SMART PRINT MANAGEMENT USING PREDICTIVE ANALYTICS
DOI:
https://doi.org/10.29121/shodhkosh.v6.i2s.2025.6710Keywords:
Predictive Analytics, Smart Print Management, Iot-Enabled Printers, Print Demand Forecasting, Predictive Maintenance, Anomaly Detection, Machine Learning, Operational Optimization, Enterprise Print Ecosystems, Data-Driven Decision-MakingAbstract [English]
This study is a proposal of a smart, foresight analytics-based Smart Print Management model that can maximize efficiency, reliability, and sustainability of enterprise print settings. The traditional print management systems are based on reactive operations and thus they have recurring device failures, consumable is used inefficiently and there is little visibility of the print behaviors. In order to seal these cracks, the suggested framework incorporates IoT-enabled telemetry, machine-learning-enabled forecasting, predictive maintenance, and anomaly detection in order to make the print management an interactive and automatic decision-making infrastructure. The system gathers multi-modal data on heterogeneous printer fleets like print volumes, device health metrics and job-level logs and processes them in an effective data acquisition and preprocessing pipeline. LMST and print volume predictive models, random forest and XGBoost predictive models for failure prediction, autoencoders models to predict anomalies are used to analyze operational trends and predict future status. The experimental use of those models proves their ability to predict workload changes, reveal the earliest indicators of a device malfunctioning, and causes of abnormal printing behavior, which allows the routing of jobs automatically, routine maintenance, and notifications about security vulnerabilities. The results indicate that there were significant gains regarding continuity of operations, cost reduction, optimization of consumables, and performance in terms of sustainability. The paper concludes that predictive analytics will offer a substantial degree of responsiveness and resiliency of the print management infrastructure. The lines of the future research involve the study of federated learning, reinforcement learning coordination, and digital twin simulation to develop automation, scalability, and privacy of smart print ecosystems further.
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Copyright (c) 2025 Dr. Soumitra Das, Dr. Charu wadhwa, Aseem Aneja, Shikha Gupta, Rutu Bhatt, Madhur Grover
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