Fractal Production Reprogramming “Industrie 4.0” Around Resource and Energy Efficiency?
Abstract
The evolution of the world of energy towards a system of decentralized production and consumption assets makes it possible to understand better the upcoming industrial revolution, which will be one of distributed information flows. A new paradigm, the “blockchain”, enables economic actors to completely decentralize information flows without intermediaries. Even though many technological locks still exist today, it becomes possible already today to imagine that each machine or connected object will be able to communicate autonomously and securely with others, learn and enrich its mode of operation thanks to artificial intelligence technologies. The business models will be strongly impacted by a greater flexibility of the manufacturing capacities, their adaptation in real time to consumer constraints and the optimization of supply management (raw material, energy and components) through smart purchase, onsite production, 3D-printing and smart logistics: a fractal production with shorter supply channels steered by resources and energy efficiency data.
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Both AR and VR proved to increase productivity.
“they proved to increase productivity [97]”
From A Review on the Application of Blockchain to the Next Generation of Cybersecure Industry 4.0 Smart Factories (2019) · cites this paper as [97]
Cites
Power to Gas: The Final Breakthrough for the Hydrogen Economy?
Winkler-Goldstein, R., & Rastetter, A. (2013) · Green · doi:10.1515/green-2013-0001 · Cited by 57 · see citing works
Self-citationYong-zhong, H. (2009) · 北京周报：英文版 · Cited by 31 · see citing works
Gesetz zur Digitalisierung der Energiewende
Kermel, C., & Dinter, J. (2016) · Recht der Energiewirtschaft · doi:10.1515/rde-2016-0403 · Cited by 30 · see citing works
Bootstrapping Trust in Distributed Systems with Blockchains.
Ali, M., Nelson, J., Shea, R., et al. (2016) · Cited by 14 · see citing works
Industrial Internet of Things supporting Factory Automation
Beyerer, J., & Usländer, T. (2016) · at - Automatisierungstechnik · doi:10.1515/auto-2016-0104 · Cited by 7 · see citing works
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Blockchain for industry 4.0: a comprehensive review
Bodkhe, U., Tanwar, S., Parekh, K., Khanpara, P., Tyagi, S., Kumar, N., & Alazab, M. (2020) · IEEE Access · doi:10.1109/access.2020.2988579 · Cited by 766 · see citing works
Fernandez-Carames, T. M., & Fraga-Lamas, P. (2019) · IEEE Access · doi:10.1109/access.2019.2908780 · Cited by 321 · see citing works
- Authors
- Winkler-Goldstein, R., Imbault, F., Uslander, T., & de la Gastine, H.
- Affiliations
- EU Twinning expert (energy, ITC and innovation), Brussels, Belgium
- Conservatoire National des Arts et Métiers (FR)
- Head of the department “Information Management and Control Technology”, Fraunhofer Institute IOSB, Karlsruhe, Germany
- Director new businesses, Intel Corporation SAS, Meudon, France
- Venue
- 2018 IEEE International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe)
- Publisher
- IEEE
- Published
- 2018-06
- Pages
- 1-5
- OpenAlex
- W2898820136
- Cited by
- 7
- References
- 5
- Citations by year
- 2024: 1 · 2022: 3 · 2020: 2 · 2019: 1
- FWCI
- 1.18
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE International Conference on Environment and Electrical Engineering and IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe) (publishedVersion)
- Publikationsdatenbank der Fraunhofer-Gesellschaft (Fraunhofer-Gesellschaft) (submittedVersion)
- Fraunhofer-Publica (Fraunhofer-Gesellschaft) (submittedVersion)
- Topics
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Digital Transformation in Industry
- Social Sciences › Decision Sciences › Management Science and Operations Research › Impact of AI and Big Data on Business and Society
- Physical Sciences › Energy › Renewable Energy, Sustainability and the Environment › Energy, Economy, and Technology Trends
- Keywords
- Industry 4.0, resource efficiency, energy efficiency, blockchain, distributed manufacturing, artificial intelligence in manufacturing, smart manufacturing, 3D printing, smart logistics, supply chain optimization, business models
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