SLIM-Estimate
How Does Uncertainty Expressed in SLIM-Estimate Relate to Control Bounds in SLIM-Control? Part III
In the previous articles in this series I presented SLIM-Estimate’s use of uncertainty ranges for size and productivity to quantify project risk, and how to build an estimate that includes contingency amounts that cover your risk exposure. In this post I will identify the project work plan reports and charts that help you manage the contingency reserve. You will see how to use SLIM-Control bounds and default metrics to keep your project on track.
Understand the project work plan documents.
How does uncertainty expressed in SLIM-Estimate relate to Control Bounds in SLIM-Control? Part II
Several months ago, I presented SLIM-Estimate’s use of uncertainty ranges for size and productivity to quantify project risk. Estimating these two parameters using low, most likely, and high values predicts the most probable effort and time required to complete the project. This post shows you how to use SLIM-Estimate’s probability curves to select the estimate solution and associated work plan that includes contingency amounts appropriate to your risk.
Begin with an unconstrained solution
Ditch the Madness: SLIM Your Brackets
Monday morning I received an email that read:
Hi All,
You can set your clocks to it: the birds flying north for spring, daylight savings time, and this email being sent on the Sunday before the tournament begins. That's right, March Madness is upon us my friends, and we’ve officially made it through winter.
The message continued with details about how to participate, but as you can see, it’s time for QSM’s annual March Madness tournament. So how do I justify spending company time filling out brackets? By blogging about how this is actually related to project management. As I went through the exercise of predicting the course of this tournament, I realized that many of the thoughts I had also go through the minds of project managers.
Achieving Goals Begins with Successful Measurement
“You can’t know where you’re going until you know where you’ve been.”
At this point, we’re about one month into 2013 and many of us have abandoned our New Year’s resolutions. Personally, I prefer to set my yearly goals about a month in because it gives me some time to reflect on what I really want to improve without being distracted by everyone’s bandwagon resolutions like getting in shape or eating less junk food.
Why Are Conversion Projects Less Productive than Development?
While doing research on projects counted in function points, the sample size was large enough (over 2000 projects) to allow me to compare the productivity of different project types. The QSM database uses these project categories:
Agile Series Part 2: Stakeholder Satisfaction
When learning something new, people often try to relate the new information back to something they already know in order to help make sense of the new concept or idea. As a psychology major now working in the software world, I’ve found myself relating a lot of what I’m learning back to the psychological theories and concepts I learned in college. Therefore, it is no surprise that upon reading The Twelve Principles of Agile Software, I’ve discovered that many of their principles map to organizational psych concepts.
Data is the New Soil
David McCandless gave a TED talk in July 2010 that focused on pairing data and design to help visualize patterns. In his talk, McCandless takes subsets of data (Facebook status updates, spending, global media panic, etc.) and creates diagrams which expose interesting patterns and trends that you wouldn't think would exist. Although the focus of McCandless' talk was about how to effectively use design to present complex information in a simple way, I was struck by his own claim that data is not the new oil, but rather that data is the new soil. For QSM, this is certainly true!
How do the uncertainty ranges in SLIM-Estimate relate to Control Bounds in SLIM-Control?
I am often asked this question during SLIM Training classes. I remember wondering about that myself. It is a logical question since SLIM-Estimate workbooks are often imported into SLIM-Control to create the baseline project plan. The answer is ‐‐ they are not directly related, because uncertainty ranges, probability curves, and control bounds are designed to perform different tasks. This post is the first in a series looking at risk associated with an estimate, risk of your project plan, and handling deviations from the plan.
Part III: The Caveats
In Part 1 of How Much Estimation? we noted that there is an optimal amount of time and effort that should be spent in producing an estimate based on the target cost of a project and business practice being supported.
In Part 2: Estimate the Estimate, we saw that the formula to calculate this optimal time (as measured at NASA) calculates the Cost of Estimate as the Target_Cost raised to the power 0.35 (approximately the cube root of the Target Cost). The factor that defines the business practice (either by early lifecycle phase or perhaps by the “expected precision” of the estimate) is a linear factor ranging from a value of 24 to a value of 115.
Tuning Effort for Best in Class Analysis and Design
After reading Best Projects/Worst Projects in the QSM IT Almanac, a SLIM-Estimate® user noted that the Best in Class Projects expended around 28% of their total project effort in analysis and design (SLIM Phase II) compared to 10% for the Worst in Class Projects. She wanted to know how she could tune her SLIM-Estimate templates to build in the typical best in class standard for Analysis and Design.
In SLIM-Estimate, effort and duration for phases I and II are calculated as a percentage of Phase III time and effort. To create a template for estimating phases II and III that will automatically allocate 28% of total project effort to analysis and design (Phase II), follow these simple steps.
