Implementation of digital twins in the food supply chain: a review and conceptual framework

Figures & data

Figure 1. Systematic literature review process (Adopted from: Tranfield, Denyer, and Smart 2003; Ghadge, Dani, and Kalawsky 2012).

Table 1. Keywords used for identification of data sources.

Keywords	Details of keywords
Digital Twin(s)	‘Digital twin*,’ ‘digital shadow,’ ‘device twin,’ device shadow,’ ‘digital alias,’ ‘virtual twin,’ ‘virtual shadow, ‘twin*,’ ‘digital replica.’
Food supply chain(s)	‘Food Supply Chain*,’ ‘Food Supply Network*,’ ‘Food Value Chain,’ ‘Food Logistics*,’ ‘Food Distribute,’ ‘Food Distribution,’ ‘Agri-food*,’ ‘Agriculture*,’ Perishable Food Supply Chain,’ ‘agricultural product*,’ ‘fresh produce,’ vegetable*,’ fruit*, poultry, meat, ‘diary product*,’ ‘bakery product*,’ ‘cold chain,’ ‘cold chain management."

Figure 2. PRISMA flow approach for data screening.

Figure 3. Year-wise publication trend.

Figure 4. Country of research origin.

Figure 5. Distribution of methodological approaches.

Table 2. Summary of technology occurrences.

Relevant technologies	Occurrences
Internet of Thing (RFID, Sensor, Camera, barcode, NFC, etc.)	60
Artificial Intelligence (BDA, Machine learning, Simulation, Optimization)	35
Cloud computing/fog computing	12
Additive Manufacturing (3D printing)	6
AR and VR	6
Blockchain	7
5G	2
GIS	1

Figure 6. Cluster analysis of selected papers.

VOS viewer generated cluster diagram.

Figure 7. Themes emerging from the selected papers.

Figure 8. Relationship between the characteristics of Digital Twins and FSC challenges.

Figure 9. Conceptual framework for Digital Twin implementation in the FSC.

Table

No	Quality criteria	0	1	2	3
1	Methodology	The article does not provide enough information to assess the criterion	Flawed research design with inappropriate quantitative methods	Justified research design; poor execution of the idea	Research design adequately examines the research argument
2	Theory robustness	Lack of information to assess this criterion	Weak theoretical base	Incomplete theoretical base	Robust theoretical grounding; contribution to the theory of practice
3	Data analysis: sample design and composition	Lack of information to assess this criterion	Inadequate data sample; inconclusive research results.	The appropriate data sample, adequate results but the inadequate explanation	Appropriate data sample: properly explained research results support theoretical arguments
4	Implications for practices	Lack of information to assess this criterion	Study fails to inform the practice of implications	Provides limited implications for practice	Provides critical implications for practice
5	Contribution plus a short statement summarising the article’s contributions	The article does not provide enough information to assess this criterion	Does not make an important contribution. It is not clear the advanced it makes	Although using others’ ideas, builds upon the existing theory	Further develops existing knowledge, expanding the way the issue was explained so far

Table

Reference	Details of digital twin definitions
Gieves 2003	A virtual replica of what has been generated and demonstrated is a three-dimensional system comprised of physical things, a virtual environment, and the data link that connects them.
NASA 2012	Defined it from a practical perspective: a DT is a multiscale, probabilistic, and ultra-fidelity simulation that presents the status of the corresponding twin in a timely manner using historical data and real-time data.
Alam and EI Saddik 2017	A digital twin is an integrated virtual model of a real-world system containing all its physical information and functional units related to the design, operation, and production of actual factories.
Asimov et al. 2018	A virtual replica of real physical installation, which can check the consistency for monitoring data, perform data mining to detect existing and forecast upcoming problems, and use an AI knowledge engine to support effective business decisions.
Tao et al. 2019a	The digital twin is a real mapping of all components in the product life cycle using physical data, virtual data, and interaction data between them.
Liu et al. 2021	A living model that continually adapts to changes in the environment or operation using real-time sensory data can forecast the future of the corresponding physical assets for predictive maintenance.
Ivanov and Dolgui 2021	computerized models representing the network state for any given moment in time.’ Researchers’ perspectives on Industry 4.0: multi-disciplinary analysis and opportunities for operations management
Van der Valk et al. 2022	The digital SC twin is a solution for better visualisation and understanding of supply chains and an opportunity for further analysis, simulation, and optimisation.
Tozanli and Saénz 2022	A combination of multiple enabling technologies, such as sensors, cloud computing, AI and advanced analytics, simulation, visualisation, and augmented and virtual reality.

Table

No.	Subject field	Name of Journal	Number
1	Operation research and management science	International Journal of Production Research (IJPR)	15
2		International Journal of Production Economics	6
3		Transportation Research Part E: Logistics and Transportation Review	3
4		International Journal of Information Management	2
5		Resources, Conservation and Recycling	3
6		Supply Chain Management: An International Journal (SCMIJ)	2
7		European Journal of Operational Research	1
8		Industrial Management & Data Systems	1
9		International Journal of Operations and Production Management	1
10		Journal of Cleaner Production	1
11		Production Planning and Control	1
12		Technological Forecasting and Social Change	1
13		The International Journal of Logistics Management	1
14		MIT Sloan Management Review	1
13	Food	Food Control	8
14		Journal of Food Engineering	5
15		British Food Journal	2
16		Computers & Electronics in Agriculture	1
17		Journal of Food Process Engineering	1
18		Trends in Food Science & Technology	1
19	Computer Science/Engineering	IEEE Access	2
20		Computers and Industrial Engineering	5
21		Computers in Industry	4
22		Journal of Manufacturing Systems	5
23		International Journal of Computer Integrated Manufacturing	4
24		Advanced Engineering Informatics	1
25		IEEE Transactions on industrial informatics	1
26		Journal of Intelligent Manufacturing	1
27		Journal of Industrial information integration	1

Table

Type of analysis	Co-occurrence
Unit of analysis	All Keywords
Counting type	Full counting
Minimum number of occurrences of a keyword	4
Meet the threshold	48

Table

Cluster category	Keyword	occurrence
Cluster 1	Agriculture	4
	Challenges	27
	Cold Chain	4
	Food Logistics	4
	Food Safety	5
	Food Supply Chain	24
	Food Traceability	4
	Framework	14
	Implementation	9
	Industry	6
	Information Asymmetry	5
	Quality	7
	RFID	10
	Risk	4
	Safety	8
	Supply Chain Management	4
	Traceability	14
	Tracking	9
Cluster 2	Applications	23
	Big Data	12
	Characteristics	25
	Cyber-Physical System	7
	Design	12
	Digital Twin	55
	Enablers	4
	Industry 4.0	14
	Manufacturing	20
	Model	10
	Optimisation	5
	Resilience	6
	Service	5
	Simulation	22
	Supply Chain	19
	Sustainability	5
	Systems	8
Cluster 3	Artificial Intelligence	6
	Barriers	14
	Benefits	9
	Blockchain	10
	Cloud Computing	4
	Data Analytics	6
	Drivers	20
	Efficiency	4
	Integration	5
	Internet Of Things	17
	Logistics	9
	Big Data	8
	Information Technology	4

Tranfield, D., D. Denyer, and P. Smart. 2003. “Towards a Methodology for Developing Evidence-Informed Management Knowledge by Means of Systematic Review.” British Journal of Management 14 (3): 207–222.

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Ghadge, A., S. Dani, and R. Kalawsky. 2012. “Supply Chain Risk Management: Present and Future Scope.” The International Journal of Logistics Management 23 (3): 313–339.

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*Tao, F., F. Sui, A. Liu, Q. Qi, M. Zhang, B. Song, … A. Y. Nee. 2019a. “Digital Twin-Driven Product Design Framework.” International Journal of Production Research 57 (12): 3935–3953.

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*Liu, J., X. Cao, H. Zhou, L. Li, X. Liu, P. Zhao, and J. Dong. 2021. “A Digital Twin-Driven Approach Towards Traceability and Dynamic Control for Processing Quality.” Advanced Engineering Informatics 50: 101395.

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*Ivanov, D., and A. Dolgui. 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.

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*Tozanli, Ö, and M. J. Saénz. 2022. “Unlocking the Potential of Digital Twins in Supply Chains.” MIT Sloan Management Review 63 (4): 1–4.

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Data availability statement

The authors confirm that the secondary data used for this study is available within the article and its supplementary materials. Derived data supporting the findings of this study are available from the corresponding author on reasonable request.