Man in a work apron and hard hat standing amid large industrial pipes with a clipboard
  • 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

snacks production food and beverage industry icon

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

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