The biological transformation of the manufacturing industry – envisioning biointelligent value adding

Miehe, Robert; Bauernhansl, Thomas; Schwarz, Oliver; Traube, Andrea; Lorenzoni, Anselm; Waltersmann, Lara; Full, Johannes; Horbelt, J.; Sauer, Alexander

doi:10.1016/j.procir.2018.04.085

Cited by 50 publications

(17 citation statements)

References 1 publication

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“…The sixth cluster deals with improvements of operational processes in logistics that can often be deduced from nature, as the examples in the framework description have shown (see also [50][51][52][53]). The topic of biological transformation is an already emerging theme promoted, e.g., by the Fraunhofer research institutes [69] and other researchers [70], and describes the change from solely bioinspired manufacturing or logistics systems towards those that are bio-integrated and bio-intelligent by combining what we can deduce and learn from the observation of processes in nature and leaping computational power as well as progress in AI, ML, and DL.…”

Section: Discussionmentioning

confidence: 99%

A Review of Further Directions for Artificial Intelligence, Machine Learning, and Deep Learning in Smart Logistics

Woschank¹,

Rauch

Zsifkovits³

2020

Sustainability

170

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Industry 4.0 concepts and technologies ensure the ongoing development of micro- and macro-economic entities by focusing on the principles of interconnectivity, digitalization, and automation. In this context, artificial intelligence is seen as one of the major enablers for Smart Logistics and Smart Production initiatives. This paper systematically analyzes the scientific literature on artificial intelligence, machine learning, and deep learning in the context of Smart Logistics management in industrial enterprises. Furthermore, based on the results of the systematic literature review, the authors present a conceptual framework, which provides fruitful implications based on recent research findings and insights to be used for directing and starting future research initiatives in the field of artificial intelligence (AI), machine learning (ML), and deep learning (DL) in Smart Logistics.

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Section: Discussionmentioning

confidence: 99%

A Review of Further Directions for Artificial Intelligence, Machine Learning, and Deep Learning in Smart Logistics

Woschank¹,

Rauch

Zsifkovits³

2020

Sustainability

170

View full text Add to dashboard Cite

show abstract

“…Therefore, biological transformation is also seen as a parallel process to digital transformation (Dieckhoff et al 2018). According to (Miehe et al 2018), biological transformation can be transferred in three levels to industrial production:…”

Section: Industry 40+: An Outlook On Future Challenges For Smesmentioning

confidence: 99%

Status of the Implementation of Industry 4.0 in SMEs and Framework for Smart Manufacturing

Rauch

Matt

2021

Implementing Industry 4.0 in SMEs

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In this chapter, we explore after 10 years of Industry 4.0 the status of the application in manufacturing companies and especially in small- and medium-sized enterprises (SMEs). Based on literature and previously conducted research we present guidelines and a modular framework for implementing smart manufacturing in SMEs. In addition, a stage model is illustrated to support SMEs in breaking down the framework from a design level to an implementation and operational level. Finally, an outlook is given on the future challenges that SMEs will face in the coming years when they want to reach the next level of Industry 4.0 in their own company.

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“…Biological transformation, [17], [18] and [9] shows that value in bio-intelligence will be either decentralized or personalized, or both. It will happen by seamless fusion among ICT, bio-, AI, robotics, etc.…”

Section: 2 Biological Transformation Of Products Processesmentioning

confidence: 99%

“…The studying of the living environment brings us closer to understand our environment and ecosystem. [20], [21], [22]. By understanding the processes, we might be able to influence and make production technology more efficient.…”

Section: 2 Biological Transformation Of Products Processesmentioning

confidence: 99%

Episodes of Robotics and Manufacturing Automation Achievements from the Past Decades and Vision for the Next Decade

Haidegger¹,

Paniti²

2019

ACTA POLYTECH HUNG

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The past 3 decades are characterized by the introduction of digital tools in the manufacturing sector, and by the advent of mechatronics being used for repetitive and fast material handling within logistics. Retrospectively this time is referred as the 3 rd Industrial Revolution. Presently, the cyber-physical systems and cyber-physical production systems are being referenced as substituents of the 4 th Industrial Revolution. The Authors have selected some outstanding episodes within this transition time to highlight the Hungarian successful achievements developed and implemented by talented researchers and innovative ideas by far-looking professors. The article also offers elements of visions for technology development, focusing on the new results of robotics and on biotransformation in manufacturing.

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The biological transformation of the manufacturing industry – envisioning biointelligent value adding

Cited by 50 publications

References 1 publication

A Review of Further Directions for Artificial Intelligence, Machine Learning, and Deep Learning in Smart Logistics

A Review of Further Directions for Artificial Intelligence, Machine Learning, and Deep Learning in Smart Logistics

Status of the Implementation of Industry 4.0 in SMEs and Framework for Smart Manufacturing

Episodes of Robotics and Manufacturing Automation Achievements from the Past Decades and Vision for the Next Decade

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