# A Survey of Digital Supply Chain Twins' Implementations

Kristine Joy Dy, Jessica Olivares-Aguila, Alejandro Vital-Soto

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Kristine Joy Dy, Jessica Olivares-Aguila, Alejandro Vital-Soto. A Survey of Digital Supply Chain Twins' Implementations. IFIP International Conference on Advances in Production Management Systems (APMS), Sep 2022, Gyeongju, South Korea. pp.502-509, 10.1007/978-3-031-16407-1_59 .
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# A Survey of Digital Supply Chain Twins’ Implementations

Kristine Joy Dy, Jessica Olivares-Aguila✉ ID, and Alejandro Vital-Soto ID

Shannon School of Business, Cape Breton University, Sydney, NS B1P 6L2, Canada
{CBU20HXJ,jessica_olivares,alejandro_vital}@cbu.ca

**Abstract.** This paper examines the implementations of digital twins (DTs) in the context of supply chains (SCs) and disruption risks. The concept of a digital supply chain twin (DSCT) has been a trending topic in recent years, but very little of the literature deals with case studies and actual applications. Therefore, this study concentrates on reviewing the implementations of DSCTs. Moreover, disruption risks associated with the current pandemic have encouraged the adoption of DSCTs to improve SC resilience and agility. For those reasons, this study analyzes the literature according to different industry sectors’ implementations (e.g., food, aerospace and automotive, construction, pharmaceutical, and general manufacturing industries). Additionally, literature concentrated on applications for SC risks is studied. This literature review unveils the current developments of DSCTs in the specific industries covered and risks applications. Hence, this work is intended to help SC practitioners and researchers identify challenges and potential research areas for DSCTs.

**Keywords:** Supply chain · Digital twin · Disruption risks · Literature review

## 1 Introduction

Supply chains (SCs) are intricate networks that represent the backbone of economies globally. Hence, paralysis in the SC escalates into paralysis of the broader economy. Disruption risks are unforeseen events triggered by incidents such as natural and human-made catastrophes, which lead to disorderly flow within the SC. Uncertainty is difficult to eradicate; hence, awareness and preparedness are imperative to overcoming and quickly recovering from disruption risks (i.e., SC resiliency) [1].

A digital supply chain twin (DSCT) is a virtual replica that represents the network state for any given time to simulate the behaviour of their real counterparts [2, 3]. The combination of a triad – simulation modelling, optimization, and data analytics – comprised the necessary technologies to design a DSCT model. DSCTs allow end-to-end SC visibility to improve resilience and test contingency plans. Implementation of DSCTs will allow for continuous feedback between the cyber and physical systems, which ultimately could help design more efficient SC processes. At the same time, DSCTs will improve the ability to collect real-time data across the SC. Although the
digital twin market size is expected to reach 48.2 billion by 2026 [4], there are a few documented DSCT implementations.

While several literature surveys are available related to DSCTs, they do not concentrate on studying applications for specific industry sectors and SC disruption risks. Therefore, this study fills this gap in the literature.

This article is organized as follows. In Sect. 2, the search methodology is described. Section 3 organizes the available literature according to the different sectors in which DSCTs have been implemented and applications for disruption risks. Section 4 discusses the findings and concludes with future research avenues.

## 2 Methodology

The search was performed on January 4th, 2022, using the SCOPUS database. Due to space constraints, only journal articles containing the keywords (supply AND chain AND digital AND twin) in the title, abstract, or keywords were selected. The search resulted in 82 articles that were read and classified as frameworks, case studies and real-life implementations. Due to space limitations, only articles related to the scope of the study are included.

## 3 State-of-the-Art of DSCTs

In this section, the body of literature on DSCTs is classified into two main categories: industry sectors and DSCTs for disruption risks.

### 3.1 Industrial Sectors

In the literature, there have been several implementations of DSCTs; these applications were grouped according to the sector recurrency.

**Food Industry Applications.** Over the years, advancements in technology have improved and increased food production, variety, and even global trade. However, food systems are still faced with the growing population, food waste, traceability, and sustainability. The vulnerability of food SCs was also exposed by the COVID-19 pandemic [5]. DTs were considered a promising approach to addressing the many challenges in the food industry [5]. Most of the available research (e.g., [6–9]) in this sector was focused on DTs’ applications to the SC transportation function to monitor changes in the quality of fresh produce (biochemical response) using sensors to adapt cooling temperatures throughout the cold chain. Therefore, DTs are a valuable tool to enhance refrigeration and logistics to prevent food wastage, resulting in a greener refrigerated SC [7].

Opportunities for cost reduction during product development using the cyber twin-based model [10] and utilization of artificial intelligence (AI) in the farming industry [11] are other valuable benefits of DTs in the food industry.

To understand the SC dynamics and behaviour, a DT-based analysis of the impact of COVID-19 disruptions on the resilience of a food retail SC was presented in [12]. Similarly, for the food retail SC, Sharma et al. [13] developed a last-mile distribution system
for food and essential goods during a pandemic that does not rely upon in-store customer visits or human employees. They used a Robotic Drive Through System and a robotic simulation software tool to create a DT of the robotic work-cell, considering real-world constraints and process requirements.

**Aerospace and Automotive Industry Applications.** The aerospace industry has one of the most stringent requirements for safety compliance. However, the fast-evolving aircraft SC industry also led to the rapid increase in the spare parts that need to be monitored for airworthiness compliance. In [14], a DT application for inventory management is presented. They implemented a blockchain-based system for the traceability and trackability of aircraft spare parts from original equipment manufacturers throughout the SC. Also, a DT conceptual framework to analyze the creation of a DT for additive manufacturing using blockchain, allowing retracing a component's history faster and in unaltered manner, was proposed in [15].

For the automotive industry, Lugaresi and Matta [16] recommended a method for discovering production system structures and automatically developing digital models starting from the manufacturing systems' event logs. The model was tested in two test cases and one real-life implementation in the automatic assembly line of electric motor vehicles. The study's findings revealed that the model could generate a real-time detailed digital model of the physical counterpart at any time within one minute. In the same way, Sharma et al. [17] proposed to automate and standardize the battery assembly line of electric vehicles using robotic simulation software to create a DT model of the robotic work-cell.

Marmolejo-Saucedo [18] developed a DT that studied the combined vehicle routing and bin-packing problems. These problems were solved by implementing a hybrid optimization-simulation framework. A hypothetical case of an automotive parts manufacturing center in Mexico was developed to verify the proposed DT performance.

**Construction Industry Applications.** Unlike other sectors, the application of DTs in the construction industry remains ill-defined, without a solid foundation for designing infrastructure [19]. Several studies provided some DSCT applications within this sector. Collectively, these studies outlined the critical role of the Building Information Model (BIM), which was considered the core technology of DTs for construction.

Sacks et al. [19] proposed a data-driven planning and control workflow for designing and constructing infrastructures using DTs. Similarly, Al-Saeed et al. [20] developed a framework to automate the construction manufacturing procedures. A case study was done within a construction product manufacturer located in the UK. That research showed that BIM Digital Objects (BDO) could be a catalyst to automate the processes and workflows, gain financial savings, and reduce material wastage. Lee and Lee [21] also provided a DT framework to simulate and coordinate logistics scenarios and predict accurate delivery in the modular construction industry, which usually operates in just-in-time delivery. That framework created a real-time virtual replica of the modules based on BIM. A geographic system information-based routing application was then used to simulate various logistic scenarios using the virtual replica in real-time.

Greif et al. [22] described the digital silo twin that monitors and tracks the silos' fill level used to store construction bulk materials. The tracking and monitoring capability
was possible by installing sensors in the silo. Shifting from a traditional to a smart silo system, it should ideally be cost-effective, safe, and with an integrated system.

**Pharmaceutical Industry Applications.** In [23], a case study was analyzed on a sterile drug product manufacturing line of the Roche Diagnostics GmbH in Germany to reduce lead-time through improved scheduling while predicting and quantifying the risks during delivery. The result demonstrated the potential of DSCTs in this industry. A DSCT application to forecast several what-if scenarios when facing a disruptive event in the pharmaceutical industry was presented in [24]. A combination of facility location models and dynamic simulation techniques were recommended to aid in the SC's mid- and short-term decision-making. For DT trackability and traceability, the usage of blockchain was suggested in [25]. They analyzed a case study for a pharmaceutical SC.

**General Manufacturing Industry Applications.** A considerable amount of literature has been published on the applications of DTs in the SC manufacturing industry. DTs are becoming an integral part of a product, asset, and infrastructure operation to achieve a sustainable manufacturing SC [26]. A case study in household appliances manufacturing in China described how enterprises could utilize DTs to promote and shift to a sustainable business model [27]. Applications of DTs are also beneficial to solving information asymmetries problems and accurately optimizing the purchasing and transportation processes of the remanufacturing industry [28]. Advancement in technology also contributes to shortened product life cycles resulting in waste management issues. Tozanliet al. [29] proposed a DT-based simulated product-recovery system for trade-in policymaking to improve reverse logistics and remanufacturing operations in electronics manufacturing.

DTs have been used to optimize the (re)design and process of the drilling operation of printed circuit boards to reduce the manufacturing time [30]. Similarly, Martin et al. [31] demonstrated the significance of DTs in reducing the required time and resources in the design and development of LED luminaires. The project experimented through a case study between a small and medium-sized enterprise (SME) and a large company wherein a method for creating a LED DT using different simulation software and modelling tools was developed. Frankó et al. [32] highlighted the significance of DTs in developing reliable, low-cost, and scalable solutions for asset tracking in less automated environments such as mid-scale manufacturing and warehousing sites.

In make-to-order manufacturing (MTO), a DT-centric architectural framework was proposed for a cyber-physical logistics and production system to provide SC control technical functionalities [33]. Moreover, a simulation and supervised machine learning model for resilient supplier selection in MTO was conceptualized [3]. The study revealed that a DSCT could predict a more reliable on-time delivery of suppliers.

The creation of a blockchain-powered DT was proposed in [34] to cover the entire data flow going from the machine or parcel parameter tracking to the operator monitoring dashboard. The model was tested on two manufacturing SC scenarios - internal (robotized warehouse and transportation) and external logistic (shipment condition monitoring and goods value real-time modification).

Open SC management was proposed in [35]. Using smart designing software and smart manufacturing machines, customers access customized products through DT technology, integrated networks, and smart devices. They implemented the concept of the open SC paradigm in the apparel industry.
## 3.2 DSCT for Disruption Risks and Resilience

In recent years, there has been an increasing interest in the design and implementation of DTs when managing SC disruption risks. Collaborative intelligent technologies such as cloud manufacturing, IoT, blockchain, data analytics, and DTs can help the manufacturing industry be resilient and viable when facing major disruptions. A Reconfigurable SC or the X-network, which exhibits some crucial design and control characteristics for SC adaptation to ever-changing environments, was proposed in [36].

In [2], a generic structure of a DSCT for managing disruption risks was proposed. At the early stage of SC design and pre-disruption, the model will visualize the potential SC risks, including disruption. In the dynamic mode, the model will use real-time data to simulate the SC disruptions impacts. Disruptions caused by long-term crises such as the pandemic urged businesses and the research community to engage in SC stress testing to examine resilience and viability [37]. Therefore, the current COVID-19 pandemic has highlighted the importance of DSCTs in analyzing the system performance. An off-line DT was developed in [38] to analyze the pandemic's impacts and potential recovery paths on a global SC with upstream facilities in China. Similarly, a DT to predict the impacts of the pandemic was presented in [39]. The DT simulation and optimization can help predict both short- and long-term effects of epidemic outbreaks on the SC [39]. In the same vein, Burgos and Ivanov [12] developed a DT-based analysis of the impact of COVID-19 disruptions on the resilience of a food retail SC with the use of simulation and optimization software.

A DSCT to forecast several what-if scenarios when facing a disruptive event was presented in [24]. DSCTs provide a breakthrough in implementing an interconnected SC structure so that managers can digitally link all the members to get the benefits of increased SC visibility, develop contingency plans, and identify and manage threads in networked SC. Thus, SMEs can benefit from DSCTs by increasing visibility across the SC and predicting risks during disruptions [40].

## 4 Discussion and Closing Remarks

Although DSCTs have been a trending topic in recent years, most articles concentrate on providing frameworks. Only a small portion of the literature deals with case studies and even a smaller fraction with actual implementations. These studies are paving the way for broader adoption. However, there are still many opportunities and challenges to be addressed. Through the review, several insights have been unveiled:

* DTs have been sparsely implemented for the different SCs functions (e.g., procurement, logistics, distribution, retail)

* Most implementations focus on an asset-centric perspective (e.g., hyper-connected objects and virtual representation). However, an SC-led perspective to enhance sensing and adjusting capabilities of the SC environment is scarcely studied

* The multi-structural composition of SC networks and the implications for the DSCT need to be addressed (e.g., organizational, financial, informational changes)

* Although DTs require on- and off-line interaction between the physical and digital world, a small fraction of studies consider it
* In the food industry, there is a risk of DTs neglecting real-world systems (farms, fields, animals) as it is easier to make decisions on the virtual system

* DSCTs are mainly applied in the manufacturing sectors. However, DT implementations in the service sector are still missing (e.g., healthcare industry)

* While DSCTs offer planning and controlling capabilities at the tactical/operational level, the advantages on the strategic decision-making level to provide business intelligence and enhance the business ecosystem need to be further exploited

* SCs include organizations of different sizes. However, there are many challenges in devising and integrating DSCTs for SMEs

* Closed-loop SC, sustainability, and circular economy objectives can be achieved using DSCTs capabilities

* The current pandemic has boosted the adoption of DTs. Combined with SC risk management, they will allow preparedness and recovery for such events. Therefore, boosting SC resilience and agility

* While there is a variety of commercial software, most of the few DSCT implementations have been devised with anyLogistix software

This literature review will be instrumental for SC practitioners and researchers that would like to know about the DSCTs' advancements in the studied industries and disruption risks. Future research should concentrate on real-case implementations, capitalize on real data to improve the DTs' fidelity and combine technologies such as blockchain to provide more secure data transfer.

## References

1. Olivares-Aguila, J., Vital-Soto, A.: Supply chain resilience roadmaps for major disruptions. Logistics 5(4), 78 (2021). https://doi.org/10.3390/logistics5040078

2. Ivanov, D., Dolgui, A.: A digital supply chain twin for managing the disruption risks and resilience in the era of Industry 4.0. Prod. Plan. Control 32(9), 775-788 (2021). https://doi.org/10.1080/09537287.2020.1768450

3. Cavalcante, I.M., Frazzon, E.M., Forcellini, F.A., Ivanov, D.: A supervised machine learning approach to data-driven simulation of resilient supplier selection in digital manufacturing. Int. J. Inf. Manag. 49, 86-97 (2019). https://doi.org/10.1016/j.ijinfomgt.2019.03.004

4. Digital Twin Market - Global Forecast to 2026. https://www.marketsandmarkets.com/

5. Henrichs, E., Noack, T., Piedrahita, A.M.P., Salem, M.A., Stolz, J., Krupitzer, C.: Can a byte improve our bite? an analysis of digital twins in the food industry. Sensors 22(1), 115 (2022)

6. Shoji, K., Schudel, S., Onwude, D., Shrivastava, C., Defraeye, T.: Mapping the postharvest life of imported fruits from packhouse to retail stores using physics-based digital twins. Res. Cons. Recycl. 176, 105914 (2022). https://doi.org/10.1016/j.resconrec.2021.105914

7. Defraeye, T., et al.: Digital twins probe into food cooling and biochemical quality changes for reducing losses in refrigerated supply chains. Res. Cons. Recycl. 149, 778-794 (2019). https://doi.org/10.1016/j.resconrec.2019.06.002

8. Defraeye, T., et al.: Digital twins are coming: will we need them in supply chains of fresh horticultural produce? Trends Food Sci. Technol. 109, 245-258 (2021)

9. Tagliavini, G., Defraeye, T., Carmeliet, J.: Multiphysics modeling of convective cooling of non-spherical, multi-material fruit to unveil its quality evolution throughout the cold chain. Food Bioprod. Process. 117, 310-320 (2019). https://doi.org/10.1016/j.fbp.2019.07.013
10. Bamunuarachchi, D., Georgakopoulos, D., Banerjee, A., Jayaraman, P.P.: Digital twins supporting efficient digital industrial transformation. Sensors **21**(20), 6829 (2021)

11. Smith, M.J.: Getting value from artificial intelligence in agriculture. Anim. Prod. Sci. **60**(1), 46–54 (2019). https://doi.org/10.1071/AN18522

12. Burgos, D., Ivanov, D.: Food retail supply chain resilience and the COVID-19 pandemic: a digital twin-based impact analysis and improvement directions. Transp. Res. Part E: Logist. Transp. Rev. **152**, 102412 (2021). https://doi.org/10.1016/j.tre.2021.102412

13. Sharma, A., Zanotti, P., Musunur, L.P.: Drive through robotics: robotic automation for last mile distribution of food and essentials during pandemics. IEEE Access **8**, 127190–127219 (2020). https://doi.org/10.1109/ACCESS.2020.3007064

14. Ho, G.T.S., Tang, Y.M., Tsang, K.Y., Tang, V., Chau, K.Y.: A blockchain-based system to enhance aircraft parts traceability and trackability for inventory management. Expert Syst. Appl. **179**, 115101 (2021). https://doi.org/10.1016/j.eswa.2021.115101

15. Mandolla, C., Petruzzelli, A.M., Percoco, G., Urbinati, A.: Building a digital twin for additive manufacturing through the exploitation of blockchain: a case analysis of the aircraft industry. Comput. Ind. **109**, 134–152 (2019). https://doi.org/10.1016/j.compind.2019.04.011

16. Lugaresi, G., Matta, A.: Automated manufacturing system discovery and digital twin generation. J. Manuf. Syst. **59**, 51–66 (2021)

17. Sharma, A., Zanotti, P., Musunur, L.P.: Enabling the electric future of mobility: robotic automation for electric vehicle battery assembly. IEEE Access **7**, 170961–170991 (2019)

18. Marmolejo-Saucedo, J.A.: Digital twin framework for large-scale optimization problems in supply chains: a case of packing problem. Mob. Netw. Appl. (2021)

19. Sacks, R., Brilakis, I., Pikas, E., Xie, H.S., Girolami, M.: Construction with digital twin information systems. Data-Centric Eng. **1**(6) (2020). https://doi.org/10.1017/dce.2020.16

20. Al-Saeed, Y., Edwards, D.J., Scaysbrook, S.: Automating construction manufacturing procedures using BIM digital objects (BDOs): Case study of knowledge transfer partnership project in UK. Constr. Innov. **20**(3), 345–377 (2020)

21. Lee, D., Lee, S.: Digital twin for supply chain coordination in modular construction. Appl. Sci. (Switzerland) **11**(13), 5909 (2021). https://doi.org/10.3390/app11135909

22. Greif, T., Stein, N., Flath, C.M.: Peeking into the void: digital twins for construction site logistics. Comput. Ind. **121**, 103264 (2020). https://doi.org/10.1016/j.compind.2020.103264

23. Spindler, J., Kec, T., Ley, T.: Lead-time and risk reduction assessment of a sterile drug product manufacturing line using simulation. Comput. Chem. Eng. **152**, 107401 (2021). https://doi.org/10.1016/j.compchemeng.2021.107401

24. Marmolejo-Saucedo, J.A.: Design and development of digital twins: a case study in supply chains. Mobile Netw. Appl. **25**(6), 2141–2160 (2020). https://doi.org/10.1007/s11036-020-01557-9

25. Alles, M., Gray, G.L.: “The first mile problem”: deriving an endogenous demand for auditing in blockchain-based business processes. Int. J. Account. Inf. Syst. **38**, 100465 (2020). https://doi.org/10.1016/j.accinf.2020.100465

26. Kamble, S.S., Gunasekaran, A., Parekh, H., Mani, V., Belhadi, A., Sharma, R.: Digital twin for sustainable manufacturing supply chains: current trends, future perspectives, and an implementation framework. Technol. Forecast. Soc. Change **176**, 12144 (2022)

27. Li, X., Cao, J., Liu, Z., Luo, X.: Sustainable business model based on digital twin platform network: the inspiration from Haier's case study in China. Sustainability (Switzerland) **12**(3), 1–26 (2020). https://doi.org/10.3390/su12030936

28. Chen, Z., Huang, L.: Digital twins for information-sharing in remanufacturing supply chain: a review. Energy **220**, 119712 (2021). https://doi.org/10.1016/j.energy.2020.119712

29. Tozanli, O., Kongar, E., Gupta, S.M.: Evaluation of waste electronic product trade-in strategies in predictive twin disassembly systems in the era of blockchain. Sustainability (Switzerland) **12**(13), 5416 (2020). https://doi.org/10.3390/su12135416
30. Balderas, D., Ortiz, A., Méndez, E., Ponce, P., Molina, A.: Empowering digital twin for industry 4.0 using metaheuristic optimization algorithms: case study PCB drilling optimization. Int. J. Adv. Manuf. Technol. 113(5-6), 1295-1306 (2021). https://doi.org/10.1007/s00170-021-06649-8

31. Martin, G., et al.: Luminaire digital design flow with multi-domain digital twins of LEDs. Energies 12(12), 2389 (2019)

32. Frankó, A., Vida, G., Varga, P.: Reliable identification schemes for asset and production tracking in industry 4.0. Sensors (Switzerland) 20(13), 1-24 (2020). https://doi.org/10.3390/s20133709

33. Park, K.T., Son, Y.H., Noh, S.D.: The architectural framework of a cyber physical logistics system for digital-twin-based supply chain control. Int. J. Prod. Res. 59, 1-22 (2020). https://doi.org/10.1080/00207543.2020.1788738

34. Mazzei, D., et al.: A Blockchain Tokenizer for Industrial IOT trustless applications. Future Gener. Comput. Syst. 105, 432-445 (2020). https://doi.org/10.1016/j.future.2019.12.020

35. Rahmanzadeh S., Pishvaee, M.S., Govindan K.: Emergence of open supply chain management: the role of open innovation in the future smart industry using digital twin network. Ann. Oper. Res. (2022). https://doi.org/10.1007/s10479-021-04254-2

36. Dolgui, A., Ivanov, D., Sokolov, B.: Reconfigurable supply chain: the X-network. Int. J. Prod. Res. 58(13), 4138-4163 (2020). https://doi.org/10.1080/00207543.2020.1774679

37. Ivanov, D., Dolgui, A.: Stress testing supply chains and creating viable ecosystems. Oper. Manag. Res. (2021). https://doi.org/10.1007/s12063-021-00194-z

38. Ivanov, D., Das, A.: Coronavirus (COVID-19/SARS-CoV-2) and supply chain resilience: a research note. Int. J. Integr. Supply Manag. 13(1), 90-102 (2020). https://doi.org/10.1504/IJISM.2020.107780

39. Ivanov, D.: Predicting the impacts of epidemic outbreaks on global supply chains: a simulation-based analysis on the coronavirus outbreak (COVID-19/SARS-CoV-2) case. Transp. Res. Part E: Logist. Transp. Rev. 136, 101922 (2020). https://doi.org/10.1016/j.tre.2020.101922

40. Nasir, S.B., Ahmed, T., Karmaker, C.L., Ali, S.M., Paul, S.K., Majumdar, A.: Supply chain viability in the context of COVID-19 pandemic in small and medium-sized enterprises: implications for sustainable development goals. J. Enterp. Inf. Manag. 35, 100-124 (2021)