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© Quva 2017 1
2
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
3
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 2017
5
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
6
Emil Ackerman
Managing Director
+358 45 208 6816
emil.ackerman@quva.fi
”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
juho.koskenranta@quva.fi
Check out our case stories

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Quva - From data to decisions

  • 2. 2 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
  • 3. 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 3
  • 4. 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
  • 5. Quva® Flow: Industrial Big Data Analytics SaaS © Quva 2017 5
  • 6. 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 6
  • 7. Emil Ackerman Managing Director +358 45 208 6816 emil.ackerman@quva.fi ”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 juho.koskenranta@quva.fi Check out our case stories