PREDICTIVE ANALYTICS FOR PRINT SUPPLY CHAIN MANAGEMENT
DOI:
https://doi.org/10.29121/shodhkosh.v7.i1s.2026.7099Keywords:
Predictive Analytics, Print Supply Chain Management, Demand Forecasting, Machine Learning, Inventory Optimization, Service-Level Improvement, Industry 4.0.Abstract [English]
Predictive analytics have become one of the most important facilitators of data-driven decision-making in supply chain management, yet the system is not actively used in the print industry, despite the growth of the complexity of operations and the unpredictability of demand. The nature of print supply chain is also marked by job production, non-homogeneous materials, short-term planning, and inflexible delivery schedules that diminish the efficiency of conventional reactive planning strategies. This paper explores the application of predictive analytics in print supply chain management and suggests a common end to end model that combines data collection, feature engineering, predictive model and decision support into an integrated operational pipeline. It suits various predictive paradigms such as classical time-series models, feature-based ensemble learning, and sequence-based deep learning so that it can flexibly adapt to data maturity like data need depending on the decision requirement. Through an experimental analysis of representative datasets of print supply chains, machine learning-based models are proven to be more effective in improving the accuracy of demand forecasting and minimizing extreme prediction errors over traditional baselines. More to the point, the combination of predictive outputs to the planning and execution processes results in quantifiable service-level benefits, such as increased on-time delivery, increased inventory fill rates, decreased stock-out rate, and decreased unplanned downtime. The results validate the claims that predictive analytics actually improves the resilience of the print supply chain by reducing the occurrence of the high-impact forecast failures and allowing risk-sensitive proactive decision-making. The suggested framework offers an effective basis of the further development of intelligence-driven supply chain management in contemporary print production settings.
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Copyright (c) 2026 Dr. Prabha D, Shwetambari Pandurang Katake, Kalpana Rawat, Dr. Vikrant Nangare, Aakash Soni, Shanthi V
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