Business Models for Industrial Smart Services – The Example of a Digital Twin for a Product-Service-System for Potato Harvesting

Kampker, Achim; Stich, Volker; Jussen, Philipp; Moser, Benedikt; Kuntz, Jan

doi:10.1016/j.procir.2019.04.114

Cited by 55 publications

(28 citation statements)

References 12 publications

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“…Applications during transportation, in particular, the stages supply and distribution (7.84% and 5.88%, respectively), determine the quality of fruits and vegetables with a focus on measuring the temperature [ 8 , 14 , 47 , 55 , 56 , 78 ]. Only one application could be assigned to the retail stage (1.96%), where it is used to determine the quality of fruits and vegetables as well as the remaining shelf-life [ 47 ].…”

Section: Resultsmentioning

confidence: 99%

“…Most of the digital twin applications are classified as intelligent or data-driven (39.22%). These applications are used for monitoring and controlling plant growth environments, in particular greenhouses or fields [ 34 , 42 , 68 , 71 ]; the twinning of plants during growing itself [ 35 , 65 ]; the detection of pests and actions to tackle them [ 65 ]; the monitoring of animals [ 23 ]; or the determination of shocks and the adaptation of process parameters during potato harvesting [ 61 , 78 , 80 ]. In addition, applications concern the monitoring of cattle with regards to their health, dairy productivity, or growth (weight gain for meat production) [ 61 , 65 , 66 ] and the control of food processing parameters [ 75 ].…”

Section: Resultsmentioning

confidence: 99%

“…The predictions could be used to suggest corrective or preventive actions (39.22%). Since most of the applications found are assigned to the production stage, many prescriptive digital twin applications belong to applications only able to assist in agricultural plants to enhance the quality during growth and harvest processes [ 42 , 43 , 61 , 62 , 64 , 69 , 70 , 78 , 80 ]. Another prescriptive digital twin is applied in a pudding production system to assist in production planning [ 73 ].…”

Section: Resultsmentioning

confidence: 99%

See 2 more Smart Citations

Can a Byte Improve Our Bite? An Analysis of Digital Twins in the Food Industry

Henrichs

Noack

Piedrahita

et al. 2021

Sensors

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The food industry faces many challenges, including the need to feed a growing population, food loss and waste, and inefficient production systems. To cope with those challenges, digital twins that create a digital representation of physical entities by integrating real-time and real-world data seem to be a promising approach. This paper aims to provide an overview of digital twin applications in the food industry and analyze their challenges and potentials. Therefore, a literature review is executed to examine digital twin applications in the food supply chain. The applications found are classified according to a taxonomy and key elements to implement digital twins are identified. Further, the challenges and potentials of digital twin applications in the food industry are discussed. The survey revealed that the application of digital twins mainly targets the production (agriculture) or the food processing stage. Nearly all applications are used for monitoring and many for prediction. However, only a small amount focuses on the integration in systems for autonomous control or providing recommendations to humans. The main challenges of implementing digital twins are combining multidisciplinary knowledge and providing enough data. Nevertheless, digital twins provide huge potentials, e.g., in determining food quality, traceability, or designing personalized foods.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Can a Byte Improve Our Bite? An Analysis of Digital Twins in the Food Industry

Henrichs

Noack

Piedrahita

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…DTs at this level elevate the productivity, resilience, and transparency of production processes, which enable end-to-end availability of data along the entire value chain as well as a holistic sustainability assessment on this basis. From a business development perspective, AI-enabled DT can additionally be considered a service agent [ 113 ], providing innovative smart services via DT network platforms [ 114 ] and subscription business models [ 115 ], thus contributing sustainably to long-term innovation for manufacturers, and helping them in accomplishing a paradigm shift from the one-time provision of production hardware to the ongoing delivery of manufacturing solutions ( SG -factor). The importance of a diversified product and service portfolio is particularly evident in times of global crisis.…”

Section: Sustainable Resilient Manufacturingmentioning

confidence: 99%

A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robotics

Huang

Shen²,

et al. 2021

Sensors

189

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Digital twin (DT) and artificial intelligence (AI) technologies have grown rapidly in recent years and are considered by both academia and industry to be key enablers for Industry 4.0. As a digital replica of a physical entity, the basis of DT is the infrastructure and data, the core is the algorithm and model, and the application is the software and service. The grounding of DT and AI in industrial sectors is even more dependent on the systematic and in-depth integration of domain-specific expertise. This survey comprehensively reviews over 300 manuscripts on AI-driven DT technologies of Industry 4.0 used over the past five years and summarizes their general developments and the current state of AI-integration in the fields of smart manufacturing and advanced robotics. These cover conventional sophisticated metal machining and industrial automation as well as emerging techniques, such as 3D printing and human–robot interaction/cooperation. Furthermore, advantages of AI-driven DTs in the context of sustainable development are elaborated. Practical challenges and development prospects of AI-driven DTs are discussed with a respective focus on different levels. A route for AI-integration in multiscale/fidelity DTs with multiscale/fidelity data sources in Industry 4.0 is outlined.

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“…The product-service system (PSS) was created in this context. The PSS was proposed at the end of the 20th century as a functional-oriented business model that integrates products, services, stakeholders, and infrastructure [4]. The PSS is mainly composed of three parts: products, services, and systems.…”

Section: Introductionmentioning

confidence: 99%

Study on the scheme‐design framework and service‐business case of product service system oriented by customer value

Zhang

et al. 2020

IET Collaborative Intelligent Manufacturing

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As the market competition is becoming fierce, to obtain long-term profits for manufacturing enterprises, under the high-frequency dynamic customer needs (CNs), many manufacturing enterprises have to transform from product manufacturers to industrial product-service providers. The product-service system (PSS) is the key to spur manufacturing companies to move from the design and production of a single product to the design and manufacture of an overall solution (including products and services). Because of the shortcomings of the current research, a customer value-oriented PSS overall scheme-design framework is proposed. On this basis, a customer value hierarchy model, value model, function model, and scheme model are established. The multi-level mapping of CNs information to product-service solutions was realised, and the leading key technologies of scheme design of the PSS were proposed. Finally, according to the different scheme-design implementation models, various service-business cases are analysed, which can provide a reference value for the research of the scheme design of the PSS.

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Business Models for Industrial Smart Services – The Example of a Digital Twin for a Product-Service-System for Potato Harvesting

Cited by 55 publications

References 12 publications

Can a Byte Improve Our Bite? An Analysis of Digital Twins in the Food Industry

Can a Byte Improve Our Bite? An Analysis of Digital Twins in the Food Industry

A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robotics

Study on the scheme‐design framework and service‐business case of product service system oriented by customer value

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