driving innovation with kanban at jaguar land rover
TRANSCRIPT
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Leankit Webinar 29 Jun 2016 Hamish McMinn
Introducing Kanban to Automotive Product Development: A New Vehicle Case Study
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Who? Why?
Hamish McMinn M.A. PMP® • Engineering Apprentice MOD Aquila • IT Operations • Project Manager (Automotive & IT) 2003 • Kanban epiphany 2012 Objectives: • How Automotive NPD offers rich opportunities for
improving time, cost and quality equations • Challenges transferring agile software techniques into
hardware development • Highlights of our learning
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What Happened?
Kanban proof of concept
Independent study reported delivery rate and quality up
Delivery rate and quality up with 30% fewer resources
2nd vehicle programme
Rollout to all new vehicle programme
Quantitative data on time & cost improvements, quality improvements
dec jan feb mar apr may jun jul aug sep oct nov dec jan feb mar apr
Users 60 60 60 60 60 60 60 80 80 80 80 80 180 220 280 330 380
Support 2 2 2 2 2 2 2 2 2 2 2 3 4 4 4 10 10
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Time: 2-4 years
cost of delay - Clark, Chew, Fujimoto estimated nearly $1M/day in 1987
(over $2M / day in today’s dollars)
Cost: £100M - >£1B (9 to 11 figure sums) Quality: cost of poor quality:
defect containment (inspect, palliatives)
escaped defects:
warranty
cost of lost sales
So What?
Sources:
Kim B. Clark, W. Bruce Chew, and Takahiro Fujimoto Product Development in the World Auto Industry
US Bureau of Labor Statistics, CPI Inflation Calculator, www.bls.gov/data/inflation_calculator.htm
Investment
Return
Cashflow
-45
-35
-25
-15
-5
5
15
25
35
45
Time Breakeven
Cash
5
Project Scaling
PCDS v2 345
Pilot
PCDS v2 666/664
Time
Pilot vs PCDS v2
UNV1 UPV0 UNV2 UPV1 UPV2 UPV3
Pilot delivered a 666 scale programme with 345 resource (30% fewer) and improved timing and quality
Planned
Planned
Actual
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Engineering Team Structure
Body S
tru
ctu
re
Mechs
Clim
ate
Sa
fety
Ext T
rim
Int T
rim
Se
atin
g
Ca
bin
Syste
ms
Do
or
Syste
ms
Chassis
Ste
erin
g
Bra
ke
s
Wh
ee
ls &
Tyre
s
Su
sp
en
sio
n
Fra
me
s &
M
ou
nts
Electrical
Dis
trib
utio
n
Info
tain
me
nt
Sw
itch
ge
ar
Drive
r A
ssis
t
Powertrain
Engin
e
Tra
nsm
issio
n
Co
olin
g
Exh
au
st
Drive
line
Hyb
rid
Project
Leaders
Module
Leaders
Lead Engineers
Component Engineers
CAD Engineers
100 - >300 Engineers, multiple sites, countries, continents
Circa 7000 parts to release
700-7000 CAD files
Requirements, FMEA, Test Plan, Cost, Weight, Supplier…
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1. How do they ensure compatibility of their design? 2. How can they collaborate effectively? 3. What parts do they need to interface to? Give clearance to? 4. What is latest design intent? 5. Software complexity (lines of code)
• Boeing 787 14M
• F35 Fighter 24M
• Modern Luxury Car 100M
The Challenge for Engineers
Source: http://www.informationisbeautiful.net/visualizations/million-lines-of-code/
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Visual Management
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Improving Collaboration
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Continuous Feedback
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1. Queues are the root cause of the majority of economic waste in product development
2. Queues are the analogue of inventory
3. We do not measure or manage queues (practically no one does)
4. Every transaction in product development is a potential queue
5. We have thousands of transactions (opportunities to improve)
To improve data supply stability:
1. Make process visible
2. Limit WiP (optimise batch sizes)
3. Focus on flow
4. Identify and reduce blockages and feedback delay
Flow and Kanban
Donald G. Reinertsen The Principles of Product Development Flow: Second Generation Lean Product Development 2009
David J. Anderson Kanban: Successful Evolutionary Change for Your Technology Business 2010
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Applying Software Development Techniques
Constraints in physical product development
• Minimum viable product
• Architectural hard points
• 6 degrees of freedom to control
• Material requirements and properties
• Material lead times
• Production representative prototype parts
• Build time and cost
• Duplication time and cost
• Modular design constrained by all of the above
Mitigation
• Decompose interim releases (internal
customers)
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Automotive Product Development Lead Time
Source: James M. Morgan, Jeffrey K. Liker: The Toyota Product Development System: Integrating People, Process, and Technology 2006, 71
Concept
Styling
CAD Design
Prototype
Mfr. Eng.
Tooling
Launch
Design Concept Start of Production Time
Marketing Business Model
Clay Model Theme Selection
CAD Engineering Change
Launch Support
Product Quality Process Development
Tooling Construction
Supplier Development
= Non Value Add Time (Waste)
= Value Add Time
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Automotive Product Development Lead Time
Concept
Styling
CAD Design
Prototype
Mfr. Eng.
Tooling
Launch
Design Concept Start of Production Time
= Non Value Add Time (Waste)
= Value Add Time
Value Added Time is only a very small percentage of the Lead-Time
Source: James M. Morgan, Jeffrey K. Liker: The Toyota Product Development System: Integrating People, Process, and Technology 2006, 71
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Automotive Product Development Lead Time
Concept
Styling
CAD Design
Prototype
Mfr. Eng.
Tooling
Launch
Design Concept Start of Production Time
= Non Value Add Time (Waste)
= Value Add Time
Value Added Time is only a very small percentage of the Lead-Time
Source: James M. Morgan, Jeffrey K. Liker: The Toyota Product Development System: Integrating People, Process, and Technology 2006, 71
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Virtual
Virtual Series
CAD Progression
UNV1
CAD Progression
UNV2
CAD Progression
UPV2
CAD Progression
UPV3
Analysis
Issue
Resolution
Data
Freeze
Data
Freeze
Data
Freeze
Data
Freeze
Analysis
Issue
Resolution
Analysis
Issue Resolution
Analysis
Issue Resolution
Virtual Series Loops
10-16 weeks duration
Data
Freeze
M1 Prototype Release VP Prototype
Release
M1 Build and Test
Physical
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Virtual Series
CAD Progression
UNV1
CAD Progression
UNV2
CAD Progression
UPV2
CAD Progression
UPV3
Analysis
Issue
Resolution
Data
Freeze
Data
Freeze
Data
Freeze
Data
Freeze
Analysis
Issue
Resolution
Analysis
Issue Resolution
Analysis
Issue Resolution
Virtual Series Loops
10-16 weeks duration
Data
Freeze
Current batch sizes and feedback delay render virtual series data delivery systemically
unstable, forcing a stark choice: scale upstream resource, or tolerate delays
Defect Created
Defect Detected
Defect Resolved
Detection Delay Resolution Delay
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Late “hockey stick”
delivery results in
asynchronous
engineering i.e low
quality, incompatible
data.
Sprint 1 Sprint 2 Sprint 3 Sprint 4
The Hockey Stick G
ate
wa
y D
ata
Re
ad
ine
ss
-12 -9 -6 -3
Countdown (weeks)
+2
100%
Av loop slip 2 weeks
Reduced delta represents
improved compatibility at the
same point
Data flow is driven by Sprint
glidepaths, not single deadline.
Data integrity improved
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Shortening Feedback: Sprints
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Shortening Feedback: Sprints
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Effect of Batch Size
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0
2
4
6
8
10
12
14
16
18
20
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56
Batch size 1
Batch size 1 with Errors
Batch size 5
Batch size 5 with Errors
Batch size 10
Batch size 10 with Errors
Effect of Batch Size
Un
de
tec
ted
Defe
cts
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0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%Combined Status
Combined Completion Prediction
Combined Compatibility Target %
Compatibility Achieved
Metrics
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0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%UPV2 Geometry - Chassis Assessment Readiness
Chassis Completion Prediction
Chassis Compatibility Target %
Compatibility Achieved
Metrics
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0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%UPV2 Geometry - Chassis Assessment Readiness
Chassis Completion Prediction
Chassis Compatibility Target %
Compatibility Achieved
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%UPV2 Geometry - Electrical Assessment Readiness
Electrical Completion Prediction
Compatibility Achieved
Electrical Compatibility Target %
Metrics
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Daily Stand up Meetings
In front of the board, three questions: 1. What did we accomplish yesterday? 2. What will we do today? 3. What obstacles are impeding our
progress? Objective is not to discuss details in the meeting, but to agree offline help required
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So What is the Board Telling Us?
The board is a signalling system, its effectiveness relies on our ability to read the signals and
raise the questions it prompts. E.g.:
• What needs to happen to progress these items?
• Why is this item blocked?
• Who has the next action?
• What is date to green?
• When will overdue be ready?
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The Big Picture
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Highlights of Our Learning to Date
• Visual Management
• Accelerate feedback
• Decompose large batches
• WIIFM
• Success breeds success
• People’s behaviour (not tools) delivers outcomes
• Replicate good practice
• Deep vs superficial learning
31 Contact: [email protected] W: Flowlogic.co
Summary
• Reduced batch size
• Shortens feedback loops
• Reduces defects / rework
• Increases throughput and quality
• Adopt Kanban / visual management to enable intense collaboration
• Result – complex programme achieved in less time with
• Improved quality
• 30% fewer resources
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Summary
• Reduced batch size
• Shortens feedback loops
• Reduces defects / rework
• Increases throughput and quality
• Adopt Kanban / visual management to enable intense collaboration
• Result – complex programme achieved in less time with
• Improved quality
• 30% fewer resources
Contact: [email protected] W: Flowlogic.co