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  • Improve the process efficiency and yield of production facilities while maintaining optimal product quality control.
  • Increase throughput and safeguard asset uptime with continuous intelligence monitoring by integrating IoT hardware and sensor data.
  • Improve visibility and efficiencies within utility resources: water, power and raw materials.
  • Enable insights from raw material through production to finished product as well as outbound shipments and logistics.
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Sectors Plutoshift supports:

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Breweries

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Dairy

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Bottled Water

meat sector food and beverage industry icon

Meat Processing

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Snack Food
Production

sweeteners production food and beverage industry icon

Sweeteners

baked goods sector food and beverage industry icon

Baked Goods

oils sector food and beverage industry icon

Edible Oils &
Extracts

condiments sector food and beverage industry icon

Condiments

soups production food and beverage industry icon

Soups

wineries sector food and beverage industry icon

Wineries

spirits sector food and beverage industry icon

Spirits

juices sector food and beverage industry icon

Juices

carbonated drinks sector food and beverage industry icon

Carbonated Drinks

coffee production food and beverage industry icon

Coffees/Teas

Use Cases

1.
Reverse osmosis membrane productivity
2.
Heat exchanger performance
3.
Refrigeration / cooling systems
4.
Sensor health
5.
Process and equipment performance optimization
1.
Optimizing the frequency of cleans, membrane lifecycle
2.
Model data from heat exchangers to identify performance degradation, predict heat transfer patterns and recommend actions
3.
Predictive maintenance and energy efficiencies
4.
Automatically identifying poor quality sensors
5.
Moving from schedule based to condition based maintenance

Use Cases

Reverse osmosis membrane productivity.
Optimizing the frequency of cleans, membrane lifecycle
Heat exchanger performance.
Model data from heat exchangers to identify performance degradation, predict heat transfer patterns and recommend actions
Refrigeration / cooling systems.
Predictive maintenance and energy efficiencies
Sensor health.
Automatically identifying poor quality sensors
Process and equipment performance optimization.
Moving from schedule based to condition based maintenance

News & Blog

100th Episode Of The Dan Smolen Podcast

Prateek Joshi, Founder and CEO of Plutoshift, discusses how A.I. makes the world a better place on the 100th episode of The Dan Smolen Podcast. The Dan Smolen Podcast...
Data quality dimensions for machine learning

8 Dimensions of Data Quality

By Prateek Joshi Large companies have enormous physical infrastructure. This infrastructure is well instrumented and data is collected continuously. The Plutoshift platform uses this data to help them monitor...

Databases, Infrastructure, and Query Runtime

By: Andrew Carlisle Recently, my team was tasked with making a switch from a combined MySQL and Cassandra infrastructure to one in which all of this data is stored...

Machine Learning In 20 Words Or Less

By John Sizemore, Vice President – Data Intelligence & Analytics I’m often told that Machine Learning sounds complicated – but it doesn’t have to be. If I was asked to...
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Food & Beverage Industry One Pager
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