Senseye Case Studies Canadian Energy Firm Started Its Digital Transformation

	Canadian Energy Firm Started Its Digital Transformation Senseye

Canadian Energy Firm Started Its Digital Transformation

Senseye

Technology Category	Analytics & Modeling - Big Data Analytics Application Infrastructure & Middleware - Data Visualization
Applicable Industries	Oil & Gas
Applicable Functions	Business Operation
Use Cases	Immersive Analytics
Services	Cloud Planning, Design & Implementation Services Software Design & Engineering Services
Challenge	Seeks to improve operational decision-making, safety management and sustainability. A key element in these initiatives is asset maintenance, representing approximately 25% of Cameco’s overall operating costs at its mining operations. Improving asset management began with automating data collection. Cameco had struggled to analyze multiple regular condition monitoring data from assets in the past. As a result, the company found it hard to understand what went wrong in the event of asset failure. Read More
About Customer	Cameco is a Canadian energy firm that started its digital transformation in earnest in 2019. Its longest-running initiatives are in operations management. Read More
Customer Name	Cameco Read More
Solution	Senseye PdM, using data collected by existing sensors, now gives Cameco a continuous stream of information on the condition and predicted future condition of its machines. Using the technique of multivariate analysis, Cameco is poised to make better operational decisions across the business: Better data collection means a clearer understanding of what went wrong and stronger insight to avoid repeated issues in the future Importantly, it gives a clearer understanding of where and what you should be monitoring to find the best insights on asset health Continuous data analysis reveals which forms of condition data are relevant for measuring asset health – and how the relationship between data points can show an upcoming fault Read More Log in to view content
Contents

Technology Category

Analytics & Modeling - Big Data Analytics

Application Infrastructure & Middleware - Data Visualization

Applicable Industries

Oil & Gas

Applicable Functions

Business Operation

Use Cases

Immersive Analytics

Services

Cloud Planning, Design & Implementation Services

Software Design & Engineering Services

Challenge

Seeks to improve operational decision-making, safety management and sustainability.

A key element in these initiatives is asset maintenance, representing approximately 25% of Cameco’s overall operating costs at its mining operations. Improving asset management began with automating data collection.

Cameco had struggled to analyze multiple regular condition monitoring data from assets in the past. As a result, the company found it hard to understand what went wrong in the event of asset failure.

About Customer

Cameco is a Canadian energy firm that started its digital transformation in earnest in 2019. Its longest-running initiatives are in operations management.

Customer Name

Cameco

Solution

Senseye PdM, using data collected by existing sensors, now gives Cameco a continuous stream of information on the condition and predicted future condition of its machines.

Using the technique of multivariate analysis, Cameco is poised to make better operational decisions across the business:

Better data collection means a clearer understanding of what went wrong and stronger insight to avoid repeated issues in the future
Importantly, it gives a clearer understanding of where and what you should be monitoring to find the best insights on asset health
Continuous data analysis reveals which forms of condition data are relevant for measuring asset health – and how the relationship between data points can show an upcoming fault

Impact #1	[Efficiency Improvement - Operation] Machine learning systems are used specifically around operations and advanced process control to make optimal operating decisions.
Impact #2	[Data Management - Data Analysis] They provide more insight into how your operating conditions or regime impact asset health and give businesses new approaches for optimizing their overall operations.

Impact #1

[Efficiency Improvement - Operation]

Machine learning systems are used specifically around operations and advanced process control to make optimal operating decisions.

Impact #2

[Data Management - Data Analysis]

They provide more insight into how your operating conditions or regime impact asset health and give businesses new approaches for optimizing their overall operations.

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Overview

Canadian Energy Firm Started Its Digital Transformation

Operational Impact