PAPER TECHNOLOGY INTERNATIONAL 2026, The next decade of paper converting: Automation, AI and flexibility. Carlos Muriel, Sales & Marketing Director, Pasaban S.A.

PAPERTECHNOLOGYINTERNATIONAL
This requires integration at multiple levels:
•
Between different machines within the converting line
•
Between converting equipment and factory
management systems (MES, ERP)
•
Between production data and decision-making
processes
For example, a sheeting line may receive production
orders directly from an MES system, automatically con昀椀gure
itself, and provide real-time feedback on performance and quality.
Similarly, winding operations may be synchronized with upstream
and downstream processes to optimize material 昀氀ow and reduce
bottlenecks.
Integration also enables advanced analytics and continuous
improvement. By collecting and analyzing data from multiple
sources, companies can identify trends, optimize processes, and
improve overall ef昀椀ciency.
Achieving this level of integration requires not only
advanced technology, but also collaboration between equipment
manufacturers, converters, and software providers.
Implications for Converters
The transition towards intelligent and 昀氀exible converting
operations has important implications for converters.
Investment decisions must go beyond immediate
performance considerations and take into account long-term
adaptability. Equipment should be evaluated based on its scalability,
openness, and compatibility with future technologies.
Data management becomes a strategic capability.
Companies must develop the ability to collect, structure, and analyze
data effectively, turning it into actionable insights.
Workforce skills must also evolve. Operators and technicians
will need to interact with more advanced systems, requiring training
in digital tools and data interpretation.
Finally, strong partnerships with technology providers
become essential. As systems become more complex, the ability to
rely on experienced partners is a key factor for success.
Image 6: (Below) Remote connection for technical issues.
Image 7: (Below Right) High precision design and
commissioning.
36
Practical Applications: Bringing Intelligence to Real Converting
Environments
While the concepts of automation, arti昀椀cial intelligence, and
昀氀exibility are often discussed at a theoretical level, their real value
becomes evident when applied to day-to-day converting operations.
In sheeting processes, for example, the combination
of advanced automation and data analysis is already enabling
signi昀椀cant improvements in productivity and quality. Modern
sheeters can automatically adjust knife positioning, cutting speeds,
and stacking parameters based on production requirements and
material characteristics. When this is combined with data monitoring
and analysis, it becomes possible to identify patterns that afect
performance—such as variations in reel quality or environmental
conditions—and compensate for them proactively.
Many aspects related to e昀椀ciency that were previously not
even questioned and were handled through operational adjustments
— requiring a certain amount of setup time with the machine
stopped or running at limited speed — are now being analyzed in
depth.
Through extensive programming and automation taken to
the highest level, the machine availability time is maximized, and
downtime is reduced to the minimum possible.
Parameters and operational settings are now optimized
automatically to ensure the highest productivity and ef昀椀ciency.
In practical terms, this means fewer interruptions, more
stable production, and improved consistency in sheet quality.
Operators are supported by systems that provide clear information
and recommendations, reducing the risk of human error and
improving overall e昀椀ciency.
Similarly, in winding operations, intelligent control systems
are transforming how reels are produced. By continuously
monitoring parameters such as tension, nip pressure, and reel
geometry, modern winders can adjust their behavior in real time to
ensure optimal reel structure. This is particularly important when
processing recycled or lightweight materials, which tend to exhibit
greater variability.
The integration of data analytics further enhances these
capabilities. By analyzing historical production data, it is possible
to identify optimal operating conditions for different materials and
applications, reducing trial-and-error adjustments and improving
reproducibility.

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