SUSTAINABLE PRINT MANAGEMENT VIA DATA-DRIVEN APPROACHES
DOI:
https://doi.org/10.29121/shodhkosh.v7.i1s.2026.7114Keywords:
Sustainable Print Management, Data-Driven Optimization, Predictive Analytics, Resource Efficiency, Green Information SystemsAbstract [English]
Sustainable print management has become an issue of paramount importance in respect to organizations that want to mitigate the effects they have on the environment whilst still being able to operate efficiently. The conventional print management systems tend to be based on fixed set of rules and manual controls hence resulting in wastage of paper, energy and unchecked waste. The research is a proposed comprehensive data-oriented system of sustainable print management, which combines data analytics, predictive modeling, and optimization methods in order to allow intelligent, adaptative, and resource-efficient printing environments. The suggested solution will take advantage of past print logs, device level energy logs, and patterns of user behavior to predict print demand, uncover inefficiencies, and make informed decisions. Predictive models are also used to forecast print volumes in future and possible waste in order to allow proactive measures to be taken like job consolidation, duplex enforcement and digital alternatives. The optimization algorithms also contribute to sustainability by dynamically assigning the print jobs to devices which consume less energy and scheduling workloads to reduce maximum consumption of energy. Moreover, user-friendly analytics is also integrated in order to gain an insight into the printing activity and implement nudging technologies that promote responsible printing without interfering with productivity. The experimental results show that there are quantifiable decreases in the amount of paper, energy use, and print-related emissions in comparison with the traditional systems and serviceable levels are attained. The results show the possibility of the data-driven strategies to make print management more of a proactive sustainability contributor as opposed to an operational reactive mechanism.
References
Agogué, R., Shakoor, M., Beauchêne, P., and Park, C. H. (2023). Analysis and Minimization of Race Tracking in the Resin-Transfer-Molding Process by Monte Carlo Simulation. Materials, 16, 4438. https://doi.org/10.3390/ma16124438 DOI: https://doi.org/10.3390/ma16124438
Bodaghi, M., Mobin, M., Ban, D., Lomov, S. V., and Nikzad, M. (2021). Surface Quality of Printed Porous Materials for Permeability Rig Calibration. Materials and Manufacturing Processes, 37, 548–558. https://doi.org/10.1080/10426914.2021.1960994 DOI: https://doi.org/10.1080/10426914.2021.1960994
Chia, H. Y., Wu, J., and Yan, W. (2022). Process Parameter Optimization of Metal Additive Manufacturing: A Review and Outlook. Journal of Materials Informatics, 2, 16. https://doi.org/10.20517/jmi.2022.18 DOI: https://doi.org/10.20517/jmi.2022.18
Fernández-León, J., Keramati, K., Garoz, D., Baumela, L., Miguel, C., and González, C. (2022). A Machine Learning Strategy for Race-Tracking Detection During Manufacturing of Composites by Liquid Moulding. Integrating Materials and Manufacturing Innovation, 11, 296–311. https://doi.org/10.1007/s40192-022-00263-6 DOI: https://doi.org/10.1007/s40192-022-00263-6
Jung, S., Kara, L. B., Nie, Z., Simpson, T. W., and Whitefoot, K. S. (2023). Is Additive Manufacturing an Environmentally and Economically Preferred Alternative for Mass Production? Environmental Science and Technology, 57, 6373–6386. https://doi.org/10.1021/acs.est.2c04927 DOI: https://doi.org/10.1021/acs.est.2c04927
James, G. G., Archibong, M. N. F. E. O., Abraham, E. E., Okafor, P. C., and Ufford, U. V. (2024). Development of the Internet of Robotic Things for Smart and Sustainable Health Care. ShodhAI: Journal of Artificial Intelligence, 1(1), 9–27. https://doi.org/10.29121/shodhai.v1.i1.2024.3 DOI: https://doi.org/10.29121/shodhai.v1.i1.2024.3
Kumar, S., Gopi, T., Harikeerthana, N., Gupta, M. K., Gaur, V., Krolczyk, G. M., and Wu, C. (2023). Machine Learning Techniques in Additive Manufacturing: A State of the Art Review on Design, Processes and Production Control. Journal of Intelligent Manufacturing, 34, 21–55. https://doi.org/10.1007/s10845-022-02029-5 DOI: https://doi.org/10.1007/s10845-022-02029-5
Lee, D., Lee, I. Y., and Park, Y. B. (2025). Real-Time Process Monitoring and Prediction of Flow-Front in Resin Transfer Molding Using Electromechanical Behavior and Generative Adversarial Network. Composites Part B: Engineering, 298, 112382. https://doi.org/10.1016/j.compositesb.2025.112382 DOI: https://doi.org/10.1016/j.compositesb.2025.112382
Lee, J., Duhovic, M., May, D., Allen, T., and Kelly, P. (2025). Physics-Informed Neural Networks for Real-Time Simulation of Transverse Liquid Composite Moulding Processes and Permeability Measurements. Composites Part A: Applied Science and Manufacturing, 193, 108857. https://doi.org/10.1016/j.compositesa.2025.108857 DOI: https://doi.org/10.1016/j.compositesa.2025.108857
Machado, J., Bodaghi, M., Nikzad, M., Camanho, P. P., Advani, S., and Correia, N. (2024). On the Understanding of Bubble Dynamics Through a Calibrated Textile-Like Porous Medium Using a Machine Learning Based Algorithm. Composites Part A: Applied Science and Manufacturing, 177, 107955. https://doi.org/10.1016/j.compositesa.2023.107955 DOI: https://doi.org/10.1016/j.compositesa.2023.107955
Nikooharf, M. H., Shirinbayan, M., Arabkoohi, M., Bahlouli, N., Fitoussi, J., and Benfriha, K. (2024). Machine Learning in Polymer Additive Manufacturing: A Review. International Journal of Material Forming, 17, 52. https://doi.org/10.1007/s12289-024-01854-8 DOI: https://doi.org/10.1007/s12289-024-01854-8
Sing, S. L., Kuo, C. N., Shih, C. T., Ho, C. C., and Chua, C. K. (2021). Perspectives of Using Machine Learning in Laser Powder Bed Fusion for Metal Additive Manufacturing. Virtual and Physical Prototyping, 16, 372–386. https://doi.org/10.1080/17452759.2021.1944229 DOI: https://doi.org/10.1080/17452759.2021.1944229
Yu, S., Liu, H., Zhao, G., Zhang, H., Hou, F., and Xu, K. (2024). A Code-Based Method for Carbon Emission Prediction of 3D Printing: A Case Study on the Fused Deposition Modeling (FDM) 3D Printing and Comparison With Conventional Approach. Journal of Cleaner Production, 484, 144341. https://doi.org/10.1016/j.jclepro.2024.144341 DOI: https://doi.org/10.1016/j.jclepro.2024.144341
Zhang, X., Li, J., Wang, Y., Liu, Y., and Zhang, H. (2024). Research on the Formation Mechanism of Surface Defects in 5182 Aluminum Alloy Strip During Hot Rolling. International Journal of Minerals, Metallurgy and Materials, 31, 1–12.
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Copyright (c) 2026 Dr. Nitin Dhawas, Dr. Reshma Sonar, Mahesh Ashok Bhandari, Dr. Shalini Wankhade, Kalyani Ghuge, Ganesh Chandrabhan Shelke, Chandrakant Kokane
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