New Version 3 of Statistical PERT Normal Edition Coming January 2019!

I’ve been working on Version 3 of Statistical PERT Normal Edition for several weeks now.  The big change with Version 3 are the new visualization features that make it easier to see and understand the statistical probabilities associated with each estimate you make.

The visualization features include:

  • Sparklines, which show the implied, bell-shaped curve based upon your inputs
  • Tri-colored bar charts, which show the left-side, right-side and middle area of a bell-curve.

The Sparklines features will make it easy to immediate see whether an estimate has a lot of uncertainty or not, based upon the shape of the bell-curve.  While you could imply this by looking at the “Most Likely Confidence” selection, this visualization feature makes it easy to scan multiple rows to find those rows that have the most uncertainty.

Sparklines also show whether the 3-point estimate is perfectly symmetrical, or skewed either to the left or right.  This visualization of the 3-point estimate wasn’t possible in the current, Version 2 of SPERT.

The tri-color bar charts will be very helpful to see how much risk there is that a planning estimate may be exceeded.  This can be a powerful tool for project managers who must convey risk to project sponsor unwilling to provide sufficient resources to their project teams.

Aside from these major enhancements, I’m making minor tweaks and changes to further refine the SPERT spreadsheet.

Version 3 will be officially released very shortly after my new Pluralsight course on estimating is published — probably in late January, 2019.  Meanwhile, if you want to download a pre-release of Version 3, it’s available for downloading right now.  Just be aware that I haven’t yet done extensive testing with pre-release versions of Statistical PERT.

Statistical PERT Beta Edition (new Version 2) will also receive the same visualization updates as the SPERT Normal Edition is getting.  SPERT Beta Edition Version 2 will be released in the first half of 2019.

Update Coming to Pluralsight Course in 2019

I recently published a new course on Pluralsight, a leading provider of online course content for IT and creative professionals.  It’s called Scrum Essentials Exam Prep, and it’s a course to help someone be well-prepared to take — and pass — the Professional Scrum Master Level One exam from Scrum.org.

Pluralsight and I are now in the early stages of negotiating a new agreement to create an updated course to replace the original one I did for them back in 2015, Easily Estimate Projects Using Statistics and Excel.  The new course will use Statistical PERT’s forthcoming Version 3, which I hope to have available in January, 2019.  The new course will show users how, exactly, to use and modify the freely-licensed Statistical PERT example workbooks and templates.

A lot has changed with Statistical PERT since 2015 when my original course with Pluralsight was published.  I’m very eager to show everyone all the cool estimation problems that Statistical PERT can solve!

If you’re interested to learn more about this new Pluralsight course, stay tuned to this blog, as I’ll begin writing about the course development here.

How Probable Are Your “Most Likely” Estimates?

Are you using “most likely” estimates in your project schedule?  If so, how likely are your “most likely” estimates?

This week, ProjectManagement.com published an article I wrote that shares the story of a PMO director who was frustrated with the late delivery of his projects.  Although his teams created “most likely” estimates that they asserted could be accomplished with 90% certainty, the reality was that most of the time, their “most likely” outcomes were too optimistic.

Learn how one PMO director used Statistical PERT Beta Edition to model schedule uncertainty, and see the difference between a “most likely” outcome and a “highly probable” outcome.

https://www.projectmanagement.com/articles/370549/Getting-Beta-with-Statistical-PERT

Statistical PERT® Beta Edition Released!

Yesterday, on March 1, 2017, I released the first production release — Version 1.0 — of Statistical PERT® Beta Edition.  It was a long journey to get to that day!  Click here to download it now, or read the press release.

About two and one-half years ago, I pondered over how to model a risk uncertainty based upon expert opinion rather than historical data.  Very often, project managers estimate projects using expert opinion, so the question in my mind was highly relevant to my job.  In fact, in the 15 years I’ve worked as a project manager, and the 15 years I’ve worked as a software developer, I can’t remember any time where I did not rely on expert opinion to estimate the work activities on a project schedule.

At the end of that weekend in October, 2014, I had created something I called SPERT, short for Statistical PERT.  My work was rooted in PERT (Program Evaluation and Review Technique, which every PMI-credentialed project manager knows).  I started with PERT’s simple formula to estimate the mean of an uncertainty.  But the trouble is using the mean is that it only has about a 50/50 chance of being correct.  Most project uncertainties are bell-shaped risks, and the mean of a bell-shaped uncertainty lies at or near the top of the bell-shaped curve, where half the area is to the left of the mean, and half the area is to the right of the mean.

I wrestled with how to come up with a standard deviation for an uncertainty where there was no historical data.  That was the key problem that Statistical PERT cracked.  Once I came up with a way to do that, then it was simply a matter of using Excel’s two normal distribution functions, NORM.DIST and NORM.INV, to come up with probabilistic estimates.

But most project uncertainties aren’t symmetrically shaped.  Most often, things take longer than expected rather than less time than expected.  Most project uncertainties, then, are skewed to the right, where the area under the bell-shaped curve is greater to the right of the mode (and the mean) than to the left.  Using the normal distribution gave “good enough” results, but was there way to approximate a skewed uncertainty using just the built-in, statistical functions of Excel?

The answer was (and is), Yes.  The beta distribution, which is the distribution originally used with PERT, is a wonderfully flexible distribution that can model left-skewed, right-skewed and symmetrical uncertainties.  It does it all!  The challenge with using the beta distribution (and the reason almost no project managers even know about it, much less use it) is that it uses two shape parameters, or arguments in Excel, called alpha and beta, and nobody except statisticians and data geeks know how to choose the right combination of alpha and beta to get a bell-shaped curve that matches the risk nature of an uncertainty.

That was the nut to crack with Statistical PERT Beta Edition.  How could I create an easy-to-use, Excel template that employed Excel’s two beta distribution functions, BETA.DIST and BETA.INV, to model risk uncertainty?  How could the template know what combination of alpha and beta to choose from using as little input from the estimator as possible?

So, 18 months ago I started walking down that road.  I spent a long time using an approach which, later, I jettisoned because it resulted in bell-shaped curves with modes that didn’t match the estimator’s three-point estimate input.  The second approach, which in future blog posts I’ll try to describe, was the one I settled on because it was simple for the estimator to use, could be matched to similar results using SPERT Normal Edition and a regular Monte Carlo simulation, and required nothing new from the estimator than what the original Statistical PERT approach required:

  1. Create a three-point estimate for the uncertainty (minimum, most likely, maximum)
  2. Render a subjective opinion about how likely was the most likely outcome to occur
  3. Select a probabilistic estimate or make a risk-based, range forecast

Statistical PERT Beta Edition is really easy to use.  It has the distinct advantage over its older brother, the Normal Edition, of being able to more accurately represent skewed uncertainties.  Its main disadvantage is that, unlike the Normal Edition, it cannot be easily changed, and you can’t employ the approach to a blank spreadsheet like you can with the Normal Edition.  But as long as you live within those constraints, downloading and then using a SPERT Beta Edition example workbook or template is wonderfully easy for the estimator, who is shielded from having to interact directly with Excel’s statistical functions.  The template does that for you.

I look forward to sharing both editions of Statistical PERT with project managers, business analysts, educators and students throughout 2017 and beyond.  It’s my firm conviction that probabilistic estimates create better alignment among people on the nature of an uncertainty, and it fosters better decision-making among those responsible to make choices about future uncertainties, such as how long a project might take, or how much it might cost.

I welcome your feedback!  Feel free to reach out to me with any questions about Statistical PERT.

Statistical PERT Version 2.1 Released

Today, I released a minor version update to Statistical PERT® Normal Edition.  Version 2.1 adds a new worksheet, SPERT Normal® (Mixed entry).  This new worksheet offers estimators a very flexible way to enter uncertainties into a SPERT worksheet.  Using this worksheet, estimators can very easily create three-point estimates with little effort using heuristics to generate the minimum and maximum extreme values for each uncertainty, and then estimators can selectively choose to change those auto-created values with row-level, exception-based values.

In this worksheet, estimators have three ways to create the necessary 3-point estimate that every Statistical PERT calculation requires.  Firstly, two global heuristics are specified above the Minimum and Maximum column headings.  The Minimum heuristic will reduce the value entered in the Most Likely column by a percentage value.  Similarly, the Maximum heuristic will increase the value in the Most Likely column by a percentage value.  These two heuristics will affect all entered uncertainties in this particular worksheet.

Estimators can, at the row level, override any globally-derived Minimum and Maximum point-estimates in one of two ways,  One way to do that is specify a row-level heuristic by selecting a percentage value in either the Min % or Max % column.  These percentages only affect a single row.  They both either reduce or increase the value in the Most Likely column, just like the global heuristics do, but only at a single row level.  The other way estimators can override a calculated minimum and/or maximum point-estimate is to manually enter a value in the Min point and/or Max point column.  Manually entering a minimum and/or maximum point-estimate will always override any point-estimate value that is a calculated result.

Most Likely point-estimates must always be manually entered by an estimator; there is no built-in way to calculate a Most Likely point-estimate.  But once a Most Likely point-estimate is entered, an estimator can either use a global or row-level heuristic to create the minimum and/or maximum point-estimates for each uncertainty, or estimators can continue to manually enter specific values for those point-estimates.

While this new worksheet may look a little more complex because of the extra columns, using it is still very simple.  This simply makes it easier for an estimator to create exception-based point-estimates when dealing with a large number of uncertainties in the SPERT worksheet.

The example workbook includes examples of all three ways to create minimum and maximum point-estimates.  The first three rows (ID 1, 2 and 3) have minimum and maximum point-estimates that are created from the global heuristics.  For these three uncertainties, minimum point-estimates are 50% less than the most likely point-estimate, and maximum point-estimates are 100% greater than the most likely point-estimate.

ID 4, however, overrides the global heuristic with two row-level heuristics:  the minimum point-estimate is only 10% less than the most likely outcome, and the maximum point-estimate is only 50% greater.

ID 5 uses a row-level heuristic to reduce the most likely outcome by 25%, but the maximum point-estimate (160) is manually entered and overrides the global heuristic for creating maximum point-estimates.

ID 6 specifies a value of 40 for the minimum point-estimate, which overrides the global heuristic for creating minimum point-estimates, and the maximum point-estimate of 200 is manually entered, too.

ID 7 specifies 80 for the minimum point-estimate, but uses a row-level heuristic to create a maximum point-estimate that is 75% greater than ID 7’s most likely value.

So, the new Mixed entry worksheet offers estimators the greatest amount of flexibility to create 3-point estimates in the easiest way possible.  Download Version 2.1 now!

Statistical PERT Comparison with @Risk

From the very beginning of the creation of Statistical PERT, I have used Palisade’s excellent risk analysis Excel add-in program, @Risk, to compare Statistical PERT’s probabilistic results with those obtained by @Risk using, mostly, the RiskPERT function that comes with @Risk.  RiskPERT is a special form of the beta distribution that models the kind of probability curve traditionally associated with PERT.  I very often gauge Statistical PERT’s accuracy against the results from a Monte Carlo simulation using @Risk.

As I ready the release of Statistical PERT – Normal Edition Version 2 and Statistical PERT – Beta Edition Version 0.6, I wanted to compare the results of these new versions with what I would get using the RiskPERT function.  To make this comparison, I have to set Statistical PERT to use “Medium Confidence” in the Most LIkely outcome to obtain the closet match to a RiskPERT distribution curve.

In performing a sample of six different three-point estimates (only one of which implied a symmetrical bell-curve, one which implied a left-skewed curve, and four which implied right-skewed curves), I was very pleased with the results.

The Normal Edition, of course, extends the implied probability curve to infinity both to the left and to the right; hence, the comparison results with RiskPERT tends to be very different at the extreme ends (1%, 5%, 95% and 99%).  But the Normal Edition performed admirably even though it fits asymmetrical bell-curves to a symmetrical normal curve.

The Beta Edition, as expected, did very well tracking to RiskPERT even at the extreme ends, and it tended to track more closely to RiskPERT overall than what the Normal Edition could do.  This is, in fact, the key reason I wanted to develop a Beta Edition of Statistical PERT was to improve the implied fitting of a probability curve to a 3-point estimate that implied a skewed uncertainty.

Overall, the difference between an estimate from @Risk and a SPERT estimate was usually around 2% or less, and very rarely was it more than 3.5%.  SPERT-Beta’s average difference with RiskPERT was 1.6% across all the 3-point estimates I tested and never greater than 2.2% except when dealing with a perfectly symmetrical bell curve.  SPERT-Beta’s kurtosis for a normal curve is slightly lower than the kurtosis used by RiskPERT, which explains why SPERT-Normal did a better job tracking to RiskPERT for a normal bell-curve than what SPERT-Beta did.

Download an Excel spreadsheet showing a full comparison of @Risk’s RiskPERT function, Statistical PERT – Normal Edition, and Statistical PERT – Beta Edition.  And stay tuned:  these new releases of Statistical PERT happen on January 2, 2017!

 

What’s New in Version 2

Like Santa in the North Pole, I’ve been working hard in my workshop, preparing the new release of Statistical PERT which I’ll be tentatively releasing on Monday, January 2, 2017.

Initially, Version 2 of Statistical PERT (Normal Edition) was just going to offer users an easy way to estimate using just a single point-estimate and heuristics to generate the minimum and maximum point-estimates, which are necessary to create an implied, bell-shaped probability curve.  But as I worked to prepare for Statistical PERT – Beta Edition, which uses Excel’s two beta distribution functions (BETA.DIST and BETA.INV), I found myself doing a lot more to the original Statistical PERT spreadsheet (which is now dubbed Statistical PERT – Normal Edition, since it uses Excel’s two normal distribution functions, NORM.DIST and NORM.INV).

The biggest change, philosophically, is that when I release Version 2 of SPERT-Normal, there will only be one example workbook and one template, rather than the six example workbooks and six templates that are available under the current 1.4 version.  Instead of creating special “flavors” of Statistical PERT for task duration, expenses, revenue, agile sprints, event attendance and portfolios, there will only be one “flavor.”  This will make it easier for me to release new versions of Statistical PERT without the added effort of updating multiple example workbooks and templates.  But the good news is that Version 2 will make it easy to estimate any bell-shaped uncertainty by letting estimators choose whether they want to view the left-side or right-side area of the implied probability curve, and whether they want to see currency formatting or not in all the different cells.

So for task duration, for instance, most estimators will want a high-probability estimate with 70% confidence or higher.  Version 2 of SPERT-Normal lets you obtain cumulative probability estimates from the left-side area of the implied distribution curve.  But if you wanted to estimate revenue, and you wanted a conservative revenue estimate, you might want to choose an estimate that has a very high probability of being exceeded.  In that case, you want would want to see the cumulative probability from the right-side area.  In Version 2, you can toggle between the left-side and right-side area, so you can either see the probability of a SPERT estimate being equal to or greater than an uncertainty (the left-side area), or you can choose to see the likelihood of a SPERT estimate being exceeded by the uncertainty (the right-side area).

Aside from that change, here’s what else is new in Version 2 of Statistical PERT – Normal Edition:

  • A separate worksheet for entering 1-point estimates that will use heuristics to auto-create a 3-point estimate
  • A new toggle that lets you choose whether to use currency formatting or not
  • New conditional formatting for the sum of all uncertainties
  • Removal of hidden columns that aren’t needed
  • Improved visual layout and formatting throughout
  • New hover comments for each column’s heading (some worksheets)

Statistical PERT – Beta Edition won’t have its Version 1 release until later in 1Q-2017, but the January release of Version 0.6 will be vastly improved over all preceding development versions, and will look very similar to SPERT-Normal Version 2.  And I’ll make SPERT-Beta available on the main site instead of only being available on the Beta Blog.

So, look forward to a great new year with great new versions of Statistical PERT!

New Version, New Edition of Statistical PERT

Statistical PERT – Normal Edition, New Version 2.0

I’m excited to announce that early in 2017, I will be releasing a new 2.0 version of the original Statistical PERT Excel workbooks and templates that I began creating in 2015.  Version 2.0 will introduce one-point, probabilistic estimation by letting estimators choose a heuristic to calculate both the minimum and maximum point-estimates in a three-point estimate.

A heuristic is well-defined on Wikipedia as, “any approach to problem solving, learning, or discovery that employs a practical method not guaranteed to be optimal or perfect, but sufficient for the immediate goals. Where finding an optimal solution is impossible or impractical, heuristic methods can be used to speed up the process of finding a satisfactory solution. Heuristics can be mental shortcuts that ease the cognitive load of making a decision.”

What I’ve learned in the two years that I and others have used Statistical PERT is that sometimes people just don’t want to go through the mental work of figuring out a 3-point estimate for an uncertain outcome.  Sometimes, it’s easier to start with a single estimate value and have the rest of the bell-shape curve implicitly created for you.  And although that may not be as precise and optimal as specifying all three points of a 3-point estimate, using a heuristic to create two of the three points in a 3-point estimate may be “good enough” for project planning purposes.

In the 2017 release of Statistical PERT, estimators may choose to employ a simple heuristic that takes a single point-estimate — the most likely outcome — and reduces it by a specified percentage to create the minimum point-estimate, and increases the most likely outcome by a specified percentage to create the maximum point-estimate.  SPERT estimators can choose separate heuristics for the minimum and maximum calculations.

For example, if I specify a value of 100 for the most likely outcome (the unit of measure doesn’t matter), I can say that the minimum point-estimate is 25% less than the most likely outcome, and the derived minimum would be 75.  Then I can say that the maximum point-estimate is 50% greater than the most likely outcome, and the derived maximum would be 150.  The resulting three-point estimate used to model the uncertainty would be (75, 100, 150).  I only had to set the minimum and maximum heuristics once for the entire worksheet, and all my subsequent one-point estimates will use heuristics to automatically create the minimum and maximum point-estimates.  Obviously, the math formulas involved are exceedingly simple, so the innovation here is mostly just about ease-of-use in creating three-point estimates, rather than technical innovation.

Estimators will be able to override heuristically-derived minimum and maximum point-estimates by just entering an actual, numeric value in place of the formula used to create minimum and maximum point-estimates.  All SPERT download files will still offer a separate worksheet to enter 3-point estimates without using heuristics, too.  Heuristics, then, are in addition to what is available in a Statistical PERT worksheet, not a replacement of anything currently available.

All existing Statistical PERT Excel downloads will be re-labeled as “Statistical PERT – Normal Edition” to reflect that all the currently-available downloads use Excel’s built-in normal distribution functions, NORM.DIST and NORM.INV.

Which brings me to the other exciting change for 2017….

Statistical PERT – Beta Edition, New Version 1.0

It’s been one year since I began a new quest to create a simple way to create probabilistic estimates using the more-flexible beta distribution, which handles skewed uncertainties more accurately than the normal distribution does.  I wanted to use Excel’s two beta distributions, BETA.DIST and BETA.INV in a spreadsheet that was just as easy to use as the original SPERT Excel spreadsheets, which hid the complexity of creating a standard deviation that the normal distribution functions require.

Excel’s beta distribution functions use two shape parameters, alpha and beta, which determine the precise shape of the beta curve.  Specifying alpha and beta shape parameters is not at all intuitive, however, so the goal was to create a technique that figures out a good approximation of alpha and beta based solely upon the estimator’s three-point estimate and the estimator’s subjective opinion about how likely the most likely outcome really is (just like the original Statistical PERT spreadsheets using the normal distribution!).

The details behind Statistical PERT – Beta Edition are too much to include in this blog post, but you can see a development build of the forthcoming version 1.0 that will be released in early 2017, along with the version 2.0 of Statistical PERT – Normal Edition.

Together, both editions of Statistical PERT will let anyone easily make estimates using Excel’s built-in, statistical functions.  And all Statistical PERT versions and editions will continue to be freely licensed under the GNU GPL, so you can download, use, modify and share any SPERT file like you’ve always been able to do.

Download a development-only build of Statistical PERT – Beta Edition here.

Palisade Risk Conference 2016

palisade-risk-conference-2016

I’ll be heading to New Orleans for the first time in my life in a few weeks.  The Palisade Risk Conference 2016 is being held on November 1 & 2, 2016.  I have used Palisade’s DecisionTools Suite since my first year in graduate school (in the MSPM program at George Washington University) back in 2010.  I love the power and utility of @Risk, the Excel add-in program for doing Monte Carlo simulations.  @Risk has helped me create Statistical PERT because I use @Risk simulations to compare against and validate my Statistical PERT approach and results.  Without @Risk, there would be no Statistical PERT.

I liken Statistical PERT to a screwdriver:  simple, easy-to-use, solves a lot of problems, inexpensive (Statistical PERT is free, actually).  By contrast, @Risk is complex, not-as-easy-to-use (but still easy for rudimentary modeling), solves a whole lot more problems, but is expensive.  Whereas everyone has a screwdriver in their junk drawer or garage, not everyone has, say, a miter saw or some other, expensive power tool.

If you’re planning attending the Risk Conference in New Orleans, hit me up!

Predicting vs. Forecasting (Part 2)

storm

Yesterday, Hurricane Matthew swept passed my home in Boca Raton, Florida.  For the U.S., it’s caused some property damage and a few people died because 911 personnel couldn’t get to those few that had life-endangering emergencies.  In Haiti, the storm wreaked havoc on that poor nation, and hundreds have died.  ;'(

Weather forecasters make forecasts.  They make predictions, too, but we don’t call it weather predicting, we call it weather forecasting.  What’s the difference?

A prediction is a single outcome of what a future uncertainty looks like.  It ignores the possible many other outcomes, some of which are probable, some of which are improbable.

Forecasting, however, recognizes that there are many possible, future outcomes for a given uncertainty.  Some of those outcomes are improbable, some are more probable.

For hurricanes, weather forecasters use the familiar “cone of uncertainty” which looks like a funnel.  The narrow part of the funnel is the expected path of the eye of the hurricane that’s nearest to where the eye of the hurricane currently is.  The wide part of the cone or funnel is three of five days away.  Anyone who is familiar with agile estimation is likely familiar with the cone of uncertainty because it works the same way.  Agile teams can pretty accurately predict what their velocity will be in the next sprint, but it’s hard to estimate what they’ll get done three months from now.

Project managers ought to become skilled at creating project forecasts instead of project predictions.  We may still need to create predictions for schedule and budget for our project sponsors who authorize and fund projects, but the better way to align expectations among all key stakeholders and improve executive decision-making is to make forecasts — not predictions.

If we have to offer predictions — a single budget number for a project, or a single date on which a project will be complete — we ought to at least offer that budget number or calendar date with a confidence level:  “With 90% certainty, the project will cost $800,000 or less, and, with 90% confidence, we will finish the project by March 31.”

When we share predictions with calculated confidence levels, we implicitly allow that the prediction may not come to pass (and how likely is that risk).  If a project sponsor demands greater assurance that the project will be done, we can offer other, more confident predictions (which naturally cost more money and take more time).  If a project sponsor wants to shrink the budget and/or schedule, we can do that too — and then share the risk that the budget and schedule will fail using easily understood probabilities.