
International Journal For Multidisciplinary Research
E-ISSN: 2582-2160
•
Impact Factor: 9.24
A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal
Home
Research Paper
Submit Research Paper
Publication Guidelines
Publication Charges
Upload Documents
Track Status / Pay Fees / Download Publication Certi.
Editors & Reviewers
View All
Join as a Reviewer
Get Membership Certificate
Current Issue
Publication Archive
Conference
Publishing Conf. with IJFMR
Upcoming Conference(s) ↓
WSMCDD-2025
GSMCDD-2025
Conferences Published ↓
RBS:RH-COVID-19 (2023)
ICMRS'23
PIPRDA-2023
Contact Us
Plagiarism is checked by the leading plagiarism checker
Call for Paper
Volume 7 Issue 2
March-April 2025
Indexing Partners



















Industry 4.0 Revolutionizing Device Troubleshooting and Support with Thingworx Platform and Edge Computing (Transformative Benefits and Strategic Implementation)
Author(s) | Ashok Kumar Kalyanam |
---|---|
Country | United States |
Abstract | Industry 4.0 introduced advanced technologies into device troubleshooting and support, such as the ThingWorx Platform and Edge Computing. This approach is innovative. Because frequent resolutions are automated, thus avoiding L1 support by a huge margin, with easy escalation to L2. The ThingWorx Platform uses edge computing, processing data closer to the source for real-time insights and predictive analysis that improve operational efficiency. This framework integrates Internet of Things technologies and distributed intelligence to provide an efficient process for maintaining devices, optimizing resource allocation, and reducing downtime. The strategic implementation of ThingWorx in Industry 4.0 is discussed in this article, and the benefits that it offers are underlined enhanced device diagnostics, enhanced workflows for support, and scalability. Such technologies lead to intelligent, efficient, and reliable industrial systems. |
Keywords | Industry 4.0, ThingWorx Platform, Edge Computing, Internet of Things, Device Troubleshooting, L1 Support Automation, L2 Support Escalation, Predictive Maintenance, Distributed Intelligence, Operational Efficiency |
Field | Engineering |
Published In | Volume 1, Issue 1, July-August 2019 |
Published On | 2019-07-10 |
DOI | https://doi.org/10.36948/ijfmr.2019.v01i01.35940 |
Short DOI | https://doi.org/g82wnd |
Share this

E-ISSN 2582-2160

CrossRef DOI is assigned to each research paper published in our journal.
IJFMR DOI prefix is
10.36948/ijfmr
Downloads
All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.
