SlideShare una empresa de Scribd logo
1 de 48
Measure and Manage Flow in Practice
                                   by
                              Zsolt Fabok
                              2012.10.18



                    Broke the WIP limit TWICE




                         Still on the team
@ZsoltFabok                                            #lkfr12
http://zsoltfabok.com/                          http://lkfr.org/
5 Stories from the life of a team by using
        real application and data




                     The collected data is the courtesy of Digital Natives
#1 Too many open items
Visualize the situation with
Cumulative Flow Diagram


                                 WIP




          point of observation   solved
It offers more than just the WIP...
    number of work items
                           The Cumulative Flow Diagram
                           Done
                           Started
                           Queued                   backlog

                                                          WIP
                                             cycle time

                                lead time


                                            time
The simplest way of collecting data:



Q   3   4   1   2   D
~   ~   ~   ~   ~   ~
~   ~   ~   ~   ~   ~


~       ~       ~   ~
~       ~       ~   ~


~       ~
~       ~

~
~
A quick detour:




CFD doesn’t say too much about
       the throughput
/* detour */

work item




WIP


            lead time




        time
2
                        /* detour */

work item




WIP


            lead time




   throughput
2   2
                /* detour */

work item
/* detour */




  Let’s add more people to the
project so that “things speed up”!
2       2        3
                                  /* detour */

work item




       coordination + communication cost
Actually, “things slow down”, so it
 was not a good idea (solve the right
problem instead - systems thinking).



                      End of the detour.
#2 It takes too much
   time to deliver
lead time
CFD was not much help here...

                         lead time




        ...because we didn’t know much about
                    the nature of the lead time
Distribution of lead times

  count
  15

  13

  10

   8

   5

   3

   0
          1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 22 28 33 56
   days
average
                                           *Calculation of medians is a popular technique in summary statistics and
median*                            summarizing statistical data, since it is simple to understand and easy to calculate,
                                     while also giving a measure that is more robust in the presence of outlier values
Some examples of work items with 8-day
             lead time
   time spent on   time spent waiting
  implementation        (hours)         %
      (hours)
        1                 63            98

        7                 57            90

        2                 62            97

        2                 62            97

        3                 61            96
95%
    of the lead
time was spent
    on waiting
Distribution of lead times
                 Before                          After
count                              count
   15                                 15

  13                                 13

  10                                 10

   8                                  8

   5                                  5

   3                                  3

    0                                  0
 days 1     4    7   10 13 16 33    days 1   3   5   7   9 11 13 22

    average
        median
#3 Still too many open
      work items
How many
times the item
   has been
   rejected
Number of rejected work items
 count
    15



    11



     8



     4



     0
week 31-32     33-34   35-36   37-38
Number of rejected work items
 count
    15



    11



     8



     4



     0
week 31-32     33-34   35-36   37-38   39-40   42-43   44-45
#4 Being predictable
Sales: “I want to know when the
new features can hit the market!”



Management: “I want to know
how much it will cost me!”
All the work items we had so far
        (~20 work items)



                         v

             ~                    v       ~
             ~               ~        v   ~
                     v       ~
                         v
                                          ~
                             v                v
                 ~                        ~
                 ~            ~
                              ~
                                          v
Categorizing them into three groups

         S
                                  v
                  ~
                  ~
                          v




                                      ~
                                      ~

                      ~



         M
                      ~               ~
                                      ~   v
              v               ~
                              ~
                  v
                                  v




         L
                      ~
                                  v
                      ~

                              v
The lead time distribution
                                count
                                   4

                                   3

                                   3
                        ~
                        ~

            ~
                                   2
M
            ~           ~
                        ~   v
    v           ~
                ~
        v
                    v
                                   1

                                   1

                                   0
                                 days 1 2 3 4 5 6 7 8 9 12 13 16
The lead time distribution
                                count
                                   4

                                   3

                                   3
                        ~
                        ~

            ~
                                   2
M
            ~           ~
                        ~   v
    v           ~
                ~
        v
                    v
                                   1

                                   1

                                   0
                                 days 1 2 3 4 5 6 7 8 9 12 13 16

                                          SLA
The lead time distribution
                                count
                                   4

                                   3

                                   3




                                                 Ex
                        ~
                        ~




                                                   pi
            ~
                                   2
M
            ~           ~
                        ~   v




                                                     re
    v           ~
                ~
        v
                    v
                                   1




                                                       d
                                   1

                                   0
                                 days 1 2 3 4 5 6 7 8 9 12 13 16

                                          SLA
The spent time distribution
                                count
                                   6

                                   5

                                   4
                        ~
                        ~

            ~
                                   3
M
            ~           ~
                        ~   v
    v           ~
                ~
        v
                    v
                                   2

                                   1

                                   0
                                 hours   6   7   8   9   10
The spent time distribution
                                count
                                   6

                                   5

                                   4
                        ~
                        ~

            ~
                                   3
M
            ~           ~
                        ~   v
    v           ~
                ~
        v
                    v
                                   2

                                   1

                                   0
                                 hours   6   7   8   9   10

                                                         SLA
#5 Forced improvement
#2 Nothing changed.
           Still the same ratio


   #1 We decided that we would
force ourselves to keep the SLA
Evolution of the
team’s workflow
Final thoughts on
 measurements
“If you can not measure it,
         you can not improve it.”

                                  Lord Kelvin




http://en.wikipedia.org/wiki/File:Lord_Kelvin_photograph.jpg
“If you start measuring
          something you start
 optimizing it, and I know it's
the wrong thing to optimize.”

                  Paul Graham




             http://paulgraham.com/swan.html
The Hawthorne effect
might influence your
measurements
The key ideas
1. We develop software not models (value)
2. Demand first, supply second
3. Observe the system (lead time, throughput)
4. Start measuring, look back if necessary
5. Manage
6. Mind that data expires
7. Goto step 3.
Thank you very much for your attention!




http://zsoltfabok.com/               @ZsoltFabok

Más contenido relacionado

Más de Zsolt Fabok

The Road to a Fairly Predictable System
The Road to a Fairly Predictable SystemThe Road to a Fairly Predictable System
The Road to a Fairly Predictable SystemZsolt Fabok
 
Narrow Down What to Test
Narrow Down What to TestNarrow Down What to Test
Narrow Down What to TestZsolt Fabok
 
Achieving Maintenance Stabilisation with Agile, Kanban and Lean Thinking
Achieving Maintenance Stabilisation with Agile, Kanban and Lean ThinkingAchieving Maintenance Stabilisation with Agile, Kanban and Lean Thinking
Achieving Maintenance Stabilisation with Agile, Kanban and Lean ThinkingZsolt Fabok
 
The Groundhog Day of a Team Leader
The Groundhog Day of a Team LeaderThe Groundhog Day of a Team Leader
The Groundhog Day of a Team LeaderZsolt Fabok
 
Bp Meetup - Achieving Maintenance Stabilisation with Agile, Kanban and Lean T...
Bp Meetup - Achieving Maintenance Stabilisation with Agile, Kanban and Lean T...Bp Meetup - Achieving Maintenance Stabilisation with Agile, Kanban and Lean T...
Bp Meetup - Achieving Maintenance Stabilisation with Agile, Kanban and Lean T...Zsolt Fabok
 
Targu Mures - Behind the Curtain: The Agile/Lean Way of Working
Targu Mures - Behind the Curtain: The Agile/Lean Way of WorkingTargu Mures - Behind the Curtain: The Agile/Lean Way of Working
Targu Mures - Behind the Curtain: The Agile/Lean Way of WorkingZsolt Fabok
 
Targu Mures - Measure and Manage Flow in Practice
Targu Mures - Measure and Manage Flow in PracticeTargu Mures - Measure and Manage Flow in Practice
Targu Mures - Measure and Manage Flow in PracticeZsolt Fabok
 
ACCU2012 - The Groundhog Day of a Team Leader
ACCU2012 - The Groundhog Day of a Team LeaderACCU2012 - The Groundhog Day of a Team Leader
ACCU2012 - The Groundhog Day of a Team LeaderZsolt Fabok
 
Achieving Maintenance Stabilisation with Agile, Kanban and Lean Thinking
Achieving Maintenance Stabilisation with Agile, Kanban and Lean ThinkingAchieving Maintenance Stabilisation with Agile, Kanban and Lean Thinking
Achieving Maintenance Stabilisation with Agile, Kanban and Lean ThinkingZsolt Fabok
 
SPSE2012 - Measure and Manage Flow in Practice
SPSE2012 - Measure and Manage Flow in PracticeSPSE2012 - Measure and Manage Flow in Practice
SPSE2012 - Measure and Manage Flow in PracticeZsolt Fabok
 
Don't Fear Change, Let Change Fear You
Don't Fear Change, Let Change Fear YouDon't Fear Change, Let Change Fear You
Don't Fear Change, Let Change Fear YouZsolt Fabok
 
The Difficult Life of a Lean Team Leader
The Difficult Life of a Lean Team LeaderThe Difficult Life of a Lean Team Leader
The Difficult Life of a Lean Team LeaderZsolt Fabok
 
Measure and Manage Flow v2
Measure and Manage Flow v2Measure and Manage Flow v2
Measure and Manage Flow v2Zsolt Fabok
 
Evolution of the Software Development Process at Digital Natives
Evolution of the Software Development Process at Digital NativesEvolution of the Software Development Process at Digital Natives
Evolution of the Software Development Process at Digital NativesZsolt Fabok
 
Agile in Stealth Mode
Agile in Stealth ModeAgile in Stealth Mode
Agile in Stealth ModeZsolt Fabok
 
Maintenance Stabilisation
Maintenance StabilisationMaintenance Stabilisation
Maintenance StabilisationZsolt Fabok
 
Kanban Basics for Beginners Revised
Kanban Basics for Beginners RevisedKanban Basics for Beginners Revised
Kanban Basics for Beginners RevisedZsolt Fabok
 
Measure and Manage Flow in Practice
Measure and Manage Flow in PracticeMeasure and Manage Flow in Practice
Measure and Manage Flow in PracticeZsolt Fabok
 
Kanban in 5 minutes
Kanban in 5 minutesKanban in 5 minutes
Kanban in 5 minutesZsolt Fabok
 
Kanban Basics for Beginners
Kanban Basics for BeginnersKanban Basics for Beginners
Kanban Basics for BeginnersZsolt Fabok
 

Más de Zsolt Fabok (20)

The Road to a Fairly Predictable System
The Road to a Fairly Predictable SystemThe Road to a Fairly Predictable System
The Road to a Fairly Predictable System
 
Narrow Down What to Test
Narrow Down What to TestNarrow Down What to Test
Narrow Down What to Test
 
Achieving Maintenance Stabilisation with Agile, Kanban and Lean Thinking
Achieving Maintenance Stabilisation with Agile, Kanban and Lean ThinkingAchieving Maintenance Stabilisation with Agile, Kanban and Lean Thinking
Achieving Maintenance Stabilisation with Agile, Kanban and Lean Thinking
 
The Groundhog Day of a Team Leader
The Groundhog Day of a Team LeaderThe Groundhog Day of a Team Leader
The Groundhog Day of a Team Leader
 
Bp Meetup - Achieving Maintenance Stabilisation with Agile, Kanban and Lean T...
Bp Meetup - Achieving Maintenance Stabilisation with Agile, Kanban and Lean T...Bp Meetup - Achieving Maintenance Stabilisation with Agile, Kanban and Lean T...
Bp Meetup - Achieving Maintenance Stabilisation with Agile, Kanban and Lean T...
 
Targu Mures - Behind the Curtain: The Agile/Lean Way of Working
Targu Mures - Behind the Curtain: The Agile/Lean Way of WorkingTargu Mures - Behind the Curtain: The Agile/Lean Way of Working
Targu Mures - Behind the Curtain: The Agile/Lean Way of Working
 
Targu Mures - Measure and Manage Flow in Practice
Targu Mures - Measure and Manage Flow in PracticeTargu Mures - Measure and Manage Flow in Practice
Targu Mures - Measure and Manage Flow in Practice
 
ACCU2012 - The Groundhog Day of a Team Leader
ACCU2012 - The Groundhog Day of a Team LeaderACCU2012 - The Groundhog Day of a Team Leader
ACCU2012 - The Groundhog Day of a Team Leader
 
Achieving Maintenance Stabilisation with Agile, Kanban and Lean Thinking
Achieving Maintenance Stabilisation with Agile, Kanban and Lean ThinkingAchieving Maintenance Stabilisation with Agile, Kanban and Lean Thinking
Achieving Maintenance Stabilisation with Agile, Kanban and Lean Thinking
 
SPSE2012 - Measure and Manage Flow in Practice
SPSE2012 - Measure and Manage Flow in PracticeSPSE2012 - Measure and Manage Flow in Practice
SPSE2012 - Measure and Manage Flow in Practice
 
Don't Fear Change, Let Change Fear You
Don't Fear Change, Let Change Fear YouDon't Fear Change, Let Change Fear You
Don't Fear Change, Let Change Fear You
 
The Difficult Life of a Lean Team Leader
The Difficult Life of a Lean Team LeaderThe Difficult Life of a Lean Team Leader
The Difficult Life of a Lean Team Leader
 
Measure and Manage Flow v2
Measure and Manage Flow v2Measure and Manage Flow v2
Measure and Manage Flow v2
 
Evolution of the Software Development Process at Digital Natives
Evolution of the Software Development Process at Digital NativesEvolution of the Software Development Process at Digital Natives
Evolution of the Software Development Process at Digital Natives
 
Agile in Stealth Mode
Agile in Stealth ModeAgile in Stealth Mode
Agile in Stealth Mode
 
Maintenance Stabilisation
Maintenance StabilisationMaintenance Stabilisation
Maintenance Stabilisation
 
Kanban Basics for Beginners Revised
Kanban Basics for Beginners RevisedKanban Basics for Beginners Revised
Kanban Basics for Beginners Revised
 
Measure and Manage Flow in Practice
Measure and Manage Flow in PracticeMeasure and Manage Flow in Practice
Measure and Manage Flow in Practice
 
Kanban in 5 minutes
Kanban in 5 minutesKanban in 5 minutes
Kanban in 5 minutes
 
Kanban Basics for Beginners
Kanban Basics for BeginnersKanban Basics for Beginners
Kanban Basics for Beginners
 

Último

Design and Development of a Provenance Capture Platform for Data Science
Design and Development of a Provenance Capture Platform for Data ScienceDesign and Development of a Provenance Capture Platform for Data Science
Design and Development of a Provenance Capture Platform for Data SciencePaolo Missier
 
Collecting & Temporal Analysis of Behavioral Web Data - Tales From The Inside
Collecting & Temporal Analysis of Behavioral Web Data - Tales From The InsideCollecting & Temporal Analysis of Behavioral Web Data - Tales From The Inside
Collecting & Temporal Analysis of Behavioral Web Data - Tales From The InsideStefan Dietze
 
Linux Foundation Edge _ Overview of FDO Software Components _ Randy at Intel.pdf
Linux Foundation Edge _ Overview of FDO Software Components _ Randy at Intel.pdfLinux Foundation Edge _ Overview of FDO Software Components _ Randy at Intel.pdf
Linux Foundation Edge _ Overview of FDO Software Components _ Randy at Intel.pdfFIDO Alliance
 
Hyatt driving innovation and exceptional customer experiences with FIDO passw...
Hyatt driving innovation and exceptional customer experiences with FIDO passw...Hyatt driving innovation and exceptional customer experiences with FIDO passw...
Hyatt driving innovation and exceptional customer experiences with FIDO passw...FIDO Alliance
 
Extensible Python: Robustness through Addition - PyCon 2024
Extensible Python: Robustness through Addition - PyCon 2024Extensible Python: Robustness through Addition - PyCon 2024
Extensible Python: Robustness through Addition - PyCon 2024Patrick Viafore
 
Google I/O Extended 2024 Warsaw
Google I/O Extended 2024 WarsawGoogle I/O Extended 2024 Warsaw
Google I/O Extended 2024 WarsawGDSC PJATK
 
TrustArc Webinar - Unified Trust Center for Privacy, Security, Compliance, an...
TrustArc Webinar - Unified Trust Center for Privacy, Security, Compliance, an...TrustArc Webinar - Unified Trust Center for Privacy, Security, Compliance, an...
TrustArc Webinar - Unified Trust Center for Privacy, Security, Compliance, an...TrustArc
 
ADP Passwordless Journey Case Study.pptx
ADP Passwordless Journey Case Study.pptxADP Passwordless Journey Case Study.pptx
ADP Passwordless Journey Case Study.pptxFIDO Alliance
 
Continuing Bonds Through AI: A Hermeneutic Reflection on Thanabots
Continuing Bonds Through AI: A Hermeneutic Reflection on ThanabotsContinuing Bonds Through AI: A Hermeneutic Reflection on Thanabots
Continuing Bonds Through AI: A Hermeneutic Reflection on ThanabotsLeah Henrickson
 
Event-Driven Architecture Masterclass: Integrating Distributed Data Stores Ac...
Event-Driven Architecture Masterclass: Integrating Distributed Data Stores Ac...Event-Driven Architecture Masterclass: Integrating Distributed Data Stores Ac...
Event-Driven Architecture Masterclass: Integrating Distributed Data Stores Ac...ScyllaDB
 
Portal Kombat : extension du réseau de propagande russe
Portal Kombat : extension du réseau de propagande russePortal Kombat : extension du réseau de propagande russe
Portal Kombat : extension du réseau de propagande russe中 央社
 
Design Guidelines for Passkeys 2024.pptx
Design Guidelines for Passkeys 2024.pptxDesign Guidelines for Passkeys 2024.pptx
Design Guidelines for Passkeys 2024.pptxFIDO Alliance
 
Tales from a Passkey Provider Progress from Awareness to Implementation.pptx
Tales from a Passkey Provider  Progress from Awareness to Implementation.pptxTales from a Passkey Provider  Progress from Awareness to Implementation.pptx
Tales from a Passkey Provider Progress from Awareness to Implementation.pptxFIDO Alliance
 
2024 May Patch Tuesday
2024 May Patch Tuesday2024 May Patch Tuesday
2024 May Patch TuesdayIvanti
 
Human Expert Website Manual WCAG 2.0 2.1 2.2 Audit - Digital Accessibility Au...
Human Expert Website Manual WCAG 2.0 2.1 2.2 Audit - Digital Accessibility Au...Human Expert Website Manual WCAG 2.0 2.1 2.2 Audit - Digital Accessibility Au...
Human Expert Website Manual WCAG 2.0 2.1 2.2 Audit - Digital Accessibility Au...Skynet Technologies
 
Using IESVE for Room Loads Analysis - UK & Ireland
Using IESVE for Room Loads Analysis - UK & IrelandUsing IESVE for Room Loads Analysis - UK & Ireland
Using IESVE for Room Loads Analysis - UK & IrelandIES VE
 
Simplified FDO Manufacturing Flow with TPMs _ Liam at Infineon.pdf
Simplified FDO Manufacturing Flow with TPMs _ Liam at Infineon.pdfSimplified FDO Manufacturing Flow with TPMs _ Liam at Infineon.pdf
Simplified FDO Manufacturing Flow with TPMs _ Liam at Infineon.pdfFIDO Alliance
 
State of the Smart Building Startup Landscape 2024!
State of the Smart Building Startup Landscape 2024!State of the Smart Building Startup Landscape 2024!
State of the Smart Building Startup Landscape 2024!Memoori
 
How Red Hat Uses FDO in Device Lifecycle _ Costin and Vitaliy at Red Hat.pdf
How Red Hat Uses FDO in Device Lifecycle _ Costin and Vitaliy at Red Hat.pdfHow Red Hat Uses FDO in Device Lifecycle _ Costin and Vitaliy at Red Hat.pdf
How Red Hat Uses FDO in Device Lifecycle _ Costin and Vitaliy at Red Hat.pdfFIDO Alliance
 
Generative AI Use Cases and Applications.pdf
Generative AI Use Cases and Applications.pdfGenerative AI Use Cases and Applications.pdf
Generative AI Use Cases and Applications.pdfalexjohnson7307
 

Último (20)

Design and Development of a Provenance Capture Platform for Data Science
Design and Development of a Provenance Capture Platform for Data ScienceDesign and Development of a Provenance Capture Platform for Data Science
Design and Development of a Provenance Capture Platform for Data Science
 
Collecting & Temporal Analysis of Behavioral Web Data - Tales From The Inside
Collecting & Temporal Analysis of Behavioral Web Data - Tales From The InsideCollecting & Temporal Analysis of Behavioral Web Data - Tales From The Inside
Collecting & Temporal Analysis of Behavioral Web Data - Tales From The Inside
 
Linux Foundation Edge _ Overview of FDO Software Components _ Randy at Intel.pdf
Linux Foundation Edge _ Overview of FDO Software Components _ Randy at Intel.pdfLinux Foundation Edge _ Overview of FDO Software Components _ Randy at Intel.pdf
Linux Foundation Edge _ Overview of FDO Software Components _ Randy at Intel.pdf
 
Hyatt driving innovation and exceptional customer experiences with FIDO passw...
Hyatt driving innovation and exceptional customer experiences with FIDO passw...Hyatt driving innovation and exceptional customer experiences with FIDO passw...
Hyatt driving innovation and exceptional customer experiences with FIDO passw...
 
Extensible Python: Robustness through Addition - PyCon 2024
Extensible Python: Robustness through Addition - PyCon 2024Extensible Python: Robustness through Addition - PyCon 2024
Extensible Python: Robustness through Addition - PyCon 2024
 
Google I/O Extended 2024 Warsaw
Google I/O Extended 2024 WarsawGoogle I/O Extended 2024 Warsaw
Google I/O Extended 2024 Warsaw
 
TrustArc Webinar - Unified Trust Center for Privacy, Security, Compliance, an...
TrustArc Webinar - Unified Trust Center for Privacy, Security, Compliance, an...TrustArc Webinar - Unified Trust Center for Privacy, Security, Compliance, an...
TrustArc Webinar - Unified Trust Center for Privacy, Security, Compliance, an...
 
ADP Passwordless Journey Case Study.pptx
ADP Passwordless Journey Case Study.pptxADP Passwordless Journey Case Study.pptx
ADP Passwordless Journey Case Study.pptx
 
Continuing Bonds Through AI: A Hermeneutic Reflection on Thanabots
Continuing Bonds Through AI: A Hermeneutic Reflection on ThanabotsContinuing Bonds Through AI: A Hermeneutic Reflection on Thanabots
Continuing Bonds Through AI: A Hermeneutic Reflection on Thanabots
 
Event-Driven Architecture Masterclass: Integrating Distributed Data Stores Ac...
Event-Driven Architecture Masterclass: Integrating Distributed Data Stores Ac...Event-Driven Architecture Masterclass: Integrating Distributed Data Stores Ac...
Event-Driven Architecture Masterclass: Integrating Distributed Data Stores Ac...
 
Portal Kombat : extension du réseau de propagande russe
Portal Kombat : extension du réseau de propagande russePortal Kombat : extension du réseau de propagande russe
Portal Kombat : extension du réseau de propagande russe
 
Design Guidelines for Passkeys 2024.pptx
Design Guidelines for Passkeys 2024.pptxDesign Guidelines for Passkeys 2024.pptx
Design Guidelines for Passkeys 2024.pptx
 
Tales from a Passkey Provider Progress from Awareness to Implementation.pptx
Tales from a Passkey Provider  Progress from Awareness to Implementation.pptxTales from a Passkey Provider  Progress from Awareness to Implementation.pptx
Tales from a Passkey Provider Progress from Awareness to Implementation.pptx
 
2024 May Patch Tuesday
2024 May Patch Tuesday2024 May Patch Tuesday
2024 May Patch Tuesday
 
Human Expert Website Manual WCAG 2.0 2.1 2.2 Audit - Digital Accessibility Au...
Human Expert Website Manual WCAG 2.0 2.1 2.2 Audit - Digital Accessibility Au...Human Expert Website Manual WCAG 2.0 2.1 2.2 Audit - Digital Accessibility Au...
Human Expert Website Manual WCAG 2.0 2.1 2.2 Audit - Digital Accessibility Au...
 
Using IESVE for Room Loads Analysis - UK & Ireland
Using IESVE for Room Loads Analysis - UK & IrelandUsing IESVE for Room Loads Analysis - UK & Ireland
Using IESVE for Room Loads Analysis - UK & Ireland
 
Simplified FDO Manufacturing Flow with TPMs _ Liam at Infineon.pdf
Simplified FDO Manufacturing Flow with TPMs _ Liam at Infineon.pdfSimplified FDO Manufacturing Flow with TPMs _ Liam at Infineon.pdf
Simplified FDO Manufacturing Flow with TPMs _ Liam at Infineon.pdf
 
State of the Smart Building Startup Landscape 2024!
State of the Smart Building Startup Landscape 2024!State of the Smart Building Startup Landscape 2024!
State of the Smart Building Startup Landscape 2024!
 
How Red Hat Uses FDO in Device Lifecycle _ Costin and Vitaliy at Red Hat.pdf
How Red Hat Uses FDO in Device Lifecycle _ Costin and Vitaliy at Red Hat.pdfHow Red Hat Uses FDO in Device Lifecycle _ Costin and Vitaliy at Red Hat.pdf
How Red Hat Uses FDO in Device Lifecycle _ Costin and Vitaliy at Red Hat.pdf
 
Generative AI Use Cases and Applications.pdf
Generative AI Use Cases and Applications.pdfGenerative AI Use Cases and Applications.pdf
Generative AI Use Cases and Applications.pdf
 

Measure and Manage Flow in Practice

  • 1. Measure and Manage Flow in Practice by Zsolt Fabok 2012.10.18 Broke the WIP limit TWICE Still on the team @ZsoltFabok #lkfr12 http://zsoltfabok.com/ http://lkfr.org/
  • 2. 5 Stories from the life of a team by using real application and data The collected data is the courtesy of Digital Natives
  • 3. #1 Too many open items
  • 4. Visualize the situation with Cumulative Flow Diagram WIP point of observation solved
  • 5. It offers more than just the WIP... number of work items The Cumulative Flow Diagram Done Started Queued backlog WIP cycle time lead time time
  • 6. The simplest way of collecting data: Q 3 4 1 2 D ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~
  • 7. A quick detour: CFD doesn’t say too much about the throughput
  • 8. /* detour */ work item WIP lead time time
  • 9. 2 /* detour */ work item WIP lead time throughput
  • 10. 2 2 /* detour */ work item
  • 11. /* detour */ Let’s add more people to the project so that “things speed up”!
  • 12. 2 2 3 /* detour */ work item coordination + communication cost
  • 13. Actually, “things slow down”, so it was not a good idea (solve the right problem instead - systems thinking). End of the detour.
  • 14. #2 It takes too much time to deliver
  • 16. CFD was not much help here... lead time ...because we didn’t know much about the nature of the lead time
  • 17. Distribution of lead times count 15 13 10 8 5 3 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 22 28 33 56 days average *Calculation of medians is a popular technique in summary statistics and median* summarizing statistical data, since it is simple to understand and easy to calculate, while also giving a measure that is more robust in the presence of outlier values
  • 18. Some examples of work items with 8-day lead time time spent on time spent waiting implementation (hours) % (hours) 1 63 98 7 57 90 2 62 97 2 62 97 3 61 96
  • 19.
  • 20. 95% of the lead time was spent on waiting
  • 21. Distribution of lead times Before After count count 15 15 13 13 10 10 8 8 5 5 3 3 0 0 days 1 4 7 10 13 16 33 days 1 3 5 7 9 11 13 22 average median
  • 22. #3 Still too many open work items
  • 23. How many times the item has been rejected
  • 24. Number of rejected work items count 15 11 8 4 0 week 31-32 33-34 35-36 37-38
  • 25. Number of rejected work items count 15 11 8 4 0 week 31-32 33-34 35-36 37-38 39-40 42-43 44-45
  • 27. Sales: “I want to know when the new features can hit the market!” Management: “I want to know how much it will cost me!”
  • 28. All the work items we had so far (~20 work items) v ~ v ~ ~ ~ v ~ v ~ v ~ v v ~ ~ ~ ~ ~ v
  • 29. Categorizing them into three groups S v ~ ~ v ~ ~ ~ M ~ ~ ~ v v ~ ~ v v L ~ v ~ v
  • 30. The lead time distribution count 4 3 3 ~ ~ ~ 2 M ~ ~ ~ v v ~ ~ v v 1 1 0 days 1 2 3 4 5 6 7 8 9 12 13 16
  • 31. The lead time distribution count 4 3 3 ~ ~ ~ 2 M ~ ~ ~ v v ~ ~ v v 1 1 0 days 1 2 3 4 5 6 7 8 9 12 13 16 SLA
  • 32. The lead time distribution count 4 3 3 Ex ~ ~ pi ~ 2 M ~ ~ ~ v re v ~ ~ v v 1 d 1 0 days 1 2 3 4 5 6 7 8 9 12 13 16 SLA
  • 33. The spent time distribution count 6 5 4 ~ ~ ~ 3 M ~ ~ ~ v v ~ ~ v v 2 1 0 hours 6 7 8 9 10
  • 34. The spent time distribution count 6 5 4 ~ ~ ~ 3 M ~ ~ ~ v v ~ ~ v v 2 1 0 hours 6 7 8 9 10 SLA
  • 35.
  • 37. #2 Nothing changed. Still the same ratio #1 We decided that we would force ourselves to keep the SLA
  • 39.
  • 40.
  • 41.
  • 42. Final thoughts on measurements
  • 43. “If you can not measure it, you can not improve it.” Lord Kelvin http://en.wikipedia.org/wiki/File:Lord_Kelvin_photograph.jpg
  • 44. “If you start measuring something you start optimizing it, and I know it's the wrong thing to optimize.” Paul Graham http://paulgraham.com/swan.html
  • 45. The Hawthorne effect might influence your measurements
  • 47. 1. We develop software not models (value) 2. Demand first, supply second 3. Observe the system (lead time, throughput) 4. Start measuring, look back if necessary 5. Manage 6. Mind that data expires 7. Goto step 3.
  • 48. Thank you very much for your attention! http://zsoltfabok.com/ @ZsoltFabok