Industry 4.0 Trends and Challenges

Transcription

1 Industry 4.0 Trends and Challenges Markus Eisenhauer Fraunhofer FIT 12. April 2018 Global Manufacturing Festival 2018 Herning, Danmark Fraunhofer-Institut für Angewandte Informationstechnik FIT

2 Outline n Fraunhofer n Motivation n IoT and Industrie 4.0 n Data and Value in Industry n IoT Examples in Industry n IoT Challenges n Conclusion

3 FRAUNHOFER

4 Fraunhofer-Society Fraunhofer is the largest organization for applied research in Europe n More than 80 research institutions, including 72 Fraunhofer institutes n More than 24,500 employees, the majority educated in the natural sciences or engineering n An annual research volume of 2,6 billion euros, of which 2 billion Euros is generated through contract research. n 2/3 of this research revenue derives from contracts with industry and from publicly financed research projects. n 1/3 is contributed by the German federal government and the Länder governments in the form of institutional financing.

5 Fraunhofer Worldwide and Germany San José East Lansing Plymouth Hamilton London Boston Storrs Newark Gothenburg Stockholm Glasgow Dublin Nijmegen Brussels Enschede Vienna Budapest Bolzano Graz Porto Cairo Lavon Jerusalem Beijing Seoul Ulsan Sendai Tokyo Osaka Bangalore Kuala Lumpur Singapore Sankt Augustin Salvador Jakarta Campinas São Paulo Santiago de Chile p n n n n n Pretoria Stellenbosch Subsidiary Center Project Center ICON / Strategic cooperation Representative / Marketing Office Senior Advisor Auckland

6 Fraunhofer Institute for Applied Information Technology

7 Industrie 4.0 Smart Cities Energy Efficiency Smart Grids UCD IoT Platforms Smart Data

8 MOTIVATION

9 Relevance non average how long do companies remain in the S&P 500?

10 Relevance Source:

11 Relevance Source:

12 The Pace of Innovation n 1958 US corporations remained in the S&P 500 index for an average of 61 years. n 1980, the average tenure of an S&P500 firm was 25 years, n 2011 that average shortened to 18 years n On average, an S&P 500 company is now being replaced about once every two weeks. n In % of the S&P 500 firms in 2011 will be replaced by new companies. Source:

13 disruptive changes with n More severe implications n Higher speed n More complexity

14 Example - Music Industry

15 Example - Music Industry

16 Example - Music Industry

17 Get a new car just by a software-update?

18 For free?

19 IOT IN INDUSTRY

20 Enabling Business-Based Internet of Things and Services

21 Traditional challenges in Production-Systems Process Integration: limited Integration of operation and und management-process. Flexibility: Appropiate flexibility adressing dynamic market demands is a big challenge. (Volatile Product-mix and Volume). Scalebility and reconfiguration: Production systems are not build to be easy reconfigurable. Efficiency in production: Monitoring of production is limited by low diagnostic capacity; no efficient measuring devices to Discover defective design To prevent installation problems No efficient data analysis, for preventive maintenance

22 DATA AND VALUE IN INDUSTRY

23 Economy is changing and currently is on the edge to a global digital value creation.

24

25 Tremendous Growth of Sensor Data The Rise of Industrial Big Data, GE Intelligent Platforms, 2012

26 Big Data-Definition the ten Vs:

27 Daten-driven economy Source: ABB

28 Beyond Big Data: Smart Data n Big Data = Volume + Velocity + Variety n Smart Data = Big Data + Benefit + Semantics + Quality + Security n User-centered data analysis aiming at actionable insight n Purpose: n People: n Processes: n Platform: What problem to solve with the data? Who is involved? What are the surrounding processes? Usercentered Design Which IT infrastructure is necessary for realization? Internet of Things Data Analytics

29 Levels of data analysis Ø Value creation out of data needs to be put in action

30 Innovation creates values for people! n Value is what people want! (Paul Graham, Silicon Valley) n Creation/ Ideation is to find a new predominantly useful solution for a problem!

31 Proces-based approach Business Process-Model describes which machine and sensors contribute to each process step Business Modell BPNM Engine ERP / MES Domain Modell Domain-Model describes production and its composition Backend Backend App App combines both models with the current sensor values and identifies which machine(s) are involved in the Event current Manager process step ebbits Gateway Layer Intelligent data-fusion reduces the amount of data at runtime RC-API OPC 6LoWPA N Energy ETHERNET WIRELESS GUI Process Sensing

32 IOT EXAMPLES IN INDUSTRY

33 Aluminium plant in Dunkerque

34 Injection moulding in plastic Industry

35

36 Stream Data Analysis in Plastic Industry n From data to insight visual data inspection (t-sne): decision tree (CART):

37 IOT CHALLENGES

38 Challenges in IoT n Devices need not only to be seamless connected with the Internet and in peer to peer mode n but interoperability is still very limited (too many standards, no agreement)

39 Challenges in IoT n Connectivity is still problematic n to connect things with the internet is still a challenge specifically in industrial settings (harsh, hot, humid, dusty environments without wireless connection specifically if urban centers are distant)

40 Challenges in IoT n Security and Privacy are fundamental cornerstones. n Companies are just starting to get a feeling what actually can go wrong!

41 Challenges in IoT n Regulations and laws need to be adapted as well: n Drones/UAVs and autonomous vehicles, pose new types of challenges for the authorities reacting cautious and slowly

42 Challenges in Big Data n lot and Big Data n Not clear if the current data infrastructure is capable of digesting the enormous data wave that the IoT will be producing n How to process data locally on network- or sensor level in order to minimize sending enormous amounts of data into the cloud n fog computing is still in a very early stage

43 CONCLUSION

44 More INTELLIGENCE Turn data into value! Use the abundance of information to make better decisions! More FLEXIBILITY Take advantage from the Network! Organizations' and systems should be as dynamic as the most innovative ideas! More EFFICIENCY Get green and sustainable! Take the responsibility!

45 Core Competencies Smart Cities UCD Energy Efficiency Smart Grids IoT Platforms Smart Data Industrie 4.0 Smart Food 45

46 Co-creation Iterative Reality Check Areas of action Problem definition Deep dive Focus definition Prioritize Idea generation Deep Dive Scope check Feasibility check Project Visions Immersion Definition Ideation Validation Develop a shared understanding of the context and challenges Clearly articulate the problem that we want to solve What options do we have beyond obvious answers? Objectives-Solutions Converge

47 Thank you for attention! Contact: Markus Eisenhauer