Device Interaction Graph: Directed Decision Graph for Settings Auto-Completion

Kannan, Ramasamy; Pathuri, Sai Harish; Kumar, Deepak

doi:10.1109/access.2020.3048423

Cited by 1 publication

(1 citation statement)

References 32 publications

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“…The second dimension is labelled as automation (18.791% of variance) and contains eight items: auto-decision of a machine, knowledge extraction from IoT devices, cognitive knowledge of IoT devices, robots connected by cloud computing, artificial intelligence techniques, additive manufacturing robots, controlling IoT devices and promoting innovation. These factors were related to the finding of Kannan et al (2020), Ge et al (2021), and Arulanantham and Palanisamy (2021), who used a knowledge-based system for auto-decision of robots or machines in IoTs, cloud computing and big data. The third dimension was categorized as the real-time (14.080% of total variance), contains four items: wearable sensors, big data analytics engine, promote real-time data and real-time data sharing.…”

Section: Discussionmentioning

confidence: 99%

The impact of applying knowledge in the technological pillars of Industry 4.0 on supply chain performance

Sawangwong

Chaopaisarn

2021

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PurposeThe purpose of the study is to investigate the impact of technological pillars of Industry 4.0 based on knowledge to adopt the supply chain performance of Thai small and medium-sized enterprises (SMEs) 4.0. In addition, to increase knowledge and understanding of how to apply knowledge in technology 4.0 to improve the efficiency of supply chains and organizations.Design/methodology/approachAn integrated model was developed from applying knowledge in five technological pillars of Industry 4.0 such as Internet of things (IoTs), cloud computing, big data and analytics, additive manufacturing and cyber-security. The bibliometric analysis was used to find the relationship between the technological pillars of Industry 4.0 and the literature review. The survey questionnaires were sent to Thai SME 4.0 (manufacturing aspect). Of these, 240 useable responses were received, resulting in a response rate of 65.84%, after then, the exploratory factor analysis (EFA), confirmatory factor analysis (CFA), structural equation modeling (SEM) and validity were used to evaluate the model through IBM SPSS 21 and AMOS 22.FindingsEFA showed the four groups of the technological pillars of Industry 4.0, such as support human, automation, real-time and security. These groups positively impact supply chain performance (increase delivery reliability, increase resource efficiency, decrease costs in the supply chain and reduce delivery time). Another important finding is that supply chain performance positively impacts organizational performance in profitability, return on investment (ROI) and sale growth.Originality/valueThis study is a model development to support the supply chain performance and increase understanding related to applying knowledge in technology 4.0 that remains unclear for SME 4.0.

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Section: Discussionmentioning

confidence: 99%

The impact of applying knowledge in the technological pillars of Industry 4.0 on supply chain performance

Sawangwong

Chaopaisarn

2021

View full text Add to dashboard Cite

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Device Interaction Graph: Directed Decision Graph for Settings Auto-Completion

Cited by 1 publication

References 32 publications

The impact of applying knowledge in the technological pillars of Industry 4.0 on supply chain performance

The impact of applying knowledge in the technological pillars of Industry 4.0 on supply chain performance

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