Video: Use Data Foundation to Unlock Your Productivity

Manufacturers are investing in Industry 4.0 infrastructure. Plants generate huge amounts of data, and while the data is often collected, companies are finding now that without a useful data foundation,  they are wrestling with plant data instead of using it.

Useful data is the key to better productivity, sustainability, and breakthroughs in quality and efficiency.

The challenge arises because OT data is not ready to be analyzed. It comes from different sources, with different rhythms, and in different structures.

Sight Machine transforms OT data from any plant into a powerful Data Foundation, and then applies rich analysis.

By stream processing plant data through just 4 common Data Models, Sight Machine continuously generates standardized information about production, what we call Data Foundation.

One common model is the Cycle Model.  Instead of modeling assets, Sight Machine models the work done by your assets. The platform streams and transforms plant data into useful tables of information, with each row representing every cycle for a discrete process and every time period for a continuous process.

Data Foundation is flexible. It includes unlimited parameters as columnar values for each cycle and it rolls up to create real-time Digital Twins – representations of parts moving through your production process, machines, lines, plants, and supply chains.

Both the Data Foundation and Digital Twins are constantly updating to include new data sources  and missing and out of order data, providing a dynamically updated view of production.

Manufacturers need to understand production data in real time from across their fleets of plants.  Data needs to be accurate, trusted, and transparent for good decision making by every stakeholder. Only with this approach of stream processing and common models can manufacturers move beyond collecting data to using it.

And with a single Foundation of trusted, real-time information each manufacturer is able to solve hundreds of use cases.

In high-speed packaging, for example, Sight Machine analyzes thousands of sensors to prevent hundreds of micro-stops.

Another example is machine learning applied to production data in the automotive industry.   In this use case, Sight Machine finds complex interaction effects that cause scrap.

In energy-intensive industries, like Glass, yield improvements drive sustainability, because each unit of glass is made with less energy.  Here, Sight Machine predicts and prevents defects in a furnace days before they happen and improves energy use.

And in almost every industry, there’s a deep tradition of continuous improvement.  This plant held a hackathon for its operators, who with the benefit of Sight Machine raised OEE for a highly optimized process by 7% in just a few days

There’s factory data and then there’s the ability to use it.  They are not the same. Contact us for more information.