Digital Twins (DT) and Artificial Intelligence (AI) Affect Sustainability Outcomes- Operational Performance- Waste Reduction, and Energy Efficiency

DOI

10.2991/978-94-6239-674-6_3How to use a DOI?

Keywords

DT; AI; Sustainability; Operational Performance; Waste Reduction; and Energy Efficiency

Abstract

Background: Digital Twins (DT) and Artificial Intelligence (AI) have become new groundbreaking technologies, allowing real-time monitoring, predictive analytics, and data-based optimization to increase energy efficiency, minimize waste, and streamline the work process.

Purpose: The study aims to investigate the effects of the use of DT and AI on sustainable industrial activities in terms of energy efficiency, waste minimization, and performance in manufacturing and energy industries. The data were analyzed using descriptive statistics, multiple linear regression, and the ANOVA method to test the relationships between DT and AI adoption sustainability outcomes, maintaining the industry type and organization size as controlled variables.

Methodology: The total respondents in this research is 50, stratified purposive sampling was used to represent. The sources of data are primary – Manufacturing, energy, etc, organization, and sources of secondary data are reports by companies.

Findings: The performance in operations was found to have a strong and positive impact of DT and AI (β= 0.65, p < 0.01). The application of DT and AI within organizations has led to optimized predictive maintenance, reduced downtime, improved production schedules, and increased productivity. The R² of 0.691 indicates that almost 70 percent of the change in operating performance can be explained by the use of technology, demonstrating the applicability of these digital tools in developing operational excellence. In manufacturing organizations, the greatest gains in predictive maintenance (30-35 percent) and downtime reduction (30-35 percent), and moderate gains in logistics, illustrated the diversity of the operational problems in different spheres.

Conclusion: These findings imply the necessity of tailoring the plans of the implementation of DT and AI to industry-specific pressures, workflow, and sustainability objectives. Digital twins (DT) and artificial intelligence (AI) need to be strategically introduced in industrial companies to enhance energy efficiency and waste reduction, and improve operational performance.

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  - Shantnu Kumar
AU  - Abhishek Priyadarshi
AU  - Priti Rai
PY  - 2026
DA  - 2026/05/28
TI  - Digital Twins (DT) and Artificial Intelligence (AI) Affect Sustainability Outcomes- Operational Performance- Waste Reduction, and Energy Efficiency
BT  - Proceedings of the International Conference on Sustainable Computing and Artificial Intelligence (ICSCAI 2025)
PB  - Atlantis Press
SP  - 21
EP  - 28
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6239-674-6_3
DO  - 10.2991/978-94-6239-674-6_3
ID  - Kumar2026
ER  -