Revolutionizing Corrugated Factories: AI-Driven Efficiency with Digital Press

The corrugated packaging industry is undergoing a transformation as brands and manufacturers strive to meet faster turnaround times, customization demands, and sustainability goals. One of the most impactful enablers of this transformation is the integration of AI into production processes—especially in factories operating both traditional flexo and digital presses.

Let's explore how AI-driven optimization can unlock new levels of efficiency and flexibility in corrugated packaging production.

Bridging the Gap Between Flexo, Digital, and Finishing

Factories equipped with both flexo and digital presses often face challenges in maximizing the strengths of each technology. Flexo printing remains the standard for high-volume, low-cost runs, while digital printing offers flexibility for short runs, personalization, and rapid prototyping. However, managing the balance between these two technologies—and coordinating the finishing processes such as cutting, folding, and gluing—can be complex.

This is where AI can step in to streamline decision-making, improve operational efficiency, and optimize the entire production workflow, including finishing stages. By analyzing production data, AI algorithms can determine the optimal balance between flexo, digital, and finishing operations for any given job, taking into account variables such as:

• Volume Requirements: AI can automatically route larger runs to the flexo press, ensuring cost efficiency, while shorter runs, reprints, or test samples are assigned to the digital press, reducing waste and setup time.
• Print Quality vs. Speed: AI can analyze the quality requirements for each print job and decide which press to use. For example, if a job requires full-color detail but at lower volumes, the digital press can produce the needed output with precision.
• Finishing Optimization: AI coordinates finishing processes, ensuring that jobs requiring complex cutting or folding are matched with the most efficient workflow. It can predict machine capacity and optimize the sequencing of cutting and gluing to ensure that jobs move seamlessly through the finishing stages, reducing bottlenecks.
• Job Complexity: For complex jobs, such as those requiring variable data or multiple components, AI can flag portions of the work to be printed digitally, while assigning the simpler elements to the flexo press. It also ensures the finishing steps are in sync, minimizing delays in assembly.

Example: Optimizing Production with AI

Imagine a packaging factory running both flexo and a modern digital press capable of high-resolution CMYK printing, along with advanced finishing equipment. This factory must quickly produce an array of custom boxes for a new product launch with tight deadlines and varying order volumes.

Using AI, the system can analyze incoming orders and split production based on a number of factors:

• Order Splitting: Larger orders with minimal design changes are processed on the flexo press, while smaller or more complex orders that require high-quality finishes are routed to the digital press. The AI also sequences these jobs for optimal finishing—ensuring that cutting, folding, and gluing processes are aligned with the production speed and complexity of each job.
• Waste Reduction: By tracking historical production data, the AI can predict where bottlenecks or waste are likely to occur in both printing and finishing stages. It adjusts the production schedule in real-time, minimizing downtime, material waste, and unnecessary finishing steps.
• Finishing Flexibility: For jobs that require high-end finishes, such as spot varnish or intricate die cuts, AI can allocate resources to ensure the finishing equipment operates at its most efficient capacity. Jobs that don’t require complex finishes are routed to simpler, faster workflows.

Future-Proofing Corrugated Packaging Production

The integration of AI doesn’t just optimize present-day production—it future-proofs operations for increasing demands on customization, sustainability, and speed. AI can make real-time adjustments to prioritize orders based on shifting customer demands, market trends, and even environmental factors, such as reducing energy consumption or optimizing the use of eco-friendly inks, substrates, and finishing materials.

The results? A more agile, efficient, and sustainable corrugated packaging operation that can meet the evolving needs of modern brands and consumers, while maintaining the highest quality in both printing and finishing.

While the integration of AI offers significant improvements, it is essential for Manufacturing Information Systems (MIS) vendors to take a proactive role. To truly harness the potential of AI, these systems need to evolve and integrate with AI-driven workflows, making it easier for factories to adopt and benefit from optimized production and finishing processes.

Now is the time for MIS vendors to collaborate with AI developers and factories to ensure seamless integration, smarter decision-making, and better overall outcomes for the corrugated packaging industry.

About the Author:

Ran Lev is a product manager with over 20 years of experience in the printing and packaging industry, specializing in digital printing technologies and workflow optimization. He has worked with major brands and industrial operations to integrate cutting-edge solutions that streamline production, improve efficiency, and reduce costs. Ran is passionate about leveraging AI to transform traditional manufacturing processes and is always exploring new ways to innovate in this dynamic field.