Changeability in Engineering

Eisenträger, Marlene; Adler, Simon; Kennel, Matthias; Moser, Sebastian J.

doi:10.1109/ice.2018.8436295

Cited by 16 publications

(2 citation statements)

References 19 publications

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“…The tradition of using mechatronics technology within various business processes across different departments goes back to the 1950s, but its origins can be found in Ancient Greece (for instance, moving statues - Eisenträger et al 2018). Important to this topic is the transformation from simple mechanical systems to a more complex and sophisticated technology consisting of various components, such as electronics, mechanics, software, and digital control systems.…”

Section: Introductionmentioning

confidence: 99%

Effestiveness and efficiency criteria for strategic application of mechatronics in business processes

Laužikas¹,

Miliūtė²,

Morozovaitė³

et al. 2021

IRD

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The present paper is centred on the effects of mechatronics in both high-tech and technologically less intensive firms. The strategy of innovation implementation in the mechatronics industry is tackled from both the high-tech business development perspective and the mechatronics product application perspective in other industries. Although the scientific and statistics literature with regards to the modern technology's effects is well established, the scarcity of scientific research in the field of the application of mechatronics/robotics in business makes this topic of significant scientific value. The purpose of this paper is to examine the effectiveness and efficiency criteria for the strategic application of mechatronics in businesses, related to both the decision-making process at the top-management level as well as choosing, applying, and monitoring the success of mechatronics among institutional clients. Given that mechatronics' application efficiency depends on a business model and the innovation culture, a focus on two categories of companies in terms of technological intensity could help businesses to choose the right high-tech product and the proper corresponding innovation commercialization model. Moreover, the paper could serve mechatronics firms while implementing the product/service or process innovations that are closer to consumer needs or specific industry challenges. Qualitative semi-structured expert interviews with four IT and four textile managers were conducted, and the conceptual model (which derives from the literature review) was tested. The choice of Lithuania as a target market for the analysed companies is related to the transition of this Baltic economy from the efficiency to the innovation-driven category, where the combination of high and low tech may be able to intensify the innovation process as well as to answer the research question of how to unlock the potential of mechatronics in business processes.

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Section: Introductionmentioning

confidence: 99%

Effestiveness and efficiency criteria for strategic application of mechatronics in business processes

Laužikas¹,

Miliūtė²,

Morozovaitė³

et al. 2021

IRD

View full text Add to dashboard Cite

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“…Using data is currently a challenge during production. Even if the companies have identified the potentials of data [2], it is challenging to gain the added values. One reason is that IT-knowledge is in most cases not a key competence of companies in industrial production.…”

Section: Introductionmentioning

confidence: 99%

Deployment architecture for the local delivery of ML-Models to the industrial shop floor

Backhaus

Herzog

Adler

et al. 2020

Machine Learning for Cyber Physical Systems

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Information processing systems with some form of machine-learned component are making their way into the industrial application and offer high potentials for increasing productivity and machine utilization. However, the systematic engineering approach to integrate and manage these machine-learned components is still not standardized and no reference architecture exist. In this paper we will present the building block of such an architecture which is developed with the ML4P project by Fraunhofer IFF.

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Reviewing the Application of Data Driven Digital Twins in Manufacturing Systems: A Business and Management Perspective

Badakhshan

Ball

2021

Advances in Production Management Systems. Artificial Intelligence for Sustainable and Resilient Production Systems

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Simulation modelling has been a widely used tool for analyzing manufacturing systems and improving their performance. Although, little attention has been paid to the application of data-driven simulation modelling of the manufacturing systems. With the development of new-generation information and digitalization technologies, more data can be collected from the manufacturing shop floor. This has paved the way for employing data-driven simulation of manufacturing systems knowns as a digital twin. This paper reviews the literature and practice on digital twins in manufacturing systems from a business and management perspective to identify the gaps and recommend avenues for future research. The results show that 2018 has been a turning point in the literature with small scale case studies of digital twins emerging independent of commercial practice. Since 2018 the digital twin literature has moved on from descriptions and conceptual frameworks to focus on one product lifecycle phase with any reference to sustainability advance being confined to energy and resource efficiency. Practice has been advanced by manufacturers and IT vendors however the definition of digital twins lacks precision for ease comparison with the literature. Future avenues for research are identified in the areas of lifecycle phases and digital model fidelity.

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Changeability in Engineering

Cited by 16 publications

References 19 publications

Effestiveness and efficiency criteria for strategic application of mechatronics in business processes

Effestiveness and efficiency criteria for strategic application of mechatronics in business processes

Deployment architecture for the local delivery of ML-Models to the industrial shop floor

Reviewing the Application of Data Driven Digital Twins in Manufacturing Systems: A Business and Management Perspective

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