Planning for tomorrow: an S&OP that enhances scenarios, capacity and margin at Sylvamo

A demanding sector where every decision impacts the future margin

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The paper industry faces an ongoing challenge: volatility in demand, rising costs, product diversity and constant pressure to optimize installed capacity. For a company like Sylvamo —with multiple plants, thousands of production combinations and a strategic horizon of 15 years—planning wasn't just projecting volumes, but anticipating how each decision would affect margin, asset utilization and business sustainability.

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The previous process relied on an Excel model with an ad-hoc solver. While useful, this approach presented increasing risks: extremely high processing times, fragility in the face of file errors, difficulty incorporating new restrictions, and an excessive reliance on few people on the team. In addition, executing scenarios required running sequential processes per year and per stage, limiting the ability to explore real alternatives.

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Consequently, the process S&OP it was losing strategic depth: it was difficult to connect tactical decisions with long-term impacts and evaluate how each production mix influenced capacity, supply, costs and future margin.

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The need for a more strategic, flexible, scenario-based S&OP

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Sylvamo was looking for a leap of maturity in his planning process. The objective was not only to replace Excel, but to transform the way in which the organization analyzed its operation and its future. I needed:

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• A unified model that will connect demand, production mix, capacity, variable costs, freight, weights and plant restrictions.
• The ability to run simultaneous scenarios for different horizons — tactical and strategic — without limiting the speed of analysis.
• Integrate into the same process what was discussed separately today: production limitations, portfolio decisions, simulation of future demand and supply strategies.
• Provide S&OP with a tool that would allow us to understand the financial and operational impact of each production combination, holding more collaborative meetings and decisions based on data.

The company also required a modern visual interface, integrated BI and an environment where different areas—sales, supply chain, finance, planning—could participate without relying on a single critical file. The S&OP should become a transversal and scalable process, not an isolated activity from the area of strategic planning.

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How Pyplan transformed Sylvamo's decision-making

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The solution developed with Pyplan combined mathematical optimization, advanced modeling and a collaborative architecture that redefined the S&OP process in the company.

First, the model integrated the commercial base: sales targets for the next 15 years, broken down into volumes and prices by product and region. From there, Pyplan automatically built the unit contribution margin, incorporating variable costs, freight, tax benefits and premiums by brand. This made it possible to compare not only the volume, but also the real profitability of each alternative production mix.

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Then all the operating restrictions were modeled: machine capacity, grammages, pulp consumption, outsourced processes, minimums and maximums per product, market compliance and physical limitations of each plant. All of these rules became dynamic, with the possibility of activating or deactivating them depending on the scenario.

On this basis, Pyplan executed an optimization engine —developed in Python and with high performance— capable of maximizing margin subject to complex restrictions. The big difference was that every year on the horizon is processed simultaneously, eliminating the historic bottleneck of the original model.

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Integration with the S&OP process became immediate:

• Teams can evaluate mix, capacity and supply scenarios before tactical meetings.
• Finance automatically obtains the impact on margin, contribution and asset utilization.
• Operations visualizes bottlenecks and opportunities by plant.
• Sales validates business strategies with a transparent analytical basis.

The solution also included complete BI, scenario comparator and dashboards that allow us to navigate production, demand coverage, margin and capacity used with a level of detail impossible to achieve in Excel.

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The cultural impact was as important as the technical one: the process stopped depending on a few analysts and became a collaborative, traceable and sustainable cycle. Sylvamo not only gained speed—80% less time spent generating scenarios—but it also increased the quality of decisions and the accuracy of the S&OP by connecting strategy, operations and finance into a single living model.

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Learn how Pyplan can take your S&OP process to a new level of precision and agility.
Explore our solutions or schedule a conversation with an expert:
👉 https://pyplan.com/es/entre-en-contacto/

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