Simulation of Supply Chain Modeling with Digital Twins

H. Michael Chung

IS and CIST, CSULB, U.S.A.

Keywords: Supply Chain, Resilience, Digital Twins, Digital Transformation, Interviews, Qualitative Data Analysis,

Real-Time Decision-Making, Simulation.

Abstract: The current supply chain management landscape, particularly with the workflow disruptions due to COVID-

19, demands more visibility, adaptive responses, and real-time predictive capabilities. First, this research

investigates digital twins' strategies, processes, success measures, and impact, and constructs a more effective

supply chain modeling. Second, the study develops appropriate performance measures and metrics for digital

twins in supply chain management. Lastly, it constructs a digital twin prototyping framework for building a

more effective supply chain.

1 INTRODUCTION

Supply chain management (SCM) has faced

unprecedented challenges during the COVID-19

pandemic, resulting in supply shortages, sourcing

limitations, and logistical delays. These challenges

are counterproductive to the supply chain's visibility

and adversely affect its core business. Disruption

risks could significantly impact SC performance,

underscoring resilience as a key determinant of long-

term success, which needs to integrate agility,

flexibility, and collaboration.

SCM should address increasing complexity,

enhance predictive capabilities, and provide adaptive

responses. These challenges involve how humans can

access real-time information about physical assets

and interacting business processes, perform real-time

analysis with the information, and make timely,

robust, and efficient decisions.

Numerous studies were conducted on how these

issues could be addressed. For example, the studies

include the increasing digitalization of the current

product and process life cycle and its control and

analysis, the networks of component demand, and

sophisticated human-machine interfaces in a

digitalized environment (Burattini, et al., 2024; Boyes

and Watson, 2022). For this goal, digital twins in the

industrial metaverse have been introduced for SCM

(Rajagopal et al., 2017).

A digital twin is a virtual representation of a

system designed to reflect a physical system as it

resides in computer platforms. It spans the system

object life cycle, updates real-time data, and uses

simulation and machine learning to help make

decisions (Wagg & Gardner, 2020; Juarez et al.,

2021). The data used to create these replicas is often

collected from the Internet of Things (IoT). Digital

transformation refers to integrating digital

technologies into all aspects of an organization to

change how it operates fundamentally, delivers value,

and engages with stakeholders. Digital twins are a

pivotal technology within digital transformation,

offering organizations the tools to bridge the physical

and digital realms, optimize operations, and unlock

new business opportunities.

The primary research objectives are to model and

build a prototype of a supply chain in digital twins

that purports to accurately capture supply, demand,

risk, and resilience, manage the changing

environment, and increase the model's adaptability.

2 LITERATURE REVIEW

The digital twins' theoretical framework and practical

implementations have yet to reach their goal. More

research is needed to identify the actionable variables

in digital twins (Sholten et al., 2020; Razak et al.,

2021; Ivanov, 2023). There has been a dearth of

research on using digital twins in SCM (GurDur &

Schooling, 2023; Gai et al., 2023). The research areas

include identifying the scope of the physical entities

and business processes involved in the supply chain;

how the physical supply chain and the virtual

344

Chung and H. M.

Simulation of Supply Chain Modeling with Digital Twins.

DOI: 10.5220/0013569200003970

In Proceedings of the 15th International Conference on Simulation and Modeling Methodologies, Technologies and Applications (SIMULTECH 2025), pages 344-348

ISBN: 978-989-758-759-7; ISSN: 2184-2841

environment could be mapped and synchronized; and

the key data needed to measure and collect (Wang et

al., 2022).

Research Questions for digitak twin-driven

supply chain modeling are:

• What are the key physical entities and

processes, particularly dynamic and real-time

data, need to be interactively shared and

synchronized?

• What are the supply chain's critical success

factors and potential bottlenecks?

• How do we map and integrate these entities

and processes with a digital twin model?

• What are the variables that would enhance and

support supply chain resilience?

The synchronization here refers to the

transmission of bidirectional information. The data

used in the physical supply chain are either static data

related to the fundamental properties of physical

entities or real-time, dynamic data generated in

business operations (Wang et al., 20). Static data

ensures that the virtual supply chain shares the same

structure and properties as the physical supply chain.

Real-time, dynamic data synchronizes the status and

processes in the virtual world. For example, the

location of a truck and traffic conditions are updated

in real-time. Hence, the estimated arrival time is

updated continuously and precisely. Sensors and

other IoT equipment collect real-time data, typical of

manufacturing and online systems, such as

procurement and order management systems, in the

virtual world through simulation.

Smart sensors or online systems connect the

physical supply chain in a digital twin. Specific data

and information are collected to enable the virtual

supply chain to mirror the physical supply chain's

properties and dynamic business processes. The

research analyzes the actual data in the physical

supply chain and the simulated data in the virtual

supply chain. Then, the results are transferred to the

physical world to support intelligent decision-making

and implementation (Ivanov, 2024).

While challenging, real-time data acquisition and

implementation could allow the connection between

the physical and virtual supply chain to synchronize

operation dynamics, increasing supply chain

visibility. The synchronized data provides

opportunities to monitor, analyze, control, and

optimize the supply chain, resulting in up-to-date

virtual simulation and optimization. Therefore, a

digital twin model could optimize the supply chain

across different stages and establish an integrated

supply chain. It allows decision-makers to look

forward instead of backward and makes the supply

chain intelligent (Klappich, 2019).

More research is needed to understand capacity

modeling from data-driven approaches compared

with demand signals. According to de Kok et al.

(2018), most studies assume infinite capacity. Feng

and Shanthikumar (2018) suggested capacity

modeling of supply chains. Garvey et al. (2015)

applied Bayesian networks to measure risk

propagation in a supply network. In a supply chain,

the propagation of risk signals (information) and

actual risks (events) are often asynchronous.

Companies must develop new modeling approaches

to understand these asynchronous signals and events.

Analytics must be designed to predict when actual

risks occur so stakeholders can prepare.

Moreover, model adaptability with real-time data

requires more research. For example, Feng and

Shanthikumar (2018) proposed an approach that

transforms nonlinear supply and demand functions

into linear functions to model random supply and

demand. Data-driven optimization is another tool that

addresses complex supply and demand data by

solving mathematical programming problems

directly using observed data (Bertsimas & Thiele,

2006). Levi et al. (2015) illustrated applying this idea

in modeling demand with an unknown distribution.

Peron (2020) introduced a vision for a DT for SCM

spare parts enabled by additive manufacturing.

Furthermore, companies must carefully manage

sensitive customer information, which is increasingly

exposed to criminal threats (Fuller et al., 2020) and

regulated by laws. For example, the European Union's

General Data Protection Regulation regulates

personal data privacy and security. In particular, these

new regulations require data controllers (companies)

to explain their data use to data subjects (customers)

(European Union, 2018).

3 RESEARCH DESIGN AND

METHODOLOGY

The study plan is further developed from prior

research on supply chain, blockchain, and IoT pilot

implementations (Chung, 2020a; 2020b; 2022). We

have collected qualitative data, including interviews

with supply chain stakeholders, particularly in the

agricultural industry. The grounded theory will be

employed to analyze the qualitative information

collected from the domain professionals.

Simulation of Supply Chain Modeling with Digital Twins

345

a) Suppliers (Raw Material Sourcing)

• Identify & evaluate suppliers

• Negotiate contracts

• Order raw materials

• Quality check raw materials

• Package & label raw materials

• Arrange transportation

b) Inbound Logistics (Transportation & Receiving)

• Plan transportation routes

• Coordinate shipment schedules

• Track shipments

• Receive & inspect goods at the warehouse

• Log inventory in the system

c) Manufacturing & Production

• Store raw materials in inventory

• Schedule production

• Process/assemble goods

• Quality control (inspection & testing)

• Package finished goods

• Store in finished goods inventory

d) Warehousing & Inventory Management

• Receive finished goods from production

• Store & organize inventory

• Monitor stock levels (real-time tracking)

• Pick & pack products for shipment

• Conduct periodic inventory audits

• Manage returns/damaged goods

e) Order Processing & Fulfillment

• Receive customer orders

• Verify payment & order details

• Allocate stock for order fulfillment

• Print shipping labels & documentation

• Dispatch order to the logistics team

f) Outbound Logistics (Shipping & Distribution)

• Select an appropriate transportation method

• Load goods onto delivery vehicles

• Track shipments & update customers

• Manage customs clearance for international

shipping

• Deliver goods to retailers/customers

g) Retail & Sales (Physical & E-Commerce)

• Receive and stock inventory

• Display and market products

• Manage pricing & promotions

• Process customer orders

• Handle customer inquiries & support

h) Last-Mile Delivery

• Assign delivery routes

• Deliver goods to end customers

• Provide tracking & real-time status updates

• Handle customer complaints or returns

i) Customer Service & Returns Management

• Receive feedback & reviews

• Process returns & refunds

• Replace or repair defective goods

• Improve customer satisfaction & retention

4 DATA COLLECTION AND

ANALYSIS

Seven Interviews with supply chain managers and

experts were conducted to confirm critical processes

and real-time data challenges. Interpretive analysis is

being applied. In addition, two field visits are being

undertaken: A logistics company in the Port of Long

Beach in California, and an overseas company

producing, packing, and delivering agricultural

products to a shipping company are involved. Key

performance measures are collected from expert

interviews and field observations to validate the

model. A software package will be used to develop a

digital twin prototype model.

5 CONCLUSION

The research investigates the digital twins' nodes,

processes, success measures, and impact, in order to

construct a supply chain model.

REFERENCES

Ariesen-Verschuur, N., Verdouw, C., & Tekinerdogan, B.

(2022). Digital twins in greenhouse horticulture: A

review. Information Technology Group, Wageningen

University & Research; Mprise Agriware.

Boyes, H., & Watson, T. (2022). Digital twins: An analysis

framework and open issues. Computers in Industry,

143, 103763. https://doi.org/10.1016/j.compind.2022.

103763

Burattini, S., Montagna, S., Gentili N., Galardi, F.,

Vespignani, R., and Zanatto, P., A Digital Twins

Approach for Oncologic Pharmaceutical Supply

Chain," 2024 IEEE International Conference on

Pervasive Computing and Communications Workshops

and other Affiliated Events (PerCom Workshops),

SIMULTECH 2025 - 15th International Conference on Simulation and Modeling Methodologies, Technologies and Applications

346

Biarritz, France, 2024, pp. 94-99, doi:

10.1109/PerComWorkshops59983.2024.10502569

Chung, H.M., “An Implementation of a Blockchain-based

Supply Chain Model under COVID-19,” 17th Iberian

Conference on Information Systems and Technologies,

2022, Madrid, Spain.

Chung, H.M. (a), “Blockchain and Supply Chain on

COVID-19,” European Conference on Information

Systems, June 2020, via Zoom.

Ferré-Bigorra, J., Casals, M., & Gangolells, M. (2022). The

adoption of urban digital twins. Group of Construction

Research and Innovation (GRIC), Universitat

Politècnica de Catalunya BarcelonaTech (UPC).

Ferko, E., Bucaioni, A., & Behnam, M. (2022).

Architecting digital twins. IEEE Access, 10, 1-14.

https://doi.org/10.1109/ACCESS.2022.3172964

Gürdür Broo, D., & Schooling, J. (2023). Digital twins in

infrastructure: Definitions, current practices,

challenges and strategies, International Journal of

Construction Management, 23(7), 1254-1263.

https://doi.org/10.1080/15623599.2021.1966980

Hribernik, K., Cabrini, G., Mandreoli, F., & Mentzas, G.

(2021), Autonomous, context-aware, adaptive digital

twins—State of the art and roadmap, Computers in

Industry, 129, 103437. https://doi.org/10.1016/j.compi

nd.2021.103437

Juarez, M. G., Botti, V. J., & Giret, A. S. (2021). Digital

twins: Review and challenges, Journal of Computing

and Information Science in Engineering, 21(2),

030802-1. https://doi.org/10.1115/1.4050244

Jiang, Y., Yin, S., Li, K., Luo, H., & Kaynak, O. (2021).

Industrial applications of digital twin, Philosophical

Transactions of the Royal Society A: Mathematical,

Physical and Engineering Sciences, 379(2206),

20200360. https://doi.org/10.1098/rsta.2020.0360

Kapteyn, M. G., Pretorius, J. V. R., & Willcox, K. E.

(2021). A probabilistic graphical model foundation for

enabling predictive digital twins at scale, Department

of Aeronautics and Astronautics, Massachusetts

Institute of Technology; The Jessara Group; Oden

Institute for Computational Engineering and Sciences,

University of Texas at Austin

Ketzler, B., Naserentin, V., Latino, F., Zangelidis, C.,

Thuvander, L., & Logg, A. (2021). Digital twins for

cities: A state of the art review, Sustainability, 13(9),

5018. https://doi.org/10.3390/su13095018

Klappich, D. Hype cycle for supply chain execution

technologies, Available at: gartner. COM/en/docum

ents/3947306/hype-cycle-for-supply-chain-execution-

technologies-2019 (2019).

Leng, J., Wang, D., Shen, W., Li, X., Liu, Q., & Chen, X.

(2021). Digital twins-based smart manufacturing

system design in Industry 4.0: A review, Guangdong

Provincial Key Laboratory of Computer Integrated

Manufacturing System, Guangdong University of

Technology; State Key Laboratory of Digital

Manufacturing Equipment and Technology, Huazhong

University of Science and Technology; Department of

Information Systems, City University of Hong Kong.

Mihai, S., Yaqoob, M., Hung, D. V., Davis, W., Towakel,

P., Raza, M., Karamanoglu, M., Barn, B., Shetve, D.,

Prasad, R. V., Venkataraman, H., Trestian, R., &

Nguyen, H. X. (2022). Digital twins: A survey on

enabling technologies, challenges, trends, and future

prospects, IEEE Communications Surveys & Tutorials,

24(4), 2343-2373. https://doi.org/10.1109/COMST.20

22.3203027

Mylonas, G., Kalogeras, A., Kalogeras, G.,

Anagnostopoulos, C., Alexakos, C., & Muñoz, L. (2021),

Digital twins from smart manufacturing to smart cities:

A survey. IEEE Access, 9, 145796-145818.

https://doi.org/10.1109/ACCESS.2021.3120843

Piromalis, D., & Kantaros, A. (2022). Digital twins in the

automotive industry: The road toward physical-digital

convergence, Applied Systems Innovation, 5(4), 65.

https://doi.org/10.3390/asi5040065

Purcell, W., & Neubauer, T. (2023). Digital twins in

agriculture: A state-of-the-art review. Information and

Software Engineering Group, TU Wien.

Ricci, A., Croatti, A., Mariani, S.,

Montagna, S., & Picone, M. (2022). Web of digital twins,

ACM Transactions on Internet Technology, 22(4), 1-

30. https://doi.org/10.1145/3507909

Segovia, M., & Garcia-Alfaro, J. (2022). Design, modeling

and implementation of digital twins, Sensors, 22(15),

5396. https://doi.org/10.3390/s22145396

Semeraro, C., Lezoche, M., Panetto, H., & Dassisti, M.

(2021). Digital twin paradigm: A systematic literature

review, Computers in Industry, 126, 103423.

https://doi.org/10.1016/j.compind.2021.103423

Sharma, A., Kosasih, E., Zhang, J., Brintrup, A., &

Calinescu, A. (2022). Digital Twins: State of the art

theory and practice, challenges, and open research

questions. Journal of Industrial Information Integration,

30, 100383. https://doi.org/10.1016/j.jii.2022.100383

Tao, F., Xiao, B., Qi, Q., Cheng, J., & Ji, P. (2022). Digital

twin modeling, Journal of Manufacturing Systems, 64,

372–389. https://doi.org/10.1016/j.jmsy.2022.06.015

VanDerHorn, E., & Mahadevan, S. (2021). Digital twin:

Generalization, characterization and implementation,

Department of Civil & Environmental Engineering,

Vanderbilt University; American Bureau of Shipping

van der Valk, H., Haße, H., Möller, F., & Otto, B. (2022).

Archetypes of digital twins, Business & Information

Systems Engineering, 64

(3), 375–391.

https://doi.org/10.1007/s12599-021-00727-7

van der Valk, H., Haße, H., Möller, F., Arbter, M., Henning,

J.-L., & Otto, B. (2020). A taxonomy of digital twins,

Retrieved from https://www.researchgate.net/publicat

ion/341235159

Verdouw, C., Tekinerdogan, B., Beulens, A., & Wolfert, S.

(2021). Digital twins in smart farming, Information

Technology Group, Wageningen University and

Research.

Wagg, D. J., Gardner, P. (2020). Digital twins: State-of-the-

art future directions for modelling and simulation in

engineering dynamics applications, ASCE-ASME

Journal of Risk and Uncertainty in Engineering

Simulation of Supply Chain Modeling with Digital Twins

347

Systems, Part B: Mechanical Engineering.

https://doi.org/10.1115/1.4046739

Wang, Q., Chen, K., Wang, S., Cao, X., (2022), Optimal

decisions in a closed-loop supply chain fairness

concerns, corporate social responsibilities and

information value, Annals of Operations Research, 309,

277-304.

SIMULTECH 2025 - 15th International Conference on Simulation and Modeling Methodologies, Technologies and Applications

348