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9min June 14, 2024 9min Jun 14, 2024
June 14, 2024 9 minute read

Unlocking your team’s Human Intelligence with data

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In today’s paper manufacturing industry, data is king. However, having data alone is not enough: teams must know what data is telling them and how to act upon what data reveals. For this reason, operations teams can’t effectively make improvements if they lack visibility into what’s truly happening on the plant floor. Data, without proper context, is essentially meaningless. 

Take, for example, the billions invested in cloud data warehouses and data lakes. Gartner estimates that 85% of data lakes fail due to low-quality data or a lack of metadata tagging capabilities. If you’re using a data lake without tagging capabilities, it means you need skilled data scientists to uncover any meaningful insights from your data. As a result, data lakes are costly from a human resources perspective, costly assets to leverage in terms of your human resources.

of an engineer’s time is spent on data processing.

Disorganized and siloed data hurt your paper company’s productivity and troubleshooting efforts. However, when contextualized data is legible and available to your teams, they can more easily interpret information and make qualified decisions that advance your company’s vital projects. 

The key to unlocking the full potential of your manufacturing data lies in a new approach to data operations: leveraging human intelligence with high-quality data. It’s about getting the right data to the right user with the right context for the right problem at the right time. This process, known as data contextualization, involves automating the tedious work of combining relevant data points so that data is easier for your teams to interpret and act upon combining insights from various data sources and types. 

In this article, we’ll illustrate how automating data contextualization, cleansing, and accessibility can help your paper manufacturing teams overcome a wide variety of efficiency bottlenecks.  

Improve usability of existing data with automated data processing

As a paper manufacturer, it can be easy to feel disenchanted by the latest data technologies and advancements coming to the manufacturing space. The reality is that most paper facilities are using legacy assets and technologies, forcing technical team members in paper facilities to do as much as they can with few technological advancements. 

This doesn’t mean paper manufacturers can’t reap the benefits of new data tools and technologies. It just means they need a slightly different approach to what solutions they source.

Know this: the strain on your technical teams to source, compile, clean, and redistribute prepared data is real—and it’s a real problem. One of our global paper customers estimated that, before using Braincube, their teams spent 90% of their time on data processing. That’s a lot of wasted time that could be spent on improvements! 

For paper manufacturers, interoperability is key. You must find a data treatment solution that makes it possible to bring all your plant data together into an integrated platform from any source, regardless of type or format. 

“The right blend of experience, tools, and platform could move us forward, enable us to scale success, and help us achieve data autonomy.“

— Joanne Boyd, Global Service Manager of Advanced Analytics at Sappi

When existing data is properly visualized and contextualized through graphs, dashboards, and other visual analytics tools, users can interpret the information and make qualified decisions based on what the data is telling them. Teams gain the ability to see what has happened successfully before and what is happening right now. Historical data provides insights into past successes, while live data allows for addressing drifts before they escalate into breakdowns.

Take the paper company that had technical teams spending 90% of their time on data processing. They brought in Braincube to integrate 360,000 variables (!) across their entire paper production process. 

Now, they are scaling this data preparation solution to 18 sites over the next three years, with over 20 users per site actively leveraging the tool they invested in. By establishing a “single source of truth,” faster and more informed decision-making becomes possible.

Take the paper company that had technical teams spending 90% of their time on data processing. They brought in Braincube to integrate 360,000 variables (!) across their entire paper production process. 

Now, they are scaling this data preparation solution to 18 sites over the next three years, with over 20 users per site actively leveraging the tool they invested in. By establishing a “single source of truth,” faster and more informed decision-making becomes possible.

Faster decision-making with a “single source of data truth”

As mentioned above, most operations teams rely on more technical teams—engineers and data scientists, for example—to extrapolate and prepare data for usage. Not only is this time-consuming and a drain on the skill sets of these valuable team members, but it also limits data access for the majority of your plant employees. 

Think about all the data your paper facility generates on an hourly basis, across every part of production. In today’s tech-forward landscape, it’s unreasonable to expect that your technical teams can adequately gather and prepare the necessary information for different users—maintenance teams, operators, plant managers—to perform their best work. Process experts deserve a single place where they can navigate and use information to conduct more effective inspections, maintenance reviews, and other general operations.

Data democratization is one of the most impactful changes that manufacturing companies can make. Providing contextualized data in a centralized, easy-to-access hub is the simplest and most comprehensive way to achieve data democracy. Giving people access to ready-to-use data enables team members at every level of the organization the vital information they need to make the right decisions that lead to improvements. 

reduction in unplanned downtime by implementing alerting at a paper facility.

Centralizing doesn’t mean that users are required to “access” the data hub to use the data. Instead, think of the data hub as the jumping-off point for other data-driven initiatives. For example, operators may actively use the data hub to track live production. However, they can get live alerts regarding drifting conditions or a problem taking place upstream. In this way, comprehensive production data is still informing their work even if they aren’t actively analyzing or consuming data. 

You can improve your team’s efficiency even more by implementing an alerting strategy that puts the most pressing issues front and center. Directing people to the most important issues and drifts reduces the amount of human energy required for decision-making while driving more impactful production outcomes.

In fact, one of our paper customers saw a 30% decrease in unplanned downtime and a 50% reduction in waste by implementing real-time alerting at one of their paper facilities. Alerting and automated monitoring means teams are working smarter, not harder, while simultaneously generating superb results. 

Improved productivity and results using actionable data visualizations

Papermaking is a long and complex process that takes place over a massive physical space. Your operations teams can’t possibly be everywhere at once. 

However, the expectation at many paper facilities is that operations teams are everywhere at once. The norm is that operations team members walk the floor, perform periodic quality or asset checks, and make adjustments as they deem necessary based on what they see. 

It makes sense that you’d want to leverage the intrinsic knowledge of your valuable employees by keeping them close to the production process. But if you continue limiting their scope to just what they can see in front of them, it’s unlikely they are always addressing the most pressing issues at any given moment. 

Dashboards enable teams to make faster, more informed decisions that drive valuable performance improvements. For example, visibility into real-time conditions reduces reaction times to drifts or problems by putting the most important and timely production data front and center. Responding quickly to the right issues could mean the difference between correcting a quality problem upstream or dealing with a costly paper break downstream.

Identifying sources of variation and quality issues is also easier (and quicker!) to troubleshoot when data is presented visually. Dashboards play a crucial role in driving continuous improvement and worker engagement by making information more accessible and understandable.

“We’ve built a series of templated dashboards that help operators get back into the right range when things start drifting.

They provide employees with clear directions for how to course correct under specific circumstances.”

—Andrew Jones, Senior Engineer Fellow, International Paper

International Paper uses “golden batch” dashboards to help operations teams achieve optimal outputs during every run. These dashboards provide operators with a visual representation of ideal production conditions for the next run based on current production conditions, regardless of the parameters used in the previous run. By doing so, they can adjust for set-point drift between runs and produce the best possible product each time. 

During production, International Paper’s operations teams use the same dashboards to compare real-time data against these golden batch benchmarks. When conditions start drifting, operators can quickly identify deviations and take corrective action, ensuring consistent quality.

While there may initially be resistance to trusting data that implies a different way of working than “the way it’s always been done,” operations teams tend to adopt visualization dashboards more readily when they begin seeing the tangible benefits they provide.

Conclusion: Laying the foundation for advanced analytics and AI

In today’s data-driven manufacturing landscape, contextualized data is the key to unlocking human intelligence and driving continuous improvement. Data collection, treatment, and usage are foundational to leveraging data within manufacturing plants. Perhaps more importantly, automating data preparation and delivery are prerequisites for taking more advanced steps, such as implementing Artificial Intelligence (AI) and Machine Learning (ML) solutions. As teams grow accustomed to utilizing data-forward solutions when performing their everyday tasks, their resistance to new ways of working gradually wanes.

This is particularly true when teams start seeing positive results, find themselves working on less monotonous or repetitive tasks, and discover how these tools work for them rather than replace them. Using their valuable intrinsic skill sets—their true human intelligence—on a daily basis can be far more motivating than feeling stuck doing the same mundane tasks. 

Gaining team buy-in for a data-driven way of working is pivotal for taking the next step in achieving autonomous operations.