Consider a fundamental question: what does a pulp and paper enterprise understand better – its intricate paper and pulp machinery or its core Enterprise Resource Planning (ERP) system? Modern production lines are marvels of engineering, equipped with thousands of sensors relaying data in milliseconds. They are constantly monitored via detailed dashboards and optimized for peak efficiency, quality, and sustainability, guided by comprehensive operational plans. This represents a state of remarkable visibility and control over physical assets.

In capital-intensive industries like pulp and paper, the focus has traditionally been on maximizing the performance and availability of expensive physical assets. Consequently, investments flowed into their monitoring and optimization. ERP systems were implemented as necessary for transaction processing and accounting, but not necessarily understood as dynamic systems for continuous process improvement. This historical focus has led to an imbalance where the "process machine" ERP receives less attention in terms of continuous monitoring and optimization.

Yet, a striking contrast often emerges when examining the ERP system – the "process machine" that orchestrates the entire business. Frequently, these critical systems operate as "black boxes." Once configured, they run with minimal ongoing scrutiny, leaving a significant gap in understanding how closely their embedded processes align with current business necessities or optimal performance standards. This isn't merely a technical oversight; it's a critical paradox. This disparity often arises because physical machinery offers immediate, visible feedback, whereas ERP inefficiencies can remain hidden, their impacts diffused across the organization. As physical machinery becomes even more intelligent through IoT and AI enhancements, this gap with a static, unexamined ERP system can widen, potentially creating a two-speed organization where advanced manufacturing is hindered by outdated back-office processes. In an industry facing intense pressures to integrate AI, meet stringent sustainability regulations, and diversify into new growth areas like biochemicals and bioenergy ⁴, such an operational blind spot becomes a considerable liability.

Navigating the Fog: The True Cost of an Unseen "Process Machine"

The consequences of this "black box" ERP are far-reaching. Imagine attempting to operate a sophisticated paper mill without its sensor arrays or trying to upgrade critical machinery without detailed engineering blueprints. Such scenarios are unthinkable in the physical realm, yet they often reflect the reality for the "process machine" ERP. This lack of clarity leads to a cascade of issues. Are processes designed a decade or more ago still fit for purpose, or have they become overly complex and outdated? Is a more efficient standard process available within the system but simply unused? Crucial information, such as how frequently specific processes are executed—be it a few times a year or thousands—remains unknown, hampering prioritization for optimization.

Furthermore, this opacity creates significant hurdles for compliance. Identifying precisely where system modifications are needed to meet new sustainability reporting requirements or other regulations becomes a high-stakes estimation. Similarly, successful AI integration hinges on well-defined processes and high-quality data ; an opaque ERP makes it difficult to pinpoint suitable processes for AI augmentation or to ensure data integrity. Strategic diversification into new product lines, such as biochemicals, demands substantial process adaptation—a task made exponentially harder without a clear view of the existing process landscape. Over time, unexamined and unoptimized ERP processes accumulate a form of "process debt"—ingrained inefficiencies and outdated logic that become increasingly costly and difficult to resolve, actively draining resources and agility. The cumulative effect can be a state of strategic paralysis, where the organization sees market opportunities but feels incapable of seizing them due to the perceived complexity and risk of altering core systems.

Illuminating Your Digital Backbone: "Sensors" and "Blueprints" for Your ERP

While modifying and upgrading complex physical machinery is a significant undertaking, it is achievable with accurate documentation and real-time sensor data. A similar approach is now viable for the "process machine" ERP. SAP Signavio, for instance, can be likened to the "sensors" for business processes. It delivers process mining and intelligence capabilities, enabling companies to visualize how their processes actually execute in real-time. This allows for the identification of bottlenecks, deviations from standard procedures, and hidden inefficiencies. 

Complementing this, SAP LeanIX acts as the "engineering blueprints" for an enterprise's entire IT landscape, including the pivotal ERP system. LeanIX provides comprehensive enterprise architecture management, helping to map IT components, their interrelationships, dependencies, and how they collectively support overarching business capabilities. Together, these solutions work to demystify the ERP, providing the clarity essential for informed decision-making, targeted optimization, and robust strategic planning. The synergy is powerful: Signavio reveals how processes are performing, while LeanIX clarifies what systems and architectural components underpin these processes and the broader business strategy. This combined visibility allows a shift from reactively fixing process or system failures to proactively governing and designing them for optimal performance and strategic alignment, moving from crisis management to strategic control.

From Insight to Impact: Real-World Wins for Pulp & Paper

The practical application of such clarity translates into tangible benefits for pulp and paper CIOs. Consider sustainability: understanding and optimizing processes with tools like SAP Signavio is fundamental to reducing waste, minimizing energy consumption, and lowering emissions. SAP LeanIX can then map these sustainability KPIs to specific applications and processes, helping to identify improvement hotspots and track progress towards Environmental, Social, and Governance (ESG) targets. 

In the realm of innovation, a "clean core" ERP—a common objective in digital ERP transformations facilitated by enterprise architecture tools and streamlined processes create the agile foundation necessary for impactful AI initiatives. These tools also smooth the path for entry into new markets, such as biochemicals or bioenergy, by making the mapping of new business capabilities and the design of efficient supporting processes manageable endeavors. 

Charting Your Path to a Smarter Process Future

The pulp and paper industry is navigating a period of profound transformation, driven by demands for greater sustainability, efficiency, and innovation. In this dynamic environment, an opaque, inefficient "process machine" ERP is not just an operational drag; it is a critical strategic risk. The ability to truly see, understand, and optimize core business processes and the underlying IT architecture is paramount.

Read why Gartner names SAP S/4HANA Cloud 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.  

One of my favorite podcasts is “Causality”. About every 2 to 3 months, an Australian engineer named John Chidgey produces a 30–60-minute podcast addressing an industrial accident, the chain of events, what went wrong, and how it could have been prevented. It is very well done, and I strongly recommend this podcast as required listening for anyone in industry.

Episode 59 addresses the August 28, 2008 explosion at a Bayer CropScience facility in Institute, West Virginia. This accident killed 2 people, injured 8 others, advised 40,000 residents to shelter in place, resulted in a $143K fine from OSHA, payment of $5.6M in legal settlements, $975K in civil penalties, and $4.23M in required improvements to operate. 

This is not the worst industrial accident of all time, but it is an example of what happens when people make assumptions and ignore proper procedures. Industrial accidents are not always the result of one simple and obvious cause. As in this case, the causes are complex and multivariable. However, as the subject of this newsletter is automation in the 4^th industrial era, I need to call attention to a glaring issue.

One of the problems is the way they handled the migration of a distributed control system (DCS). They had a Honeywell TDC 3000 control system. I am one of the few people still around that know how to work on that system. In 2006 Bayer began migrating the system to Siemens PCS7. When doing a migration, it requires not only experience in the destination system but the original system. If you want to translate German to Spanish, you need to know both languages. By the time of the incident, the DCS upgrade had not been completed or fully tested. Starting up a new DCS without thorough commissioning is very bad and very dangerous. The PID loops had default tuning, instead of translating the tuning from the loops in Honeywell to Siemens. There were undocumented changes in control schemes implemented in the migration. Perhaps the most alarming problem was the inability of operators to see alarms and interlock status effectively. The HMI implementation did not follow good design practice, resulting in operators being overwhelmed with detailed information at the instrumentation level but not able to see the process overview views that would have helped operators notice alarms, overridden safety interlocks, and cause and effect.

I have done a bunch of migrations in my career, and each one has risk. There is great danger in treating a migration casually, either from the implementation side or the operation side. In the case of Bayer, it seems incredulous that would even think of starting up operation before commissioning the migration. Even though industry has been in a digital age for about 50 years, it seems way too common for industry to ignore the risks of control systems. The high capital steel and concrete projects are noticed, but lower capital DCS migration efforts can sometimes hide in the background with the assumption that risks are low. While the investment in control systems can be lower, the impact of a bad migration can be deadly.

This should not be an impediment to migration. Honeywell TDC 3000 reached end of life a long time ago. I know people in the business of stockpiling old TDC parts because the only other place you can find it is eBay. Facilities hanging on to these legacy systems are one IO card failure away from a lengthy unplanned shutdown. Migrations must happen, and you need the right people to deliver a well-designed and commissioned system as well as responsible operations people that will not start production until it is ready.

I ask you to make “Causality” a part of your routine. Decision makers in industry need to recognize how dependent we are on automation, and the dangers of doing automation badly.

Last month, I mentioned my "elderliness." I don't think that is an official word, but in context, I think you will understand it.

As we talk about what we can do in real time today, the software has gotten far ahead of the humans. When I was young, we didn't worry about the software, there wasn't any. But we did a poor job of planning compared to what we can do with today's software.

We are doing a poor job of producing homogenized humans that can both create the necessary software and understand the complex manufacturing processes we demand today. SAP is working on it, and along with their affiliates, is doing a yeoman's task.

But we are not there. A story is told of the machine operator sitting in the control room who is asked, "Where is the fan pump?" The control pointed to a symbol on the screen--"right there." This person has no idea where the physical fan pump is or anything about its size.

This is the type of challenge we have ahead--getting the people up to speed with the great planning the software can do now.

IoT integration is the process of linking smart devices, applications, databases and systems to facilitate data exchange and enable automated workflows.

With chemicals playing a role in 96 per cent of manufactured goods, safeguarding chemical companies against cyber threats is a top priority[1]. As digital transformation integrates operational technology (OT) with information technology (IT), chemical companies face increased exposure to sophisticated cyber threats. Cybersecurity is not just a technical issue, it requires a shift in culture, mindset and awareness of overlooked areas like physical security and supply chain integrity. Here, Duncan Lugton, Head of Policy & Impact at the Institution of Chemical Engineers (IChemE) explores how chemical engineers are uniquely positioned to take the lead in addressing these security challenges.

Strategies, principals and responses must be aligned between the two, or the business is at risk.

The promise of digital transformation is real, but the path is paved with more than just new tech. Manufacturing leaders who want to see real ROI must prioritize efficiency and discipline.