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© Quva 2017 1
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8 Countries 14 Stock listed customers 50% Productivity increase goal
“Based on the project, it is evident that Quva has skills to analyze big data and find
out the relevant information in order to enable prediction and prevention of process
related problems."Markku Kotajärvi, Maintenance Manager, Hot Strip Mill, SSAB EUROPE
Quva: Industrial Big Data Analytics Company
© Quva 2017
Analytics for
Action
What to do?
Analytics for
Future
What will happen?
Analytics for Diagnosis
Why did it happen?
Analytics for Past
What has happened?
Data Collection
What are we measuring?
Development Pyramid of Data Analytics
© Quva 2017
Quva helps
customers in all
levels of data
utilization!
Operational
Excellence
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Consequences of current data utilization
• Work safety issues
• Quality problems
• Low OEE
• Unplanned downtime
• Equipment failures
• Production losses
• High energy costs
• Laborous data processing
• Slow troubleshooting
• Suboptimized processes
• High ratio of tacit knowledge
• Lack of big picture
• Inability to forecast future
© Quva 2017 4
Quva® Flow: Industrial Big Data Analytics SaaS
© Quva 20175
Implementation Steps for Data Analytics
© Quva 2017
Lean pilot with
Quva® Flow
SaaS
Quva® Flow
implementation
into production
Routine use of
analytics
through SaaS
Scaling of
analytics into
other areas
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Emil Ackerman
Managing Director
+358 45 208 6816
”The real issue is making sense of big data and finding patterns in it that
help organizations make better business decisions.”
-Gartner
© Quva 2017 7
Juho Koskenranta
Business Development Director
+358 40 741 8498
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