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Articolo pubblicato nel fascicolo di Ottobre 2023 di Carta & Cartiere INDUSTRIAL DATA

HOW TO BUILD A DATA-DRIVEN CULTURE AT YOUR MILL

A successful data-driven culture drives productivity and profitability across the entire operations.

di: Matti Häkkinen - Trimble

In nearly every industry today, operators and decision makers are struggling with a crucial “chicken or egg” dilemma that impacts the very core of their business operation: do we invest in data tools, platforms and solutions, or build a data-driven culture first? Without an entrenched data culture, will the money we spend on these tools and new technologies simply go to waste?

It’s a question worth pondering across all sectors but in particular, for this article, in the rapidly evolving and dynamic pulp, paper and packaging industry. Many mill managers and engineers today believe that culture and tools are equally important and that they can – indeed, must – make advancements on both fronts simultaneously.

A Gartner Research study, 10 Ways CDOs Can Succeed in Forging a Data-Driven Organization takes a similar view. It lists “data-driven culture” and “advanced analytics capability” as the two most critical elements when building data and analytics team success (advanced analytics capability can be further split into employees’ capabilities and supporting tools).

In the end, a thriving data-driven culture will include three key elements:

  • Active data-driven decision making.
  • Employees with data literacy.
  • Solutions that enable easy use of data.

Now, let’s explore how to turn these core elements into a successful data-driven culture that drives productivity and profitability across the entire operation.

Step 1: Data-driven decision making starts from the top

Successful data-driven decision making must be led by example. Employees need to see that when managers make decisions, they favor data-backed arguments. This message will quickly echo through the organization and lead to a situation where employees have access to the data that supports their ideas and requests.

The ultimate goal here is that over time, and if truly integrated into the business culture, this style of decision making will become natural and employees will begin to instinctively leverage data when making small optimization decisions on a daily basis. When evaluating data-backed arguments, keep in mind that almost anything can be proven by facts.

The more data you have, the easier it becomes to misuse the data to serve the conclusions you want to draw. This highlights the importance of an open mind, transparency and reproducibility when conducting analyses. Companies that can embrace scrutiny and have a welcoming attitude when engaging with a colleague who challenges their data-backed conclusions will be rewarded.

Step 2: Foster and encourage an environment in which employees learn and want to “speak data”

Data literacy is the ability to read, write and communicate data in the context of the business. This includes an understanding of data sources, analytical methods and techniques applied, as well as the ability to describe the use case, the application and the resulting value. The best way to foster data literacy is to provide in-depth training programs and then easy-to-use tools to help employees integrate their learnings into daily workflows. In some cases this could also involve recruiting employees with the required data skill sets.

Ahlstrom’s factories in Italy, Finland and Brazil use Wedge, a solid process analytics tool for better data management and sharing.

However, it may not be necessary given that the single most pivotal determinant of success in data-driven decision making is curiosity. Creative people tend to dive into data, play with it, fail, fail a few more times and finally succeed in turning the data into information and actions that drive the company’s bottom line. For employees without this mindset, becoming a data champion may be a constant struggle, literate they are.

Step 3: Match the data solutions you integrate with the specific business needs

Don’t be surprised (or get fooled) if someone on the team suggests that this kind of data analysis can easily be done in an Excel spreadsheet. It cannot. You may have one wizard in your team who can manage this, but it will never scale and become foundational to your culture.

As you are considering investing in new data utilization tools, think about what type of data you need to be evaluating in your business. Is it event or time-series data? How is the raw data quality? Does the data contain delays? Would you like to study profile data as well? Is your process continuous, a set of batches, or a hybrid of the two? Do you want to keep the data in your own servers or will a cloud-based solution work?

Once you know what you want to analyze, come up with a list of available tools currently on the market. Make sure that the new tool provides a smooth and flexible workflow, intuitive and visual user interface and analysis capabilities ranging from basic analytics to advanced problem solving. Finally, and perhaps most importantly, make sure that your team gets good training and support during the implementation.

Now let’s return to the original question about whether an investment in data-driven decision-making tools would be a waste without a pre-existing workplace culture ready to embrace it. We would suggest that it’s not a question of “either/or” and, arguably, it can’t even be “if”. Solid data paints a picture of what’s happening across an entire organization.

This level of transparency empowers leaders and operators to make real-time decisions that drive productivity, efficiencies, profitability and ultimately sustainability gains throughout the business. Innovative leaders in the industry are embracing this path and implementing the two key pillars – investing in data-driven technologies and fostering the culture needed to make it happen – in tandem. Their success stories speak for themselves.

Case study

Wedge is the perfect tool for process development and quality assurance.

Ahlstrom is a highly international company that manufactures innovative fiber-based solutions for industrial needs. One of Ahlstrom’s factories is located in Karhula, Finland. The Karhula plant specializes in the production of high-quality fiberglass used mainly as backing material for vinyl-based flooring materials.

No more painfully slow problem solving.

“It had bothered me for a long time that it was painfully difficult to access information when problems arose in the process. Something had to be done” – explained Juhani Piispa, Engineering & Technology Manager at Ahlstrom, when asked about purchasing Wedge. The Karhula factory decided to find out how to speed up process problem solving. Ahlstrom’s Italian and Brazilian factories had long been using Wedge, a solid process analytics tool. It seemed to be the best solution for Karhula’s needs as well.

“Our problem was data sharing. There was a lot of data, but managing it was slow and cumbersome. I wish I had known earlier that such a system existed” – says Anssi Kokko, Manufacturing Process Engineer at Karhula’s factory, describing the change.

A smooth roll-out brought quick results.

Experiences with Wedge at other Ahlstrom factories and discussions with Trimble demonstrated the benefits and potential of Wedge. In addition, precise calculations were made at Karhula to determine how much savings Wedge would bring, and the figures sealed the decision to purchase.

A high-quality end product is the lifeblood of Ahlstrom and smooth quality management is important. Wedge is considered a handy and quick tool for finding the causes of quality challenges.

After three months of piloting, Wedge is now in daily use in both quality assurance and process development. The plan is to extend its use to maintenance, among other things. “Earlier, it could take days to get to the root cause of a problem and get things running normally again. With Wedge, you can do the same in 15 minutes” – says Anssi Kokko, who is delighted that things are running more smoothly.

Wedge helps find the root causes of production problems faster than before.

In quality assurance, Wedge gets a lot of use. “I always check the quality data in the morning. I also do some correlation analysis, which can be used to adjust production together with the machine operators, if necessary” – says Minna Peltola, Quality and Lean Manager, explaining how Wedge has made her daily routine easier.

Ahlstrom uses Wedge for their process improvement.

A high-quality end product is the lifeblood of Ahlstrom and smooth quality management is important. Wedge is considered a handy and quick tool for finding the causes of quality challenges. Minna Peltola explains the benefits of Wedge: “Wedge has all the necessary data in one place, which makes analysis and finding root causes much easier and faster.”

Anssi Kokko immediately thinks of one practical example of how quality loss has been effectively reduced with Wedge: “We were able to reduce edge trim by almost half by using Wedge to see how it should be done. Before we thought of using Wedge for this, we had already spent over 12 hours on it. After the adjustment, the amount of waste from edge trim was halved straight away”.

The number one tool for process engineers and quality managers.

After a few months of user experience, Minna Peltola and Anssi Kokko have already found their favorite Wedge features. “The meters offered by Wedge are excellent. The Best correlations button is amazing! It goes a long way, although there are many other things you could easily do with Wedge. It’s also easy to combine data and cut out unnecessary time periods”.

Both admit that not all Wedge’s capabilities have yet been utilized and they have only started exploring the new possibilities Wedge offers for process and quality improvement. When asked about how they would rate Wedge based on their experience so far, however, Anssi Kokko’s answer is clear: “A good acquisition! An unbeatable tool for process improvement”. Minna Peltola continues: “We highly recommend Wedge to all process industry operators who have to deal with a lot of data. Process engineers and quality managers, for example, are guaranteed to get their money’s worth”.

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