Why should I bother collecting metrics? How can they help me? My CFD is pretty and colourful, but what is it actually trying to tell me?
CFD, control chart, lead time distribution, percentiles...Metrics can be daunting to start with but if you know how to interpret them they can drive continuous improvement and forecast the future and take your Kanban system to the next level! It’s much easier than you think, no need for complex maths or expensive software.
At Sky Network Services a few teams are using Kanban and metrics. In this talk I’ll share our experience: what metrics we use, how we use each one of them, what little data we collect to get a whole lot of value, what pitfalls we encountered.
Downloads
Powerpoint: https://goo.gl/4CkKJd
PDF: https://goo.gl/VDW93U
Presentation by Em Campbell-Pretty & Adrienne Wilson at the Global SAFe Summit 2021
Mob Programming thought leader, Woody Zuill, suggests that instead of always focusing on solving problems, we also take the time to notice the things that are going well and amplify them, thereby "turning up the good". When it comes to SAFe Dean Leffingwell perhaps said it best: "There is no magic in SAFe . . . except maybe for PI Planning." I suspect most of you agree that PI Planning is the magic in SAFe. There is nothing quite like the energy created by bringing a group of 100+ people together to build a collaborative plan over a couple of days every 10 to 12 weeks. So what would it mean to "turn up the good in PI Planning"? If we focused on what is good and what we want more of, would we get more magic?! For Em and Adrienne, the answer is a resounding "Yes!" In this session, they will take the "The Facilitator’s Guide to PI Planning" and illustrate how turning up the good can bring your PI Planning magic to the next level.
Most teams need to answer questions like “When will it be done? What can I get by date X?”. However, common estimation approaches often fail to give us the predictability we want, and tend to introduce bad behaviours like hard deadlines and hiding uncertainty.
In this session, I’ll show you how, step by step and with real life examples, my team uses their historical data and metrics to forecast the future and answer these questions with confidence.
Download slides at: http://bit.ly/2pD9rfQ
Book discount link: http://leanpub.com/metricsforbusinessdecisions/c/MATTIA20-BZXib2F
An introduction to the Spotify matrix model including recent updates we've made as we have continued to grow. I presented this talk at the Spark the Change Conference in London, UK on July 1, 2015.
This document provides an overview of a training course on scaling Scrum. The course will cover Scrum frameworks, roles, events, execution, leadership patterns, and strategies for scaling Scrum across multiple teams. It will involve 4 interactive remote sessions with homework assignments and coaching. Key aspects of scaling Scrum that will be discussed include using a scale-free architecture, defining roles like the Chief Product Owner and Scrum of Scrums Master, and holding events like the Scaled Daily Scrum and Meta Scrum. Patterns like stable teams and mitosis value streams will also be presented as ways to achieve hyper-productivity when scaling Scrum.
Team data and dashboards can be misused and cause more pain than results. Having the team run blind to its historical data though is often worse, with solely opinions and gut-feel driving process change. Helping your teams see and understand a holistic balance of their data will give your coaching advice context and encourage team constant improvement through experiments and reflection.
Coaching dashboards are about balancing trade-offs. Trading something your team is great at for something they want (or need) to improve. Having the team complete the feedback loop and confirm than an experiment had the intended impact, will process improvement be continuous and sustainable.
This presentation shows how to expose data to teams in order for them to retrospect productively, determine if a process experiment is panning out as expected, and to vigorously explore process change opportunities. Recent research shows strong relationships of certain metrics to process and practices, and this session demonstrates how these metrics have and can be tied to timely coaching advice.
The real-world dashboards demonstrated in this session show most common problems and how to avoid them with before and after shots and quotes from the teams impacted by them.
In this session you will –
- Learn how you can not only gather data, but use it to improve the process, with examples!
- Learn how your can tie data insights to coaching advice (data driven coaching)
- Learn how you can detect, predict and avoid data gaming and dashboard misuse
- Learn from my mistakes, and mistakes I’ve seen others with real examples of Agile coaching dashboards (good and bad)
The Five Phases of Agile Maturity (Part 2): Phase 3 and 4
The journey to agile maturity is neither fast nor straightforward. What do you need to know? What challenges might you face? Which tools will best meet your organization where it's at?
Explore what you should expect to see across the five phases of Agile maturity. In part 2 of this series, we will focus on Phase 3 and 4. We'll share valuable advice about negotiating the turns, avoiding roadblocks, and enjoying the ride in your agile maturity journey. Plus, we’ll talk about the optimal tools to support you—enterprise product management software, like Atlassian Jira Align.
Learn:
- Common maturity elements of Phase 3 of agile maturity (The Scaling Agile Organization) and Phase 4 of agile maturity (The Agile Enterprise)
- Challenges you may face in your agile maturity journey and how to overcome them
- How Jira Align’s features and functionality can support scaling
Agile metrics: Measure and Improve:
Mattia Battiston (SKY) and David Leach (Reed Online) share their expert views on velocity, agile ROI, reporting and measuring impact.
Sponsored by Wemanity - www.wemanity.com - the agile driving force
The document discusses the role and responsibilities of a Scrum Master. It describes how the Scrum Master facilitates the Scrum process and ceremonies like daily stand-ups, sprint planning and retrospectives. The Scrum Master also helps resolve impediments, enables self-organization of the team and protects the team from external interference. Key responsibilities include enforcing timeboxes, maintaining artifacts and metrics, and guiding the team on Agile best practices. The document provides checklists and guidelines for Scrum Masters to effectively perform their duties.
Agile development works well in small teams. But we encounter problems when Scrum is applied to other teams and the rest of the organisation. Large Scale Scrum (LeSS) can help. This slide deck explains why.
Release wednesdays and the agile release train upload
The document discusses the benefits of using an "Agile Release Train" approach for software development. Some key points:
- An Agile Release Train plans work in train-like themes that are delivered on a regular cadence, such as every 2 weeks. This provides a predictable release rhythm.
- Work is broken into small, independently releasable increments called user stories. If a story is not ready for the current release, it waits for the next one.
- Planning occurs through story mapping epics and stories to future releases. The plan is updated each sprint to reflect progress and priority changes.
- Benefits include clear focus on current work, visibility of priorities and timelines, and
DevOps Transformation: Learnings and Best Practices
The presentation delves into the best practices and approach for DevOps adoption. Understand key aspects of DevOps and how it brings about speed and efficiency in the software development lifecycle
This presentation outlines how Suncorp has adopted Agile scrum and Lean kanban to effectively and efficiently deliver IT Service Management. This presentation was given at the BMC Remedy User Group forums in Sydney & Melbourne, Australia in November 2013.
Delivering value early and often, giving ourselves the best opportunity to beat the competition to market, realize revenue and discover insights that we can use to help us improve.
This document discusses various types of dependencies that can impact agile planning and development if not properly managed. It begins by covering dependencies related to requirements preparation, UI design, and inter-story dependencies that can delay development if not addressed in advance. It then discusses external dependencies on other teams, vendors, or systems that may not follow agile practices. The document provides recommendations for minimizing the impact of dependencies, such as advanced planning, cross-team coordination, and determining if dependencies are truly necessary or if workarounds are possible. The overall message is that dependencies require extra planning efforts to avoid blocking development work or impacting velocity.
DevOps 3rd way is to Improve Continuously and Experiment. lets look at important concepts like devops continuous learning ,kaizen ,improvement kata ,PDCA, and blameless post-mortem
Traditional vs Lean Portfolio Management, Agile PMO & Organisations
This deck showcases how the future can look for organisations as they attempt to scale up agile and lean practices and principles across the entire organisation.
Regardless if we have entered to do project/programme/portfolio work, once onsite I find it is a great way to introduce the wider organisation to the ideas that we use to deliver and how they can support all areas and activities in the organisation.
Key concepts;
- How traditional PMO and organisation are setup
- Legacy mindset for are alive and still driving the majority of portfolio/organisation behaviours
- Comparisons of traditional and agile/lean mindsets
- Principles of agile/lean portfolio/organisation management
- Organisational structure
- Annual vs Incremental funding (Beyond Budgeting)
- Limiting Work in Progress i.e. its only matters how many projects you finish, not start.
- Managing and visualising capability
- Coping with portfolio complexity through experimentation and validated learning
- Removing the concept of projects and focusing on continuous delivery of value
- Benefits of agile/lean portfolio/organisation management
This deck was compiled using referenced materials and the support of David Joyce (@dpjoyce) and Ian Carroll (@caza_no7)
We like the architecture of our applications to revolve around the business logic, not around technical details (and especially not around the database).
In my team at Sky Network Services we use the Clean Architecture and it has given us a great deal of benefits: the business logic is explicit, we are free to change our technical decisions, the app is easy to test, working on it is faster and scalable, it’s hard to do the wrong thing, and many more.
But it comes at a cost, of course. In this talk I’ll tell you the story of our experience with Clean Architecture and give you some tips to get the most out of it.
Example Project
https://github.com/mattia-battiston/clean-architecture-example
Downloads
Online: https://goo.gl/DTxftJ
PDF: https://goo.gl/ZAtdBN
Powerpoint: https://goo.gl/D54wdZ (but you need to install these fonts to see it properly: https://goo.gl/iH8SO5)
Kanban Metrics in practice at Sky Network Services
Why should I bother collecting metrics? How can they help me? My CFD is pretty and colourful, but what is it actually trying to tell me?
CFD, control chart, lead time distribution, percentiles...Metrics can be daunting to start with but if you know how to interpret them they can really take your Kanban system to the next level - drive continuous improvement and forecast the future! It’s much easier than you think, no need for complex maths or expensive software.
At Sky Network Services a few teams are using Kanban and metrics. In this talk I’ll share our experience: what metrics we use, how we use each one of them, what little data we collect to get a whole lot of value, what pitfalls we encountered.
Downloads
Powerpoint: https://goo.gl/19wOjU
PDF: https://goo.gl/AM69MF
Perche’ dovrei raccogliere metriche? Come possono aiutarmi? Il mio CFD e’ molto colorato ma a cosa serve?
CFD, control chart, lead time distribution…Le metriche possono incutere timore ma se sai come interpretarle puoi portare il tuo processo a un nuovo livello!
In questo experience report pieno di esempi pratici vi raccontero’ come il mio team a Sky (Londra) usa Kanban e metriche per:
- guidare il processo di miglioramento continuo
- essere prevedibili, senza bisogno di stime
Downloads
Powerpoint: https://goo.gl/mHg3nx
PDF: https://goo.gl/WFaoW8
CONTINUOUS IMPROVEMENT: HELL ON EARTH? (KATHERINE KIRK) - LKCE13
We all seem to love the term 'continuous improvement' - which is an honourable intention. But in reality 'continuous improvement' can be hell on earth - e.g. to always be 'not good enough'. In fact, some corporations, managers and teams have been known to use this phrase as an excuse for behaving badly. So, how can an honourable intention like continuous improvement create a negative impact, ie. apathy? If so - how do we avoid it? What are other ways of handling this need to consistently overcome challenges in an ever-changing industry? And does 'continuous improvement' have a limit or is it an endless source of success? Using case study examples this talk reflects on what continuous improvement really feels like on the ground and explores how we might want to approach 'getting better' by looking at and drawing from other industries, research, ideas and real-life experiences.
Lean Kanban Central Europe 2014: Beyond VSM: Understanding and Mapping Your P...
The document discusses mapping processes of knowledge discovery beyond traditional value stream mapping (VSM). It notes knowledge discovery processes differ from production processes and provides examples of mapping dominant activities over time. Participants are invited to map examples for their work item types by identifying dominant knowledge-creating activities. Mapping is a social process that can provoke improvement rather than inertia. The document concludes by encouraging readers to try mapping their own processes and sharing stories.
Metrics should be used to learn and enable change in complex systems, not to control or constrain. Short-term metrics can provide information but longer-term perspectives are needed to understand emergent behaviors. It is important to consider unintended consequences and how metrics may influence behaviors in unexpected ways.
This document discusses lead time and how measuring and understanding it can help forecast projects. It defines lead time as the time between one event preceding another. Lead time data can be more useful than just average times since it accounts for variability. The document also discusses using distributions to model different types of work instead of single numbers, as processes are probabilistic rather than deterministic. Measuring lead time and understanding its distribution is important for planning delivery rates and work in process.
Practical lessons learned from our startup growth accelerator, Sprinthack on growth, agile product management and how to integrate this approach to any organisation's way of working.
Scrum vs ScrumAnd vs ScrumBut: Which one are you doing? :: Agile Tour London ...
This document discusses different approaches to implementing Scrum: Scrum, ScrumAnd, and ScrumBut. Scrum follows the framework strictly, ScrumAnd uses Scrum along with additional practices or "addons", and ScrumBut modifies or breaks from parts of the Scrum framework. It provides examples of popular addons for ScrumAnd, such as using XP practices, and popular modifications for ScrumBut, such as having sprints longer than 4 weeks. The document emphasizes that the goal should be to deliver working software, and modifying Scrum is acceptable if done for the right reasons, rather than just calling something Scrum.
The document provides an overview of the Scrum framework, which is an agile process that focuses on delivering high business value in short iterations called sprints. It describes Scrum roles like Product Owner, Scrum Master, and cross-functional team. Key Scrum events include sprint planning, daily stand-ups, sprint reviews, and retrospectives. The product and sprint backlogs are used to track requirements and work in progress. Scrum has been successfully used in various domains for both commercial and internal projects.
Kanban India 2022 - Keynote - Todd Little | Turbocharge your Scrum with KanbanLeanKanbanIndia
Kanban can help turbocharge Scrum by improving performance without overburdening teams. The document discusses challenges with Scrum and how Kanban principles and practices can address them through evolutionary change rather than revolution. It provides examples of how one company, Posit Science, adopted Kanban practices to improve upon their Scrum process over time.
Presentation by Em Campbell-Pretty & Adrienne Wilson at the Global SAFe Summit 2021
Mob Programming thought leader, Woody Zuill, suggests that instead of always focusing on solving problems, we also take the time to notice the things that are going well and amplify them, thereby "turning up the good". When it comes to SAFe Dean Leffingwell perhaps said it best: "There is no magic in SAFe . . . except maybe for PI Planning." I suspect most of you agree that PI Planning is the magic in SAFe. There is nothing quite like the energy created by bringing a group of 100+ people together to build a collaborative plan over a couple of days every 10 to 12 weeks. So what would it mean to "turn up the good in PI Planning"? If we focused on what is good and what we want more of, would we get more magic?! For Em and Adrienne, the answer is a resounding "Yes!" In this session, they will take the "The Facilitator’s Guide to PI Planning" and illustrate how turning up the good can bring your PI Planning magic to the next level.
Most teams need to answer questions like “When will it be done? What can I get by date X?”. However, common estimation approaches often fail to give us the predictability we want, and tend to introduce bad behaviours like hard deadlines and hiding uncertainty.
In this session, I’ll show you how, step by step and with real life examples, my team uses their historical data and metrics to forecast the future and answer these questions with confidence.
Download slides at: http://bit.ly/2pD9rfQ
Book discount link: http://leanpub.com/metricsforbusinessdecisions/c/MATTIA20-BZXib2F
An introduction to the Spotify matrix model including recent updates we've made as we have continued to grow. I presented this talk at the Spark the Change Conference in London, UK on July 1, 2015.
This document provides an overview of a training course on scaling Scrum. The course will cover Scrum frameworks, roles, events, execution, leadership patterns, and strategies for scaling Scrum across multiple teams. It will involve 4 interactive remote sessions with homework assignments and coaching. Key aspects of scaling Scrum that will be discussed include using a scale-free architecture, defining roles like the Chief Product Owner and Scrum of Scrums Master, and holding events like the Scaled Daily Scrum and Meta Scrum. Patterns like stable teams and mitosis value streams will also be presented as ways to achieve hyper-productivity when scaling Scrum.
Data driven coaching - Agile 2016 (troy magennis)Troy Magennis
Team data and dashboards can be misused and cause more pain than results. Having the team run blind to its historical data though is often worse, with solely opinions and gut-feel driving process change. Helping your teams see and understand a holistic balance of their data will give your coaching advice context and encourage team constant improvement through experiments and reflection.
Coaching dashboards are about balancing trade-offs. Trading something your team is great at for something they want (or need) to improve. Having the team complete the feedback loop and confirm than an experiment had the intended impact, will process improvement be continuous and sustainable.
This presentation shows how to expose data to teams in order for them to retrospect productively, determine if a process experiment is panning out as expected, and to vigorously explore process change opportunities. Recent research shows strong relationships of certain metrics to process and practices, and this session demonstrates how these metrics have and can be tied to timely coaching advice.
The real-world dashboards demonstrated in this session show most common problems and how to avoid them with before and after shots and quotes from the teams impacted by them.
In this session you will –
- Learn how you can not only gather data, but use it to improve the process, with examples!
- Learn how your can tie data insights to coaching advice (data driven coaching)
- Learn how you can detect, predict and avoid data gaming and dashboard misuse
- Learn from my mistakes, and mistakes I’ve seen others with real examples of Agile coaching dashboards (good and bad)
The Five Phases of Agile Maturity (Part 2): Phase 3 and 4Cprime
The journey to agile maturity is neither fast nor straightforward. What do you need to know? What challenges might you face? Which tools will best meet your organization where it's at?
Explore what you should expect to see across the five phases of Agile maturity. In part 2 of this series, we will focus on Phase 3 and 4. We'll share valuable advice about negotiating the turns, avoiding roadblocks, and enjoying the ride in your agile maturity journey. Plus, we’ll talk about the optimal tools to support you—enterprise product management software, like Atlassian Jira Align.
Learn:
- Common maturity elements of Phase 3 of agile maturity (The Scaling Agile Organization) and Phase 4 of agile maturity (The Agile Enterprise)
- Challenges you may face in your agile maturity journey and how to overcome them
- How Jira Align’s features and functionality can support scaling
Agile metrics: Measure and Improve:
Mattia Battiston (SKY) and David Leach (Reed Online) share their expert views on velocity, agile ROI, reporting and measuring impact.
Sponsored by Wemanity - www.wemanity.com - the agile driving force
The document discusses the role and responsibilities of a Scrum Master. It describes how the Scrum Master facilitates the Scrum process and ceremonies like daily stand-ups, sprint planning and retrospectives. The Scrum Master also helps resolve impediments, enables self-organization of the team and protects the team from external interference. Key responsibilities include enforcing timeboxes, maintaining artifacts and metrics, and guiding the team on Agile best practices. The document provides checklists and guidelines for Scrum Masters to effectively perform their duties.
Agile development works well in small teams. But we encounter problems when Scrum is applied to other teams and the rest of the organisation. Large Scale Scrum (LeSS) can help. This slide deck explains why.
Release wednesdays and the agile release train uploadChris Smith
The document discusses the benefits of using an "Agile Release Train" approach for software development. Some key points:
- An Agile Release Train plans work in train-like themes that are delivered on a regular cadence, such as every 2 weeks. This provides a predictable release rhythm.
- Work is broken into small, independently releasable increments called user stories. If a story is not ready for the current release, it waits for the next one.
- Planning occurs through story mapping epics and stories to future releases. The plan is updated each sprint to reflect progress and priority changes.
- Benefits include clear focus on current work, visibility of priorities and timelines, and
DevOps Transformation: Learnings and Best PracticesQBurst
The presentation delves into the best practices and approach for DevOps adoption. Understand key aspects of DevOps and how it brings about speed and efficiency in the software development lifecycle
This presentation outlines how Suncorp has adopted Agile scrum and Lean kanban to effectively and efficiently deliver IT Service Management. This presentation was given at the BMC Remedy User Group forums in Sydney & Melbourne, Australia in November 2013.
Delivering value early and often, giving ourselves the best opportunity to beat the competition to market, realize revenue and discover insights that we can use to help us improve.
This document discusses various types of dependencies that can impact agile planning and development if not properly managed. It begins by covering dependencies related to requirements preparation, UI design, and inter-story dependencies that can delay development if not addressed in advance. It then discusses external dependencies on other teams, vendors, or systems that may not follow agile practices. The document provides recommendations for minimizing the impact of dependencies, such as advanced planning, cross-team coordination, and determining if dependencies are truly necessary or if workarounds are possible. The overall message is that dependencies require extra planning efforts to avoid blocking development work or impacting velocity.
DevOps 3rd way is to Improve Continuously and Experiment. lets look at important concepts like devops continuous learning ,kaizen ,improvement kata ,PDCA, and blameless post-mortem
Traditional vs Lean Portfolio Management, Agile PMO & OrganisationsBarry O'Reilly
This deck showcases how the future can look for organisations as they attempt to scale up agile and lean practices and principles across the entire organisation.
Regardless if we have entered to do project/programme/portfolio work, once onsite I find it is a great way to introduce the wider organisation to the ideas that we use to deliver and how they can support all areas and activities in the organisation.
Key concepts;
- How traditional PMO and organisation are setup
- Legacy mindset for are alive and still driving the majority of portfolio/organisation behaviours
- Comparisons of traditional and agile/lean mindsets
- Principles of agile/lean portfolio/organisation management
- Organisational structure
- Annual vs Incremental funding (Beyond Budgeting)
- Limiting Work in Progress i.e. its only matters how many projects you finish, not start.
- Managing and visualising capability
- Coping with portfolio complexity through experimentation and validated learning
- Removing the concept of projects and focusing on continuous delivery of value
- Benefits of agile/lean portfolio/organisation management
This deck was compiled using referenced materials and the support of David Joyce (@dpjoyce) and Ian Carroll (@caza_no7)
We like the architecture of our applications to revolve around the business logic, not around technical details (and especially not around the database).
In my team at Sky Network Services we use the Clean Architecture and it has given us a great deal of benefits: the business logic is explicit, we are free to change our technical decisions, the app is easy to test, working on it is faster and scalable, it’s hard to do the wrong thing, and many more.
But it comes at a cost, of course. In this talk I’ll tell you the story of our experience with Clean Architecture and give you some tips to get the most out of it.
Example Project
https://github.com/mattia-battiston/clean-architecture-example
Downloads
Online: https://goo.gl/DTxftJ
PDF: https://goo.gl/ZAtdBN
Powerpoint: https://goo.gl/D54wdZ (but you need to install these fonts to see it properly: https://goo.gl/iH8SO5)
Kanban Metrics in practice at Sky Network ServicesMattia Battiston
Why should I bother collecting metrics? How can they help me? My CFD is pretty and colourful, but what is it actually trying to tell me?
CFD, control chart, lead time distribution, percentiles...Metrics can be daunting to start with but if you know how to interpret them they can really take your Kanban system to the next level - drive continuous improvement and forecast the future! It’s much easier than you think, no need for complex maths or expensive software.
At Sky Network Services a few teams are using Kanban and metrics. In this talk I’ll share our experience: what metrics we use, how we use each one of them, what little data we collect to get a whole lot of value, what pitfalls we encountered.
Downloads
Powerpoint: https://goo.gl/19wOjU
PDF: https://goo.gl/AM69MF
Perche’ dovrei raccogliere metriche? Come possono aiutarmi? Il mio CFD e’ molto colorato ma a cosa serve?
CFD, control chart, lead time distribution…Le metriche possono incutere timore ma se sai come interpretarle puoi portare il tuo processo a un nuovo livello!
In questo experience report pieno di esempi pratici vi raccontero’ come il mio team a Sky (Londra) usa Kanban e metriche per:
- guidare il processo di miglioramento continuo
- essere prevedibili, senza bisogno di stime
Downloads
Powerpoint: https://goo.gl/mHg3nx
PDF: https://goo.gl/WFaoW8
We all seem to love the term 'continuous improvement' - which is an honourable intention. But in reality 'continuous improvement' can be hell on earth - e.g. to always be 'not good enough'. In fact, some corporations, managers and teams have been known to use this phrase as an excuse for behaving badly. So, how can an honourable intention like continuous improvement create a negative impact, ie. apathy? If so - how do we avoid it? What are other ways of handling this need to consistently overcome challenges in an ever-changing industry? And does 'continuous improvement' have a limit or is it an endless source of success? Using case study examples this talk reflects on what continuous improvement really feels like on the ground and explores how we might want to approach 'getting better' by looking at and drawing from other industries, research, ideas and real-life experiences.
Lean Kanban Central Europe 2014: Beyond VSM: Understanding and Mapping Your P...azheglov
The document discusses mapping processes of knowledge discovery beyond traditional value stream mapping (VSM). It notes knowledge discovery processes differ from production processes and provides examples of mapping dominant activities over time. Participants are invited to map examples for their work item types by identifying dominant knowledge-creating activities. Mapping is a social process that can provoke improvement rather than inertia. The document concludes by encouraging readers to try mapping their own processes and sharing stories.
Metrics should be used to learn and enable change in complex systems, not to control or constrain. Short-term metrics can provide information but longer-term perspectives are needed to understand emergent behaviors. It is important to consider unintended consequences and how metrics may influence behaviors in unexpected ways.
This document discusses lead time and how measuring and understanding it can help forecast projects. It defines lead time as the time between one event preceding another. Lead time data can be more useful than just average times since it accounts for variability. The document also discusses using distributions to model different types of work instead of single numbers, as processes are probabilistic rather than deterministic. Measuring lead time and understanding its distribution is important for planning delivery rates and work in process.
Practical lessons learned from our startup growth accelerator, Sprinthack on growth, agile product management and how to integrate this approach to any organisation's way of working.
Scrum vs ScrumAnd vs ScrumBut: Which one are you doing? :: Agile Tour London ...Pedro Gustavo Torres
This document discusses different approaches to implementing Scrum: Scrum, ScrumAnd, and ScrumBut. Scrum follows the framework strictly, ScrumAnd uses Scrum along with additional practices or "addons", and ScrumBut modifies or breaks from parts of the Scrum framework. It provides examples of popular addons for ScrumAnd, such as using XP practices, and popular modifications for ScrumBut, such as having sprints longer than 4 weeks. The document emphasizes that the goal should be to deliver working software, and modifying Scrum is acceptable if done for the right reasons, rather than just calling something Scrum.
This presentation deals with Learned Helplessness. Optimism and Pessimism as causes of Learned Helplessness. It also deals with the health and the social impacts of learned helplessness.
Servant leadership un neutered 2016 09 21Mike Burrows
This document discusses the concept of servant leadership. It begins by presenting questions about the definition of servant leadership, such as whether it means "leadership for nice guys" or "leading by example". It then discusses Robert Greenleaf's conception of servant leadership, which emphasizes serving people, teams and the organization in a purposeful pursuit of their needs. The document outlines six strategies for Lean-Agile transformation based on servant leadership: skills-first, needs-first, team-first, improvement-driven, alignment-driven, and purpose-driven. Each strategy is explained briefly. The document concludes by inviting the audience to learn more about servant leadership and Lean-Agile transformation.
The document outlines the STATIK framework for implementing Kanban, which involves understanding the system's purpose, sources of dissatisfaction, demand and capability, knowledge discovery processes, classes of service, Kanban systems design, and rollout. It emphasizes understanding multiple perspectives, balancing demand and capability, discovering needs through collaboration and validation, recognizing different expectations through classes of service, and designing systems to pursue sustained evolutionary change through Kanban values like transparency and respect.
The document discusses Kanban's value system and core practices. It outlines three "agendas" of Kanban - survivability, self-organization and better decision making, and collaboration. For survivability, the key values are understanding, agreement, and respect. Self-organization and better decision making values transparency and balance. Collaboration values customer focus, flow, and leadership. The document explores how these values and agendas can be applied within organizations.
Creating Resilient, Robust, & Antifragile OrganizationsDavid Anderson
What makes organizations fragile? What makes them resilient and able to bounce back from failure? How can they be robust to avoid major failures? However, what really differentiates business that are "built to last" is that they can mutate & evolve in response to change technology, market, economic and political conditions. What actions can leaders take to create resilient, robust & antifragile businesses and how can the Kanban Method help with that?
The document describes an exercise for mapping the foundational principles and core practices of Kanban to its nine values. It includes instructions to map the four principles to the values in a bottom-up order, then map the six practices to the values with hints that some values map to multiple practices. It then provides the principles, practices and values mapping for completion of the exercise. Participants are asked to reflect on values that resonate personally or are important/challenging for their organization.
Enterprise Services Planning - Effective Middle ManagementDavid Anderson
This may be the first time, I really explained Enterprise Services Planning (ESP) effectively. Many people in the audience seemed to understand for the first time.
Key Note - PMI Congress Poland - The Role of the Project Manager with KanbanDavid Anderson
Kanban systems are used to improve service delivery in creative knowledge work. This presentation looks at how use of Kanban affects the role of a project manager. It takes a look at how planning, scheduling, estimating, issue and risk management are affected by adopting Kanban.
STATIK is a systems thinking approach to implementing Kanban developed by Mike Burrows. It is a repeatable process with 6 steps: 1) understand sources of dissatisfaction, 2) analyze demand and capability, 3) model the knowledge discovery process, 4) discover classes of service, 5) design Kanban systems, and 6) roll out. The document discusses each step in STATIK and how it can be used both to initially implement Kanban and to reinvigorate existing Kanban implementations.
Creating Space to Be Awesome -- Offentlig ChefMeri Williams
The document discusses creating an inclusive work environment where people feel they have purpose, autonomy, opportunities for mastery, and a sense of belonging. It argues traditional management is ineffective and that the best performing teams have employees who feel their work is important, have a say in their responsibilities, and are able to continually learn and improve. It emphasizes the importance of understanding employee motivation and addressing factors like implicit bias that can prevent some groups from feeling they can succeed. The overall message is that managers should focus on cultivating an environment where everyone, regardless of their background, feels they have space to do their best work.
Agile estimation involves estimating at multiple levels:
1) Product roadmap uses high-level estimates like 12 months to sequence features by business value and cost.
2) Release planning estimates in story points to prioritize features for the next 4 iterations based on team velocity.
3) Iteration planning breaks down epics and estimates user stories in story points or t-shirt sizes for the current sprint.
Relative estimation methods like planning poker and fibonacci sequence are preferred over absolute estimates due to uncertainty in large projects. Regular refinement of estimates is important in agile.
We’re agile, so we don’t have to estimate and have no deadlines, right? Wrong! This session will review the problem with estimations in projects today and then give an overview of the concept of agile estimation and the notion of re-estimation. We’ll learn about user stories, story points, team velocity, and how to apply them all to estimation and iterative re-estimation. We will take a look at the cone of uncertainty and how to use it to your advantage. We’ll then take a look at the tools we will use for Agile Estimation, including planning poker, Visual Studio TFS and much more.
Speak To The Business! Agile Metrics That Inform Rather Confuse the Businesstroytuttle
Given to PMI KC Professional Development Days 2014 Conference.
In this session, we will investigate the challenges with the popular Agile planning and reporting concepts like story points, planning poker, and average velocity. We will explore some practical alternative planning and reporting practices that the business can understand. And we will look at metrics that are less of an abstraction from reality and more actionable by teams and management.
I love the smell of data in the morning (getting started with data science) ...Troy Magennis
Data Science 101 for software development. I know it misses the purist view of Data Science, but this is intended to get you started! First presented at Agile 2017 in Florida.
Have your Agile practices become stale or redundant? Does it feel like your team is just going through the motions? Have team members asked to discontinue “critical Agile practices” and ceremonies?
In Lean product development, the minimum viable product or MVP, is defined as the product with the highest return on investment versus risk. It’s a strategy to avoid building products that customers don’t need or want by maximizing our learning of what is valuable to the customer.
Agile is typically learned through exposure to a series of Agile practices, a recipe of sorts. But what if that recipe goes beyond minimal? Have we replaced heavy waterfall process with heavy Agile process?
This session will interrogate the thinking behind some of the Agile sacred cows like detailed sprint planning, detailed release planning, and even some popular estimation techniques. We will try to identify what is truly needed to be Agile, based on needs instead of prescribed recipes. What is minimally sufficient to start realizing the benefits of Agile?
What is your MVA? It might be different than you think!
Agile estimating 12112013 - Agile KC Dec 2013molsonkc
Story point estimating using the Fibonacci sequence is the most common agile estimating technique. It provides better and more accurate estimates than hourly estimates with less variation. Story points also cut estimation time by 80% allowing teams to estimate and track work more. Regularly measuring a team's velocity enables accurate forecasting of schedules and costs for a release. Agile estimating with story points is more efficient than traditional techniques.
When will it be done? (Lean Agile Forecasting)Rodrigo Vieira
This document summarizes key points from the book "When will it be done?" by Daniel Vacanti. It discusses how traditional software estimating techniques often fail and presents better approaches. These include using data from past cycle times to generate forecasts in the form of percentiles, tracking item ages, and applying Monte Carlo simulations to forecasts for multiple items. Keeping work in progress limited and flows smooth helps improve predictability. Focusing standups and retrospectives on queue times, scatterplots, and histograms can help teams reduce cycle times and make more reliable forecasts. The document emphasizes that reliable forecasts require a predictable process with minimal waste.
Practical Agile Analytics: Reduce uncertainty and stop making such a big deal...Steven J. Peters, PhD
This presentation analyzes user story size estimate and task hour data from several Agile teams to understand the impact of uncertainty on estimates. The analysis shows there is significant variation in actual task hours for each story size, indicating estimates are often too high or low. This variation is caused by uncertainty about the work needed. Reducing this uncertainty, such as by splitting stories smaller, would improve estimation accuracy. The key is gaining a better understanding of the work scope for each story.
This document discusses the NoEstimates approach to software development. It begins by defining estimates and explaining that NoEstimates is about minimizing estimates rather than eliminating them entirely. The main goals of NoEstimates are to evaluate progress in a concrete way and force teams to slice work into smaller stories. Key benefits include being faster, easier iteration planning, and minimal time spent estimating. Progress is measured by the number of "Running Tested Stories" completed. The document also covers challenges, case studies, and debates around using NoEstimates versus more traditional estimating approaches.
The document discusses using feature points for agile release planning. It defines feature points and how they can be used to estimate user stories, features, and epics at different levels of a project. The key points are: feature points provide relative estimates independent of time units; epics are estimated by POs and architects, features by team leads, and stories by scrum teams; velocity is tracked in feature points to predict sprint and release completion; and principles for agile estimation emphasize basing estimates on facts, estimating often and small chunks, and communicating assumptions.
Machine Learning Project - Default credit card clients Vatsal N Shah
- The model we built here will use all possible factors to predict data on customers to find who are defaulters and non‐defaulters next month.
- The goal is to find the whether the clients are able to pay their next month credit amount.
- Identify some potential customers for the bank who can settle their credit balance.
- To determine if their customers could make the credit card payments on‐time.
- Default is the failure to pay interest or principal on a loan or credit card payment.
Story Points considered harmful – a new look at estimation techniquesVasco Duarte
The document discusses alternatives to using story points for estimating work in agile software development projects. It analyzes claims made about the benefits of story points and finds limited evidence to support these claims. The document proposes using the number of completed backlog items per sprint as a simpler and more efficient metric for planning, tracking progress, and estimating release dates. Correlation data from multiple projects shows story points and number of items completed tend to provide similar information about the amount of work done.
The document discusses using metrics to improve decision making for software projects, explaining that metrics should be focused on outcomes that teams can influence and that predict future performance, and provides examples of different types of metrics and modeling techniques that can help teams forecast delivery and make better decisions.
When will it be done? - Agile Camp ChicagoDanilo Garcia
The document discusses using systems thinking and Little's Law to increase predictability in software development projects. It recommends defining the development system, measuring cycle time, work in progress, and throughput to limit work in progress. Monte Carlo simulations can then be used based on backlog size, growth rate, and work types to understand forecasting possibilities and repeat measurements daily for predictability. The goal is to stabilize the system to enable meaningful forecasts.
How to use data to improve software development teams and processes. Presented at the Prairie Dev Con Deliver conference October 2016. http://www.prdcdeliver.com
Data Driven Decision Making - Chapter 1. Team PerformanceRevengg
This document discusses how burn down charts can provide insights into team performance over time. It analyzes metrics like work completed, work remaining, and work added on a quarterly release burn down chart. These metrics can indicate things like whether the team's velocity is improving, if there is scope creep, and how many sprints are forecast to complete the remaining work. The document also outlines some key team performance indicators that can be measured, like velocity, debt, scope creep, and forecast, to help understand the team's progress and identify areas for improvement.
This document discusses establishing agile financial operations. It recommends aligning organizational objectives and key results before defining deliverables, establishing initial budgets, and building prioritized backlogs. It also suggests modeling value streams to create forecasts and develop release plans. An agile financial hierarchy is proposed where finance functions reside with agile teams and project managers use agile earned value management to track results against compliance requirements. Important changes include early agile team participation in budgeting and compliance engagement from the start.
7. space the estimation aid for bringing agile delivery predictability - p...Nesma
This document introduces SPACE, an estimation tool to help improve agile delivery predictability. It discusses current challenges with agile estimation like lack of repeatability and comparability. SPACE provides a standardized sizing methodology, collects data from past deliveries, and suggests story points to aid planning poker sessions. A case study shows how SPACE helped a company establish KPIs to track agile performance, improve productivity over 3 years, and enhance estimation accuracy. In the end, SPACE is presented as a tool to add value to agile practices by helping bring structure and data to planning sessions.
The document provides an overview of key concepts in the Scrum agile methodology. It defines Scrum as an iterative and incremental process combining elements of iterative and incremental development models. It then describes 10 important Scrum terminology: Scrum Team, Sprint, Product Owner, Scrum Master, User Story, Epics, Product Backlog, Sprint Backlog, Story Points, and Burn Down Chart. The document is intended to help software developers and testers understand the Scrum methodology.
Similar to Kanban Metrics in practice for leading Continuous Improvement (20)
Lots of bloggers are using Google AdSense now. It’s getting really popular. With AdSense, bloggers can make money by showing ads on their websites. Read this important article written by the experienced designers of the best website designing company in Delhi –
Software development... for all? (keynote at ICSOFT'2024)miso_uam
Our world runs on software. It governs all major aspects of our life. It is an enabler for research and innovation, and is critical for business competitivity. Traditional software engineering techniques have achieved high effectiveness, but still may fall short on delivering software at the accelerated pace and with the increasing quality that future scenarios will require.
To attack this issue, some software paradigms raise the automation of software development via higher levels of abstraction through domain-specific languages (e.g., in model-driven engineering) and empowering non-professional developers with the possibility to build their own software (e.g., in low-code development approaches). In a software-demanding world, this is an attractive possibility, and perhaps -- paraphrasing Andy Warhol -- "in the future, everyone will be a developer for 15 minutes". However, to make this possible, methods are required to tweak languages to their context of use (crucial given the diversity of backgrounds and purposes), and the assistance to developers throughout the development process (especially critical for non-professionals).
In this keynote talk at ICSOFT'2024 I presented enabling techniques for this vision, supporting the creation of families of domain-specific languages, their adaptation to the usage context; and the augmentation of low-code environments with assistants and recommender systems to guide developers (professional or not) in the development process.
Ansys Mechanical enables you to solve complex structural engineering problems and make better, faster design decisions. With the finite element analysis (FEA) solvers available in the suite, you can customize and automate solutions for your structural mechanics problems and parameterize them to analyze multiple design scenarios. Ansys Mechanical is a dynamic tool that has a complete range of analysis tools.
Seamless PostgreSQL to Snowflake Data Transfer in 8 Simple StepsEstuary Flow
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Discover how to seamlessly transfer your PostgreSQL data to Snowflake, leveraging Estuary Flow's intuitive interface and powerful real-time replication capabilities. Harness the power of both platforms to create a robust data ecosystem that drives business intelligence, analytics, and data-driven decision-making.
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Try it Free: https://dashboard.estuary.dev/register
Are you wondering how to migrate to the Cloud? At the ITB session, we addressed the challenge of managing multiple ColdFusion licenses and AWS EC2 instances. Discover how you can consolidate with just one EC2 instance capable of running over 50 apps using CommandBox ColdFusion. This solution supports both ColdFusion flavors and includes cb-websites, a GoLang binary for managing CommandBox websites.
5. Why do we need metrics?
#1: drive continuous improvement #2: forecast the future
6. But I thought metrics were bad....
Typical problems:
gaming
dysfunctions
7. Good vs Bad metrics
● look at improving the whole system ● reward/punish individuals
“95% performance is attributable to
the system, 5% to the people”
W. Edwards Deming
● feedback about state of reality ● used as target
● leading (let you change behaviour) ● lagging (tell you about the past)
● all metrics must improve ● local optimisations
10. The Spreadsheet
Inputs: story details; start time and duration of each state
Public version: https://goo.gl/0A9QSN
For you to copy, reuse, get inspired,
etc.
11. All the maths you need
● Min, Max
Normal: data is distributed
around a central value
e.g. height of UK population
Skewed: data has a long tail
on one side (positive or
negative)
e.g. income of UK population
(positive skew)
Lead time of stories follows
skewed distribution
● Average (mean)
avg(1,2,2,2,3,14) = (1+2+2+2+3+14)/6 = 4
● Median: separates the high half from the low half. Less impacted by outliers
median(1,2,2,2,3,14) = 2
● Mode: value that occurs more frequently
mode(1,2,2,2,3,14) = 2
● Standard Deviation: measures the amount of dispersion from the average. When
high, values are spread over a large range.
stdev(1,2,2,2,3,14) = 4.5; stdev(1,2,2,2,3,5) = 1.2;
● Percentile: percentage of elements that fall within a range
50% perc(1,2,2,3,7,8,14) = 3; 80% perc(1,2,2,3,7,8,14) = 7.8;
● Normal Distribution vs Skewed Distribution:
14. Cumulative Flow Diagram
● Objective: retrospect (but needs a good facilitator)
CFD used for Retrospective
● Objective: demonstrate effectiveness of changes
changed WIP limit in DEV from 3 to 2
15. Cumulative Flow Diagram
● Objective: decide what you should work on today
● Objective: forecasting: rough info about lead time, wip, delivery date (although
they’re easier to use when tracked separately)
WIP
Lead Time
16. CFD Patterns
(taken from CFD article by Pawel Brodzinski)
growing lines: indicate large WIP + context switching.
action: use WIP limits
stairs: indicates large batches and timeboxes
action: move towards flow (lower WIP,
more releases, cross-functional people)
flat lines: nothing’s moving on the board
action: investigate blockers, focus on finishing, split in
smaller stories
single flat line: testing bottleneck
action: investigate blockers, pair with testers,
automate more
typical timeboxed iterationdropping lines: items going back
action: improve policies
18. Control Chart
Description: For each story it shows how long it took. Displays Upper and Lower control
limits; when a story falls out of limits something went wrong and you should talk about it.
stories
leadtime(days)
19. Cycle/Lead Time stats + History
Description: Stats to get to know your cycle time and lead time. They let you predict “how
long is the next story likely to take?”. Visualize trends of improvement
20. Lead Time distribution
lead time (days)
n.storiesthattookthatlong
Description: For each lead time bucket (#days), how many stories have taken that long.
Useful to show as a percentage to know probability.
50%
80%
21. Story Health
Description: Indicates if the story is in good health or if we should worry about it. Based
on lead time distribution
50-80% >90%80-90%0-50%
0-4 gg 5-7 gg 8-10 gg >10 gg
22. Cycle Time vs Release Prep. Time
stories
days
Description: For each story shows how long it spent in the iteration and in release
preparation (Context specific). Used to discuss cost vs value of release testing.
26. Arrivals Rate
Description: how often a story is started, aka pulled into our system (arrival). This is how
often you can change your mind about what to do next
27. Points vs Lead Time
leadtime(days)
story points
Description: Shows low correlation between estimated points and actual lead time
33. Flow Efficiency
Description: Shows how long stories have spent in queues - nobody was working on
them. Shows how much you could improve if you removed waiting time.
36. Resources
Books
Metrics
● Data driven coaching - Troy Magennis
● Seven Deadly Sins of Agile Measurement - Larry Maccherone
● The Impact of Lean and Agile Quantified - Larry Maccherone
● Kanban at Scale: A Siemens Success Story - Bennet Vallet
● FocusedObjectives@Github - Troy Magennis
● Visual feedback brings key Agile principles to life - Bazil
Arden
● How visualisation improves Psychological Safety - Bazil
Arden
Forecasting
● Cycle Time Analytics - Troy Magennis
● Top Ten Data and Forecasting Tips - Troy Magennis
● Forecasting Your Oranges - Dan Brown
● Using Maths to work out Potentially Deliverable Scope -
Bazil Arden
● Forecasting Cards - Alexei Zheglov
Story Points
● Story Points and Velocity: The Good Bits - Pawel
Brodzinski
● No correlation between estimated size and actual time
taken - Ian Carroll
Lead Time
● Analyzing the Lead Time Distribution Chart - Alexei
Zheglov
● Inside a Lead Time Distribution - Alexei Zheglov
● Lead Time: what we know about it, how we use it - Alexei
Zheglov
● The Economic Impact of Software Development Process
Choice - Troy Magennis
More
● Flow Efficiency - Julia Wester
● Cumulative Flow Diagram - Pawel Brodzinski
thank you everyone for coming to my talk. Other sessions are great
Can I ask:
who here has an idea of what Kanban is?
who here already knows what a CFD is?
Great, you are in the right place!
My name is Mattia, I’m from a small lovely town in Italy called Verona. You might know it as the city of Romeo and Juliet
Background of software development, now team leader. I don’t consider myself a real coach but I like to help teams improve (run workshops, retros, talks, etc.)
I work at Sky Network Services, we’re the department in Sky that deals with the Voice and Broadband. The way I explain it to my wife is “we make the internet work”. Oh btw we’re hiring
this presentation is a slightly modified version of one that I do internally for teams that want to learn about Kanban metrics
I use it to share our experience about why we like using metrics, what metrics we use and how, and how we collect the data
not mandatory, but it helps if you have an idea of what kanban is and its values (e.g. limiting WIP)
if you need help on this consider going to the London Lean Kanban Day, last year it was great and I’m sure this year it’s going to be even better!
Why do I even need metrics? what problem are we trying to solve?
#1 - improve (hints on where you should improve, validate experiments); Kanban is all about continuous improvement -> start with what you do, and use data to improve
basically you are constantly running experiments and validating them with your data
#2 - forecast future, move away from estimates and use historical data to predict the future
Forecasting is a big topic that deserves to be discussed on its own, for this presentation I’m focusing on #1 (but if you want to know more we can have a chat later, or look up Troy Magennis, Kanbandan, Dimitar Bakardzhiev
the typical reaction when you start talking about metrics is “how about no, thanks”
The argument is usually that metrics can be gamed and they will cause dysfunctions (you’ll destroy the system but the metrics will say you’re doing great)
example: velocity; we have to complete 10 points in each iteration -> double the points for each story!
So yes, we are stepping in a high risk area when we talk about metrics
That’s why we distinguish between good and bad metrics:
good metrics are about improving the system as a whole, rather than rewarding/punishing individuals. We recognize Deming’s rule about the performance of a system (95/5). They always work with a systemic approach, team metrics
absolutely not used as target! they are a feedback mechanism. Only for internal use, no exposure to management, PMs, etc.
they are usually expressed as trends rather than single numbers; single numbers tends to become targets, trends instead can tell you a more generic “you are improving”
they are leading to let you act on them, rather than lagging when you can’t do anything about it
we use Jira but that has been setup with a very generic process to fit all teams; we’re pretty much a physical shop; we have physical boards to represent the real workflow and we either print cards or use post its
this is the main board of my team, representing the main part of our process
we represent our WIP limits with placeholders -> next = 3, dev = 2, test = 3; an empty placeholder is a pull signal
we’ve got the next two stories to work on in next, then dev, functional testing (with “waiting” as a buffer), and waiting for a cut
our point of commitment is when a story enters Next, so that’s when our Lead Time starts
CLICK
for “Iteration based” apps, stories have to wait in “waiting for cut” until the end of the the even iteration (2-4 weeks). Then we do a cut of the application and it goes through release testing, then to production.
the “on demand” apps we can do the cut as soon as we want, so we try to do it as soon as possible and release as soon as possible
“direct” stories are small things that have to be done (emails, reports, etc.) and go straight to done
we treat these three as 3 different work item types, based on either different workflow or different speed in the same workflow
This is very important because for most metrics you want to differentiate between work item types. They will have different lead time and different demand
In particular for “On demand” and “Direct” we calculate the lead time from Next to Done
But for “Iteration-based” we only count the time from Next to Cut and call it “Iteration Cycle time”, as the rest of the time is fixed time
collecting the data is really simple. We record transitions; we stamp the card each time it moves from one state to the next
this piece of information is enough to get most of the metrics we use
then we regularly put this information in a spreadsheet, where we record the work item type and the transitions. You might decide to track some extra pieces of information, e.g. for bugs we want to record the environment it was found in. It’s up to you
btw there are formulas to only count working days
you’re probably wondering why we don’t use a tool.
for collecting the data: make sure you are recording reality (real workflow) and you can change the data (e.g. if you forget to update it) Jira is quite bad, can’t update the dates
for displaying and analysing the data: Jira is rubbish. Other tools do something, but in my experience you still want access to the raw data and go crazy with a spreadsheet. You will want to reorganise data, rearrange, split it differently, play with it...tools don’t have enough flexibility to do data mining. If you use a tool, make sure you have access to the raw data
Our spreadsheet: the only input is some details about the story, what state it’s in, and then for each state when it enters that state and how long it stayed in there
Collect CFD data every day - it’s inferrable but it makes it easier
All the rest is calculated or inferred (organized in various sheets)
Our spreadsheet has grown in complexity over time because of many experiments, eventually I will redo it and publish it
I left this slide in but I’ll skip it, you can read it later if you want; the math involved is really easy, and all the formulas are already in excel
Probably the most famous Kanban chart
for each day it shows how many stories are in each state
can be used in retrospectives and root cause analysis to look at history (but needs good facilitator)
can be used as leading, but it might be just easier to look at the board
keep queueing states as thin as possible
don’t let any state grow too much
alert when you don’t see flow (when chart is not going steadily up)
one of the most famous (with CFD); usually done with dots, but it was easier to use columns (because of non-numeric data)
Objective:
retrospect: talk about stories that took longer or shorter than expected, and improve your process or policies; as you improve you should see trends of improvement
leading: see stories approaching the limit and decide if you want to act on it
Tips & Traps:
use percentiles instead of std. deviation for the limits (std. dev only if you have a normal distribution)
hard to talk about problems all the time (too busy to improve)
we calculate some stats about our cycle time (or lead time).
Objective: forecasting. Lets you answer questions like “how long do stories usually take? what are the chances that the next story is going to take longer than 10 days?”
Tips and traps:
distinguish between “all time” and “last X months”. I usually look at the last 5 or 6 months (the process is constantly improving, older data is not representative anymore)
shows trends of improvements, but doesn’t really tell you why
on x-axis you’ve got days, on y the number of stories that took that long.
You should find a skewed distribution. You can draw the curve that interpolates the data - it’s called Weibull distribution
Get the probability of each bucket, and if you sum them you can find that 50% of stories take 6 days or less, 85% of stories take 10 days or less
So next time you ask “how long will the next story take?” you can decide how certain you want to be and pick a number. “how long will the next story take with a 80% confidence?”
If I want a story to be done by a particular date, I know it needs to be in Next at least 10 days before the fixed date to have 83% confidence
Objective: forecasting (on a story level, but this is the data you’d use to do a montecarlo simulation)
Tips & Traps:
long tail is symptom of high standard deviation (high variability)
multiple peaks are often hiding multiple work item types
from the shape of the weibull you can draw some conclusions
concept of Health of a story, based on how long the story has been in progress for
based on the lead time distribution I know that 50% stories take up to 6 days, so I consider that green. After 6 days it becomes yellow, we start worrying about it. And then red and black.
Objective: leading. what should I work on today? It’s a way to escalate problems and raise alarms
Tips and traps:
remember to do it per work item type (can’t expect different work items to take the same amount of time)
this is quite specific to our context
for those stories that are iteration-based we show how long they wait in “release preparation”
You can see that release testing takes up most of the time. That’s the effect of having a big batch, with all the dysfunctions that come from it.
We put this together with the low value that we get from it, which is number of bugs found, to decide that it would have been crazy for new projects to follow the same approach
That’s why for new applications we moved to a flow approach
context specific, but it’s an example of how you can use data to drive your argument
throughput: how many stories are done in a particular amount of time?
we use the iteration as cadence, so “how many stories are done in two weeks?”
Objective:
how many stories should I plan in next iteration?
are we going faster?
Traps:
If you split by work item type you might have iterations where you did nothing of that particular work item type; so the average is kind of weird
depending on what project we’re working on, if it only involves iteration-based applications I would look at one throughput or another
Shows how often a story is started
Also called takt time in lean
This is how often you can change your mind about what to do next (instead of deciding every two weeks)
Arrivals and departures should be balanced
this is a controversial one, people tend to have strong feelings for or against story points
for stories of 1 points, they took from 2 to 10 days; stories of 2 points took 2 to 20 days; etc.
very low correlation between story points and actual lead time
this worked as a shock factor and we decided to stop wasting time with planning poker, or fingers in the air
Now we do story breakdown and use historical data (e.g. lead time distribution) to forecast how long they’ll take
like at disneyland “how long do I wait from here?”
we use 50th and 80th percentile to show how long stories will take from here, and how long they’re going to spend in here
Tips and Traps:
remember, it’s only valid on a “per-work item” basis (can’t mix)
useful to take decisions in the middle of the iterations
another context specific metrics. When stories are in next we create a list of tasks for the story to agree on the scope and the acceptance criterias
we monitored how long tasks take for a while, and now we can predict quite accurately how long a story is going to take based on the n. of tasks
highly accurate for dev time (+ creating tasks makes scope clearer)
Objective:
forecast: how long is a story going to take, based on n. of tasks?
leading: how much is left to do on this story? should we swarm? or should I rather start another story?
Tips & Traps:
high correlation between dev and n. tasks
very low correlation between test and n. tasks (makes sense)
also helps with defining scope of stories
one simple way of keeping track of quality: count number of bugs;
we express it as “n. of bugs per stories” so that we can keep them into account when we plan in future
one of the best incarnation of lean mindset. Shows how long stories have spent in queue states, therefore no one was actively working on them (pure waste in lean)
It’s a demonstration of deming’s rule - just by removing wait time we could improve our performance of 50%
How do you reduce wait time? probably reduce WIP, have true cross-functional people, attack sources of variability
Tips: represent queue states on your board by using red labels
shows where stories spend most of the time; interesting to compare it with what the team perceives are the states that take longer.
Clearly shows that development is only a small fraction of time; is this our intended process, or are we here just by chance? does this reflect the importance of each state? e.g. if we think development is the main state, are we happy with it being just a small fraction?
lots of potential but hard to use for the risk of people feeling blamed
GOOD
drive changes to process: tells you what you should improve on and gives you directions for what to change
validate experiments and arguments: can see if a change is having the effect you wanted, or find good arguments for your point
enable forecasting
helps answer “what should I work on today”
infinite learning possibilities, only your imagination is the limit
google spreadsheet as tool worked very well
WATCH OUT
resist the temptation to automate everything, or even worse create a custom tool! You will keep changing charts, metrics, reorganize them, etc. Use intelligent automation, just to make manual steps easier. example: you could capture the CFD data automatically, but I still prefer to have a look and copy/past them when I’m happy they’re right
don’t obsess over precision, you’re looking for trends rather than precision. Number won’t always 100% precise but it doesn’t matter
only use the last period of time, for example the last 6 months. Data older than that might not be accurate anymore, or might not reflect your current process
people still know it better: if you have a reason to think that reality is different from what a metric is saying, you’re probably right; it’s worth investigating, you’re probably on to learning something new!
PROBLEMS
it was hard to get the team on board, and I actually never succeeded. NIM team is particularly difficult, people tend to have strong opinions and you have to find the right moment when they’re willing to listen. This is true in general. Be prepared to do it yourself, don’t expect people to help you until they think it’s helping them. It still works, you can look at the metrics and decide what’s the next change you’ll introduce. Just make sure you’re never forcing anything on the team, that’s an instant fail
it’s hard to make these metrics speak: what do they mean? how do I interpret a particular chart? how do I read this? It’s hard to make them user friendly enough. So again, you can still use them as a management tool to drive the changes you introduce, but you need to make then easier to use if you want people to look at them
as soon as you have work item types with different process the complexity explodes; I don’t know how to fix this, you need to make your metrics even more user friendly
difficult to make it visible; ideally these metrics should be printable as some kind of dashboard, so before standup and after every iteration someone refreshes them and prints them. But I never managed to do it
as soon as you open the spreadsheet you get an information overload that often scares people. Again, need to improve the usability. Add instructions, help, etc.
should organise the metrics better, probably by their usage (example: daily vs iteration, lead time vs predictability)
write down when important changes or important events happen, so that when you’re looking at the past you know “that’s when we changed the WIP limit in dev, have we improved?”
it doesn’t matter what the numbers are saying, you’ll never be able to convince people with just numbers. You need to translate that to a feeling, make them feel a problem and then they’ll listen. example: it doesn’t matter that all metrics are telling you that you have too much WIP, people will be scared of WIP limits no matter what