May 31, 2019
To the Cloud! A Success Story with a What If...
20 years ago, pre-cloud, I worked in the quality department for a company that manufactured wood products. Great work, but I was at a crossroads in deciding what I was going to do with my life. I was looking for a chance to improve the opportunities available to me in my career. So, at 24 years old, I chose to go back to college and finish my degree. After I received my degree, I returned to my previous employer as a manager at a different facility in a different state.
Prior to leaving, we had started getting serious about statistical process control (SPC). The facility I now worked at had chosen an InfinityQS quality intelligence solution. This was a great improvement from pencil and paper, with which we had started. We would use the software to capture data for all our testing in the quality lab—measurements, weights, etc. It was good work, and we did some interesting things, but as I look back I can see that a cloud solution—like what InfinityQS now offers with Enact®
, would have made life so much easier. But I’m jumping to the end. Let me tell you a story about the way in which we worked, and why a cloud solution would have been such a boon to our business. Who knows, maybe it can help you or someone else decide if a cloud solution is right for your company.
It’s in the Software
Each of our production shifts had six operators and a crew of around 20 people overall in a huge facility; it was about a quarter-mile long. I worked in the quality lab, and we did all our work in the InfinityQS quality software. In the early going, it was pretty simple stuff—calculating data points for cutting a big panel into 4’ X 8’ panels, things like that. Then (just to paint a complete picture here) we would perform some measurements—keyed into the system by hand: weight, thickness, and such. Then there were water soaks and thickness swells.
We used lots of different types of equipment. Since we were manufacturing a variety of wood products, there was naturally lots of sawdust around. Therefore, it was not all data collection—there was plenty of clean up, sweeping, and pressurized air to blow all the equipment clean.
It was a wide variety of work for the crew of twenty or so people, but most of it was automated and was managed by only six operators. And all the equipment was interconnected. To keep it all running smoothly and in unison, we had PLCs—programmable logic controllers—all over the place, connecting all this disparate equipment together using ladder logic. It was quite an operation.
Like I said, at the end we were cutting big panels into smaller panels to be used to build homes. But first we needed to create the big panels. That was the main process we worked on.
The Quality Job: Using SPC
We used statistical process control (SPC)—but all we were really doing was capturing information. We didn’t make anything in the lab (of course); our job was to go around the facility and record measurements, keeping an eye on all the equipment, and help the operators. For all intents and purposes, we were essentially “quality control”, but had started making the shift towards “quality assurance” by empowering operators to make their own checks in their respective work areas.
So, mainly, our job in the quality lab was to enforce quality checks. We were responsible for all the product that left the facility. Our customers had to receive quality products. When we found an issue, we would look at the process—we’d involve the shift supervisor, the operator, and the production aide. We’d sit down and go through the entire process and troubleshoot.
Here’s the rub: at this point, we were mostly only testing finished
So, as I mentioned, this was a really big operation—large facility, tons of machinery. Lots to keep an eye on. Let the job of making boards begin!
Huge logs were de-barked. Then the logs were “waferized”—spinning blades sliced them into wafers and they were conveyed into bins, where they would await drying. After drying, they were “screened,” or separated by size for processing. Lots of machinery, lots of processes.
By the way, the mountains of ground-up bark were sold to bark companies for their use (mulch and such). Later on, it was burned for fuel to heat the dryers. Nothing went to waste.
…Become Something Else
Eventually, the wafers were separated by size, prepared with resins and wax, and then oriented in layers. These layers were then separated into large mats (12’x24’), which eventually became panels of pressed wood. These panels were the heart of the matter. And they were expensive. Just to give you an idea of what I’m talking about here: at that time, a single pound of panel weight was worth about a million dollars to the bottom line of the mill each year in cost savings. It added up fast.
So, I explain all this, and more, for a reason: even a process you might think is quite simple—something like creating wood products—has many facets, many intricacies, and involves many machines and processes. SPC is invaluable in a complicated, machine-laden environment like this, where there’s a lot of money on the line.
Looking for Answers
So, as I mentioned, our job (as quality folks) at this facility was to make the rounds of the machines and the processes, performing all these different checks. When we had issues, we were glad we had the InfinityQS software and the automated data collection there to help us figure out the genesis of the problem.
The company adopted Lean Six Sigma. We began training our project teams in furthering continuous improvement. We started working on black belt and green belt projects throughout the organization. We would find savings in various black belt projects that would then spawn a green belt project to make even further improvements in that area.
The more projects we worked on, the more interest we had in our various inputs. Always trying to correlate data from the respective processes and understand their effect on the outputs. The other side of the coin was how to leverage these improvements across all our other facilities. Those were things corporate quality managers were trying to figure out. At that time, there was no simple way to look at data and compare the various facilities across the enterprise.
One time, we had a quality issue related to what we called “blows” but we couldn’t figure out what was causing them. We deduced that the origin had to be moisture. Had to be. And the way you understand moisture when you are dealing with wood is by looking at weight.
Too much moisture obviously adds water weight, but it will “cook out” in pressing—resulting in lighter panels. When heated, the moisture converts into steam and escapes the panel, oftentimes causing “blows” where the panel looks like it just blew apart. The inverse is true in that when the moisture content is too dry, the panels will be heavy. The target moisture content has a certain amount of water that is intended to “cook out.” Therefore, when the moisture isn’t there, it doesn’t cook out and results in additional panel weight.
The data we were gathering in terms of weight and moisture led us to look at the drying process. There we looked at the main input factors for that process: inlet temperature, live bottom speed, and bin levels. By looking at these inputs, we could determine the best possible way to maintain moisture consistency. All of that was by collecting data and analyzing it to understand how each variable affected the outputs collectively. That allowed us to create run plans and share best practices across the operators. We even setup a friendly competition wherein if each dryer operator maintained moisture content for the month, we fed their entire shift a pizza lunch/dinner for a job well done. Everyone bought in and worked together.
When temperatures are too high or too low, the moisture content is affected. Then there is also time: when things are left in a dryer too long or not long enough, again, moisture content and therefore weight are affected. In addition, we also had to keep track of how much material was going through the dryer at any given time: too much material affects how dry it becomes, and vice-versa.
So, clearly, a lot of different variables to consider—and they all had a direct impact on quality. We were glad InfinityQS was there to help us understand all this data.
The Data Determines What to Monitor
The issue that we encountered was that if you had a moisture problem at any given time, you would not be alerted to it for at least 20 minutes. When these issues occurred, we had to make production decisions to preserve quality. To prevent from making bad product, we had to slow the process down, sometimes by half. So, we put inline monitors on the drying process. Prior to that, when collecting data manually using pencil and paper, an aide had to go around once every hour or half-hour and collect a sample. They would bring the sample back and cook it in the dryer console and record the results. But, as you can see, that’s probably not going to help very much in terms of helping you identify moisture problems. It only helps in terms of verifying that there is a moisture problem after the fact.
What we want to do is look at trends—that’s SPC. We don’t want to write down a number every once in a while and hope to catch an issue before it goes too far. InfinityQS software helped us with that. How? Looking for trends means charting the data.
On the Floor
A cool thing about the InfinityQS software is that not only does it help with looking at trends, it’s also designed to be used on the shop floor. And that’s where the action is. We put a computer in the dryer console and monitored those processes. We looked at the charts that data produced and discovered exactly how all the different dryers were running—moisture points, dryer capacity, etc.
And we put another computer in the press console, so we could see into the panel weights and what they were doing. The shop floor operators were all involved. They had a vested interest. They wanted to see how the processes were truly doing.
We kept going. We hooked the PLCs into the system and gathered the measurements they were collecting—straight from the scales instead of by hand. Soon we knew all the dryer moisture readings every three minutes instead of every 30 minutes prior to inline measurements. We were charting that data.
Soon after, we installed a computer at the saw line and started collecting defect data from our downgraded panels. All of this was working great. We were getting a clear picture of what the machinery was doing. The neat part about this was all this data could be seen in the different dashboards set up in the various consoles. The downgrade, panel weights, and dryer moistures could be seen from any dashboard throughout the facility, including the quality lab and foreman’s office. Quality tests from the lab were visible to the operators. Everyone knew what was happening at various times and everyone was working together for a common goal of making great products. It was an amazing thing to see.
Spread the Wealth
So, as I mentioned earlier, those large panels are expensive. And we were only working on our own processes at our own plant. What if we expanded the use of SPC to other divisions within the company? That could mean huge savings for the organization. By reducing panel weights at each facility, we were able to make great cost savings. At the time, when you annualized it out over a 10-year period, the company could have the potential to save something like $88 million for the 20 or so plants that were operating. Monstrous!
Other divisions in the organization heard about the cost-savings potential and were interested in leveraging that for their own products. We got the opportunity to do just that. We introduced the software and data collection techniques to another division, and it spread throughout the organization.
To the Cloud!
One of the things we wanted to do at the time was see the correlations between variables like panel weight and moisture. We wanted to look at all the processes across all the lines—from raw log to finished product—and understand every data point and trend along the way. And we wanted to see the comparisons between plants.
With a cloud solution, like InfinityQS Enact, we would have been able to do that, and so much more:
- See all that information as it happened, in real time
- Slice and dice the process data as much as we liked
- Create checklists and share them across plants, creating best practices
- Update instead of upgrade—since Enact is in the cloud, when the software is updated everyone has the latest and greatest version—such a relief for IT
- All sites using one database and the information in one place
- No concerns about individual workstation issues or breakdowns—a mill is a pretty active, hazardous environment for a computer
- Data is data—with Enact, you don’t have to worry about all the different types and formats of data being collected…data is all treated the same way in Enact
Well, so that’s where I’m at. We had great success with InfinityQS and their SPC software and expertise. We managed to spread that success to the rest of the company. If we’d had a native cloud solution, well, you can see how much easier that might have been to accomplish. With the cloud, the sky is the limit!
Read more about InfinityQS quality intelligence solutions, like ProFicient