IoT and the Smart Connected World
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Paper Type
Complete
Paper Number
1139
Description
The ‘smartification’ of physical products represents a key trend in the digital era. This trend also applies to the mechanical engineering industry, where companies have started to implement smart machines as cornerstones for the digitalization of their traditional business models. However, despite the significant business potential of smart machines, recent industry surveys suggest that implementation rates remain low. Against this backdrop, our study aims to support the design of smart machines by structuring the solution space. Specifically, following established guidelines and drawing on an analysis of 72 smart machines, we develop a taxonomy—consisting of 13 dimensions and 45 characteristics—and illustrate its usefulness by applying it onto four machines. Our study contributes to the existing body of knowledge by offering a nuanced understanding of smart machine design, as well as by providing practitioners with a conceptual tool for the systematic identification and assessment of available design options.
Recommended Citation
Scharfe, Philipp and Wiener, Martin, "A Taxonomy of Smart Machines in the Mechanical Engineering Industry: Toward Structuring the Design Solution Space" (2020). ICIS 2020 Proceedings. 1.
https://aisel.aisnet.org/icis2020/iot_smart/iot_smart/1
A Taxonomy of Smart Machines in the Mechanical Engineering Industry: Toward Structuring the Design Solution Space
The ‘smartification’ of physical products represents a key trend in the digital era. This trend also applies to the mechanical engineering industry, where companies have started to implement smart machines as cornerstones for the digitalization of their traditional business models. However, despite the significant business potential of smart machines, recent industry surveys suggest that implementation rates remain low. Against this backdrop, our study aims to support the design of smart machines by structuring the solution space. Specifically, following established guidelines and drawing on an analysis of 72 smart machines, we develop a taxonomy—consisting of 13 dimensions and 45 characteristics—and illustrate its usefulness by applying it onto four machines. Our study contributes to the existing body of knowledge by offering a nuanced understanding of smart machine design, as well as by providing practitioners with a conceptual tool for the systematic identification and assessment of available design options.
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