Here is how Trimble’s Wedge transforms the concept of Intuitive Data Refinement from a theory into a practical, everyday toolkit for all data users.

Cleansing: Visual and immediate

Analysis derived from uncleansed raw data is usually worthless. Outliers, sensor failures, and shutdown periods skew results and ruin all analysis. In many systems, removing these requires complex SQL queries or manual, laborious data manipulation.

Wedge approaches this differently. It acknowledges that an experienced process expert is the best judge of which values are irrelevant. Using built-in “cutting tools,” users can visually identify outlier spikes or breaks and remove invalid data with a single mouse click. The system instantly recalculates all analysis and diagnostic results based on this refined dataset, allowing for an iterative, “what-if” style of investigation without altering the original database.

For recurring cleansing of known phenomena from data, manual cleansing is not needed. To address this, Wedge includes automated, rule-based data cleansing capabilities. Users can create custom formulas and rules, such as trimming artifact values during state changes or excluding data when a machine is down, to automatically refine data. This feature automates repetitive data cleansing tasks, ensuring results are always based on high-quality, pre-screened data without requiring constant human intervention.

Filtering: Focus on what matters

Data refinement isn’t just about removing bad data; it’s about isolating relevant data. You might need to analyze specific conditions, such as a particular product grade, a specific raw material batch, or a single shift.

Wedge allows users to filter data by process state. For example, if you are troubleshooting a quality issue that only appears during the production of “Grade X,” you can apply a filter to exclude all other grades from the view. All result windows, for example: histograms, X-Y plots, and statistics, automatically reflect this selection, ensuring you are comparing apples to apples.

Dynamic delay compensation: Eliminating process lags

Perhaps the most critical aspect of data refinement in continuous processes is handling time delays (lags). A change in chemical feed at the wet end of a paper machine does not impact the reel scanner for some time. Similarly, in steelmaking, an alloy adjustment made at the furnace won’t be reflected in the quality sensors until the slab has traveled hundreds of meters through the caster and cooling segments. If you analyze these signals based on their raw timestamps, you will find no correlation.

Wedge solves this with Dynamic Process-Delay Compensation. Unlike simple static time-shifting, Wedge allows users to build a delay model using drag-and-drop blocks representing pipes, tanks, and towers. The system calculates the true delay based on current process conditions, taking variable flows, speeds, and tank levels into account.

This capability extends to complex scenarios involving intermediate storage and distinct production phases, even where the First-In-First-Out (FIFO) principle does not apply. This capability allows the system to handle complex processing steps, such as reel or coil rewinding, and to handle varying production speeds across different production phases.

For example, Wedge can track specific production batches, such as a roll or a coil, allowing users to examine the history of an end-product batch even after it has been rewound. By selecting the product ID, users can see exactly what process conditions that specific batch experienced throughout the entire production line.

The delay feature includes a Virtual Tracer, which visualizes material flow through the process. It aligns the data so that the cause (upstream) and effect (downstream) appear simultaneously in your analysis view, regardless of how long the material actually took to travel between those points.

Accurate data for real-world analysis

By combining instant visual cleansing, automated rule-based refinement, state-based filtering, and intelligent delay compensation, Wedge ensures that the data you analyze reflects the physical reality of your process. It turns the chore of data preparation into a seamless part of the troubleshooting mindflow.

Want more insights?

Our sales team is full of data analysis experts ready to help you. Learn more about Wedge process data analysis tool.

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Building on decades of industrial experience, the company is now accelerating its digital transformation journey—adopting modern systems that enhance transparency, traceability, and operational collaboration. This shift reflects a strategic commitment to future‑ready manufacturing, where decision-making is increasingly informed by real‑time data.

By integrating advanced process intelligence tools, MEL empowers its teams to gain deeper insight into production behavior, reduce waste, and support consistent product quality. These improvements strengthen the company’s competitive position and ensure that MEL continues to deliver dependable, sustainability-oriented cartonboard solutions to its international customer base.

“Wedge proved its value almost immediately. We achieved several successes during the commissioning of a CHP (Combined Heat and Power) plant with Wedge, even before the Wedge system was fully rolled out.”, adds Martin Bergner, Mill Manager at MEL Paper.

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The new plant in Tervasaari will benefit from Wedge’s advanced data analysis capabilities, allowing SharpCell to utilize process data swiftly and effectively. This integration supports the company’s goals of achieving zero waste, zero emissions, and optimal resource utilization, aligning with its vision of being a quality leader in the airlaid segment.

By expanding the use of Wedge, SharpCell continues to demonstrate its dedication to innovation and excellence in the production of airlaid products for various applications, including hygiene, personal care, and household cleaning.

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When a process engineer must tackle new phenomena or unexpected issues, ad hoc data analysis is required. This exploratory work rarely follows a strict, system-driven sequence; rather, it proceeds in undefined steps of investigation, requiring analysts to adapt their approach as insights continually evolve.

For this essential, creative problem-solving to flourish, the data analysis platform must empower individual users to easily and intuitively create and fine-tune their environment for specific tasks. 

Customization: The key to agile troubleshooting and process optimization 

A highly effective data analysis tool must serve the user’s “mindflow,” not compel them to adhere to a rigid theoretical workflow. This flexibility is realized through deep personalization capabilities:

1. On-the-Fly Customization: The capability to define one’s own workspaces, customizing the layout and selecting visualization options via intuitive drag-and-drop functionality, is essential. This allows process experts to tailor dashboards and create ad-hoc diagrams in seconds to contextualize a specific issue.
2. Intuitive Data Refinement: Raw data rarely offers reliable analysis results immediately. Domain expertise is needed to judge which values are irrelevant, necessitating tools that support immediate data cleansing, filtering, and refinement. This includes the crucial ability to compensate for complex process delays (lags) to ensure reliable correlation findings. [Read more]
3. Ad Hoc Calculation Engine: To derive truly useful measurements, users frequently need to combine information from several sources, performing unit conversions or creating new calculated measurements (soft sensors). Systems must include an embedded calculation engine that allows users to easily create and utilize these custom measurements in diagrams and analyses as if they were real physical measurements.
4. Accessible Causal Analysis: Understanding the “why” is the ultimate goal. The system must empower users to intuitively apply advanced analysis methods to uncover hidden root cause-and-consequence relationships. This functionality must be intuitive, enabling process experts to diagnose the source of anomalies without requiring data science intervention.

The best data analytics tools combine automation and customization capabilities

For effective data analysis and utilization, the best data analysis systems enable both automated analysis and flexible customization.

Standard views and automated reports fulfill the need for consistency and knowledge dissemination, but they are insufficient for the agile, spontaneous demands of modern troubleshooting and process optimization. 

The competitive edge belongs to organizations that deploy systems that allow users to seamlessly modify datasets, views, and analytic parameters, ensuring that the system is always precisely tuned to the singular task—no matter how novel—that the user is trying to solve.

Want more insights?

Our sales team is full of data analysis experts ready to help you. Learn more about Wedge process data analysis tool.

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Why Traditional Methods Fall Short

For decades, paper quality has been monitored using visual 2D-profile maps. While effective for identifying major flaws, these maps struggle to reveal deeper, detailed information crucial for understanding quality deviations. The inability to connect these visual maps to process measurements creates a critical blind spot, making it difficult to pinpoint the root cause of paper quality disruptions. To truly master quality, robust methods are needed to manage extensive historical data and understand the impact of process variable changes on profile stability.

Decoding Profile Deviations

No two paper profiles are perfectly identical. While an ideal scan would show perfectly uniform values, real-world production always introduces some variation. The goal isn’t zero variation but rather values that are normally distributed around a set point with minimal spread. Spotting deviations from this optimal state quickly is paramount. 

Profile changes primarily fall into two categories:

• Changes in the shape of scan vectors: These range from simple slopes to complex U-shapes, S-shapes, or W-shapes.
• Changes in the statistical distribution of scan values: Analyze characteristics like mean value, deviation, distribution symmetry (skewness), and overall distribution shape (kurtosis).

Combining insights from profile maps with process data dynamics generates specific profile variation indices—metrics that signal the emergence of profile anomalies. Tracking these indices as trends allows for direct analysis alongside other process measurements, improving the ability to identify the causes of profile changes. This approach also enables efficient study of long-term trends in paper profile uniformity.

Introducing the Wedge Advantage

Trimble’s Wedge system offers a transformative solution by treating intricate profile data with the same intelligence and flexibility as any other process measurement. This advanced data analytics system empowers users to:

• Dive Deep: Freely select any time frame for study, whether it’s a single production run or a specific paper grade over months. Filter out outlier data points to ensure clear analysis.
• Characterize Deviations: Calculate distinct indices that precisely characterize and pinpoint problems within profile measurements, turning visual anomalies into actionable insights.
• Uncover Root Causes: Compare profile variation indices with process measurements to quickly identify process changes triggering shifts in CD profiles, ensuring consistently uniform and high-quality paper.

From Monitoring to Mastery

The Wedge system employs advanced pattern recognition to diagnose complex profile shapes, such as a W-shaped profile. It analyzes relevant process measurements to identify correlations that occurred concurrently with or before the profile deviation, generating a prioritized list of potential root cause candidates. The pattern-recognition tool compensates for process delays, ensuring accurate root cause identification.

This integrated methodology significantly enhances the ability to swiftly identify the root cause of paper profile problems, ensuring consistent production of uniform, high-quality CD profiles. Defined by reliability, speed, and user-friendliness, the Trimble Wedge system streamlines the diagnostic process, elevating paper quality and contributing to operational efficiency and sustained profitability.

Learn more 

If you are interested in delving deeper into the innovative methodologies discussed in this article, Trimble offers a detailed white paper titled “Improve Paper Quality by Combining Cross Direction (CD) Profile Measurements with Process Measurements.” This document provides an in-depth exploration of the techniques and benefits of integrating profile and process data. To request your copy and gain further insights into optimizing paper production, please contact [email protected].

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Ad Hoc Data Analysis: Flexibility and Exploration

Ad hoc data analysis is characterized by its flexibility and exploratory nature. It is particularly valuable for addressing new and often unanticipated questions, troubleshooting, and exploring new phenomena. This approach involves a dynamic study of data, allowing analysts to adapt their methods based on evolving insights and the problem at hand.

Ad hoc analysis is iterative and non-linear, often requiring multiple cycles of redefining tasks, selecting measurements, refining and analyzing data, and drawing conclusions. This flexibility makes it ideal for generating fresh insights and understanding process behavior dynamically. For instance, when a mill experiences a sudden drop in paper quality, ad hoc analysis can be employed to investigate various factors such as raw material quality, machine settings, and environmental conditions. By iteratively refining their analysis, analysts can identify specific issues and take corrective actions.

However, the iterative nature of ad hoc analysis can be time-consuming and requires skilled analysts and significant resources. Additionally, the lack of predefined workflows can lead to variability in the analysis process, potentially affecting the repeatability of the results.

Automated Data Analysis: Consistency and Efficiency

Unlike ad hoc methods, automated data analysis employs a systematic and structured approach that capitalizes on existing process knowledge and is characterized by its repeatability. 

This technique emphasizes data visualization, pre-configured workflows and process modeling. It incorporates predefined algorithms, dashboards, and reporting tools to process data. For instance, automated dashboards can track key performance indicators (KPIs) such as production rates, energy consumption, and quality metrics, offering real-time insights and alerts for swift decision-making.

The advantages of automated analysis include its consistency, efficiency, scalability, and the capacity to spread established information across the organization. However, it is less adaptable to new or unforeseen situations and relies heavily on existing understanding. Incorrect assumptions or models can lead to inaccurate outcomes, and substantial initial investment and ongoing maintenance are necessary to maintain accuracy and relevance.

Integrating Ad Hoc and Automated Approaches

Both ad hoc and automated data analysis play crucial roles in the pulp and paper industry. Each approach has unique contributions to process optimization and decision-making, and their integration can lead to more robust and adaptive operations.

By integrating both approaches, organizations can achieve a balance between flexibility and consistency, leading to enhanced operational efficiency and improved quality control. Ad hoc analysis provides the flexibility to respond to new challenges and opportunities, while automated analysis ensures the widespread application of proven insights. Insights gained from ad hoc analysis can be incorporated into automated workflows, continuously improving the accuracy and relevance of automated systems.

Requirements for Data Analysis Tools

To effectively support both ad hoc and automated data analysis, tools must possess distinct capabilities. For ad hoc analysis, tools should allow dynamic data exploration, support advanced analytical techniques, and facilitate an iterative workflow. They should also integrate seamlessly with various data sources and offer a user-friendly interface to enable quick adaptation to new challenges.

For automated analysis, tools should support predefined workflows and models, enable real-time data monitoring, incorporate statistical process control (SPC), and be scalable to handle large volumes of data. They should also facilitate the dissemination of established knowledge across the organization.

Conclusion

In conclusion, integrating ad hoc and automated data analysis can significantly enhance the pulp and paper industry’s ability to optimize processes and maintain high-quality standards. By leveraging the strengths of both methods, organizations can achieve a balanced approach that is both adaptive and consistent, ultimately leading to more robust and efficient operations.

Learn more 

For those interested in exploring the innovative methodologies discussed in this article further, Trimble provides a comprehensive white paper titled “Innovative Data Analytics in the Pulp and Paper Industry: Balancing Ad Hoc Insights with Automated Consistency.” This document offers an in-depth examination of the techniques and advantages of integrating ad hoc and automated data analysis. To request a copy and gain additional insights into optimizing processes in the pulp and paper industry, please contact [email protected].

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Take the opportunity to connect with our process data analytics specialists, Matti Häkkinen, Holger Wagner, and Teemu Möykkylä, to explore how Wedge can enhance your process improvement efforts.

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Billerud is a leading provider of sustainable packaging materials and solutions. The decision to implement Wedge at their Gävle mill is a way to leverage advanced technology to ensure high process efficiency and product quality, meeting customer demands on runnability and printability in the conversion processes, as well as the functionality of the final packaging solutions.

Wedge‘s powerful analytics and real-time monitoring capabilities will support Billerud in optimizing its production processes, reducing variability, and enhancing overall product quality.

We are excited to partner with Billerud and contribute to their mission of delivering superior packaging solutions.

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At Zellcheming, Matti Häkkinen will deliver a presentation titled “Dynamic vs. Systematic: Exploring Data Analysis Strategies in the Pulp and Paper Industry” on Wednesday, July 2, from 10:10 to 10:30. This session will explore various approaches to data analysis within the pulp and paper sector.

Simultaneously, at Congresso Nacional da Bioenergia, Alan Oliveira will present “Aproveitamento de dados industriais: Transformando dados fragmentados em vantagem competitiva” during the Industrial panel. This presentation will focus on leveraging industrial data to create competitive advantages.

We look forward to meeting you soon!

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Our team, including Teemu Möykkylä and Matti Häkkinen, will be there to showcase Wedge, our powerful tool for process data analytics. Wedge is designed to help you make the most of your process data, driving efficiency and sustainability in your operations. Whether you’re looking to optimize performance or gain deeper insights into your processes, Wedge is the solution you need.

We also welcome you to join us for an insightful presentation by Teemu Möykkylä on Tuesday, May 6th at 9:20 AM. He will be presenting “Innovative Data Analytics in the Pulp and Paper Industry: Balancing Ad-Hoc Insights with Automated Consistency.” This presentation is part of the session “Unlocking the Power of Advanced Data Analytics: Techniques and Practical Strategies for Successful Implementations.” Don’t miss this opportunity to learn about cutting-edge data analytics techniques that are transforming the pulp and paper industry.

Let’s meet at TAPPICon!

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