Agile Metrics - Modeling, Simulation, and Data MiningRally Software
Modeling, Simulation & Data Mining: Answering Tough Cost, Date & Staff Forecasts Questions provides techniques for modeling projects to answer difficult questions about costs, dates, and staffing needs. The presentation discusses using simulation modeling languages and Monte Carlo analysis to forecast outcomes while accounting for uncertainty. Sensitivity analysis helps identify key drivers to focus on. Outcomes-driven metrics and risk management are emphasized over averages. Communicating forecasts with appropriate levels of uncertainty to executives is also covered.
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.
Prioritization – 10 different techniques for optimizing what to start next ...Troy Magennis
10 different prioritization techniques to help understand what to START next. Shows the evolution between choosing at random up to full economic analysis. First presented at Agile 2017 in Florida.
Everyone has been given a 2 paragraph document listing the "scope of services" for a potential project. The client would like an estimate in 48 hours and there are no more details to help you deliver that required fixed bid contract. At the same time, many teams have also been given (or created) a detailed PRD or backlog document and still had a project budget balloon out of control. In this session I would like to discuss the not only the problems associated with estimation and how to avoid them, but more importantly how we can plan for them, turning our estimation process into not only an art, but a science. Well cover how to sell your estimate internally, and arm you with the methodologies to support your numbers. The problem with software estimation The morale The metrics The reality - an estimation metaphor Avoiding Risk Project entry point of sale At what point of the project lifecycle is your first sale? Risk association with point of sale Products in the front, estimations in the back The Elusive Discovery phase How to estimate a discovery How to sell a discovery How to include discovery in a full fixed bid RFP Planning for Risk Estimation types Gut - An art form Comparables - An art/science Factors/formula - A science Contingency Rating systems Formulas Granularity
Key Note - DoSE Berlin - Qualitative Risk ManagementDavid Anderson
This document summarizes a presentation on qualitative risk management and using a Lean approach with Kanban. The presentation argues that qualitative techniques for assessing and prioritizing risk are often faster, cheaper, and better than quantitative techniques. It provides an example of how Kanban systems can qualitatively assess the cost of delay for different types of work using function sketches to categorize risk rather than attempting precise calculations. This qualitative approach helps limit waste and dysfunction that can occur when trying to precisely quantify risk in complex work with many uncertainties.
Building a Problem Statement with Matt Hansen at StatStuffMatt Hansen
The document discusses problem statements and their importance in Six Sigma projects. It defines a problem statement as a clear, 3-sentence description of the problem a project aims to solve. An ideal problem statement explains the symptoms, why the problem matters, and any assumptions, without proposing solutions or strategies. The document provides examples of problem statements and guidelines for crafting effective ones, including asking questions to validate a statement addresses the root problem.
The document discusses issues with estimation in software projects. It notes that traditional estimation approaches fail because they ignore uncertainty and complexity. While Agile aims to help with lighter estimation practices, there is still risk of falling into the same traps as traditional methods. The key problems are how estimates are used, with unrealistic targets, imposed deadlines, and lack of respect causing issues. Respecting uncertainty and using estimates appropriately is emphasized as important.
Agile Metrics - Modeling, Simulation, and Data MiningRally Software
Modeling, Simulation & Data Mining: Answering Tough Cost, Date & Staff Forecasts Questions provides techniques for modeling projects to answer difficult questions about costs, dates, and staffing needs. The presentation discusses using simulation modeling languages and Monte Carlo analysis to forecast outcomes while accounting for uncertainty. Sensitivity analysis helps identify key drivers to focus on. Outcomes-driven metrics and risk management are emphasized over averages. Communicating forecasts with appropriate levels of uncertainty to executives is also covered.
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.
Prioritization – 10 different techniques for optimizing what to start next ...Troy Magennis
10 different prioritization techniques to help understand what to START next. Shows the evolution between choosing at random up to full economic analysis. First presented at Agile 2017 in Florida.
Everyone has been given a 2 paragraph document listing the "scope of services" for a potential project. The client would like an estimate in 48 hours and there are no more details to help you deliver that required fixed bid contract. At the same time, many teams have also been given (or created) a detailed PRD or backlog document and still had a project budget balloon out of control. In this session I would like to discuss the not only the problems associated with estimation and how to avoid them, but more importantly how we can plan for them, turning our estimation process into not only an art, but a science. Well cover how to sell your estimate internally, and arm you with the methodologies to support your numbers. The problem with software estimation The morale The metrics The reality - an estimation metaphor Avoiding Risk Project entry point of sale At what point of the project lifecycle is your first sale? Risk association with point of sale Products in the front, estimations in the back The Elusive Discovery phase How to estimate a discovery How to sell a discovery How to include discovery in a full fixed bid RFP Planning for Risk Estimation types Gut - An art form Comparables - An art/science Factors/formula - A science Contingency Rating systems Formulas Granularity
Key Note - DoSE Berlin - Qualitative Risk ManagementDavid Anderson
This document summarizes a presentation on qualitative risk management and using a Lean approach with Kanban. The presentation argues that qualitative techniques for assessing and prioritizing risk are often faster, cheaper, and better than quantitative techniques. It provides an example of how Kanban systems can qualitatively assess the cost of delay for different types of work using function sketches to categorize risk rather than attempting precise calculations. This qualitative approach helps limit waste and dysfunction that can occur when trying to precisely quantify risk in complex work with many uncertainties.
Building a Problem Statement with Matt Hansen at StatStuffMatt Hansen
The document discusses problem statements and their importance in Six Sigma projects. It defines a problem statement as a clear, 3-sentence description of the problem a project aims to solve. An ideal problem statement explains the symptoms, why the problem matters, and any assumptions, without proposing solutions or strategies. The document provides examples of problem statements and guidelines for crafting effective ones, including asking questions to validate a statement addresses the root problem.
The document discusses issues with estimation in software projects. It notes that traditional estimation approaches fail because they ignore uncertainty and complexity. While Agile aims to help with lighter estimation practices, there is still risk of falling into the same traps as traditional methods. The key problems are how estimates are used, with unrealistic targets, imposed deadlines, and lack of respect causing issues. Respecting uncertainty and using estimates appropriately is emphasized as important.
A detailed roadmap through the Analyze phase of the DMAIC methodology that navigates the user through the various tools and concepts for leading a Six Sigma project.
Issue based work planning and hypothesis problem solvingSTRATICX
DESCRIPTION
The principles behind Issue-Based Work Planning are a powerful concept for use on all business issues and help to align the approach with the overriding issues, rather than the traditional process structure.
They help ensure that all relevant project issues are covered and to arrive at the most robust and creative answer, by linking analyses and end products to a methodical analysis of key issues.
This powerpoint is suitable for anyone who is looking for a robust methodology to solve the most complex of issues.
Hypothesis Testing: Central Tendency – Non-Normal (Compare 1:1)Matt Hansen
This document provides instruction on using the Mann-Whitney test to compare the medians of two independent samples. It discusses when to use the Mann-Whitney test, how to run it in Minitab, and provides an example comparing the medians of two columns of sample data labeled MetricC1 and MetricC2. The results of running the Mann-Whitney test on this example are interpreted to determine if the medians are statistically different between the two samples. The document encourages applying the test to factors identified in a previous lesson and discussing how the results could impact an organization.
This document discusses various problem solving tools and techniques. It begins by describing the importance of root cause problem solving over simply treating symptoms. It then discusses different problem solving tools like 5 whys, logic trees, and 7 step problem solving and how to select the appropriate tool based on the situation. It provides examples of each tool. The key takeaways are that the level of complexity will determine the best tool, and many problems can be solved quickly with root cause analysis or 5 whys. Logic trees are useful for organizing problem solving efforts.
An extension on hypothesis testing, this lesson introduces the concepts of a correlation and regression as part of measuring statistical relationships.
This Slideshare presentation is a partial preview of the full business document. To view and download the full document, please go here:
http://flevy.com/browse/business-document/issue-based-work-planning-and-hypothesis-problem-solving-377
The principles behind Issue-Based Work Planning are a powerful concept for use on all business issues and help to align the approach with the overriding issues, rather than the traditional process structure.
They help ensure that all relevant project issues are covered and to arrive at the most robust and creative answer, by linking analyses and end products to a methodical analysis of key issues.
This powerpoint is suitable for anyone who is looking for a robust methodology to solve the most complex of issues.
This document provides an introduction to forecasting. It begins by defining forecasting as predicting the future based on past patterns and trends. It discusses the four main laws of inference used in forecasting: repetition, continuity, mean, and decomposition. It then describes different types of forecasting approaches, including subjective, objective time-series models, and causal models. Finally, it provides examples of specific forecasting methods like moving averages, exponential smoothing, and surveys.
Beyond Churn Prediction : An Introduction to uplift modelingPierre Gutierrez
These slides are from a talk I at the papis conference in Boston in 2016. The main subject is uplift modelling. Starting from a churn model approach for an e-gaming company, we introduce when to apply uplift methods, how to mathematically model them, and finally, how to evaluate them.
I tried to bridge the gap between causal inference theory and uplift theory, especially concerning how to properly cross validate the results. The notation used is the one from uplift modelling.
Technical debt is something that most project teams or independent developers have to deal with – we take shortcuts to push out releases, deadlines need to be met, quick fixes slowly become the standard. In this talk, we will discuss what technical debt is, when it is acceptable and when it isn’t, and strategies for effectively managing it, both on an independent and team level.
This presentation provides a clear path for your agile project by using a handful of simple steps. Don’t expect an ambiguous restatement of the Agile Manifesto. You will learn specific steps that will challenge your team and delight your customers.
Rethinking Risk-Based Project Management in the Emerging IT initiatives.pptxInflectra
The pressure to deliver faster to the market has never been more insistent and pervasive than today’s business environment. The Agile world of iterative and incremental delivery has enabled great advances in terms of delivery speed; however, the lack of an integrated risk framework is creating challenges in terms of matching speed with quality. On the one hand, the standards-setting organizations such as the Project Management Institute (PMI) have updated their book of knowledge (PMBOK v7) to move away from highly prescriptive processes to lean thinking. On the other hand, Agile standards themselves have started to emerge, recognizing the need for some prescriptive guidelines on coming up with release and iteration goals. Struggling in between this continuum are the innovative technology projects that wonder how “creativity can be timeboxed” to deliver value!
While the impact of leadership to form the team and the organizational culture to embrace continuous learning are unquestionable, it is important to realize that the areas of strategy, leadership, and culture are not substitutes for the lack of risk-based project thinking. When delivering IT applications that are contain inherent conceptual, technical, and compliance risks, a more systematic approach is needed. In this presentation, you will hear about the emerging space of IT initiatives that are impacted by such risks and the need to adopt risk-based frameworks in application lifecycle management. You will also see practical examples of how risk-based lifecycle management can be done in real-time.
This document discusses principles for successful release management in large enterprises. It recommends: (1) defining an environment strategy upfront to plan testing sandboxes; (2) integrating code early through continuous integration; (3) building quality through techniques like regression testing; (4) measuring changes through metrics like bugs introduced; and (5) automating deployments through repeated processes. The goal is to reduce risk, increase stability, and improve throughput during the software release process.
This document contains the syllabus for a course on software verification, validation, and testing (CSE 565). It lists the topics that will be covered each week, including testing techniques like requirements-based testing, exploratory testing, structure-based testing, integration testing, and usability testing. It also covers testing at different stages like unit testing, integration testing, and system testing. The document provides an overview of the areas and concepts that will be learned throughout the course.
Agile Software Development in Practice - A Developer PerspectiveWee Witthawaskul
This document provides an overview of agile software development practices from a developer perspective. It recommends adopting agile practices to increase productivity and recommends Scrum and XP as agile frameworks. It describes common agile practices like user stories, daily standups, iteration planning, testing practices like TDD, mocks and continuous integration to automate testing.
Agile * Agile Principles * Agile Practices * Pair Programming * Extreme Programming * SOLID design principles * SDLC * Software Development
After working 10 years in multiple major "from-scratch" development projects, I finally got a chance to work in a truly Agile development project. Here is my take on how to make Agile work for your project.
A detailed roadmap through the Analyze phase of the DMAIC methodology that navigates the user through the various tools and concepts for leading a Six Sigma project.
Issue based work planning and hypothesis problem solvingSTRATICX
DESCRIPTION
The principles behind Issue-Based Work Planning are a powerful concept for use on all business issues and help to align the approach with the overriding issues, rather than the traditional process structure.
They help ensure that all relevant project issues are covered and to arrive at the most robust and creative answer, by linking analyses and end products to a methodical analysis of key issues.
This powerpoint is suitable for anyone who is looking for a robust methodology to solve the most complex of issues.
Hypothesis Testing: Central Tendency – Non-Normal (Compare 1:1)Matt Hansen
This document provides instruction on using the Mann-Whitney test to compare the medians of two independent samples. It discusses when to use the Mann-Whitney test, how to run it in Minitab, and provides an example comparing the medians of two columns of sample data labeled MetricC1 and MetricC2. The results of running the Mann-Whitney test on this example are interpreted to determine if the medians are statistically different between the two samples. The document encourages applying the test to factors identified in a previous lesson and discussing how the results could impact an organization.
This document discusses various problem solving tools and techniques. It begins by describing the importance of root cause problem solving over simply treating symptoms. It then discusses different problem solving tools like 5 whys, logic trees, and 7 step problem solving and how to select the appropriate tool based on the situation. It provides examples of each tool. The key takeaways are that the level of complexity will determine the best tool, and many problems can be solved quickly with root cause analysis or 5 whys. Logic trees are useful for organizing problem solving efforts.
An extension on hypothesis testing, this lesson introduces the concepts of a correlation and regression as part of measuring statistical relationships.
This Slideshare presentation is a partial preview of the full business document. To view and download the full document, please go here:
http://flevy.com/browse/business-document/issue-based-work-planning-and-hypothesis-problem-solving-377
The principles behind Issue-Based Work Planning are a powerful concept for use on all business issues and help to align the approach with the overriding issues, rather than the traditional process structure.
They help ensure that all relevant project issues are covered and to arrive at the most robust and creative answer, by linking analyses and end products to a methodical analysis of key issues.
This powerpoint is suitable for anyone who is looking for a robust methodology to solve the most complex of issues.
This document provides an introduction to forecasting. It begins by defining forecasting as predicting the future based on past patterns and trends. It discusses the four main laws of inference used in forecasting: repetition, continuity, mean, and decomposition. It then describes different types of forecasting approaches, including subjective, objective time-series models, and causal models. Finally, it provides examples of specific forecasting methods like moving averages, exponential smoothing, and surveys.
Beyond Churn Prediction : An Introduction to uplift modelingPierre Gutierrez
These slides are from a talk I at the papis conference in Boston in 2016. The main subject is uplift modelling. Starting from a churn model approach for an e-gaming company, we introduce when to apply uplift methods, how to mathematically model them, and finally, how to evaluate them.
I tried to bridge the gap between causal inference theory and uplift theory, especially concerning how to properly cross validate the results. The notation used is the one from uplift modelling.
Technical debt is something that most project teams or independent developers have to deal with – we take shortcuts to push out releases, deadlines need to be met, quick fixes slowly become the standard. In this talk, we will discuss what technical debt is, when it is acceptable and when it isn’t, and strategies for effectively managing it, both on an independent and team level.
This presentation provides a clear path for your agile project by using a handful of simple steps. Don’t expect an ambiguous restatement of the Agile Manifesto. You will learn specific steps that will challenge your team and delight your customers.
Rethinking Risk-Based Project Management in the Emerging IT initiatives.pptxInflectra
The pressure to deliver faster to the market has never been more insistent and pervasive than today’s business environment. The Agile world of iterative and incremental delivery has enabled great advances in terms of delivery speed; however, the lack of an integrated risk framework is creating challenges in terms of matching speed with quality. On the one hand, the standards-setting organizations such as the Project Management Institute (PMI) have updated their book of knowledge (PMBOK v7) to move away from highly prescriptive processes to lean thinking. On the other hand, Agile standards themselves have started to emerge, recognizing the need for some prescriptive guidelines on coming up with release and iteration goals. Struggling in between this continuum are the innovative technology projects that wonder how “creativity can be timeboxed” to deliver value!
While the impact of leadership to form the team and the organizational culture to embrace continuous learning are unquestionable, it is important to realize that the areas of strategy, leadership, and culture are not substitutes for the lack of risk-based project thinking. When delivering IT applications that are contain inherent conceptual, technical, and compliance risks, a more systematic approach is needed. In this presentation, you will hear about the emerging space of IT initiatives that are impacted by such risks and the need to adopt risk-based frameworks in application lifecycle management. You will also see practical examples of how risk-based lifecycle management can be done in real-time.
This document discusses principles for successful release management in large enterprises. It recommends: (1) defining an environment strategy upfront to plan testing sandboxes; (2) integrating code early through continuous integration; (3) building quality through techniques like regression testing; (4) measuring changes through metrics like bugs introduced; and (5) automating deployments through repeated processes. The goal is to reduce risk, increase stability, and improve throughput during the software release process.
This document contains the syllabus for a course on software verification, validation, and testing (CSE 565). It lists the topics that will be covered each week, including testing techniques like requirements-based testing, exploratory testing, structure-based testing, integration testing, and usability testing. It also covers testing at different stages like unit testing, integration testing, and system testing. The document provides an overview of the areas and concepts that will be learned throughout the course.
Agile Software Development in Practice - A Developer PerspectiveWee Witthawaskul
This document provides an overview of agile software development practices from a developer perspective. It recommends adopting agile practices to increase productivity and recommends Scrum and XP as agile frameworks. It describes common agile practices like user stories, daily standups, iteration planning, testing practices like TDD, mocks and continuous integration to automate testing.
Agile * Agile Principles * Agile Practices * Pair Programming * Extreme Programming * SOLID design principles * SDLC * Software Development
After working 10 years in multiple major "from-scratch" development projects, I finally got a chance to work in a truly Agile development project. Here is my take on how to make Agile work for your project.
The document compares traditional waterfall and agile product development approaches. It summarizes research finding that agile projects succeed three times more often than waterfall projects. Key aspects of agile methodologies like Scrum are outlined, including roles, ceremonies, and values. Challenges of adopting agile approaches are also discussed.
The document discusses how Agile project management differs from traditional project management. It explains that Agile uses iterative planning with short timeboxes, focuses on delivering working software frequently in small batches, emphasizes individuals/interactions over processes/tools, and values customer collaboration over contract negotiation. The document outlines how Agile manages scope, time, costs, risks, and other factors using techniques like user stories, burndown charts, and frequent inspection and adaptation. It encourages the reader to start implementing Agile practices like iterative planning and daily stand-ups.
How To Handle Exploding Complexity in Product DevelopmentPerforce
Was everything tested before the product shipped?
Were all requirements met?
Are you sure?
As product development explodes with complexity (and as requirements & tests evolve during development), many design teams struggle to answer these questions with confidence.
Knowledge gaps in product development add risk. Never good—especially in regulated industries.
But there’s more to it. For teams using Jira or spreadsheets or similar, answering these questions can take time and effort away from development. It can burden productivity and invites human error.
Is there a better way?
Watch this webinar to learn about:
-Growing challenges of product development teams.
-Ways to manage change, improve visibility throughout the lifecycle.
-How traceability is becoming the linchpin for modern design teams.
-Cost-effective tools to help simplify the complexity.
The document discusses agile methodology and Scrum in particular. It outlines some key disadvantages of traditional SDLC approaches, including delayed deployment, inability to incorporate new requirements, and lack of early customer feedback. It then introduces agile principles like iterative development, collaboration, and responding to change. Scrum is presented as an agile methodology consisting of roles like Product Owner and Scrum Master, ceremonies like sprint planning and daily standups, and artifacts like product backlogs. Benefits of Scrum include continuous improvement, delivering working software frequently to gather early customer feedback, and higher return on investment.
If you like the ideas raised in this presentation, don't forget to check out my latest book, Directing the Agile Organisation (http://theagiledirector.com/book).
Lean DevOps - Lessons Learned from Innovation-driven CompaniesXavier Amatriain
This document summarizes a presentation about lean DevOps practices. It discusses how companies can optimize for innovation through experimentation while balancing factors like cost, availability, scalability, speed, security, and developer happiness. The presentation outlines lessons learned, including that quality pays off by reducing technical debt, metrics are important, and competing priorities like speed and cost can conflict so teams must find the right balance. It concludes that lean approaches benefit companies and DevOps teams by encouraging innovation, but also add risk, so processes are needed to optimize across concerns like customer experience, infrastructure stability, and business outcomes.
The document provides an overview of project management modules and topics, including:
- Module 1 defines a project as a "temporary endeavor undertaken to create a unique product or service."
- Module 2 outlines the nine knowledge areas of project management according to PMI: integration management, scope management, time management, cost management, quality management, human resource management, communications management, risk management, and procurement management.
- Module 3 discusses the triple constraint of project management involving balancing the constraints of time, cost, and quality/scope.
- Modules 4 and 5 cover risk management and project selection methods such as payback period, net present value, weighted and unweighted selection criteria, and forced pair comparisons for priorities
Benefits of Agile Software Development for Senior ManagementDavid Updike
This is a presentation to Senior and Executive Managers which is used to explain how Agile Software Development processes and practices benefit them, their organization and their customers.
This document provides an introduction to the Scrum framework for agile software development. It describes Scrum as an iterative, incremental framework that uses self-organizing cross-functional teams to deliver complex products. The key aspects of Scrum covered include the roles of product owner, Scrum master and development team, the Scrum events of sprint planning, daily stand-ups, sprint reviews and retrospectives, and the artifacts of product and sprint backlogs and burn-down charts. The document provides an overview of how Scrum is intended to provide transparency, inspection, and adaptation to optimize predictability and control of risk.
The document discusses testing within a Scrum environment at Planon, a software company. It covers how Planon integrated testers into development teams, emphasized automated regression testing, and adapted traditional test practices like documentation, activities, and reporting to fit an agile process. The lessons learned section emphasizes treating quality as a team responsibility and coaching testers to work effectively within Scrum.
The document discusses testing within a Scrum environment at Planon, a software company. It covers how Planon integrated testers into development teams, emphasized automated regression testing, and adapted traditional test practices like documentation, activities, and reporting to be more iterative and team-focused. The lessons learned section emphasizes treating quality as a team responsibility and coaching testers to work effectively within Scrum.
The Zen of Scrum document provides an overview of Scrum and its principles for agile software development. It summarizes problems with traditional development approaches and how Scrum addresses these issues through its roles, processes, and focus on delivering working software frequently through short iterations called sprints. The document outlines the Scrum roles of Product Owner, Development Team, and Scrum Master and the core Scrum events of sprint planning, daily standups, sprint review and retrospective.
Similar to Using Simulation to Manage Software Delivery Risk (20)
What is the story with agile data keynote agile 2018 (Magennis)Troy Magennis
This document discusses using data to improve agile practices and outcomes. It argues that agile has lost the "data war" by not capturing and utilizing data from teams effectively. It suggests that data needs to be handled safely to avoid embarrassing people and destroying the utility of historical data. Better ways are needed to measure outcomes rather than just output, and to balance predictability with creativity. The document also discusses visualizing and managing dependencies, comparing performance across teams, and using the right metrics depending on a team's characteristics and challenges. The overarching message is that data needs to be used carefully and conversationally to drive the right actions and improve agile practices.
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.
Forecasting using data workshop slides for the Deliver conference in Winnipeg October 2016. This session introduces practical exercises for probabilistic forecasting. http://www.prdcdeliver.com
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 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)
Risk Management and Reliable Forecasting using Un-reliable Data (magennis) - ...Troy Magennis
To meet expectations and optimize flow, managing risk is an important part of Kanban. Anticipating and adapting to things that "go wrong" and the uncertainty they cause is topic of this session. We look at techniques for quantifying what risks should be considered important to deal with.
Although discouraging, forecasting size, effort, staff and cost is sometimes necessary. Of course we have to do as little of this as possible, but when we do, we have to do it well with the data we have available. Forecasting is made difficult by un-reliable information as inputs to our process – the amount of work is uncertain, the historical data we are basing our forecasts on is biased and tainted, the situation seems hopeless. But it isn't. Good decisions can be made on imperfect data, and this session discusses how. This session shows immediately usable and simple techniques to capture, analyze, cleanse and assess data, and then use that data for reliable forecasting.
Second and hopefully draft of LKCE 2014 talk.
LKNA 2014 Risk and Impediment Analysis and Analytics - Troy MagennisTroy Magennis
Software risk impact is more predictable than you might think. This session discusses similarities of uncertainty in various industries and relates this back to how we can measure and analyze impediments and risk for agile software teams.
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Nathalie zal delen hoe DEI en ESG een fundamentele rol kunnen spelen in je merkstrategie en je de juiste aansluiting kan creëren met je doelgroep. Door middel van voorbeelden en simpele handvatten toont ze hoe dit in jouw organisatie toegepast kan worden.
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1. Using Simulation to Manage
Software Delivery Risk
Effective Modeling and Simulating
Kanban and Scrum Projects using
Monte Carlo Techniques
Troy Magennis
Troy.magennis@focusedobjective.com
@AgileSimulation
12. Monte Carlo Simulation
Performing a simulation of a
model multiple times using
random input conditions and
recording the frequency of
each result occurrence
13. Simple to more complex model and simulation of a software project
DEMO: VISUAL MODEL SIMULATION
DEMO: MONTE-CARLO SIMULATION
In case of demo disaster, press here…
14. History
Stan Ulam Holding
the FERMIAC
Credits: Wikipedia
15. When to use Monte Carlo Simulation
When there is no correct
single answer (knowable in
advance) or when the
time/effort taken to compute
an answer is beyond realistic
16. When to use Monte Carlo Simulation
When a range of input
conditions can MASSIVELY
alter the final outcome
17.
18. Who Uses Monte Carlo Simulation
High risk industries
Natural resource exploration, insurance,
finance, banking, pharmaceutical…
Software Development == High Risk!
Just look at our reputation, and on-time, on-budget success rate…
20. Why? To Answer Tough Questions…
Date and cost forecasts
Impact of staff hire/loss
Cost of defects
Cost of blocking events
…
And my three 1:1 questions each week!
21. But doesn’t it require estimates?
Yes, but very few…
MUST: Estimate major risks
SHOULD: Column cycle-times
and story counts
22. We need to estimate risk events
**Major risk events have the predominate role
in deciding where deliver actually occurs **
We spend all our
time estimating here
1 2 3
23. Is it Accurate?
1. Gin still equals Gout
2. Doesn’t suffer from the
“Flaw of Averages”
25. The average Major issue: Race
release condition, third party
date!!! component failure…
25
Frequency of Result
20
15
10
5
1
Major Risk Event Shifts
Developer Estimates
Delivery Shape Right
26. We need to estimate risk events
**Major risk events have the predominate role
in deciding where deliver actually occurs **
We spend all our
time estimating here
1 2 3
See model example…
31. DEMO: FORECASTING (DATES & COST)
DEMO: SENSITIVITY (COST OF DEFECTS)
DEMO: STAFF IMPACT (STAFF RISK)
In case of demo disaster or no internet, press here…
33. Sensitivity Model
Test (a little)
The Model
Creation
Cycle
Monte- Visually
Carlo Test Test
34. Make
Informed Baseline
Decision(s)
The
Experiment
Cycle
Make
Compare
Single
Results
Change
35. Best Practice 1
Start simple and add ONE
input condition at a time.
Visually / Monte-carlo test
each input to verify it works
36. Best Practice 2
Find the likelihood of major
events and estimate delay
E.g. vendor dependencies,
performance/memory issues,
third party component
failures.
37. Best Practice 3
Only obtain and add detailed
estimates and opinion to a
model if Sensitivity Analysis
says that input is material
38. Best Practice 4
Use a uniform random input
distribution UNTIL sensitivity
analysis says that input is
influencing the output
39. Best Practice 5
Educate your managers’
about risk. They will still want
a “single” date for planning,
but let them decide 75 th or
95 th confidence level
(average is NEVER an option)
40. Q1. Are we meeting our commitments?
Is the likelihood of the models forecast date
increasing or decreasing?
Q2. What are the top three risks
jeopardizing on-time delivery?
Top three items in the Sensitivity report
Q3. What skillsets do your next three
hires need to have?
Skills applicable to the top three WIP limit increases
that cause the biggest reduction in forecast
41. Call to action
• Read these books
• Download the software FocusedObjective.com
• Follow @AgileSimulation
• Learn: http://strategicdecisions.stanford.edu/
42. Questions?
My Contact Details and to get these slides, the
software or the book used in this session -
FocusedObjective.com
Me: Troy.magennis@FocusedObjective.com
Follow: @AgileSimulation and @t_magennis
45. Manual Kanban Model & Simulation
2 3 4
Design Develop Test
Backlog 1 – 2 days 1 – 2 days 1 – 2 days
Deployed
1
2
5
PLUS: For this manual example, at least 1 defect,
blocking event and scope-creep item.
46. Day 1
Design Develop Test
Backlog 1 – 2 days 1 – 5 days 1 – 2 days
Deployed
1 Day picked at random
2 for this columns cycle-
time range
47. Day 2
Design Develop Test
Backlog 1 – 2 days 1 – 5 days 1 – 2 days
Deployed
2
1 day
48. Day 3
Design Develop Test
Backlog 1 – 2 days 1 – 5 days 1 – 2 days
Deployed
2
1 day
49. Day 4
Design Develop Test
Backlog 1 – 2 days 1 – 5 days 1 – 2 days
Deployed
Added
Scope
2
1 day
50. Day 5
Design Develop Test
Backlog 1 – 2 days 1 – 5 days 1 – 2 days
Deployed
Added
Scope
2
1 day
51. Day 6
Design Develop Test
Backlog 1 – 2 days 1 – 5 days 1 – 2 days
Deployed
Added
Scope
2
1 day
52. Day 7
Design Develop Test
Backlog 1 – 2 days 1 – 5 days 1 – 2 days
Deployed
Added
Scope
2
1 day
53. Day 8
Design Develop Test
Backlog 1 – 2 days 1 – 5 days 1 – 2 days
Deployed
2
1 day
Added
Scope
54. Day 9
Design Develop Test
Backlog 1 – 2 days 1 – 5 days 1 – 2 days
Deployed
Added
Scope
2
55. Day 10
Design Develop Test
Backlog 1 – 2 days 1 – 5 days 1 – 2 days
Deployed
Added
Scope
2
56. Day 11
Design Develop Test
Backlog 1 – 2 days 1 – 5 days 1 – 2 days
Deployed
Added
Scope
2
57. Result versus Frequency (50 runs)
More Often
25
Frequency of Result
20
15
10
5
1
10 15 20
Less Often
Result Values – For example, Days
58. Result versus Frequency (250 runs)
More Often
25
Frequency of Result
20
15
10
5
1
10 15 20
Less Often
Result Values – For example, Days
59. Result versus Frequency (1000+ runs)
More Often
25
Frequency of Result
20
15
10
5
1
10 15 60
Less Often
Result Values – For example, Days
61. Flaw of Averages
50% 50%
Possible Possible
Outcomes Outcomes
Return to main presentation…
62. Software Development Model
4
3 Blocking 5
Events Added
Defects
Work
2 6
Staff
Work
Vacations
1 7
Columns
& WIP Model …
Return to main presentation…