Using sensor data and predictive analytics to keep plant moisture levels in check

The client operated a manufacturing plant where maintaining consistent moisture levels was critical to product quality. Environmental factors such as temperature and pressure caused moisture levels to fluctuate throughout production, and without a reliable way to anticipate these changes, the plant often had to react to quality issues after they had already occurred.

These fluctuations led to inconsistent product output, higher energy and material waste, and periods of downtime whenever moisture-related issues disrupted production. The client needed a way to predict moisture trends ahead of time and make data-driven adjustments before quality was affected, rather than correcting problems after the fact.

• Microsoft Azure
• Azure Synapse
• Snowflake
• Azure Machine Learning

1. Moisture levels fluctuated due to environmental factors like temperature and pressure, making consistent product quality difficult to maintain.
    
2. The plant lacked a reliable way to predict moisture trends in advance, relying on reactive adjustments rather than proactive management.
    
3. Without a unified view of historical and real-time sensor data, it was difficult to identify the patterns driving moisture inconsistencies.
   Inconsistent moisture management contributed to wasted energy, material waste, and unplanned production downtime.
    

Cymetrix implemented a predictive analytics solution that analyzed temperature and pressure data collected from IoT sensors across the plant, giving the client visibility into the environmental factors driving moisture fluctuations. Built on Microsoft Azure, Azure Synapse, and Snowflake, the solution brought historical and real-time sensor data together in one place.

Using Azure Machine Learning, Cymetrix developed predictive models that forecast moisture levels based on this combined data, allowing the plant to make data-driven adjustments before inconsistencies affected product quality. Real-time IoT data streams were integrated with historical sensor data so the system could continuously optimize plant settings, supporting proactive rather than reactive moisture management.