Digital Twins in Food Supply

An Overview of potentials and challenges

Christian Krupitzer, Elia Henrichs
JournalIndustrie 4.0 Management
Issue Volume 38, 2022, Edition 5, Pages 17-20
Open Accesshttps://doi.org/10.30844/IM_22-5_17-20
Bibliography Share Cite Download

Abstract

The food sector is facing many challenges, including food loss and waste. To cope with those challenges, digital twins that create a digital representation of physical entities by integrating real-time and real- world data seems to be a promising approach. This article presents the results of a literature review on digital twin applications in the food industry and analyze their challenges and potentials.

Keywords

, , , ,

Bibliography

[1] Tao, F.; Zhang, M.; Nee, A.: Five-Dimension Digital Twin Modeling and Its Key Technologies. In Tao, F.; Zhang, M.; Nee, A. (Hrsg): Digital Twin Driven Smart Manufacturing. London San Diego Cambridge Oxford 2019.
[2] Koulouris, A.; Misailidis, N.; Petrides, D.: Applications of process and digital twin models for production simulation and scheduling in the manufacturing of food ingredients and products. In: Food and Bioproducts Processing (2021) 126.
[3] Verboven, P.; Defraeye, T.; Datta, A.K.; Nicolai, B.: Digital twins of food process operations: the next step for food process models? In: Current Opinion in Food Science 35 (2020), S. 79-87.
[4] Werner, R.; Takacs, R.; Geier, D.; Becker, T.; Weißenberg, N.; Haße, H.; Sollacher, R.; Thalhofer, M.; Schumm, B.; Steinke, I.: The Challenge of Implementing Digital Twins in Operating Value Chains. In Herwig, C.; Pörtner, R.; Möller, J. (Hrsg): Digital Twins: Applications to the Design and Optimization of Bioprocesses. Heidelberg 2021.
[5] Rohleder, B.; Minhoff, C.: Die Ernährung 4.0 – Status Quo, Chancen und Herausforderungen, 2019. URL: www.bitkom.org/sites/de-fault/files/2019-03/Bitkom-Charts%20190326%20Digitalisierung%20der%20Ern%C3%A4hrungsindustrie.pdf, Abrufdatum 09.04.2022.
[6] Krupitzer, C.; Stein, A. Food Informatics—Review of the Current State-of-the-Art, Revised Definition, and Classification into the Research Landscape. In: Foods (2021) 10.
[7] Henrichs, E.; Noack, T.; Pinzon Piedrahita, A. M.; Salem, M. A.; Stolz, J.; Krupitzer, C.: Can a Byte Improve Our Bite? An Analysis of Digital Twins in the Food Industry. In: Sensors 22 (2022) 115. www.mdpi.com/1424-8220/22/1/115.

Your downloads

You might also be interested in

Experiencing Digital Twins in Production and Logistics

Experiencing Digital Twins in Production and Logistics

The fischertechnik® Learning Factory 4.0 as a development platform for possible expansion stages
Deike Gliem ORCID Icon, Sigrid Wenzel ORCID Icon, Jan Schickram, Tareq Albeesh
The fischertechnik® Learning Factory 4.0 has proven to be a suitable experimental environment for testing digital twins. Depending on the targeted maturity stage, the functions of a digital twin range from status monitoring and forecasting to the operational control of production and logistics systems. To systematically classify these functions, this article presents a maturity model that serves as a framework for the development of a digital twin. Building on this, selected use cases are implemented in a test and development environment based on a system architecture with multi-layered logic structure. These initial implementations serve to highlight application purposes, relevant methods, and typical challenges and potentials in the transfer to real factory environments.
Industry 4.0 Science | Volume 42 | Edition 2 | Pages 30-37 | DOI 10.30844/I4SE.26.2.30
Empathic Assembly Assistance

Empathic Assembly Assistance

Combining AI-based data analysis and empathic human digital twins
Matthias Lück ORCID Icon, Katharina Hölzle ORCID Icon, Christian Saba-Gayoso, Joachim Lentes
Industrial companies in Germany face demographic change and stagnating productivity in an increasingly complex world. Manual assembly remains essential for complex, low-volume products, yet productivity and quality lag due to human variability. This paper introduces a concept and demonstrator for an empathic assembly assistance system that merges a human digital twin and AI-based screwdriver data analytics within a modular architecture. Tightening anomalies are classified, linked to inferred worker states and translated into information and recommendations.
Industry 4.0 Science | Volume 41 | 2025 | Edition 5 | Pages 6-13 | DOI 10.30844/I4SE.25.5.6
Enabler for the Digital Twin

Enabler for the Digital Twin

Requirements for Technical Documentation 4.0
Christian Koch, Lukas Schulte, René Wöstmann, Jochen Deuse ORCID Icon
The increasing heterogeneity and complexity of industrial plant components from different manufacturers make it difficult to handle technical documentation consistently. In addition, the flexibility required for system changes challenges the long-term usability and legally compliant design of this documentation throughout the entire life cycle of cyber-physical production systems. This article contributes to the discussion on Technical Documentation 4.0 by systematically analyzing existing specifications and approaches and by proposing a concept for a holistic documentation framework.
Industry 4.0 Science | Volume 41 | 2025 | Edition 4 | Pages 76-85
Real-Time Monitoring of the Carbon Footprint for SMEs

Real-Time Monitoring of the Carbon Footprint for SMEs

Sustainability in real time — from operation to finished products
Henning Strauß ORCID Icon, Julian Sasse ORCID Icon
Although SMEs are not directly affected by the statutory reporting obligations for carbon accounting, as suppliers they are obliged to meet the requirements of sustainability reporting. In addition to a holistic life cycle analysis, this requires a high-quality database within production in order to determine the specific CO₂ footprint. A central element is the implementation of a Machine Carbon Footprint (MCF). This article aims to develop and implement an MCF focusing on its applicability for SMEs. For this purpose, data is recorded and visualized in real time on a machine tool. The measurement data is then processed, stored and visualized using open-source low-code platforms. Real-time data flows enable the precise determination of the production-specific carbon footprint and, in conjunction with order data, the Product Carbon Footprint.
Industry 4.0 Science | Volume 41 | Edition 3 | Pages 102-109
Enabling the Future of Manufacturing with Digital Twins

Enabling the Future of Manufacturing with Digital Twins

Opportunities and obstacles
Javad Ghofrani, Darian Lemke, Tassilo Söldner
Digital twins connect physical and digital systems, furthering efficiency, enabling predictive maintenance, and allowing the production of more customized products. Despite these advantages, challenges such as high costs, data synchronization, and security risks hinder widespread adoption. This article explores the potential of digital twins and examines key barriers to integration and implementation, also considering some industrial applications including additive manufacturing as a relevant use case.
Industry 4.0 Science | Volume 41 | Edition 3 | Pages 72-81
Digital Supply Chain Twin: The Pathway to Success

Digital Supply Chain Twin: The Pathway to Success

A catalyst for increasing competitiveness
Gökhan Cenk ORCID Icon, Jonas Andersson, Tobias Engel ORCID Icon
Companies face a variety of challenges when optimizing global supply chains. Economic interests must be balanced with legal requirements, such as the German Supply Chain Due Diligence Act (SCDDA) and the European Sustainability Reporting Standards (ESRS). A digital supply chain twin (DSCT) enables the visualization of value creation networks and supports key business functions, such as purchasing, supply chain management, distribution, service, and sales. By leveraging immersive technologies, the DSCT helps generate sustainable competitive advantages across the entire supply network.
Industry 4.0 Science | Volume 41 | 2025 | Edition 3 | Pages 52-60 | DOI 10.30844/I4SE.25.3.52
Christoph Brocks