Post on 20-Mar-2017
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SMART FACE Shifting Automotive Production to Industry 4.0
Dr. Christian Schwede, Fraunhofer IML
Prague, June 9th, 2016
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Common Core of Recent Buzzwords and Upcoming Trends: Digitization of the World
Big Data
Cyber-Physical Systems
Industry 4.0
Social Media
Google Glass Smart
Devices
RFID
Cloud Computing
Service-Based Architectures
DronesAgent-Based
Systems
Internet of Things
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Election of the Pope 2005
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Election of the Pope 2013
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Industry 4.0 – The “classic” ladder of progress
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From truth to probability
Industry 3.0
• standard variants
• stable demands
• centralised organisation
• cycle driven work
• rigid
• focus on production volumes
Industry 4.0
• highly individual products
• uncertain global markets
• decentralised organisation
• autonomous decision making
• Flexible
• focus on the customer demand
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Car Production between Individualisation and Efficency
Number of variants
Output per variant
1850
1913
1955
1980
2000
Quelle: Koren (2010), cited in Bauernhansl (2014).Pictures: https://en.wikipedia.org (2015), https://www.impulse.de (2015), audi.de (2015), o2.co.uk (2015), computerbild.de
(2015).
Ford Model T
VW Käfer
Production
Audi Configurator
Mass-
production
Individualisation
»Shareconomy«
Complexity
Globalisation
iPhone
3D-Printed-Car
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Autonomous behaviour of everything is the core aspect of the revolution
Humansplan, control, interact…
Binsknow their content and control their processes
Containerorganize loads and routes on their own
Trucksno driver to transport goods
Vehicles and Forkliftsorganized in swarms
Racksorder their supplies autonomously
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SMART FACE is an approach towards a factory of the future
Development of a decentrally controlled small-scale production of electric vehicles
based on Industrie 4.0 technologies & concepts
Main Objective
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The Great Transformation on the shop floor -Current situation
Sequencing (part A 1:2)
Customer order
Weekly Program Planning
Frozen Zone
Restrictions (suppliers, assembly line) based on prognosis (e.g. 50% part A)
BodyPaint Shop
Buffer Buffer
Paint shop sequenceOverload
Floater
Distribution rows(buffer time 2 days)
Assembly sequence
assemblytime
Re-sequencing
Cycle time (e.g. 90 seconds)
Workstation boundaries
assembly time
Throughput time (e.g. 2-3 days)
racks racks
Pre-sequencing
Tuggertrain
Train Station
JIS call-off
JIS delivery
Super market
Large to small load carrier
LLC storageFinal call-off to supplier (3-4 weeks before)
suppliers
Rework (e.g. quota 40%, duration 1-2 days)Quality
analysis
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The Great Transformation on the shop floor -Current situation
Sequencing (part A 1:2)
Customer order
Weekly Program Planning
Frozen Zone
Restrictions (suppliers, assembly line) based on prognosis (e.g. 50% part A)
BodyPaint Shop
Buffer Buffer
Paint shop sequenceOverload
Floater
Distribution rows(buffer time 2 days)
Assembly sequence
assemblytime
Re-sequencing
Cycle time (e.g. 90 seconds)
Workstation boundaries
assembly time
Throughput time (e.g. 2-3 days)
racks racks
Pre-sequencing
Tuggertrain
Train Station
JIS call-off
JIS delivery
Super market
Large to small load carrier
LLC storageFinal call-off to supplier (3-4 weeks before)
suppliers
Rework (e.g. quota 40%, duration 1-2 days)Quality
analysis
What Problems do exists?
1. Managing unpredictable situation with an inflexible system
a. Manpower in planning and control
b. Resequencing, rework
2. Technical problems and overloads lead to stop of the whole assembly line
3. Huge stocks in distribution logistics due to instability
4. Production of stock orders without customers to fill the capacity gabs
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The Great Transformation on the shop floor –Industry 4.0
BodyPaint Shop
Buffer Buffer
Paint shop sequence
Distribution rows –transport assignment criteria for AGV
Final call-off to supplier (3-4 weeks before)
suppliers
Customer order
Volume cycle
Restrictions (suppliers) based on prognosis
Size
Time
Calculation of volume cylce
setting
Frozen Zone
Large to small load carrier
LLC storageConsumption-driven parts
(common parts)
Market Place
Demand-driven
(individual parts)
Size, value, variety
Quality analysis
racks
Rework performed by regular Workstations
By call-off
Kanban
Workstation
racks
racks
Part container
Assembly priority chart
AGV
Workstation
Station with various abilities
Cycle independent throughput times
JIT call-off
JIT delivery
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Simulation to evaluate the performance
• What do we expect?
1. Both systems running optimally
2. Run-up period
3. Instable volumes
4. Disruptions
Scenario/KPI
Reliability Stock inbound
Stock outbound
Capacityutilization
Resources Throughputtime
Working costs
1 0 - + 0 0 - -
2 0 - + + + - ?
3 0 - + + + - ?
4 + ? + + + ? -
- deterioration 0 unchanged + improvement
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Thank you for your attention!
www.smartfactoryplanning.de
Christian.Schwede@iml.fraunhofer.de