Artificial Intelligence in the Printing Industry: A Systematic Review of Industrial Applications, Challenges and Benefits

The commercial printing industry is undergoing a significant transformation with the integration of Artificial Intelligence (AI), yet there is limited literature on its specific applications and impact. This systematic review addresses two critical research questions: What are the key industrial domains where AI is applied in the printing industry, and what are the associated benefits and challenges? Our study identifies the primary AI applications across Production Planning and Control (PPC), Quality Management (QM), Maintenance Management (MM), and Supply Chain Management (SCM). PPC employs optimization algorithms to optimise scheduling and resource allocation, improving production efficiency. In QM, Machine Learning and Computer Vision detect defects and optimize print quality. MM focuses on minimizing downtime by ensuring machines operate efficiently through predictive maintenance. Despite these advancements, challenges such as high-quality data requirements, algorithmic complexity, and integration difficulties persist. This research fills a critical gap in the literature by providing a comprehensive overview of AI’s role in the printing industry, offering valuable insights into its potential to drive efficiency, cost savings, and quality improvements. Our findings suggest that while AI holds substantial promise, its benefits are contingent upon overcoming significant implementation challenges. This study contributes to the ongoing discourse on digital transformation by providing a robust framework for understanding AI’s current and future impact on the printing sector. Our methodology followed PRISMA guidelines, with a thorough review and thematic analysis of peer-reviewed studies. The insights gained from this review can guide future research and practical implementations, positioning AI as a crucial enabler in the evolving landscape of the printing industry.