Article summary
- How Proficy Plant Applications first proved out MES concepts in demanding pulp and paper mills
- Why capturing tribal knowledge as structured recipes changed the way plants handled grade changes
- How “continuous” mills used batch thinking to unlock traceability and smarter planning
- The role of electronic records and audit trails in making compliance part of normal operations
- Practical lessons modern manufacturers can apply to their own MES roadmaps today
Before MES became a line item in every digital transformation plan, pulp and paper mills were already living with its core problems: complex grade mixes, heavy automation that still depended on a few experts, and rising pressure to prove exactly how each ton was made.
Proficy Plant Applications entered that world not as a grand vision, but as a practical tool to solve very specific operational headaches.
Those early deployments in pulp and paper quietly defined what we now think of as “modern MES”: recipes that capture the best way to run, batch logic wrapped around continuous processes, and electronic records that turn day‑to‑day work into a trustworthy history.
Expanding on this, today we take a closer look at how those early adopters in pulp and paper used Proficy to lay the groundwork for the modern MES practices manufacturers now rely on.
Turning Tribal Knowledge into Executable Recipes
(*Note: Companies mentioned in today’s article are completely hypothetical and for demonstration purposes only. Examples – though based on real world experiences – have been slightly altered to better illustrate our thesis.)
At one tissue and towel mill (Mill A), grade changes were a kind of controlled chaos.
On some shifts, transitions from one furnish to another were smooth and predictable; on others, the same change meant extra broke, longer downtime, and a spike in complaints.
The difference wasn’t the equipment… It was who sat at the operator console.
Veteran operators carried the recipe in their heads.
… They knew the order in which to adjust refining energy, stock consistency, steam, and chemicals.
… They watched a handful of trends and understood which shapes meant “go ahead” and which meant “wait.”
Much of this was written down only as scattered notes and habits.
When Proficy appeared, the mill’s leadership realized that if they didn’t find a way to formalize that know‑how, they were one retirement away from losing it.
The team used Proficy’s recipe capabilities to capture this experience as something the system could guide.
Engineers and operators worked through each grade change as a sequence of steps and targets, turning intuition into a master recipe: start conditions, safe ramps, interlocks, and clear criteria for moving from one stage to the next.
The control system still handled valves and drives, but it now did so under the direction of that recipe instead of relying entirely on operator timing.
Over time, grade changes became less about who was on shift and more about following a proven method.
New operators had a structured path to follow instead of trying to imitate a veteran’s instinct.
Adding a new grade meant cloning and adapting an existing recipe rather than inventing a new approach from scratch.
In modern terms, Mill A had separated “how we intend the process to run” from “how the plant is wired,” and that separation became a foundation for every improvement that followed.
Seeing Batches Inside a “Continuous” Process
From the outside, a paper machine looks like the definition of continuous production… Pulp goes in, reels come out, and the line never stops unless something has gone wrong.
Inside the mill, everyone knew the reality was less straightforward.
- Stock prep ran campaigns of particular pulps.
- Coating and additive kitchens produced discrete mixes.
- Broke was collected, processed, and fed back in defined chunks.
Operators naturally spoke about “this mix” or “that campaign,” but the data treated everything as one long stream.
This disconnect became painful whenever the mill needed to answer questions like “Which materials were behind this reel?” or “Why do complaints seem to cluster around certain runs?”
Without clear batch boundaries in the data, every answer was an educated guess.
Proficy gave the mill a way to bring the language on the floor into the system.
Instead of modeling only equipment, the team began modeling units and batches.
A chest became the point where one batch ended and another began.
A mix tank became an operation with a start, a sequence of additions, and a defined end.
Each pulp campaign and coating run received a name and a history: when it started, what went in, what conditions were present, and how long it lasted.
As those batches flowed forward, the MES associated them with specific reels and orders, effectively creating an electronic batch record around what had once been a continuous blur.
The payoff showed up in several places at once.
Quality teams could link a single customer claim to the exact combination of pulp campaigns and coating batches involved, rather than sifting through days of data.
Production planners saw the real cost of jumping between certain campaigns and could redesign the schedule to reduce unnecessary changeovers.
Process engineers finally had clean, comparable “stories” they could analyze and improve.
Without turning the plant into a classic batch facility, the mill had used batch thinking to draw useful lines inside its continuous process.
Making Compliance and Proof Part of the Job
In another mill (Mill B), the pressure point wasn’t grade changes or campaigns… It was proof.
This site made specialty papers for tightly regulated applications, and external parties regularly asked hard questions: which recipes were active for this product, which materials were actually used, who approved changes, and how often limits were challenged.
- The controls could enforce limits in the moment, but the plant’s memory was largely handwritten.
- Every audit triggered a familiar scramble.
- Supervisors pulled logbooks, printed trend charts, and copied values into spreadsheets.
- Missing pages and ambiguous entries were treated with suspicion.
- Internal investigations into process upsets or customer complaints followed the same pattern.
The people in the control room felt like they were doing the right things, but they had no easy way to show that they had done them consistently.
With Proficy, Mill B approached the problem from a different angle…
Rather than building a separate “compliance system,” they decided that good records had to be a by‑product of normal work.
When an operator started a run, acknowledged an alarm, or performed a check, the system captured that action in context.
When someone changed a recipe or a critical limit, Proficy recorded who made the change, when it happened, and what justification was given.
Quality results were attached directly to the batches and orders they described, instead of living as detached files.
To operators, the change felt less like extra paperwork and more like a change in the fabric of their tools.
They still used familiar HMI and SCADA views, but those views now sat on top of an application that quietly assembled a coherent story.
Months later, when an auditor or customer asked how a particular job was run, the answer came from a query, not a chase through file cabinets.
Events that once became anecdotes were now part of a searchable, analyzable history.
The Patterns that Still Define Modern MES
Looking back, it’s easy to see these projects as early case studies for Proficy.
It’s more useful to see them as the moment when MES stopped being an abstract concept and started to mean something tangible inside a plant.
Across different mills, common patterns emerged.
First, they all invested in making their processes explicit.
Whether the focus was grade changes, campaigns, or compliance, success depended on writing down how the plant was supposed to run and then encoding that understanding in recipes, workflows, and models.
The software could only amplify clarity that already existed.
Second, they refused to let their automation architecture define their thinking.
Instead of accepting that every change required new control logic, they used the MES layer to separate process intent from the details of I/O and programs.
Third, they treated data not as exhaust, but as part of the production asset.
Batch records, genealogy, and audit trails weren’t just there to satisfy auditors; they became raw material for improvement.
These ideas have become table stakes for modern MES, but they were not obvious at the time… Rather, they were discovered in the middle of real production pressure, by teams that wanted fewer surprises and better stories in their data.
What Today’s Manufacturers Can Take from Pulp and Paper
Manufacturers weighing MES options today face a more crowded market and a louder buzzword environment than those early pulp and paper mills did.
Yet the core questions remain remarkably similar:
- Where is your operation still running on unwritten rules known only to a few people?
- Where are you pretending your plant is continuous when everyone on the floor thinks in terms of campaigns, lots, or runs?
- Where are you assuming you will be able to reconstruct what happened months from now, without actually having the records to do it?
The pulp and paper experience suggests that the right place to start is not with a catalog of features, but with these uncomfortable gaps.
Once you know where tribal knowledge, invisible batches, and proof problems are hurting you, you can use a modern MES—Proficy or any other—to give those areas structure.
That structure, in the form of recipes, batch definitions, and electronic records, becomes the backbone for every other digital initiative.
The early mills that treated Proficy as a way to formalize and execute how they wanted to run, rather than as just another data system, are the ones whose results still stand-up years later.
The Wrap Up
Proficy’s early life in pulp and paper was less about a software brand conquering a vertical and more about a set of ideas finding their first real test.
Those mills showed that when you capture the best way to run as recipes, honor the batch‑like nature of your process, and let records grow naturally out of daily work, you get more than visibility… You get a plant that can remember, learn, and improve on its own history.
Modern MES platforms are broader and more connected, but they still succeed or fail on the same basics.
The questions pulp and paper answered out of necessity—how to preserve expertise, how to see the true structure of production, how to prove what really happened—are the ones every manufacturer still has to answer today.
FAQ
Q: What made pulp and paper a good proving ground for MES?
A: Pulp and paper combined highly automated assets with complex grade mixes and tight margins. That mix exposed every weakness in informal recipes, poor traceability, and manual recordkeeping, forcing early MES projects to solve real operational problems rather than chase abstract digital goals.
Q: How did Proficy help capture tribal knowledge in mills?
A: Teams used Proficy’s recipe capabilities to turn experienced operators’ unwritten methods—especially around grade changes—into structured, executable procedures. This made transitions more consistent across shifts and reduced the risk of losing critical knowhow when people moved on.
Q: Can batch concepts really apply to continuous processes?
A: Yes. Even in continuous mills, there are natural “chunks” of production, such as pulp campaigns, coating mixes, or order runs. By naming and tracking these as batches, mills gained the traceability and planning benefits of batch management without changing the fundamental nature of the paper machine.
Q: Why are electronic records so important in MES?
A: Electronic records and audit trails turn everyday actions—starting a run, changing a limit, recording a test—into a coherent history that can be trusted later. This reduces audit pain, speeds up investigations, and gives engineers better data for improving processes over time.
Q: What is the main lesson for modern MES buyers?
A: The main lesson is to focus MES efforts where they codify and execute how you want the plant to run, not just where they generate more dashboards. Recipes, batch definitions, and built‑in records are the pieces that make future reporting, analytics, and optimization truly meaningful.
P.S. We’ve given you the questions, now we’re interested to hear your answers…
If you’re looking at how to bring these lessons into your own MES journey, Rain Engineering can help you design and implement Proficy‑based solutions that do exactly what those early pulp and paper projects did best: capture expertise, structure complex processes, and turn everyday production into data you can trust—and act on.
Are you ready to take your MES to the next level?
