The purpose of this paper is to identify, analyze and model Internet of Things (IoT) enablers essential for the success of Industry 4.0.
IoT enablers for Industry 4.0 are identified from literature and inferable discussions with industry experts. Three different techniques namely, principal component analysis (PCA), interpretive structural modeling (ISM) and decision making trial and evaluation laboratory (DEMATEL) are applied to model IoT enablers. In addition to this, DEMATEL is also applied under two different situations representing the behavioral characteristic of experts involved. These are termed as optimistic (maximum) and pessimistic (minimum).
The integrated approach of PCA-ISM-DEMATEL shows that IoT ecosystem and IoT Big Data are the most influential or driving IoT enablers. These two enablers have been identified as the pillars for Industry 4.0. On the other side, IoT interchangeability, consumer IoT, IoT robustness and IoT interface and network capability have also been identified as the most dependent enablers for Industry 4.0.
The findings enable the industry practitioners to select the most appropriate driving enablers for an effective implementation of Industry 4.0.
The integrated approach-based hierarchical model and cause-effect relationship among IoT enablers are proposed which is a novel initiative for Industry 4.0. Moreover, two different variants of DEMATEL namely, pessimistic and optimistic are applied first time.
Rajput, S. and Singh, S.P. (2019), "Identifying Industry 4.0 IoT enablers by integrated PCA-ISM-DEMATEL approach", Management Decision, Vol. 57 No. 8, pp. 1784-1817. https://doi.org/10.1108/MD-04-2018-0378
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