The Smart Factory and the evolution towards Industry 4.0
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Transcript of The Smart Factory and the evolution towards Industry 4.0
0 © 2017, Fujitsu EMEIA
FujitsuForum2017
#FujitsuForum
1 © Copyright 2017 FUJITSU
The Smart Factory and the evolution towards Industry 4.0
Christian Schregel
Chief Evangelist Industry 4.0
Industry 4.0 Competence Center, CE
Frank L. Blaimberger
Head of Services & Tools
A Division of Factory Operations
2 © Copyright 2017 FUJITSU
Industry 4.0 Competence Center
3 © Copyright 2017 FUJITSU
The path to Industry 4.0
Digital Transformation
has high relevance for
future business model
Digital Transformation
is high priority
Executive Council
topic
Strategy is defined
in detail, a roadmap
is available
Top-3 hurdles:
• Lack of IT skills and resources
(64%)
• Lack of standards for M2M and
IoT (57%)
• Compliance regulations (53%)
and High Security Demands
(52%)
The path towards the 4th industrial
revolution is an evolutionary one
Agree
Disagree
0% 50% 100%
Agree
Disagree
Source: IoT in German Manufacturing Industry, PAC GmbH on behalf of Fujitsu (n=160), 2017
4 © Copyright 2017 FUJITSU
Smart Business
Models Cross-Company
EcosystemsEfficient
Operations
New Production Principles
Data-driven Enhancements
Horizontal Integration
Partner Management
Reduce to Max
Fast IT ready
Evolution of Fujitsu Smart Factory at Campus Augsburg
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The Story So Far ….
Easy
Difficult
Complex
KaikakuKaizenWork Load2000 2011 20191985 2016
Complicate
WorkLoad
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Areas of Smart Factory Based Solutions
Digitalization and Transformation
Worker‘s Place
Assistance SystemsBridging Digital Gaps
Technologies to support and enhance @ Shopfloor and Office
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eInk @ Shopfloor
Substitution of Printing Materials
Printer, Paper Rols, Labels, Blades, etc.
Environmental protection (zero emission)
Dynamic Information Distribution
Last minute change ‘on the run’
Information distribution based on status
Display of Additional Content
Warnings, Changes, Instructions, etc.
Code Labels
Hidden control information
8 © Copyright 2017 FUJITSU
Smart Button @ Shopfloor
Digitally Integration of…
Legacy devices
Appliances w/o digital interfaces
Secured ‘black boxes’
Inexpensive goods
Non electrical production related elements
Commodity Technology
Standard wireless technology WiFi 802.11xx
Low efforts of integration into corporate infrastructure
Seamless integration into already existing supply data flow
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Dynamic Test Control
Process Control Test Controlling
Locked / Closed Q-Loops Dynamic „Q-control loops“
„Linestop“- Function
Assembly & Soldering AOI & ICT Test
Intelligent, Dynamic Test Proceedings Separation of test steps
Automatic controlling of test depth and test details
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APS-Connect
Context Based Instruction Display Multi-Use capability
Context based filtered / visualization
Smart Instruction Separation
Timing Dynamic flow per single product
Automatic product recognition
Flexible material assignments
Interactions Push services for support and material supply
Bi-directional information flow
Data Interface for Quality Tools
APS: Worker’s Place Control Area
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Dynamic Light @ Workbench
Need Based Driven
Content driven illumination assistance
Support for employees with special needs
Increase of ergonomically issues, based on product / configuration related operation
Economical
Power down after operation
Multi functional desk approach to reduce non functional space
12 © Copyright 2017 FUJITSU
Adaptive Testing Event assistance Dynamic test control Problem visualization Multiple operation / less invest
Quality Improvement Online problem solver Process instruction Escalation assessment Trigger on event
Predictive Maintenance Improve OEE Production stabilization Resource allocation Avoid unnecessary waste
Operator Support Context based Instruction Quality increase
Agility and fast
reaction
Digital Supply Chain Paperless Kitting: eInk eKanban Transparency along supply chain “Pull” control
Client Interface Cloud based App Seamless DataFlow towards
customer Data value for clients Desk view load
Shopfloor
Management Worker’s place control Real time data Mobile App
Power / Energy
Management Peak reduction Energy cost saving Consumption supervision
Current Smart Factory Readiness Level
13 © Copyright 2017 FUJITSU
Future Challenges ….
Cost Sensitive
Reduce to the Max
Ready to change IT accounting schemes
(e.g. pay per use)
Fast IT ready
Scalability and automation capability to
decrease costs and expenditure
Smart
Industry 4.0 / IoT Ready
To support and deploy new production principles
and technologies
Enhancements
e.g. being ready for Smart Analytics / AI
Comprehensive
Partner-Management
Embrace new partner and collaboration models
Fujitsu’s Live UseCase
Demonstrate manufacturing capability
towards customers
14 © Copyright 2017 FUJITSU
Enabler for Cyber Physical Systems – Tool as a Service
Request for Data
Receive Value
Fujitsu ‘Cyber Security’ Layer
Factory Platform @ Fujitsu Cloud (K5)
Data Regression Reconcile
Smart Analytics & Prediction
Smart Devices e.g. Sensor Grids
IoT Apps
Fujitsu Augsburg
Customer
Data Stream
Supervision Monitor
Fujitsu Gateway Appliance / Edge Computing Operation
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Co-Creation Approach, e.g. Intelligent Dashboard
Fujitsu Campus Augsburg
Partner
Requirements
Customizing / Consulting
Colmina @ K5
Smart Factory
Fujitsu Global Product / Solution Portfolio Partner Co-Operation
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Industry 4.0 Solution Stack
Engineering Manufacturing Operation & Maintenance
Process FacilityProduct Design Factory Design EnvironmentProduct Worker
Consulting & Managed Security Services
Collaborative Engineering
Industrial Analytics
Industrial-IoT & Edge Computing
Fujitsu Industry 4.0 – Co-Create Industrial Value NetworksTransparency, Interoperability and Innovation in internal and cross-organizational Value Chains
Collaborative Engineering
Product Lifecycle Management, based on transparent Value Chain
Innovation Management Machine Utilization Quality Assurance Business Planning
Product Design WIP Management Supply Chain Traceability MRO-Optimization
Process Automation Demand Forecast
Process Analytics
Worker Assistant Solution
Predictive Analytics Condition Monitoring
Location Based Solutions
Asset Tracking & TracingSolution Offerings
17 © Copyright 2017 FUJITSU
Process Automation Demand Forecast
Process Analytics
Worker Assistant Solution
Predictive Analytics Condition Monitoring
Location Based Solutions
Asset Tracking & Tracing
Process FacilityProduct Design Factory Design EnvironmentProduct Worker
Consulting & Managed Security Services
Engineering Cloud Orchestration
Design & Simulation Tool Integration
Design-Data Management
Collaborative Engineering
Fujitsu Industry 4.0 – Co-Create Industrial Value NetworksTransparency, Interoperability and Innovation in internal and cross-organizational Value Chains
Industrial Reporting Smart Analytics & Machine Learning
Big Data
IoT Platform Event Processing Platform Process Automation Platform
RTLS / AIT Edge Computing UI-Applications
Devices / Sensors / Tags / Beacons Industrial IoT Gateway PAN/ LAN / WAN / Mobile
Product Lifecycle Management, based on transparent Value Chain
Innovation Management Machine Utilization Quality Assurance Business Planning
Product Design WIP Management Supply Chain Traceability MRO-Optimization
Engineering Manufacturing Operation & Maintenance
Industry 4.0 Solution Stack
18 © Copyright 2017 FUJITSU
Smart Quality Control with AI
• Ultrasonic quality inspection generates massive amount of blade scanning data
Deep learning solution automates the scan data evaluation process
High gains in time efficiency by enabling skilled operators to focus on the important part of the data
19 © Copyright 2017 FUJITSU
Industry 4.0 Show Case
K5 GERSensor Data
Access
Dashboarding
&
Alerting
Machine Sensor Data
Motor current flow Data
Dashboarding
Alerting
Predictive
Data generation/Digital Twin• Native data from robot arm
• Add. Sensors:
• Temperature
• Vibration gearing box (microphone)
• Acceleration, Gyroscope via Smartphone
Micro Services• IoT Data GW
• Dashboarding and EPP
• Data visualization on AR via table/HMD
Data for animated Twin
Customer Product DataCustomer Product
Analytics results on Twin
Production Machine
Analytics Services• Predictive Analytics
• Dashboarding
• AI Sound analyse including machine learning
pattern definition (SWP)
20 © 2017, Fujitsu EMEIA
Industry 4.0 Competence Center Team
Andreas Rohnfelder
E-mail: [email protected]
Head of Industry 4.0 CC
Wieland Kordas
E-mail: [email protected]
Christian Schregel
E-mail: [email protected]
Industry 4.0 Evangelist
Leopold Sternberg
E-mail: [email protected]
Program Manager
Collaborative Engineering
Frank Zedler
E-mail: [email protected]
Program Manager
Industrial-IoT & Edge
Computing
Program Manager
Industrial Analytics
21 © 2017, Fujitsu EMEIA