Digital Twin Enabled Product Lifecycle Management for Smart and Sustainable Factories

DOI

10.2991/978-94-6239-750-7_45How to use a DOI?

Keywords

Product lifecycle management; Smart factories; Digital twin; Industry 4.0; Cyber-physical systems; Green manufacturing; Predictive maintenance; AI-driven analytics; Energy optimization; Sustainable manufacturing

Abstract

The fast development of Industry 4.0 technologies has turned the classical factories into intelligent, information-driven manufacturing systems. Nevertheless, a great number of Product Lifecycle Management (PLM) solutions are still largely static PLM and are not directly tied to real-time shop-floor intelligence. This restricts the visibility of the lifecycle and limits predictive and sustainability-conscious decision-making through the lifecycle of the product. Possible impacts of these limitations include increasing unintended downtime, decreasing resource efficiency, and increasing lifecycle energy consumption. In a bid to close this gap, the present paper suggests a Digital Twin-Enabled Product Lifecycle Management (DT-PLM) model combining cyber-physical systems, real-time sensing, and smart analytics to sustain a constantly updated virtual image of products, processes, and factory assets. The suggested four-layer framework comprises (1) sensing and IoT, (2) digital twin and lifecycle synchronisation, (3) AI-driven predictive analytics, and (4) decision intelligence and sustainability optimisation. Degradation prediction and energy consumption prediction are realised through a hybrid deep learning model composed of Temporal Convolutional Networks (TCN) and Gated Recurrent Units (GRU), through multi-objective optimisation balancing productivity, maintenance cost, and carbon footprint. According to testbed results, better predictive performance and quantifiable sustainability benefits, such as a decrease in energy consumption, a decrease in maintenance cost, and a decrease in downtime, have been realized as opposed to the conventional PLM. In general, DT-PLM is evidenced to have a scalable model of smart and sustainable manufacturing.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Anjali Goswami
AU  - Kanchan Thakur
PY  - 2026
DA  - 2026/08/31
TI  - Digital Twin Enabled Product Lifecycle Management for Smart and Sustainable Factories
BT  - Proceedings of the International Conference on Advanced Design, Manufacturing, and Sustainable Energy Systems (ICADMSES 2026)
PB  - Atlantis Press
SP  - 616
EP  - 628
SN  - 2589-4943
UR  - https://doi.org/10.2991/978-94-6239-750-7_45
DO  - 10.2991/978-94-6239-750-7_45
ID  - Goswami2026
ER  -