Abstract
Evaluating Industry 4.0 technology application in small and medium-sized enterprises (SMEs) is an issue that requires a multi-criteria strategy comprising quantitative and qualitative elements. The purpose of this study is to integrate performance estimation of Industry 4.0 technology application using the technology–organization–environment (TOE) framework. Relating TOE to Industry 4.0 technology application evaluation is more multifaceted than other methods and it requires comprehensive analysis. In this study, we applied a multiple-criteria decision-making (MCDM) approach to develop a model which integrates MCDM to perform an assessment that prioritizes the influence weights of Industry 4.0 technology application to SMEs’ factors. Firstly, we carried out a review of the literature and the TOE framework was selected to generate nine elements, along with three aspects used to measure Industry 4.0 technology application in SMEs. Secondly, the approach of the decision-making trial and evaluation laboratory (DEMATEL) was set up using an influence network relations digraph (INRD). The DEMATEL-based analytic network process (DANP) was used to indicate the influence weights linking the above aspects and elements. Lastly, the modified VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) technique applied influence weights to assess the aspects/elements in the gaps identified and to investigate how to reduce the gaps so as to estimate the application of Industry 4.0 technology by SMEs. The results show that the technology aspect is the most influential factor.
Highlights
Owing to the advancement of science and technology, numerous fields are gradually moving toward automation and intellectualization
This study examines the effect of aspects of technology, organization, and environmental criteria on Industry 4.0 technology application
This paper has examined issues surrounding the application of Industry 4.0 technology, and proposes a beneficial approach for the estimation and choice of elements for Industry
Summary
Owing to the advancement of science and technology, numerous fields are gradually moving toward automation and intellectualization. The technology of Industry 4.0 represents the convergence of a range of emerging and novel technologies, such as cloud computing, smart sensors, big data, additive manufacturing (AM), robotics, artificial intelligence (AI), machine learning (ML), Internet of Things (IoT), and augmented reality [1,2,3,4,5,6,7] These progressive technologies’ participation in Industry 4.0 is reorganizing entire operational structures via centralized workflows and transforming analog to decentralized and digital manufacturing procedures [8,9,10,11,12]. Machines, and people, thereby establishing more responsive and agile supply chains
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