The global trade environment faced significant upheaval in early 2025, as the United States implemented broad tariffs, triggering swift retaliation from major trading partners like Canada, the EU, and China. For the US paper and packaging (P&P) industry, this new reality translates into a complex set of challenges: escalating costs for essential imported materials like pulp and certain paper grades, disruptions to deeply integrated supply chains, particularly with Canada, squeezed profit margins, and diminished competitiveness in key export markets.  

Successfully navigating this volatile landscape demands more than just reactive adjustments; it requires a proactive, strategic approach focused on building resilience and optimizing operations. Information Technology (IT) emerges as a critical enabler in this transformation, providing the tools and insights needed to adapt and thrive across the short, medium, and long term.

Immediate Actions (0-6 Months): Triage and Assessment with IT

The initial phase focuses on understanding the immediate shockwaves and stabilizing the business. A crucial first step is conducting a comprehensive tariff impact assessment. This involves meticulously mapping the entire supply chain, identifying suppliers across all tiers, correctly classifying imports and exports to determine applicable duties, and quantifying the financial hit from both US tariffs and foreign retaliation. IT plays a vital role here. Supply chain risk software helps visualize complex dependencies, while scenario planning tools allow companies to model various tariff outcomes and pinpoint critical vulnerabilities. For example, mills in certain regions or product ranges depending on essential raw materials with tariffs might no longer be profitable. Or US raw materials might become scarce. Furthermore, AI-powered analytics can rapidly process complex cost impacts across numerous products and suppliers, providing clarity much faster than manual methods.  

Simultaneously, proactive stakeholder communication is essential. Engaging transparently with customers about potential cost adjustments and collaborating with suppliers on contingency plans requires robust communication channels. Companies can also consider using dynamic and AI-supported pricing solutions such as PriceFX or Vistex. 

Collaborative supply chain platforms facilitate this by enabling real-time data exchange on inventory, lead times, and potential disruptions.  

Reviewing the inventory strategy is another immediate priority. Companies must evaluate stock levels considering potential supply disruptions and cost increases, possibly considering strategic pre-purchasing or building safety stocks for critical items. Inventory management systems and analytics help model the cost-benefit trade-offs, while specialized software can manage the complexities of utilizing Foreign Trade Zones (FTZs) to potentially defer or reduce duties. Finally, shoring up cash flow management through rigorous financial planning, supported by FP&A software, provides the necessary flexibility to navigate the financial pressures.  

Medium-Term Strategies (6-18 Months): Adaptation and Optimization Enabled by IT

Once the initial impact is assessed, the focus shifts to adapting operations and enhancing efficiency. Implementing supplier diversification becomes key, moving beyond assessment to actively qualifying and onboarding alternative suppliers in lower-risk regions. IT streamlines this process through Source to Pay and Supplier Risk Management platforms which help identify, vet, and manage new partners while assessing their reliability and risk exposure.  

Launching operational efficiency programs is crucial to offset external cost pressures. This involves tackling major expense areas like energy, manufacturing waste, logistics, and labor productivity. IT provides a suite of tools: Manufacturing Execution Systems (MES) improve shop-floor control, Asset Performance Management (ASM), Transportation Management Systems (TMS) optimize routes, and predictive maintenance tools minimize equipment downtime.  

This phase is also the time to pilot and adopt key technologies. Companies should begin phased implementations of high-impact solutions like dynamic pricing engines to respond to cost volatility, advanced collaborative platforms for deeper supply chain visibility, and AI/ML-based demand forecasting and inventory optimization tools for improved accuracy in uncertain markets.  

Exploring alternative export markets less affected by retaliatory tariffs, such as those in Southeast Asia or Latin America, requires thorough research and outreach, supported by market intelligence platforms and Customer Relationship Management (CRM) systems. Lastly, renegotiating commercial agreements with suppliers and customers to reflect new cost structures and incorporate flexibility can be streamlined using Contract Lifecycle Management (CLM) software. On the sales side, other solutions that can help include Configure Price Quote applications or solutions such as such as PriceFX or Icertis Contract Intelligence platform. 

Long-Term Strategic Positioning (18+ Months): Building Enduring Resilience with Integrated IT

The long-term vision involves embedding resilience and agility deep within the business model, leveraging fully integrated technology. This includes making definitive decisions on supply chain reconfiguration, potentially involving significant investments in nearshoring or reshoring. Advanced supply chain network design and simulation software is invaluable here, allowing companies to model the total cost and risk implications of different configurations before committing capital.  

Achieving full technology integration is paramount. Moving beyond pilots, companies must ensure seamless data flow between ERP, SCM, CRM, pricing engines, and analytics platforms, often requiring investments in data warehousing, integration platforms (iPaaS), and Master Data Management (MDM). Continuously evaluating and adopting next-generation AI and automation maintains a competitive edge.  

Driving product innovation, particularly towards sustainable and higher-margin solutions, creates differentiation beyond price. Product Lifecycle Management (PLM) software supports this innovation pipeline. Maintaining vigilance through ongoing policy monitoring, aided by risk intelligence platforms, and embedding a culture of resilience, supported by Integrated Risk Management (IRM) platforms and AI-powered monitoring tools, ensures the organization is prepared for future disruptions.  

The Takeaway: Turning Disruption into Advantage

The 2025 tariffs, while disruptive, act as a powerful catalyst for necessary transformation in the US paper and packaging industry. Companies that proactively embrace strategic adaptation—diversifying supply chains, optimizing operations, seeking new markets, and innovating—will be far better equipped to navigate the current volatility. Information Technology is the linchpin, providing the essential visibility, agility, and data-driven insights required at every stage. By strategically investing in and integrating technology, P&P companies can not only mitigate tariff risks but also build a more resilient, competitive, and future-proof business, ultimately turning today's challenges into tomorrow's competitive advantage. 

Discover how SAP Business Suite can help your business be more resilient to business challenges and responsive to opportunities.

While as a business owner I need to obey Jim’s maxim to spin the invoice printer, I also engage in pro bono activities. One of them is to serve as chair of the Technical Advisory Committee for Digital Transformation with the International Society of Automation (ISA).

This week ISA introduced new forums to foster discussion amongst professionals in automation. In the Digital Transformation forum, the current topics are Artificial Intelligence, Data Science, Digital Strategy, Digital Twin, Smart Manufacturing, and Virtual, Augmented, and Mixed Reality. More topics may be added in the future, but these topics are clearly hot in industry today. 

In many of my prior articles in this newsletter I have addressed defining terms. We seem to have a tower of Babel in industry where terms like Digital Twin mean entirely different things depending on who is saying it and what the application is. For example, ISA has a very helpful artificial intelligence bot on its website called MIMO. Like any chatbot, it responds to your questions by searching for answers within the training dataset it was given. If you ask MIMO to define Digital Twin, you get:

1. A process model must adapt from realtime data, and the learning is unsupervised (no human review, approval, or intervention)
2. There is automatic flow of data from the digital twin back to the process that can influence or control the physical process
3. It must encompass the entire process

There are no applications in the manufacturing industry that meet those requirements: 

1. Realtime data can be noisy, inaccurate, or invalid. There are times when the process is down, and the data is useless. If a human does not pre-process the data to remove noise and outliers, the model learns garbage. I know of no applications in industry that entirely replace subject matter or data analytics engineers with unsupervised software pre-processing.
2. It is very risky to automatically connect an adapting model to a physical process. Every deployment I know of requires a human in the loop to evaluate and approve predictive process models before they can impact the process. 
3. Most applications of realtime prediction models are applied to unit processes, not an entire process. In a paper mill there can be such applications for each paper machine, a coater kitchen, a lime kiln, each evaporator set, each continuous digester, and each boiler. In a chemical facility the process model may apply to one reactor. If a PID loop is not big enough to be considered a digital twin, how many inputs and outputs are required to make it big enough? I see no clear definition of the size of a digital twin.

Therefore, digital twins as defined by ISA do not exist.

I am not alone in this opinion. I asked Dr Michael Grieves, who is credited for introducing the concept of digital twin in 2002, to render his opinion. He agreed that the answer MIMO gives does not match what he originally defined as a digital twin.

Perhaps clarity on defining terms is not as important as spinning an invoice printer, but they are related. If I sell a project only to find the customer had very different expectations at the outcome, it will hurt my reputation and my business. I try to be careful to manage expectations so that my customers clearly know what is being provided. If I am providing a digital twin, what does the customer think they will get?

I believe ISA can be the one place where automation professionals can provide clarity on terms. I encourage you to join the forums at ISA.org and make your pro bono contribution to automation.  

Kai, you are not alone, everyone is talking about tariffs.

I was with a client just this week who is a major supplier to the pulp and paper industry. They have manufacturing facilities in the United States and China and serve customers around the world.

Life just became more difficult for them. For instance, the goods they bring into the US from China will now have a drastically different price than identical goods shipped to customers in Canada or Mexico. Imagine selling a complex system to Twin Rivers Paper which straddles the Maine and New Brunswick international border.

Then there are the long lead projects shipped in many partial shipments. Perhaps half the shipment has already landed in the United States and half is yet to come.

How about the spare parts built into the contract that may not even be made, let alone shipped for perhaps another year or two.

Robust enterprise systems are the only solution to keeping in compliance with these conditions and maintain profitability. Even more important than compliance will be detailed accountability to properly make the case with the customers for proper payment and preserving customer relations.

Manufacturers have spent years adding sensors to lay the groundwork for digital transformation and data-driven decision making. Now the focus turns to scaling this connectivity for predictive maintenance, process optimization and energy monitoring with help from AI.

The IoT (Internet of Things) has been around for decades—and here at Connected World we have been covering the space since before Kevin Ashton uttered the three-word phrase. I am sure many of you are asking who? 

Report found growth is being driven by automation, AI advancements and the rise of Industry 4.0

As organizations scale their IoT networks, they encounter a growing tangle of challenges: how do you manage thousands—or even millions—of connected devices efficiently? How do you ensure security without sacrificing agility? And how do you prevent infrastructure from becoming an unmanageable bottleneck?