[14]  Zhe Wang,et al.  A framework for shopfloor material delivery based on real-time manufacturing big data , 2018, Journal of Ambient Intelligence and Humanized Computing.

[15]  Stefan Boschert,et al.  Digital Twin—The Simulation Aspect , 2016 .

[16]  Qiang Liu,et al.  Digital twin-driven manufacturing cyber-physical system for parallel controlling of smart workshop , 2018, Journal of Ambient Intelligence and Humanized Computing.

[17]  E. Manavalan,et al.  A review of Internet of Things (IoT) embedded sustainable supply chain for industry 4.0 requirements , 2019, Comput. Ind. Eng..

[18]  Qi Li,et al.  Big Data Driven Supply Chain Management , 2019, Procedia CIRP.