The future of S&OP: How AI is revolutionising supply chain planning for paper & packaging Jari Kaukiainen, Advisor Integrated Business Planning and SCP, I Plan

PAPERTECHNOLOGYINTERNATIONAL
Another relevant use case involves rebalancing supply to
accommodate shortterm demand shifts. Simulation scenarios test
how shifting product availability or logistics flows can satisfy updated
regional demands within existing constraints. A real-life example of
such is where paper supplier must redirect volume from Spain to
Germany. Simulation assesses the impact on service levels, lead
times, and costs, guiding the planner’s next-best actions.
6. Sales and Operations Execution (S&OE)
Non-generative ML use case:
• Dynamic replanning algorithms. Real-time demand
signals (orders, POS data, weather, etc.) are ingested
by ML models that adjust short-term supply signals like
delivery plans or material allocations.
Simulation use case:
Short-term execution playbooks. Simulation tools support
rapid evaluation of next-best actions in case of unexpected
changes—e.g., customer delays or line shutdowns—helping teams
decide within hours, not days.
Why Non-Generative AI and Simulation Work Best in Practice
• Machine learning excels in repeatable, data-rich
environments where relationships are too complex for
human detection but structured enough for algorithms
to optimise.
• Simulation excels in uncertain, constraint-heavy
environments where scenario testing helps de-risk
decisions and align stakeholders around trade-offs.
Rather than replacing planners, these tools augment their
capability—freeing time from number crunching and enabling more
strategic, data-informed discussions across the business.
The Power of Edge AI, Predictive Alerts, and Collaboration
There are several steps paper industry operators need to
take to address the still commonly fragmented supply chains and
disconnected data flows. Improving these processes toward a
more agile and resilient setup is critical to responding effectively to
disruptions occurring anywhere along the supply chain. While many
companies have already made significant progress, there is still
important work to be done.
It is still common in the paper and packaging industry to
operate at a relatively slow pace when it comes to supply chain
decision-making—particularly in S&OP. Many companies rely on
quarterly or monthly S&OP cycles, based on the belief that faster
planning is not feasible. However, this assumption is outdated.
Increasingly, it’s important to integrate S&OP with short-term Sales
& Operations Execution (S&OE) and to build tactical planning
capabilities that support weekly and ad-hoc scenario creation—
especially with the help of modern planning tools.
In many cases, real-time, end-to-end data sharing remains
absent across the supply chain—even when both ends of the chain
are owned by the same company. This lack of integration makes it
even more essential to establish real-time platforms that enable key
partners to collaborate more effectively.
It is strongly recommended that organisations across the
ecosystem begin leveraging AI to better coordinate key processes,
such as demand planning and timely capacity adjustments.
To fully capitalise on AI’s potential, several innovations are
being rapidly adopted:
• Edge AI: Localised AI models installed directly at
mill or plant level enable nearinstant insights on shop
floor realities. As an example these models can adjust
production forecasts based on machine availability,
material delays, or shift patterns, driving smarter
micro-decisions.
• AI-Augmented Collaboration: AI-generated
scenario visualisations and forecasts can be
shared in S&OP meetings to enrich discussions.
This promotes cross-functional alignment, helping
commercial, operations, and finance teams cocreate action plans.
• Predictive Alerts: AI tools continuously monitor
real-time data and generate alerts when conditions
deviate from plan. For instance, sudden drops in
raw material availability or order volumes trigger
automatic suggestions for replanning.
Connecting AI Forecasts with Execution: The
ERP & APS Integration Imperative
AI-powered forecasts only realise their full
potential when seamlessly connected to execution
systems like ERP and APS. For example:
• ERP Integration (SAP, IFS, Oracle, Microsoft, etc.):
I-Plan integrates with leading ERP systems to push
accurate demand forecasts and receive execution
feedback. This enables closed-loop planning where
short -term demand sensing helps to adapt forecasts
based on actual order intake, production realities, and
shipment schedules.
• End-to-End Visibility: Integration ensures that insights
are shared across the business, aligning planning
with procurement, logistics, and finance. It removes
data silos and makes real-time collaboration possible
between upstream suppliers and downstream
customers.
• Execution Alignment: Accurate forecasting feeds
directly into production orders, transport planning, and
inventory restocking—ensuring that planning isn’t just
theoretical but operationally actionable.
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