Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Multimachine Stability01:25

Multimachine Stability

163
Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
163

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Improving Deep Learning Based Lung Nodule Classification Through Optimized Adaptive Intensity Correction.

Bioengineering (Basel, Switzerland)·2026
Same author

Artificial intelligence-based intrusion detection and secure communication model for sustainable 6G-IoT networks.

Scientific reports·2026
Same author

Injectable platelet rich fibrin (i-PRF) versus platelet rich fibrin (PRF) both mixed with beta-tri calcium phosphate in bone regeneration using metacarpal bone defect in goats: micro CT comparative study.

The Saudi dental journal·2026
Same author

Protein Kinase Cδ deficiency in Arab children: A link to fatal monogenic lupus and BCGitis susceptibility.

Lupus·2025
Same author

Efficient sepsis detection using deep learning and residual convolutional networks.

PeerJ. Computer science·2025
Same author

Project based learning framework integrating industry collaboration to enhance student future readiness in higher education.

Scientific reports·2025

Related Experiment Video

Updated: Jul 9, 2025

Data Communication Based on MQTT in a Polymer Extrusion Process
08:15

Data Communication Based on MQTT in a Polymer Extrusion Process

Published on: July 15, 2022

3.5K

Analysis of barriers affecting Industry 4.0 implementation: An interpretive analysis using total interpretive

R Ben Ruben1, C Rajendran1, R Saravana Ram2

  • 1Department of Mechanical Engineering, Sri Krishna College of Engineering and Technology, Coimbatore, 641008, India.

Heliyon
|December 4, 2023
PubMed
Summary

This study uses Total Interpretive Structural Modeling (TISM) and Fuzzy MICMAC to prioritize Industry 4.0 implementation barriers. Key driving barriers include management commitment and training, while IT infrastructure and communication models are most dependent.

Keywords:
Barriers of Industry 4.0Driving and dependent barriersFuzzy MICMACISMImplementing Industry 4.0 technologies

More Related Videos

Evaluating Usability Aspects of a Mixed Reality Solution for Immersive Analytics in Industry 4.0 Scenarios
06:02

Evaluating Usability Aspects of a Mixed Reality Solution for Immersive Analytics in Industry 4.0 Scenarios

Published on: October 6, 2020

2.3K
Integrative Toolkit to Analyze Cellular Signals: Forces, Motion, Morphology, and Fluorescence
14:55

Integrative Toolkit to Analyze Cellular Signals: Forces, Motion, Morphology, and Fluorescence

Published on: March 5, 2022

4.0K

Related Experiment Videos

Last Updated: Jul 9, 2025

Data Communication Based on MQTT in a Polymer Extrusion Process
08:15

Data Communication Based on MQTT in a Polymer Extrusion Process

Published on: July 15, 2022

3.5K
Evaluating Usability Aspects of a Mixed Reality Solution for Immersive Analytics in Industry 4.0 Scenarios
06:02

Evaluating Usability Aspects of a Mixed Reality Solution for Immersive Analytics in Industry 4.0 Scenarios

Published on: October 6, 2020

2.3K
Integrative Toolkit to Analyze Cellular Signals: Forces, Motion, Morphology, and Fluorescence
14:55

Integrative Toolkit to Analyze Cellular Signals: Forces, Motion, Morphology, and Fluorescence

Published on: March 5, 2022

4.0K

Area of Science:

  • Industrial Engineering
  • Operations Management
  • Information Systems

Background:

  • Industry 4.0 adoption faces significant implementation barriers.
  • Prioritization of these barriers is crucial for effective strategic planning.
  • Existing models may not fully capture the complex interrelationships between barriers.

Purpose of the Study:

  • To develop a structural relationship model for analyzing and prioritizing Industry 4.0 implementation barriers.
  • To apply Total Interpretive Structural Modeling (TISM) and Fuzzy MICMAC for barrier analysis.
  • To identify key driving and dependent barriers influencing Industry 4.0 technology deployment.

Main Methods:

  • Literature review to identify 10 crucial barriers to Industry 4.0 implementation.
  • Application of Total Interpretive Structural Modeling (TISM) for structural relationship analysis.
  • Utilization of Fuzzy MICMAC for barrier classification and prioritization.

Main Results:

  • Identified IT infrastructure, lack of cyber-physical systems, and improper communication models as most dependent barriers.
  • Identified lack of top management commitment and inadequate training as most driving barriers.
  • Demonstrated the superiority of TISM over traditional Interpretive Structural Modeling (ISM).

Conclusions:

  • The study provides a clear hierarchy of barriers, highlighting those requiring immediate management attention.
  • Decision-makers can use the findings to implement targeted mitigation strategies for Industry 4.0 adoption.
  • Detailed explanation of TISM facilitates its adoption by researchers and practitioners.