Unlock the Full Power of Machine Data – A Day in the Life of an Engineer, With and Without TrendMiner

Based on an anonymized real-world example from a high-tech manufacturing customer.

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Without TrendMiner:

Morning: Mary, a process engineer at a leading high-tech manufacturing plant, hears about a quality issue: a critical product parameter has drifted out of spec. She heads to the shop floor and manually extracts time-series data from a production machine — a 15-minute task involving a USB stick and patience.

Mid-Morning: Back at her desk, Mary opens MATLAB to process the raw data. As she’s not a coding expert, she needs Peter, the data scientist, to help clean and structure the dataset. Together, they meticulously clean and process the machine data, ensuring it's ready for analysis. The back-and-forth takes over an hour — time-consuming, manual, and reliant on expert support.

Lunch: After a quick lunch, Mary attempts to visualize the data in Excel, plotting and overlaying machine runs and checking parameters for anomalies. It’s tedious and error-prone. With no native tools for root cause analysis, she’s left to make assumptions based on charts alone. This takes another hour, as she needs to ensure the accuracy of her visualizations.

Afternoon: She shares her findings via spreadsheet with John, the machine operator on the shop floor, and verbally recommends that a specific parameter be kept below a certain threshold. John nods and promises to implement the changes and pay attention, but Mary knows that the insights might be forgotten soon after. Without a system in place to monitor or alert in real time, follow-through depends on memory and best intentions.

Late Afternoon: Mary returns to her office, feeling a bit frustrated. The whole process has taken her several hours, and there’s no efficient way to monitor the results or set up alerts to monitor the process in the future. She realizes that the analysis might end up in a drawer, never to be looked at again.

And while Mary spends hours analyzing manually, the line continues producing out-of-spec products — driving up scrap and costs.

With TrendMiner: A Data-Driven Day

Morning: Mary logs into TrendMiner and immediately investigates the reported quality deviation. Within seconds, she overlays time-series data across machines and identifies when and where the issue occurred. Using built-in search and layering features, she pinpoints a threshold breach of a critical process parameter and correlates it with recent maintenance data pulled from SAP DM — all within one platform.

Mid-Morning: Instead of wrangling code or spreadsheets, Mary uses TrendMiner’s intuitive interface to configure smart alerts. Mary doesn’t need to wait for IT or a data scientist — she finds answers on her own. TrendMiner automatically flags any deviations in the critical parameters, ensuring early detection and fast response times.  

Confident in the system's real-time monitoring, Mary enjoys a stress-free lunch. Alerts are automatically shared with her and the operations team, fostering a culture of transparency and shared responsibility.

Afternoon: Mary and John meet on the shop floor and look at the shared dashboard. TrendMiner has made data accessible to the operational experts - no more waiting on engineers for answers. Together, they review root cause analysis results and co-create a plan to optimize machine performance. They decide to integrate the TrendMiner dashboard into their daily morning meeting. Her team’s daily meeting now starts with data-driven decisions, not gut feel.

John is quite enthusiastic about TrendMiner, as it provides him with fast and easy access to machine data he didn’t have before. Empowered by data, he takes ownership and suggests rolling out similar dashboards across other lines.

Late Afternoon: Mary focuses on continuous improvement projects, knowing TrendMiner is handling process monitoring and alerting. What once took a full day now takes minutes — cutting analysis time by over 80% and driving faster action.

Conclusion: Empowering Engineers, Enabling Excellence

With TrendMiner, Mary has shifted from reactive problem-solving to proactive, data-driven decision-making. She’s empowered to analyze, act, and collaborate — without depending on external experts or manual workarounds. Her team is more engaged, her insights are actionable, and her time is spent where it matters most. The plant is evolving — from ad hoc analysis to continuous, scalable insights.

TrendMiner helps high-tech manufacturers scale their data maturity and empower every employee to become a data-driven problem solver.

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Ready to empower your engineers and unlock the full value of your machine data? Discover what TrendMiner can do for your smart factory.