World-class technology to enable world-class manufacturing
Connect, unify and label all of your factory data
Restructure data from multiple plant sources
Benchmark, monitor, and improve productivity
Drive operational improvements across your enterprise applications
A large paper manufacturer was challenged by high scrap rates in its production mills. Implementing Sight Machine’s application suite enabled the manufacturer to analyze thousands of sensor readings for every machine that touched individual paper rolls throughout the production process. The manufacturer used this analysis to rapidly identify hidden relationships between production parameters and quality issues to reduce scrap rates and improve quality and yield.
One of the largest global paper manufacturers was focused on building a scalable platform to analyze production data to address quality challenges. The manufacturer operates paper mills that take in bark and mix it with water and additives to create various grades of paper. A key initiative is improving profitability by addressing the 10-15% scrap rate of this continuously running process.
A data historian at each facility captures information from hundreds of PID controllers used throughout the production process. This data is forwarded to a cloud-based data lake for centralized storage of production data from multiple facilities.
Defect count/type data was captured at the last machine in the production process, the winder, but the manufacturer did not have the ability to determine which sensor readings from all the equipment involved in the production process related to a specific paper roll. This made it difficult to determine how individual or cumulative variances—for example, in pressure, temperature or tension—from one or more machines involved in the production process impacted the overall quality of a specific roll.
To investigate an issue, process engineers would make assumptions about which machines and parameters might be impacting quality, and query the data lake for a specific production run. They would then manually build data models associating sensor readings to specific production runs and apply algorithms to look for correlations and trends. Not only did this process need to be repeated for each inquiry, but the effectiveness of the data modeling was highly dependent on the skills and experience of each engineer.
The manufacturer was looking for a scalable IoT data analytics platform that continuously analyzed all data in real time rather than manually-selected, historical samples, allowing them to investigate and react faster. These needs aligned with Sight Machine’s approach of creating an accessible data platform built for analyzing readings and parameters from thousands of sensors and hundreds of machines.
Sight Machine’s AI Data Pipeline created Part and Quality models that allow process engineers to perform deep analysis of how the entire production process is impacting a batch of paper:
Leveraging the AI Data Pipeline models, Sight Machine’s analytics application allows process engineers to quickly diagnose quality issues.
The Analytics Correlation Heatmap tool quickly determines which variables and machines might be impacting quality. Unlike the manual models and process created by data scientists, the Correlation Heatmap removes the guesswork by automatically analyzing tens of thousands of data points that relate to the production of a specific product.
Once engineers have identified the most likely contributors to scrap, Sight Machine’s Advanced Analytics Toolkit enables the team to perform deeper analysis on the associated assets. In this case, the team analyzed dryer pressure and temperature readings for specific production cycles that generated a large amount of scrap to pinpoint the cause. Unlike manual data models which cover a set period of time, the team can verify their analysis by reviewing data from the same sensor during all scrapped runs using historical data.
All of this comes together to enable transformational levels of analysis providing the manufacturer with the ability to perform:
Sight Machine’s Enterprise Manufacturing Analytics application suite pulls from the manufacturer’s data lake to deliver insights from data captured by multiple historians deployed at multiple facilities.
Sight Machine helps a major industrial manufacturer reduce scrap costs by 30% within 3 weeks, identifying more than $500,000 in potential savings.
Sight Machine drives quality for a major global manufacturer by providing push-button multivariate root cause analysis on more than 60 data fields.
