An extra month of production every year: how Lubelski Fornir increased output by 9% in six months
Founded in 1996, Lubelski Fornir is a premium sliced-veneer manufacturer in Łaszczów, Poland. The factory makes thin, grained sheets of real wood for high-end furniture, doors and panels, slicing around 1,000 cubic metres of premium hardwood into roughly a million square metres of natural veneer (85% of it beech) every month. Most of their products go to large furniture manufacturers, with IKEA the biggest name on the customer list.
GlobalReader had been installed at Lubelski Fornir for about two years, but nobody was using it.
That changed in December 2025, when a new production manager, Bartłomiej Kusiak, arrived from an IKEA plant with a habit of managing production through data and became the first person at Lubelski Fornir to actually work with the system.
In this case study, Bartłomiej explains what a veneer plant looks like when a monitoring system sits idle, how he built his management routine on GlobalReader, and how he got the same machines and the same team to produce 9% more within six months.
Company profile:
What they produce: Sliced natural veneer from European hardwoods (85% premium beech), FSC-certified production. Part of the Möhring Group since 2019.
Company size: 230 employees (office + shop floor). Part of the larger global Möhring Group since 2019.
Customers: Large furniture manufacturers (such as IKEA and Meble Wójcik)
Production volume: Around 1,000 m³ of veneer per month
Shifts per day: 3 shifts a day, 5 days a week
Machines: 13 machines total (3 new and 10 older) across five production stages
Connected to GlobalReader: 7 machines that make up the critical path of the factory
Most important KPIs for Lubelski Fornir: OEE, machine running time, output quantity, and downtime broken down by reason
GlobalReader modules in use: Monitoring Core (Hardware + Analytics + Notifications)
Systems landscape: GlobalReader is the only production system in the factory
Before GlobalReader: two years of data nobody opened
GlobalReader sensors had been running on Lubelski Fornir's machines for two years, but it wasn’t used to its true potential. Operators were sporradically logging their stop reasons, the data was then copied into an Excel sheet and that spreadsheet became the factory's version of the truth. Problems travelled by word of mouth, and the real picture of downtime lived in people's memories.
“Before GlobalReader, everything was tracked in Excel. Very easy, very simple, but it tells you nothing about what is really happening on the machines.”
Management by conversation has a failure mode every production manager knows: whoever isn’t in the room doesn’t know, and whatever isn’t written down didn’t happen.
“Information moved only by conversation. Somebody forgot about something, and then somebody would randomly recall something later on. Nothing was written down or measured.”
Weekly production meetings kept circling back to the same issues, and maintenance happened irregularly. Meanwhile, a factory slicing premium beech was running three shifts a day essentially from memory, even though every hour of downtime there means lost output of high-value veneer and puts delivery deadlines for customers at risk.
The biggest pain point: the same problems, discussed for weeks and never solved
The biggest source of lost time turned out to be how issues were handled. Problems were raised, talked about, and left unresolved, because nobody wrote down the root cause, the action, or the owner. A week later, the same problem was back on the table. Everyone left the meeting to do their own job, and the issue still remained.
“Sometimes the night shift had to do unexpected repairs themselves, and the spare parts were not prepared from the warehouse. A small job turned into a long stop.”
The loop had a direct cost on the shop floor. Because machine service wasn’t planned against real running data, maintenance was irregular and breakdowns happened at the worst possible moment.
“Machine service was not regular. Repairs happened when something broke, not when the data said it was time.”
Night shifts found themselves doing repairs without spare parts prepared from the warehouse, turning what should have been a planned swap into a lost stretch on a premium slicing line. In a plant that runs three shifts, the shifts with the least management cover were paying the highest price.
Why did Lubelski Fornir choose GlobalReader?
The original decision was simple, GlobalReader was used heavily and worked well at the sister plant, Balti Spoon (Möhring Group’s Estonian veneer producer). As the Production Manager puts it, Lubelski Fornir is “like the little sibling of the Estonian plant”: solutions that prove themselves in there get replicated in the Polish sister plant.
“I saw how the team at Balti Spoon works with GlobalReader. The management there is on a really high level, and GlobalReader is a big part of how they run the plant.”
The second, more important decision to actually use GlobalReader to its full potential, came with the new Production Manager, Bartłomiej. He had seen GlobalReader working at Balti Spoon and, just as importantly, had seen the management culture around it. Coming from an IKEA plant where a single CNC line produced 25,000 parts a day, he knew the importance of data-driven production.
How Lubelski Fornir runs the factory today with GlobalReader
The new workflow with GlobalReader is built around identifying where the factory is losing time, finding the pattern and planning the fix.
“I’ve been using GlobalReader for six months now and see a very big potential in it. We are only at the beginning of what we can do with it.”
The Production Manager reviews downtime reasons over a few days, then sits down with the maintenance manager and turns the pattern into scheduled service actions that they run every other day, with spare parts prepared in advance. As a result, the extra breakdown stops on the night and second shifts disappeared almost immediately. Shifts that used to improvise repairs in the middle of the night, now run on a plan.
The endless problem discussions that never led to actual fixes also changed. Problems raised in the daily production meeting now get written down with a root cause, a corrective action and an owner.
“When I’m on the production floor, I can only see what is happening right and if there is a lot of scrap or a lot of output. The data shows me the whole picture, what my eyes can’t: which machine, which reason, how much time. And that is what I bring to the management”
Additionally, the conversation with the shop floor is now focused on revenue. Instead of telling operators to book their stops “because management said so,” the data is now used to show what lost time and scrap actually cost, and what that money could mean as bonus potential. On premium beech, where scrap is extremely expensive and its causes hide between machine, material and process, the operators’ reason-coding in GlobalReader is what makes the cost visible at all.
“I try to show people how much money we lose when a machine stands still. That money can be potential bonus for them. Working with the people, that is the key.”
However, this is just the beginning. By the end of the year, with operator training completed, standard work rolled out, and the whole team confidently working from GlobalReader data, the factory expects to be working with GlobalReader “on a very high professional level.”
Lubelski Fornir’s results: 9% output increase in six months
GlobalReader is built to uncover hidden losses so that existing machines can produce more, without new investments or extra headcount. At Lubelski Fornir, the first six months of working with the GlobalReader data, is already showing up in solid results:
9% more production output in six months: that’s roughly a full extra month of output per year unlocked from existing capacity
80-90 m³ of additional veneer producedper month with the same team, on the same machine park
Extra breakdown stops on night and second shifts eliminated: downtime-pattern data turned irregular firefighting into preventive service actions
Less rework, better quality: the number of defective products has dropped since downtime and scrap causes became visible in the data
