In the pulp and paper industry across the Americas, a quiet but profound shift is rendering traditional business models obsolete. From the sophisticated, value-driven markets of North America to the volatile, cost-sensitive economies of South America, a single, uncomfortable question is emerging: are your pricing strategies capable of navigating today’s turbulent reality? For many, the answer is a resounding no. The era of static, cost-plus price lists is over. Survival and profitability in the next decade will depend on a radical pivot to a more intelligent, transparent, and dynamic approach to pricing.

The Americas Market at a Crossroads

The challenges are hemispheric, though their flavors differ. In North America, the market is defined by a pivot to value. While overall growth is modest, demand is surging in high-margin segments like sustainable packaging and sanitary products. Consumers are often willing to pay more for eco-friendly goods, and companies are making nine-figure investments in plant modernization and digital innovation to meet this demand. Here, the failure lies in not capturing the premium for innovation.

In South America, the challenge is managing extreme volatility. Fluctuating raw material costs, currency devaluations against the US dollar, and unpredictable supply chains create a chaotic operating environment. When pulp prices can spike over 40% in six months, a quarterly price list becomes a recipe for disaster, eroding margins before you can react. Here, the failure lies in an inability to respond with speed and precision. Whether your challenge is capturing value or hedging against volatility, the root problem is the same: a pricing model built for a world that no longer exists.

The Paradigm Shift Beyond the Price List

Relying on legacy price lists is the Achilles' heel of profitability. This is not simply a software issue; it is a fundamental business process and paradigm failure. The transition to a modern pricing model is an organizational transformation, shifting the company culture from one based on historical assumptions to one driven by real-time data.

This is not a thinly veiled strategy for arbitrary price hikes. On the contrary, the goal is to create transparency and stability for both you and your customers. Dynamic pricing, which adjusts to small market changes frequently, prevents the kind of drastic, delayed price shocks that damage long-term commercial relationships. It’s about achieving a price that is fair, defensible, and reflective of both the immense value you create and the current market realities. This requires a new commercial nervous system, one built on a modern digital foundation.

The Digital Foundation for Intelligent Pricing

Such a profound shift is impossible with outdated technology. Legacy ERP systems, with their siloed data and batch-processing limitations, cannot provide the clean, real-time information required for dynamic pricing. This is why the migration to a modern digital core like SAP Business Suite should not be just a technical upgrade, but a strategic business imperative. SAP Business Suite creates a single source of truth, breaking down the data silos that have plagued operations for decades. Its in-memory computing power provides the real-time visibility into costs, inventory, and sales data that is the non-negotiable prerequisite for any intelligent pricing strategy.

While SAP Business Suite serves as the central nervous system for core business processes, unlocking its full commercial value requires a comprehensive data strategy and a specialized intelligence engine. This is where SAP Business Data Cloud provides the essential business data fabric, harmonizing data from SAP Business Suite and other enterprise sources.

On this unified data foundation, specialized partner solutions like Pricefx and Visitex can turn insights into profitable action. Integrated seamlessly via the SAP Business Technology Platform, these solutions leverage AI and machine learning to analyze the comprehensive data landscape, model scenarios, and deliver optimized, value-based prices directly into your SAP Business Suite commercial processes.



This symbiotic architecture gives CIOs a powerful, future-proof platform for commercial excellence: SAP Business Suite runs the real-time business, SAP Business Data Cloud provides a unified, business-centric view of all data, and partners like Pricefx and Vistex deliver the specialized pricing intelligence.

From Theory to Tangible Results

This transformation is not theoretical; it is delivering tangible returns today. A leading global manufacturer of wood-based panels was hampered by a slow quoting process that took several days and resulted in significant margin leakage due to manual errors and legacy system inefficiencies. By implementing a Pricefx solution, the company used machine learning models to provide country-specific price recommendations and anticipate margins. The result was a dramatic reduction in quote time from several days to just a few minutes, boosting pricing confidence and efficiency for 350 users across its European operations.

Similarly, a Fortune 500 leader in packaging products found its legacy pricing tool could not support a data-driven global strategy and suffered from poor user adoption. The company implemented a complete, cloud-native Pricefx solution, rolling it out region by region. The new system streamlined processes and gave sales teams instant access to critical data, enabling them to maximize margins and adopt a more consultative sales approach. Since the implementation, the company has already put 10,000 quotes into production with the new, fully integrated tool.

These cases validate the two-part strategy: first, establish a clean data foundation; second, deploy an intelligence engine to unlock its value.

Your Roadmap to Pricing Excellence

As a CIO, your migration to SAP Business Suite is a once-in-a-decade inflection point. It is the moment to move beyond a technical-lift-and-shift and lead a strategic transformation of your company’s commercial capabilities. By architecting a clean digital core with SAP Business Suite and integrating an AI-powered pricing engine like Pricefx, you can build a business that is not just resilient to market volatility but is poised to master it. The tools to unlock the true, dynamic value of your products are here. The time to act is now.

To learn more about how SAP Business Suite and integrated partner solutions can transform your pricing strategy, begin the conversation with SAP’s experts for the mill products industry.

Read why Gartner names SAP a Leader in its 2024 Magic Quadrant™ reports for cloud ERP for product-centric enterprises.

For more about how SAP has partnered with the Mill Products industry – including paper and packaging - for more than 50 years click here.  

I recently read “The Sentient Machine” by Amir Husain. It was written in 2017, so it may be considered outdated since artificial intelligence (AI) is a very rapidly progressing technology. However, the book is still a worthy read.

A considerable portion of the book deals with the potential dangers of AI that have been expressed by people like Geoffrey Hinton, Bill Gates, Stephen Hawking, and Elon Musk. In the middle position was Marvin Minskey, one of the pioneers in AI, who acknowledged some risks but was less alarmist. On the other side of the spectrum are people like Ray Kurzweil who see the tremendous benefit and brave new world that AI can build.

Like Kurzweil, Husain sees a relatively rosy future for AI. In his book he explains the many potential applications of AI that can provide benefits unachievable otherwise. Husain begins with a chapter entitled “What is AI?”, which is necessary to explain terms before talking about them.

Husain states “Artificial Intelligence is a broad field of study”. Broad means that it is more important to define what is NOT AI than what is. So much of what we use computers for can be considered AI. Husain says that machines that can “reason, learn, and act intelligently” are application of AI. Consider a software application for the game of chess. The application has the rules of the game embedded as well as the optimization algorithm for determining the best move. Such an application is “hard coded” with these rules and algorithms, but under Husain’s definition is considered AI. Chess games have been in computers since the first consumer market for them appeared. You may not have thought the Commodore 64, Apple Lisa, IBM PC Jr, or Tandy 1000 had AI, but Husain says it did under a broad definition.

Much of what we consider AI today is a subset called Machine Learning (ML). ML uses data to train algorithms. In contrast to hard coded logic, algorithms are built to learn logic from data. ML is where most of the recent advances in AI have come from.

What is NOT AI? Humans obviously. Artificial means non-human intelligence, so we are not AI. That means anything else that is not intelligent is not AI, and intelligence was described by Husain as “reason, learn, and act intelligently”. This is obviously circular logic. We haven’t really defined intelligence.

Obviously, a hammer is not reasoning, learning, or acting. Neither was a Ford Model T. However, today’s cars can sense the road and detect when you are out of your lane to automatically steer you back between the lines. Is a dog AI? The bigger challenge is to explain what AI is not and why.

A common method for discriminating AI is the Turing test. While Alan Turing, the great mathematician who invented this test, apparently meant it as a joke, it might be the best approach for defining AI. Put a human in a room where they don’t know what is outside the room. They must interact with something outside the room. If the human can’t tell whether they are interacting with a human or an intelligent machine, the intelligent machine is AI. 

Consider a thermostat. Your interaction is to give it a temperature setpoint. A human outside the room could look at that setpoint, compare it to a thermometer, and manually decide to turn on or off a heating or cooling unit. This would be tedious, unrewarding work and very inefficient use of human labor. The thermostat does the same work more effectively and efficiently, but the human inside the room wouldn’t know the difference. Is a thermostat AI? 

In manufacturing, we have the equivalent of thousands of thermostats which are implemented as PID loops, which are the foundation of supervisory and advanced automation strategies built on top. We have had this in industry since the 3^rd industrial era arrived about 50 years ago, and under Husain’s definition would be considered AI. ML is in its infancy in manufacturing but is maturing. Usually when we talk about AI in manufacturing, we really mean ML.

Considering the dangers of AI applied to manufacturing, we are very far away from the nightmares that some fear. I don’t know of any AI application in industry without humans in the loop. Our data has noise and outliers. Training ML models without humans to pre-process data with filters and outlier detection is very dangerous. Any closed loop application in industry required human supervision.

I recommend “The Sentient Machine”. It is relatively concise, well written, and pertinent work for any of us that interact with AI.

My friend Kai Aldinger, above, is describing a real problem in the economics of the pulp, paper and solid wood industries.

The method of setting and accepting prices goes back to green eye shades and no. 2 pencils. It is slow and loaded with assumptions. This was the only choice when computing power was non-existent or very expensive.

To me, the challenge is the execution of the transition from days of old to today.

There is another transition that may give us a hint. We made boxes according to Rule 41 for one hundred years. This was a weight-based metric. When we moved to a strength-based metric, STFI, the transition was fairly rapid and the boxes became a lot cheaper.

Why did this work with boxes? Everyone saved money. Paper machines produced a lot more paper when the basis weight was lower. A lot more paper meant a lot more boxes. This reduced capital expenditures per 1,000 boxes. For the customer, they were not overpaying for boxes heavier than they need. Everyone wins.

Same thing with Kai's vision. Make it demonstrably obvious that everyone wins and you have a winner.

Responsible AI is getting a lot of buzz. With policy conversations around the deregulation of AI, we’ve been led to believe that ethical practices are falling on enterprises, as they largely have since the inception of the technology. This, however, is wrong. The days of “AI washing” are coming to an end. And while we may see lags in federal oversight, that's not the case for state and local governments. 

Focusing on precompetitive problems, such as enabling communication between digital twins, U-M and ASU are seeking industry partners for a potential NSF research center.

Companies that adopt industrial artificial intelligence see productivity losses before longer-term gains, according to new research.

Christian Klein, SAP’s CEO, insists there is “not so much demand” for data centres in Europe, and the continent should focus on the AI software race.