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1
Leankit Webinar 29 Jun 2016
Hamish McMinn
Introducing Kanban to Automotive Product
Development: A New Vehicle Case Study
2
Who? Why?
Hamish McMinn M.A. PMP®
• Engineering Apprentice MOD Aquila
• IT Operations
• Project Manager (Automotive & IT...
3
What Happened?
Kanban proof of concept
Independent study reported delivery rate and quality up
Delivery rate and quality...
4
Time: 2-4 years
cost of delay - Clark, Chew, Fujimoto estimated nearly $1M/day in 1987
(over $2M / day in today’s dollar...
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 ...
6
Engineering Team Structure
Body
Structure
Mechs
Climate
Safety
ExtTrim
IntTrim
Seating
CabinSystems
DoorSystems
Chassis
...
7
1. How do they ensure compatibility of their design?
2. How can they collaborate effectively?
3. What parts do they need...
8
Visual Management
9
Improving Collaboration
10
Continuous Feedback
11
1. Queues are the root cause of the majority of economic waste in product development
2. Queues are the analogue of inv...
12
Applying Software Development Techniques
Constraints in physical product development
• Minimum viable product
• Archite...
13
Automotive Product
Development Lead Time
Source: James M. Morgan, Jeffrey K. Liker: The Toyota Product Development Syst...
14
Automotive Product
Development Lead Time
Concept
Styling
CAD Design
Prototype
Mfr. Eng.
Tooling
Launch
Design Concept S...
15
Automotive Product
Development Lead Time
Concept
Styling
CAD Design
Prototype
Mfr. Eng.
Tooling
Launch
Design Concept S...
16
Virtual
Virtual Series
CAD Progression
UNV1
CAD Progression
UNV2
CAD Progression
UPV2
CAD Progression
UPV3
Analysis
Iss...
17
Virtual Series
CAD Progression
UNV1
CAD Progression
UNV2
CAD Progression
UPV2
CAD Progression
UPV3
Analysis
Issue
Resol...
18
Late “hockey stick”
delivery results in
asynchronous
engineering i.e low
quality, incompatible
data.
Sprint 1 Sprint 2 ...
19
Shortening Feedback: Sprints
20
Shortening Feedback: Sprints
21
22
Effect of Batch Size
23
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...
24
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Combined Status
Combined Completion Prediction
Combined Compatibility Targe...
25
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
UPV2 Geometry - Chassis Assessment Readiness
Chassis Completion Prediction
...
26
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
UPV2 Geometry - Chassis Assessment Readiness
Chassis Completion Prediction
...
27
Daily Stand up Meetings
In front of the board, three questions:
1. What did we accomplish yesterday?
2. What will we do...
28
So What is the Board Telling Us?
The board is a signalling system, its effectiveness relies on our ability to read the ...
29
The Big Picture
30
Highlights of Our Learning to Date
• Visual Management
• Accelerate feedback
• Decompose large batches
• WIIFM
• Succes...
31
Contact: hamish@flowlogic.co W: Flowlogic.co
Summary
• Reduced batch size
• Shortens feedback loops
• Reduces defects /...
32
Questions and Answers
Contact: hamish@flowlogic.co W: Flowlogic.co
33
Summary
• Reduced batch size
• Shortens feedback loops
• Reduces defects / rework
• Increases throughput and quality
• ...
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Driving Innovation with Kanban at Jaguar Land Rover

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Find out how Kanban is accelerating product design and development at Jaguar Land Rover.

Watch the recorded webinar here: https://vimeo.com/172780037

Hamish McMinn, Automotive and IT Project Manager, will explain how Kanban is improving time, cost and quality across new vehicle development projects at Jaguar Land Rover.

You'll learn:
-Why new product development provides rich opportunities for continuous process improvement.
-Benefits and challenges of transferring agile software techniques to hardware design and development.
-How to visualize work, focus on flow and increase cross-functional collaboration using LeanKit.

Hamish will share learnings from the initial pilot project, and how Kanban is now being scaled across multiple engineering teams.

Published in: Software
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Driving Innovation with Kanban at Jaguar Land Rover

  1. 1. 1 Leankit Webinar 29 Jun 2016 Hamish McMinn Introducing Kanban to Automotive Product Development: A New Vehicle Case Study
  2. 2. 2 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
  3. 3. 3 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
  4. 4. 4 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 TimeBreakeven Cash
  5. 5. 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
  6. 6. 6 Engineering Team Structure Body Structure Mechs Climate Safety ExtTrim IntTrim Seating CabinSystems DoorSystems Chassis Steering Brakes Wheels&Tyres Suspension Frames& Mounts Electrical Distribution Infotainment Switchgear DriverAssist Powertrain Engine Transmission Cooling Exhaust Driveline Hybrid 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…
  7. 7. 7 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/
  8. 8. 8 Visual Management
  9. 9. 9 Improving Collaboration
  10. 10. 10 Continuous Feedback
  11. 11. 11 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
  12. 12. 12 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)
  13. 13. 13 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 ProductionTime Marketing Business Model Clay Model Theme Selection CAD Engineering Change Launch Support Product QualityProcess Development Tooling Construction Supplier Development = Non Value Add Time (Waste) = Value Add Time
  14. 14. 14 Automotive Product Development Lead Time Concept Styling CAD Design Prototype Mfr. Eng. Tooling Launch Design Concept Start of ProductionTime = 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
  15. 15. 15 Automotive Product Development Lead Time Concept Styling CAD Design Prototype Mfr. Eng. Tooling Launch Design Concept Start of ProductionTime = 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
  16. 16. 16 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 Prototyp Release M1 Build and Test Physical
  17. 17. 17 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 DelayResolution Delay
  18. 18. 18 Late “hockey stick” delivery results in asynchronous engineering i.e low quality, incompatible data. Sprint 1 Sprint 2 Sprint 3 Sprint 4 The Hockey StickGatewayDataReadiness -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
  19. 19. 19 Shortening Feedback: Sprints
  20. 20. 20 Shortening Feedback: Sprints
  21. 21. 21
  22. 22. 22 Effect of Batch Size
  23. 23. 23 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 Undetected Defects
  24. 24. 24 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Combined Status Combined Completion Prediction Combined Compatibility Target % Compatibility Achieved Metrics
  25. 25. 25 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
  26. 26. 26 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
  27. 27. 27 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
  28. 28. 28 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?
  29. 29. 29 The Big Picture
  30. 30. 30 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. 31. 31 Contact: hamish@flowlogic.co 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
  32. 32. 32 Questions and Answers Contact: hamish@flowlogic.co W: Flowlogic.co
  33. 33. 33 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: hamish@flowlogic.co W: Flowlogic.co

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