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Rapid Estimation for Agile and Conventional Projects,[object Object],cPrime, Inc.,[object Object],4100 E. Third Ave, Suite 205,[object Object],Foster City, CA 94404,[object Object],650-931-1651,[object Object],www.cprime.com,[object Object],The leader in training and consulting for project management and agile development,[object Object]
Overview,[object Object],The purpose of this course is to teach you how to provide good estimates for project tasks, quickly, using some best practices in “expert estimation”,[object Object],You will also learn how uncertainty limits our ability to make accurate estimates, and how we can reduce the effects of uncertainty to produce better estimates,[object Object]
Outline,[object Object],The Relationship between Uncertainty and Estimation,[object Object],Techniques for Expert Estimation,[object Object],Example of the “Planning Poker®” method,[object Object]
Outline,[object Object],The Relationship between Uncertainty and Estimation,[object Object],Techniques for Expert Estimation,[object Object],Example of the “Planning Poker®” method,[object Object]
Estimation is Necessary for any Planning Process,[object Object],We can estimate anything that can be quantified,[object Object],Time,[object Object],Resources,[object Object],Materials,[object Object],Common examples:,[object Object],Waterfall projects estimate effort in order to predict the schedule and cost of a project,[object Object],Agile projects estimate effort in order to predict scope that can be implemented for a fixed schedule and known resources,[object Object],We may estimate effort required for,[object Object],Scope: A set of features, or requirements,[object Object],Task: An activities performed to accomplish necessary work,[object Object],For convenience, we will use “task” throughout,[object Object]
We Create Schedules Based on Task Estimates,[object Object],A schedule contains a set of tasks,[object Object],Example: Remodel a House,[object Object],Remodel Kitchen,[object Object],Remodel Bedroom,[object Object],Remodel Living Room,[object Object],Each task takes time. To plan a schedule, we need to know ,[object Object],Effort required per task,[object Object],Resources available per task,[object Object],The logical sequencing of tasks,[object Object],We will focus on effort estimates in this course,[object Object]
Estimating a Single Task is Challenging,[object Object],Uncertainties arise in many ways. Some examples:,[object Object],Incomplete understanding of scope,[object Object],What features are assumed but not understood?,[object Object],Incomplete understanding of work per scope,[object Object],What bits of work did we omit?,[object Object],Imperfect understanding of how much work is required even when the task is well understood,[object Object],Variance due to skill level, materials issues, etc.,[object Object],Inability to forecast the unexpected,[object Object],What if the paint is out of stock?,[object Object]
Uncertainty Decreases as Scope Decreases,[object Object],Big tasks have more uncertainty than small tasks,[object Object],We can reduce uncertainty by breaking big tasks into smaller tasks, and estimating smaller tasks.,[object Object],“Remodel Bedroom” becomes,[object Object],Remove old carpet,[object Object],Paint room,[object Object],Cut new carpet to fit,[object Object],Install new carpet,[object Object],If we study and estimate each smaller task, we can get a better estimate for the larger task to “Remodel Bedroom”,[object Object]
So why not Make Tasks very Small?,[object Object],Isn’t it better to make lots of small tasks?,[object Object],Won’t we get much better estimates this way?,[object Object],Only to a point,[object Object],Two factors limit the usefulness of breaking work into a large number of small tasks,[object Object],First, estimating many small tasks takes too long,[object Object],Second, relative error decreases with scope only so far. There is little benefit to breaking a task with a 20% relative error into five smaller tasks which also have 20% errors.,[object Object],We’ll address the issue of “granularity” next,[object Object]
We Need to Pick the Right Granularity,[object Object],“Granularity” means size, or level of detail.,[object Object],Choose a granularity that is practical for estimation,[object Object],Too big: We can’t estimate with any reliability,[object Object],Too small: Estimation of all items takes too long to be practical,[object Object],Choose granularity appropriate to the project’s stage,[object Object],Initial planning needs reasonable estimates quickly,[object Object],Select “coarse-grained“ tasks that are small enough to estimate with moderate confidence, large enough to work through all of them in reasonable time,[object Object],Detailed planning may be required at a later stage,[object Object],Break coarse-grained subsets into fine-grained tasks,[object Object],Estimate fine-grained tasks,[object Object],Aggregate to improve coarse-grained estimates,[object Object],Adjust plans based on results,[object Object]
Estimation Errors Behave in Surprising Ways,[object Object],Each task estimate has some uncertainty,[object Object],Tasks are more likely to take longer than estimated, than shorter,[object Object],First reason is logistical: We omitted some work in our estimation,[object Object],Second reason is mathematical: There is more room for work to grow (be over estimate) than shrink (be under estimate),[object Object],Example: Suppose we estimate “Paint Bedroom” at 3 person-days,[object Object],Work can never be under the estimate by more than 3 days,[object Object],Work can easily be over the estimate by more than 3 days,[object Object],This observation has important implications for scheduling,[object Object]
Think of Uncertainties as Factors, not Increments,[object Object],We cannot say a task should complete in “X plus or minus Y days,” and have this work for any X and Y,[object Object],We can say that a task should complete in the range described by an uncertainty factor F, between “X/F” and “X × F,” and have this work for any F,[object Object],Example:  “Paint Bedroom” is estimated at 3 days, with an uncertainty factor of 2,[object Object],Most likely case is 3 days,[object Object],Best case is 1.5 days (under by 1.5 days),[object Object],Worst case is 6 days (over by 3 days),[object Object],More sophisticated models rely on lognormal probability distributions and Monte Carlo methods, but this simple model shows the right kind of behavior,[object Object]
Errors Add Up in Surprising Ways,[object Object],Suppose 10 tasks have the same estimate of 10 days,[object Object],The sum is 100 days. Call this the “naïve schedule.”,[object Object],Now say each task has an uncertainty factor F = 2,[object Object],The worst case is when every task is slow by 2:,[object Object],This means the project takes 200 days,[object Object],Ratio of Actual to Expected = F,[object Object],Not good, but not very likely,[object Object],A more typical case will have some tasks under, some over,[object Object],Assume half are faster by 2, at 5 days (under by 5),[object Object],Assume half are slower by 2, at 20 days (over by 15),[object Object],Total time is 5 x 5 + 5 x 20 =  125 days,[object Object],Still significantly over the naïve schedule of 100 days,[object Object]
Errors Add Up in Surprising Ways,[object Object],The factor-of-two uncertainty for tasks added 25% to the actual time, compared to estimate,[object Object],Ratio of Actual to Expected = (F + 1/F) / 2,[object Object],Conclusion: Actual schedule will always exceed sum of most-likely task durations if uncertainty exists,[object Object],We see this in a simple scenario,[object Object],Expect real-life cases to be more complex, but not better,[object Object],Remember, the worst case has no upper bound!,[object Object]
How do we Deal with Uncertainty?,[object Object],We cannot eliminate uncertainty, only cope with it,[object Object],We cope by reducing it to a practical minimum,[object Object],We design our process to handle uncertainty gracefully,[object Object],How we deal with uncertainty depends on the process,[object Object],We add buffers to waterfall schedules to preserve scope,[object Object],We adjust scope for agile projects to preserve schedule,[object Object]
What Lessons should we Learn about Uncertainty?,[object Object],Estimation is prone to uncertainty,[object Object],Smaller things are easier to estimate than bigger things,[object Object],Breaking work into many small tasks helps, but only to a point,[object Object],We may not have the time to estimate many small tasks,[object Object],If breaking a task into smaller tasks doesn’t decrease the uncertainty factor, there is no benefit to the additional breakdown,[object Object],Uncertainties for small tasks do not cancel. They accumulate, which limits the value of breaking large tasks down.,[object Object],The process we are using must take uncertainty into account, and not assume that the future can be predicted accurately,[object Object]
Outline,[object Object],The Relationship between Uncertainty and Estimation,[object Object],Techniques for Expert Estimation,[object Object],Example of the “Planning Poker®” method,[object Object]
Practical Guidance for Estimation,[object Object],We will now provide some practical guidance for effective estimation,[object Object],We will cover,[object Object],Standard tools and techniques for estimation, from the PMBOK®,[object Object],The Delphi and Wideband Delphi methods,[object Object],A modern, and fast, version of the Wideband Delphi method known as “Planning Poker®”,[object Object]
When we  Estimate, we Rely on Expertise and Experience,[object Object],We rely on three basic (and overlapping) tools to produce estimates,[object Object],These are described in the PMBOK®,[object Object],All rely on past experience,[object Object],The three tools are,[object Object],Expert Judgment,[object Object],Analogous Estimating,[object Object],Parametric Estimating,[object Object]
The Three Tools for Estimation,[object Object],Expert Judgment,[object Object],Experts with experience in the field produce estimates based on their knowledge and history of past projects,[object Object],Analogous Estimating,[object Object],Estimators identify specific analogs to the work, in past projects, and estimate based on known effort for those analogs,[object Object],Sometimes called “Affinity Estimating,” when used to estimate many tasks quickly by comparison to known tasks,[object Object],Parametric Modeling,[object Object],Estimators build quantitative models that predict effort, based on historical data,[object Object],Useful when inputs are quantitative: Square feet of carpet, linear miles of road, etc.,[object Object],Often not possible: Software development, unique projects, etc,[object Object]
How do we Use Time wisely when Estimating?,[object Object],Estimation is time-consuming—Be aware of the cost!,[object Object],Trade off between cost and benefits of estimation efforts,[object Object],[object Object]
More effort doesn’t always yield better results
Avoid “analysis paralysis!”
Good enough, now, beats best possible, later
We want to get reasonable estimates quickly
How do we get good practical results from a set of experts?
How do we get those results quickly?,[object Object]
Delphi Methods Tap “Collective Wisdom”,[object Object],Important characteristics of this approach include,[object Object],Reliance on a Team of experts, not individuals, for estimation,[object Object],Avoidance of expert bias (“anchoring”),[object Object],Focus on variance to improve estimates,[object Object]
Reliance on a Team,[object Object],We draw on a team of estimators, not a single person, for each estimate,[object Object],The Team has more knowledge than any individual,[object Object],Tapping the Team’s “collective wisdom” yields greater understanding and better estimates than one person can provide,[object Object],Best case: Estimators are the people who will also do the work,[object Object],Do this whenever possible,[object Object]
Avoidance of Expert Bias,[object Object],“Anchoring” refers to the tendency of estimators to defer to the judgment of someone they believe to be an expert,[object Object],Team members often have different degrees of expertise in different areas, and are aware of this,[object Object],If the “expert” gives his opinion first, other estimators may say nothing, or simply agree,[object Object],We aren’t tapping collective wisdom when this happens,[object Object],We’re just getting one estimate, several times,[object Object],We reduce the problem of anchoring by gathering all responses anonymously before revealing the results,[object Object]
Focus on Variance to Improve Estimates,[object Object],Different estimators have different backgrounds, different areas of expertise, and consider different factors for each item estimated,[object Object],These differences usually lead to a range of estimates,[object Object],Discussion about why the estimates differ produces insights into assumptions and issues,[object Object],These insights, once shared, usually produce convergence of estimates over time, to more reliable values,[object Object],Failure to converge indicates unresolved issues that require further study,[object Object]
The Planning Poker® Method Taps Wisdom Quickly,[object Object],This version of Wideband Delphi is very quick,[object Object],It is popular for Agile projects, but useful for any kind,[object Object],Planning Poker® is a Team-based iterative voting process that converges to an estimate,[object Object],Purpose is to find “good enough” estimate quickly, not best possible estimate,[object Object],It uses Planning Poker® cards (or equivalent) to show individual estimates,[object Object],Cards are not anonymous, but do prevent anchoring,[object Object],PLANNING POKER® is a reg. trademark of Mountain Goat Software, LLC,[object Object]
Estimates are Restricted to a Pre-defined Set,[object Object],Only allowed values are 0, ½, 1, 2, 3, 5, 8, 13, 20, 40, 100, ∞, ? ,[object Object],Initial integer values are from the Fibonacci sequence,[object Object],“?” means “I don’t know enough to estimate”,[object Object], “∞”(infinity) means “It’s so big there’s no point in estimating it”,[object Object],Spacing increases with size because absolute uncertainty increases with size,[object Object],We restrict allowed values to discourage unproductive debate (“Is it 13 or 14?” Who cares?),[object Object],Granularity may be too large if estimates are at 20 or above,[object Object],Consider breaking the item into smaller pieces,[object Object],The specific sequence of values is © 2007 by Mountain Goat Software, LLC,[object Object]
Estimates have Units,[object Object],Everything that we estimate has some kind of unit,[object Object],Material goods have weight in pounds, volume in liters, length in miles, and so forth,[object Object],Tasks have effort-based units (e.g., person-days),[object Object],Requirements (“Stories,” in agile terms) do not have unique units. Possibilities include,[object Object],Person-days (for total implementation effort),[object Object],Function points (for complexity of software),[object Object],Story points (for complexity of agile Stories),[object Object],Units should be chosen to be what works best for the organization, especially the estimators and implementers,[object Object]
Three Roles Participate in the Estimation Process,[object Object],Facilitator,[object Object],Often provides quality control for specifications to be estimated,[object Object],Runs the estimation meeting, enforces schedule, and keeps the Team focused and moving,[object Object],Keeps discussions short and productive in meetings,[object Object],Re-focuses or terminates non-productive discussions,[object Object],Requirements Owner,[object Object],Authors and/or is the authority on the items to be estimated,[object Object],Answers questions to clarify requirements,[object Object],Team Members (Estimators),[object Object],Provide estimates,[object Object],Discuss issues and ask questions to improve their understanding,[object Object]
Estimation Meetings have Pre-requisites,[object Object],Time, date, and duration of meeting are set,[object Object],Decide duration (“Time box”) in advance (e.g., one hour), and stick to it,[object Object],Items to estimate have been identified,[object Object],Team members have reviewed and discussed all items prior to the meeting,[object Object],Time is allocated to review the items in advance,[object Object],They investigate items, as appropriate,[object Object],They bring open issues and questions about requirements and implementation to the meeting,[object Object]
How to Conduct a Planning-Poker® Estimation Meeting,[object Object],Facilitator reads item to be estimated, moderates brief discussion to clarify details, and calls for estimates.,[object Object],Each Estimator places estimate face down, hiding the value.,[object Object],Facilitator calls for vote, and all Estimators turn over cards at the same time.,[object Object],If all cards agree, their value is recorded as the estimate.,[object Object],Otherwise, Facilitator asks high and low Estimators to explain their reasoning, and moderates brief discussion to clarify issues.,[object Object],Repeat 2-5 until estimates converge.,[object Object]
Outline,[object Object],The Relationship between Uncertainty and Estimation,[object Object],Techniques for Expert Estimation,[object Object],Example of the “Planning Poker®” method,[object Object]
Example Walk-Through for Planning Poker®,[object Object],We will use this technique to estimate, “How many chickens are required for a dinner party for twenty people?”,[object Object],The Facilitator is Ralph Runner,[object Object],The Requirements Owner is Debbie Diner,[object Object],The Estimation Team has three members,[object Object],Bob Cook,[object Object],Sue Chef,[object Object],Ted Baker,[object Object],We begin with the first round of voting, and follow the process through to a final estimate,[object Object]
Round 1 Vote,[object Object],Ralph reads the description, and the Team has a short discussion to clarify a few issues.,[object Object],Ralph counts down: “5, 4, 3, 2, 1, Vote!”,[object Object],Bob has 20,[object Object],Sue has 13,[object Object],Ted has 5,[object Object],Ralph asks high and low voters for their reasoning.,[object Object],Bob says, “I can eat a whole chicken, so we need one per person.”,[object Object],Ted says, “I thought one chicken would feed three people, and we’d have some vegetarians.”,[object Object],Sue has nothing to add. ,[object Object]
Discussion after Round 1,[object Object],Ralph asks Patty to respond.,[object Object],Patty says, “Oh, I wrote ‘chicken,’ but I was thinking ‘squab,’ so use squab instead of chicken. And we’ll probably have one-third vegetarians, who will get mushroom risotto.”,[object Object],Sue asks, “What’s a squab?”,[object Object],Patty says, “It’s what you call a pigeon when you cook it.”,[object Object],Ted says he doesn’t want to eat a pigeon. He starts to tell a long joke about pigeons, but Ralph says, “Hey, folks, let’s stay focused!”,[object Object],Bob asks, “How many squabs does one person eat?”,[object Object],Patty says, “It’s one squab per person.”,[object Object],There are no more questions, so Ralph calls for a new vote.,[object Object]
Round 2,[object Object],Ralph counts down: “5, 4, 3, 2, 1, Vote!”,[object Object],Bob has 5,[object Object],Sue has 13,[object Object],Ted has 20,[object Object],Ralph asks high and low voters for their reasoning.,[object Object],Bob says, “Ted was right last time. Most people won’t eat pigeons. They’ll have risotto.”,[object Object],Ted says, “Bob was right last time. We need 20 to cover late-comers and spoilage.”,[object Object],Sue adds, “Thirteen is just right for one-third vegetarians.”,[object Object]
Discussion after Round 2,[object Object],Ralph asks Patty to respond.,[object Object],Patty says, “I know who will be coming, and the non-vegetarians will love the squab. I don’t want to buy extras, because the squabs are very expensive. Also, the risotto is very good, we’ll have plenty, and I’ve told everyone that it’s first-come, first-served.”,[object Object],Sue asks, “What kind of wine do you serve with squab? White or red?”,[object Object],When the wine discussion threatens to drag on, Ralph reminds everyone that they have a lot of estimation to do in a short time, and need to move quickly.,[object Object],There are no more questions, so Ralph calls for a new vote.,[object Object]

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