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Lean Metrics

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Estimating software projects, feature delivery dates, and even task completion times are notoriously difficult and unwieldy even for experienced teams. Guessing the future in terms of gut feeling or …

Estimating software projects, feature delivery dates, and even task completion times are notoriously difficult and unwieldy even for experienced teams. Guessing the future in terms of gut feeling or past experiences is a hit or miss practice that often leaves teams working overtime to meet unrealistic deadlines. Some simple metrics tracking borrowed from Lean software development can help. In this session, you'll learn very simple techniques that enable you to project timelines and determine probabilities that are based on a team's actual performance instead of a guess.

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  • 1. LEAN METRICSHOW TO PREDICT THEFUTUREPhil LedgerwoodPowerPoint is Terrible and I‟m Sorry
  • 2. PLATINUM SPONSORSGOLD SPONSORSSILVER SPONSORS
  • 3. Phil LedgerwoodApplication DeveloperNetchemia, LLCwww.Netchemia.comTwitter: @pledgerwoodLinkedIn: http://www.linkedin.com/in/philledgerwood
  • 4. The Estimation Process
  • 5. Let‟s Play a Game How long does ittake you to go to thestore to buybananas? Traffic? Accident? Johnson Countymoms talking inaisle? Person in front ofyou pays with checkand has no id? Yes, we have no
  • 6. Your Estimates SuckBecause… They are based on your view of your ownproductivity. They assume all unknowns take zero time. You have never written that software before.People are terrible estimators.
  • 7. Stopping the FoolishnessHere Come Metrics!
  • 8. Metrics are AwesomeBecause… They are based on your actual productivity. They factor in unknowns, divergent sizes, andwhen Steve goes on vacation, but he didn‟t tellanyone he was going on vacation until like twodays before he did, and he‟s gone for two weeksand it‟s like, oh great, thanks a lot, Steve. You can identify impediments visually.They are eerily accuratebecause Math.
  • 9. Estimates vs. Metrics Estimates Based on guessing Deviate over longtimespans False sense ofsecurity Can easily begamed Take a long time togenerate Up for debate Pretty much useless Metrics Based on actuals Become moreaccurate over longtimespans Cold, hard reality Can‟t be gamed Take almost no timeto generate Not up for debate Ignore at your peril
  • 10. Scatterplot DiagramThe Control Chart
  • 11. Lead Time Record the date something enters yourworkflow and the date something leaves it. This amount of time is called “Lead Time” End points need some agreed definition When an item is ready for work? When you start work on an item? When an item is ready for release? When an item is actually deployed? Typical Lead Time: Ready for Work >>Deployed
  • 12. Control Chart Data Dates (or day # of project) along the X axis Number of days along Y axis Lead times are plotted on chart as items arecompleted Median Lead Time Standard Deviations Percentages of Lead Time values (ex. “75% ofour lead times are 10 days or less”)
  • 13. What Can This Tell Us? How fast we‟redelivering The likelihood of anitem taking a certainamount of time Predictability When events occurthat screw everythingup Trends in velocity
  • 14. Hey Let‟s See One
  • 15. CFD (But you could probably figure that out)Cumulative Flow Diagram
  • 16. Value Stream How things get from “idea” to “delivered” For developers, it usually looks something likethis: Analysis (Acceptance Criteria) Design (Automated Tests) Code Test Deploy “To Do,” “Doing,” and “Done” is ok for chorelists, but not so good for organization workflowmanagement
  • 17. Cumulative Flow Data Dates (or day # of project) along the X axis Number of work items along Y axis Plot stacked series of # of items in each “valuestream state” from day to day
  • 18. What Can This Tell Us? Horizontal distance: Lead Time Vertical distance: Work in Progress (WIP) Slope of Top Line: Average arrival rate Slope of Bottom Line: Average completion rate
  • 19. LOL WUT? Where are our bottlenecks? Where are things flowing smoothly? What is our predictability? Do we have too much WIP? Are we startingmore than we‟re finishing?
  • 20. And I Care About This Why? “Improvement comes by managing flow, nottrying to get faster.” –Phil Ledgerwood, JustNow, 2013 Improve the flow, and the speed will come. How do you improve flow? Limit WIP Give love to the area that needs it most Look at what is skewing the lines and respond
  • 21. Hey Let‟s See One
  • 22. What Metrics Don‟t Tell You
  • 23. Metrics Tell You What, But NotWhy Charts and numbers are a catalyst forconversation, not a substitute for it You cannot manage “by the numbers” Data is not a substitute for cultural analysis,critical thinking, people‟s stories, or just beingin the trenches. Data can show you your trends and providefodder for discussions, but productivity andworkflow is ultimately a people issue.
  • 24. Appendix A: The Retrospective Are you tired of retrospectives being free formwhine sessions? I am also tired of it. Yourwhining, I mean. The metrics represent your current state. Ask the team what a better state would looklike. Ask the team for ideas on moving closer tothat state. Pick one, try it, and see if the metrics change.
  • 25. I‟m Phil Phil Ledgerwood I work at Netchemia Blog: http://thecuttingledge.com/ Twitter (rare): @pledgerwood LinkedIn (often):http://www.linkedin.com/in/philledgerwood Ask me for a business card or something,because, man, I‟ve got a ton of „em.