A global approach to Industry 4.0 Strategy definition & kick-off of initial workstreams. Giampaolo Burello Berlin, September 24th 2018

Transcription

1 A global approach to Industry 4.0 Strategy definition & kick-off of initial workstreams Giampaolo Burello Berlin, September 24th 2018

2 Husqvarna Group Robotic lawn mowers Walk-behind lawn mowers Ride-on lawn mowers Chainsaws Cutting equipment and diamond tools for the construction and stone industries Other handheld, petrol-powered products Watering products (Europe) 2

3 Megatrends influencing our industry Battery and electric technology Digital revolution eg. Big Data, IoT, Robotics New consumer groups, urbanization and change of economic gravity Changing consumer values and purchasing behavior Environment. Shortage of resources eg. water, raw materials, land etc. Manufacturing ongoing priorities Delivery Safety Cost Quality Sustainability Customization Manufacturing increasing priorities Faster response to the market Agility Make to support business models 3

4 Manufacturing trends evolution in our industry Lean Production Product cost Life cycle cost Flexible automation Collaborative automation Make to Order Design to order / Customise to order Plan execution Adaptive plan Hardware Software 4

5 Industry 4.0 What is it? User experience Augmented Reality Elements Easy interfaces, mobile, social Low Disruptiveness High Connectivity (IoT) Horizontal and vertical data integration Digitization Industrial cloud and Edge computing Big data and Analytics Digital-Twin Equipment/ Process Enhanced robots and equipment Additive Manufacturing for serial production 5

6 Started activities User experience Augmented Reality Elements Easy interfaces, mobile, social Pilot project Connectivity (IoT) Horizontal and vertical data integration IIoT Platform investigation Digitization Industrial cloud and Edge computing Big data and Analytics Pilot project Digital-Twin Equipment/ Process Enhanced robots and equipment Additive Manufacturing for serial production Pilot project Learn and inform 6

7 Pilot project characteristics Approach: Start small Be specific & agile: experiment, learn & modify Group Function funding Find the factories where to run projects Get divisions and local managers to buy-in Complete the project Define next steps 7

8 Big Data and Analytics Big Data and Analytics Pilot project IIoT Platforms Augmented reality Additive Manufacturing Summary 8

9 Big Data and Analytics Project set-up Background: Scientific papers, academic studies, Limited information on industrial applications Scope: proof of concept in manufacturing Objective: to experiment and learn: Complexity of the discipline Reasonable level of achievement Skills and resources needed Time frame: 3 months 9

10 Big Data and Analytics Production path Stamping tools Presses Pre assembly Assembly Quality control machine Tool 1 Tool 2 Tool 1 Tool 2 Tool 1 Tool 2 Press 1 Press 2 Press 3 Press 4 Press 5 Press 6 Machine 1 Machine 2 Machine 1 Machine 2 Quality check Press 1 Machine 3 Machine 3 Tool 1 Tool 2 Tool 3 Press 2 Press 3 Press 4 Machine 4 Machine 4 Press 5 10

11 Big Data and Analytics The model Machine 1, process 2 6 Tooling 6, process 2 Machine 3, process Machine 2, process 87A Riveting Tooling 6, process 8 3 Only 7 features are relevant for the final result 11

12 Big Data and Analytics Uses of the model Choose the optimal path for a production batch E.g.: batch produced with press x and tool y: the model recommends which assembly machine to use to minimize the defectivity Tool to support the quality team working on reducing product defectivity Model development: improve accuracy and learn how use it at best 12

13 Big Data and Analytics Main learnings Data quantity: more is better. Log everything available Data have to be actionable: use tags, time stamps, keep data linked and synchronized Model the data having in mind the digital representation of the processes Connectivity: the equipment has to be connected and stream data Collaboration between data scientists, process engineers, IT architects B A Vs. 13

14 The Big Data Analytics journey Data Virtuoso on data analytics Data driven decisions Automated decisions visibility 14

15 Big Data and Analytics Industrial IoT Platforms Investigation IIoT Platforms Augmented reality Additive Manufacturing Summary 15

16 Industrial IoT platforms investigation Equipment, processes and products produce data (The Big Data) IoT platforms are new software tools that allow better utilization of the data The functions of an IoT Platform are: Source Contextualize Analyze Synthesize Orchestrate Engage 16

17 Current state: Tools-centrical Product management Product creation Product development Product engineering Corporate Governance Strategic decisions Overview (Control tower) CAD PLM IoT Platform S&OP ERP IoT Platform MRP ERP IoT MRP Platform Supply chain planning Supply chain planning Supplier demand info MES MES 17

18 Next step: IoT Platform as a glue Product management Product creation Product development Product engineering IoT Platform Source Contextualize Analyze Corporate Governance Strategic decisions Overview (Control tower) CAD PLM S&OP ERP MRP ERP MRP Supply chain planning Supply chain planning Supplier demand info MES MES 18

19 Vertical data integration Vertical data integration Future state Operative System Source Contextualize Analyze Synthesize Orchestrate Engage PLM CAD MDM Divisions and Corporate Governance Strategic decisions Overview (Control tower) S&OP MRP Digital Twins ERP MES Horizontal data integration 19

20 Industrial IoT Platforms Market players 20

21 Big Data and Analytics Augmented Reality Pilot project IIoT Platforms Augmented reality Additive Manufacturing Summary 21

22 Augmented Reality pilot project Remote guidance for maintenance Software: Hardware: R-7 or smartphone or tablet How it works: 22

23 Augmented Reality pilot project Results Advantages Time to execute a reparation reduced by 30% to 70% compared to a phone call Easily scalable solution Our equipment suppliers are starting to use it too Learnings It needs procedures and discipline in use AR goggles: 30 mins battery. Usually we use the smartphone Privacy: filming people that are not part of the scope 23

24 Big Data and Analytics Additive Manufacturing Pilot project IIoT Platforms Augmented reality Additive Manufacturing Summary 24

25 Time horizon for 3D printing in manufacturing Intensity Product design for 3D printing Parts designed for 3D printing Whole product designed for 3D printing The revolution start at Product Development Intensity Serial production Intensity Parts for Production Equipment (Tools parts, replacement parts, fixtures, ) Today

26 Challenges and opportunities in producing with Additive Manufacturing AM is a new process. Know-how needs to be built, no history, no standards It needs a full industrialization process Opportunities: Customization Supply chain model Design Tool make Produce Store Use Variable design 3D print Use 26

27 Pilot project 3D printed spare parts Target: learn and be ready to scale-up Industrialization process Prioritization of parts eligible for 3D printing 3D printed part approval All tests done? Need redesign? Drawing and other specs Insource or outsource. Supplier selection and cost model Production Approval Parts on sale Customization opportunities

28 Printing technologies on our focus + Speed + Surface finish - Small size parts + Speed - Surface finish - Limited materials choice 28

29 Big Data and Analytics IIoT Platforms Summary Augmented reality Additive Manufacturing Summary 29

30 Framing the pilots in the Smart Manufacturing journey Short term Long term Smart Manufacturing Additive Manufacturing IoT Platform investigation Strategic initiative 1 Strategic initiative 2 Strategic initiative 3 Actions with good ROI Exploratory projects Augmented Reality Big Data Analytics Run-the-business Lean Differentiate and principles innovate Technology capabilities People Run-the-business Lean principles Differentiate and innovate Technology capabilities People Run-the-business Asset intense Data intense 30

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