Author(s): Amit Sharma

Abstract: The print production industry is undergoing a technological transformation, driven by the need for cost efficiency, speed, and quality. Traditional cost control methods such as Lean, TQM, and Six Sigma are increasingly limited in managing the complex variables of modern workflows. This paper explores how Artificial Intelligence (AI) and Machine Learning (ML) are reshaping cost control by enabling predictive, proactive, and prescriptive decision-making. Applications discussed include predictive maintenance, ink and substrate optimization, dynamic job scheduling, defect detection, and inventory forecasting. A hybrid research approach—combining analytical review with industry insights—highlights the capabilities of supervised, unsupervised, and reinforcement learning models. The findings demonstrate that AI/ML systems can reduce unplanned downtime, minimize material waste, and enhance pricing accuracy, offering savings between 10-35%. Despite adoption challenges like data silos and integration with legacy systems, the study concludes that AI-enabled cost governance is critical for printers seeking long-term competitiveness in an increasingly digital and data-driven industry.

DOI: 10.33545/27076571.2025.v6.i2a.179

Pages: 70-76 | Views: 225 | Downloads: 165

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