Learn how the Mayr-Melnhof Group implemented production process visualization in a highly automated and fragmented industrial, process-control environment with the Elastic Stack.

1. 1
Industrial Production Process Visualization
with the Elastic Stack in Real-time at MM
Karton
Stephan Hampe
Jürgen Kerner
October, 30, 2018

2. 2
Agenda
• Introduction & context of the project
• Finding the right technology
• Use case presentation
• Conclusion & next steps

3. 3
It would be really great to see the
usage of all the relevant materials
and chemicals from production in
one dashboard, if possible in near
real-time!
Stephan Hampe, Technologist, MM Karton

4. 4
CHALLENGE ACCEPTED
CANNOT BE THAT HARD
;)

5. 5
Let’s talk about machines
What IT has in mind

6. 6
Let’s talk about machines
What technologists have in mind – board making machine

7. 7
Let’s talk about machines
What technologists have in mind – board winder

8. 8
Let’s talk about machines
What technologists have in mind – cross cutting machine

9. 9
Let’s talk about machines
What technologists have in mind – printing machine

10. 10
Let’s talk about machines
What technologists have in mind – die cutting machine

11. 11
Let’s talk about machines
What technologists have in mind – gluing machine

12. 12
Glossary
Common standards in IT and OT field
IT OT
Cables Twisted pair copper, fiber Twisted pair copper, fiber
Connectors RJ45, ST, LC, … RJ45, ST, LC, …
Protocols TCP, UDP, ICMP, … ModbusTCP, Profinet, …
Messaging protocols JSON, REST, SOAP, XMPP,…
OPC-DA, OPC-UA, MQTT, Modbus,
Profibus,..
Standard MTU 1500 bytes up to 255 bytes
Standard latency < 3ms ~ 50µs
Standard speeds 1-40 Gbit/s 2-100 Mbit/s

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WE JUST NEED TO BUY A SYSTEM
WHERE ALL DATA COMES IN AND
ML, AI AND DEEP LEARNING DOES
THE REST

14. 14
Data heat cycle
Predict
Optimize
Empower
EmbedDiscover
Trust
Describe
Find available data and
check if there is value
in my data?
Can I trust
my data?
What happened
and why?
What might
happen next?
What is the right conclusion
for your business?
Is insight being delivered
to the right people at the
right time?
How do you embed the new data
analytics knowledge to your
organization?
Start
Source: PWC
How it should be

15. 15
Data heat cycle
Predict
Optimize
Empower
EmbedDiscover
Trust
Describe
Find available data and
check if there is value
in my data?
Can I trust
my data?
What happened
and why?
What might
happen next?
What is the right conclusion
for your business?
Is insight being delivered
to the right people at the
right time?
How do you embed the new data
analytics knowledge to your
organization?
Start
Source: PWC
Where most approaches stop
Still valid and
usefull

16. 16
Elastic Stack journey of MM
Okt.
2016
Apr.
2017
Mai
2017
Nov
2017
Start with basic
license features
Switch to platinum
license features
Implementation of IT
use cases. Mainly
logging
Start of Business
Use Case –
Production process
visualization
Mar.
2018
Finished
Development
of Business Use
Case

17. 17
Why MM chose Elastic Stack?
• Open code
• Right to contribute
• Speed of development from Elastic
• Same stack across the group in different editions
‒ Production locations basic subscription license features
‒ Corporate IT platinum subscription license features
‒ No additional effort when migrating projects to Corporate IT
• Kibana
‒ Ease of use for not yet IT affine people (like electrical-, automation-, maintenance
engineers)

18. 18
Why MM chose Elastic Stack?
• Specific trainings for target user groups
‒ IT Elastic Search Engineer
‒ BI Elastic Stack Data Administration
‒ Production Kibana
• Sufficient feature set for production in one tool
• Nice UX across user groups
• Performance

19. 19
LET’S TALK ABOUT BOARD
MAKING

20. 20
Board making
Stock-
Preparation
Approach-
Flow
Board
Machine
• “treat the fiber”
• Chemical pulp
• Mechanical pulp
• Other fiber qualities
• “mix them together!”
• Fibers/pulp
• Additives
• Chemicals
• “build the board!”
• 3 different fiber layers
• 2 surface applications
• 3 coating layers

21. 21
Sensors and relevant process data
Stock-
Preparation
Approach-
Flow
Board
Machine
• Flows
• Consistencies
• Flows
• Consistencies
• Speeds
• I/O´s (BM condition)

22. 22
From 4-20mA to hard facts
DigitalTwin
Mass balance
&
Virtual board
structure
Controlling/BI
Recipes
&
Production data
Visualization
DashBoard
with
KIBANA

23. 23
Why?
Responsible use of resources
Recipe
Grade
change
Process
stability
inventory
30 day´s
1 hour
---
“traditional situation”

24. 24
Why?
Responsible use of resources
Recipe
Grade
change
Process
stability
real-time
real-time
real-time
“situation today”

25. 25
Visualisation:
Example: ply weight

26. 26
Effect:
Example: consumption of special chemical
Background: Same performance of the board but improved dosing strategy!
60
77
93
110
05 2018 06 2018 07 2018 08 2018
specific consumption [%]
- 20 %

27. 27
Recipe
System
Calculation
Engine
Securables
Database
(Tags, Materials)
API
Gateway
PLC Systems
Datawarehouse
Group reporting
Process
Information
Systems
DA / UA
Our approach
Combining tradition with Elastic Stack

28. 28
Example dashboard running in control rooms

29. 29
Real life view from dashboard in control rooms

30. 30
Lessons learned
Visualization board making process
Have a clear vision of what you want to achieve
Kibana rulez ;) – people love it3
Best performance from Elastic is useless when sensors are not
calibrated inside board machine
4
Once it is visualized it’s the most normal thing in the world5
Have the data heat cycle in mind – we often had to go back to the
beginning and in the end bring missing sensors to the board machine
2
1

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Next steps
Visualization board making process
Roll out process monitor to all production locations
Include data from other systems (lab, quality system, etc.)3
Machine learning for analysis of critical process conditions4
Canvas dashboards for mill management (mobile applications)5
Include other areas with potential (finishing, cutting, etc.)2
1

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This was just the
beginning
Visit us at the AMA booth
Stephan Hampe stephan.hampe@mm-karton.com
Jürgen Kerner juergen.kerner@mm-karton.com