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Spotfire for Manufacturing

This club is for those working in the Manufacturing industry and looking to engage in discussions with Manufacturing professionals who are currently exploring or applying advanced analytics solutions. Hear from our experts in the field and like-minded peers, share best practices in our discussion forum, and find useful articles and supporting materials to gather the richest insights for smarter data-driven decision-making. Click on the “Manufacturing Forum” tab to get started.

Key Manufacturing Priorities and Use Cases:

Spotfire helps manufacturers meet their core business objectives and deliver better product outcomes. Here are some of the ways that today’s manufacturing leaders use Spotfire analytics to improve operations:

Anomaly Detection

Anomaly Detection aims to optimize production efficiency by quickly identifying anomalies and preventing cost failures which negatively impacts yield. Anomalies can manifest as sudden spikes, dips, or fluctuations of parameters like temperature, pressure, electrical characteristics, vibration or energy consumption which may indicate equipment malfunction, material defects, or other issues that could impact product quality and production efficiency.

Predictive Maintenace

Predictive Maintenance involves using data analytics, machine learning, and other advanced techniques to predict when equipment or machinery is likely to fail so that maintenance can be performed proactively, thus avoiding unplanned downtime and costly repairs. By analyzing data from sensors, historical maintenance records, and equipment performance metrics, predictive maintenance algorithms can identify patterns and indicators of potential equipment failure, allowing manufacturers to plan maintenance activities at optimal times.

Fail Patterns and Defect Classification

Fail Patterns and Defect Classification in manufacturing refers to identifying and labeling recurring trends or patterns in equipment failures or product defects, which can provide valuable insights into underlying issues such as equipment degradation, process inefficiencies, or material quality problems. Defect classification involves categorizing defects based on their characteristics, causes, and impacts, allowing manufacturers to identify root causes and prioritize corrective actions effectively. 

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