How it Works
How It Works
Use Cases

Sustainability

Making the Earth More Sustainable: One Manufacturer at a Time

Sustainability efforts lead to better efficiency, quality and profitability

Energy and Sustainability Podcast

Sight Machine, Microsoft, and Accenture on the MPI Vodcast

Jon Sobel, Co-Founder and CEO of Sight Machine, discusses manufacturing sustainability and energy optimization for manufacturers in this new podcast from Microsoft.

Sustainability White Papers and Presentations

Benefits of Sustainability

Reducing energy and increasing yield rate

There are a lot of buzzwords floating around improving manufacturing sustainability – green manufacturing, IIoT, the circular economy, the Fourth Industrial Revolution. All of them have real meaning. But in the plant the reality is more basic. Having a continuous “Earth Day mentality” leads not only to sustainability but to better efficiency, quality and profitability.

Glass Production
Predictive Maintenance Requires a Stable Foundation

Unlock Productivity

What production data can do

Most manufacturers already have plenty of production data: terabytes of input streaming in continuously from hundreds to thousands of shop-floor sensors. Blend this insight-filled data together with information on quality, downtime, and energy, and voila, you have complete real-time visibility into production – the foundation for optimization.

Sight Machine Insights about Sustainability

Industry Insights about Sustainability

Real Customer Stories about Sustainability

Real Outcomes In Weeks

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Fertilizer Manufacturer

High-speed, highly automated producer with outstanding production practices created real-time Data Foundation at multiple plants. New OEE gains achieved by detecting elusive bottlenecks and improving scrap.

Fertilizer Manufacturer

Challenge

Toros Tarim is the leading manufacturer of fertilizer for Turkey and neighboring regions. This large scale process requires a huge industrial chemical complex with each step in the production pathway (sulfur acid to phosphoric acid to ammonium diphospate for example) being dependent upon the former. Downtimes are extremely costly, with potential to impact all areas, including the on-site co-generated electricity whose surplus also bolsters the Turkish electrical grid. Toros approached Sight Machine as part of their digital transformation and lean journey with the clear expectation to start at building a contextualized data platform of all their data, facilitating an holistic view of their process network, before moving to data visibility and automated dashboarding of reports and key performance indicators (KPIs).

Solution

The next part of the journey is now underway as Sight machine and Toros partner to use their data platform to explore advanced analytics such as predictive and AI, process optimization (including for sustainability and emissions management), and maximizing plant reliability / minimizing asset downtime. This project was implemented entirely remotely during the COVID-19 pandemic and was possible because of the unique way Sight Machine is able to rapidly ingest and use automated tool sets to transform and contextualize tens of thousands of data fields in parallel.

Large Paper Manufacturer

Insights within the first 2 weeks. Large paper manufacturer used Artificial Intelligence and Machine Learning to pinpoint discrepancies with Warp, defects and energy consumption.

Large Paper Manufacturer

Challenge

Corrugated paper plant had trouble knowing which specific process parameters drove up scrap and customer claims. In addition, plant wanted to know when and which SKUs resulted in driving peak energy consumption.

Solution

Sight Machine integrated process data, energy data, quality data and customer claims data to provide integrated models that were then utilized to run advanced analytics including AI/ML algorithms.

Outcome

AI/ML yielded insight within 2 weeks about causes of quality issues that had previously eluded company data scientists. Similarly, connections between process variation and energy use and defects and process variation represented new insight into mission-critical questions.

Pulp and Paper Production

Leading paper manufacturer improved energy use by analyzing thousands of data sources across all its processes.

Pulp and Paper Production

Challenge

Steam usage had been high on a paper machine coming up from an outage with no identified root cause.

Solution

Variance analysis and correlation analysis identified a single valve that was found to not be 100% open. Root cause was found and corrective action was taken for a valve cut back.

Outcome

This single insight and the corresponding best practice developed from it – one of dozens generated annually – yields millions per year at the enterprise level in steam savings and increases in production rate.

Leading Glass Manufacturer

Leading glass manufacturer incorporated predictive analytics into workflow to help operators anticipate quality issues and reduce scrap. Scrap is a perennial source of energy loss in the glass industry, and improving it drives significant gains in sustainability. Predictive analytic had almost 80% accuracy in anticipating scrap.

Leading Glass Manufacturer

Challenge

The lag between the causes of quality issues in a furnace and the emergence and detection of faults in glass 3 days later, makes it difficult for glass producers to manage yield and results in glass being returned to the furnace at every glass company. This results in increased energy use when glass has to be re-melted to remove defects.

Solution

By continuously analyzing 45 furnace parameters, Sight Machine predicted increases in fault density up to three days in advance with an extremely high degree of accuracy.  

Outcome

The furnace analytic predicted 78% of all defects from the entire process.