Spotfire® for Manufacturing

Unlock your data with advanced analytics & visual data science to drive innovation and efficiency for your smart factory

Spotfire powers your smart factory

Cost pressures, supply chain disruptions, compliance, and changing customer demands all require you to identify bottlenecks in production lines, detect expensive quality problems, and predict machine failures before they happen. Spotfire is a visual data science platform that makes smart people even smarter in solving complex manufacturing problems to reduce costs, improve operations, and increase profitability.

Your visual data science platform for the smart factory

Mitigate risk and improve yield with anomaly detection

Optimize production efficiency, and prevent costly anomalies and failures. Detect sudden spikes, dips, or fluctuations in parameters such as temperature, pressure, vibration, or energy consumption that may indicate potential equipment malfunctions, or material defects, and prevent operational inefficiencies or low product quality.

Minimize downtime with predictive maintenance

Operate more efficiently, reduce costs, enhance product quality, and maintain a competitive edge in the market by leveraging data-driven insights to effectively manage equipment assets. Maximize the uptime of your factory and increase operational efficiency.

Gain insights into product performance with field patterns and defect classifications

Visualize valuable insights into the performance of products and production processes, understand the root causes of failures, identify areas for improvement, and take proactive measures to enhance product quality, reliability, and customer satisfaction.

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Customer stories

Hemlock Semiconductor uses Spotfire to optimize outputs and lower costs

$300K savings per month in electricity consumption via analytic-driven asset utilization

With Spotfire for Manufacturing, Hemlock Semiconductor can manage energy demand in real time, increase product quality, and detect production anomalies.

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Hemlock Semiconductor uses Spotfire manufacturing analytics to optimize outputs and lower costs

Explore Spotfire for Manufacturing

Statistical Process Control Monitoring

This demo shows how process control, monitoring, and alerting in operations use statistical methods to monitor and reduce the variability of measured processes.

Wafermap Pattern Recognition

This demo uses classification techniques to identify and cluster patterns in industrial wafermaps. Additionally, it also offers a means for labeling and then classifying new wafers based on the chosen labels which use reinforcement learning.

Predictive Maintenance with Weibull Curves

Starting with failure event data, this demo fits a parametric failure model with a Weibull curve distribution to help make decisions for an optimal maintenance schedule.