This document summarizes an approach called eScop for integrating manufacturing systems. It discusses challenges in physical layer integration using web services, knowledge representation, orchestration of changes, visualization of complex knowledge, and implementing manufacturing execution system (MES) functions as modular components. The eScop approach aims to make integration re-configurable, adaptive, modular, loosely-coupled, knowledge-driven, and dynamic by addressing these challenges through techniques like semantic web services, hypermedia-driven APIs, and knowledge modeling. Future challenges discussed include developing taxonomies for manufacturing data and functions as well as leveraging implicit knowledge, big data, and artificial intelligence.

1. Integration for manufacturing
The eScop Approach
•Date: October, 2015
•Linked to: eScop
Contact information
Tampere University of Technology,
FAST Laboratory,
P.O. Box 600,
FIN-33101 Tampere,
Finland
Email: fast@tut.fi
www.tut.fi/fast
Conference: ARTEMIS Technology
Conference, ATC 2015. Turin, Italy –
October 6-7 2015.
Title of the presentation:
Integration for manufacturing
The eScop Approach
Authors: Sergii Iarovyi

2. Integration for manufacturing
The eScop Approach
Sergii Iarovyi, eScop

3. From enterprise solutions to embedded devices
- how to use new technologies to overcome
current challenges in manufacturing.

4. Outline:
• Introduction
• Challenges
• Technology review
• Approaches
• Conclusions

5. Introduction:

6. How to…
…reduce vendor dependency for MES
components?
…introduce open ecosystem for MES function
implementations?
…adjust MES for the mass customization?
…adapt MES for SMEs requirements?

7. Background:
1. Cyber-physical systems
2. Service orientation
3. Knowledge representation
4. Automation system hierarchy

8. 60s:
PLC born
ARPANET
70s:
Terminals,
Networking,
PC
80s:
Networking
(MAP)
Ethernet
90s:
IEC61131-3
the Internet
00s:
IEC61499
Web
standards
Background: Cyber-Physical System

9. Standardized
service contract
Service loose
coupling
Service
abstraction
Service reusability
Service autonomy
Service
statelessness
Service
discoverability
Service
composability
increased federation
increased intrinsic interoperability
increased vendor diversity options
increased business and technology alignment
increased ROI
increased organizational agility
reduced IT burden
StrategicgoalsBenefits
Principles
Background: Service orienation

10. Source: Benjamin Nowack, J. (2009). http://bnode.org/blog/2009/07/08/the-semantic-web-not-a-piece-of-cake.
Background: Knowledge Representation

11. ERP
MES
SCADA
Background:
Automation
Systems

12. eScop Approach

13. Challenges on :
- Physical layer
- Representation layer
- Orchestration layer
- Visualization layer
- Function layer

14. Challenges on Physical layer:
- Smart devices on the shop-floor level for Web
Services
- Web services for all levels
- Web service implementation
- Plug and Produce

15. Challenges on Physical layer:
- Smart devices on the shop-floor level for Web
Services

16. Challenges on Physical layer:
- Web services for all levels
Flexible
Simple
Accessible
Widely used
REST

17. Challenges on Physical layer:
- Web service implementation
Data
Service
Event
Notification

18. Challenges on Physical layer:
- Plug and Produce
Description
Swagger
Hypermedia
Semantics
Hydra API
Discovery
Hello/Bye
Probing
Heartbeat

19. Challenges on Representation layer:
- Service inventory
- The model of the manufacturing system
knowledge

20. Challenges on Representation layer:
- Service inventory
Discovery
• Discover API
• Get initial links
• Traverse through hypermedia
• Analyze semantics
• Populate Ontology
• Provide access to operations by semantics

21. Challenges on Representation layer:
- The model of the manufacturing system
knowledge

22. Challenges on Orchestration layer:
- Decisions vs Composition
- Orchestration on changes

23. Challenges on Orchestration layer:
- Decisions vs Composition
http://www.slideshare.net/razvanr/bpmn-primer-razvan-radulian-aspe-webinar
Orchestrator
DS1
DS2

24. Challenges on Orchestration layer:
- Orchestration on changes
Orchestrator
DS1
DS2
Manufacturing
system
representation

25. Challenges on Visualization layer:
- Knowledge defined visualization
- Exploitation of knowledge complexity

26. Challenges on Visualization layer:
- Knowledge defined visualization

27. Challenges on Visualization layer:
- Knowledge defined visualization

28. Challenges on Visualization layer:
- Exploitation of knowledge complexity
E1
D2
BL1 D1
BP1 O1 BP2
T2 T3
T1
BL2

29. Challenges on Function layer:
- MES Functions as modules

30. Conclusion
- Re-configurable
- Adaptive
- Modular
- Loosely-coupled
- Knowledge-driven
- Dynamic

31. Future challenges:
- Widely accepted taxonomy for data in
manufacturing
- Taxonomy on functional level
- The better service definition
- Usage of implicit knowledge
- Knowledge-driven orchestration
- Safety and security
- Big data & Artificial cognition

32. Thank you
for your attention!
Any questions?