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Project Manager Estimate at Completion (PMEAC)
- by Rob Seiler, PMP, Jerry B. Insall, PMP, and Lynn W. Heenan, PMP
Project Manager Estimate at Completion (PMEAC) is the official version — it incorporates the project manager’s professional judgment. Readers will be able to use PMEAC to reduce errors in estimates at completion, save time in communicating completion estimate variances, and reduce the confusion around multiple ways to calculate EAC.
Software Estimation using a Combination of Techniques
- by Klaus Nielsen, MBA, PMI-ACP, PMI-RMP, PMP
Estimation is not one-size-fits-all! Software estimation with a combination of techniques is a bit of an art, but mostly, it relies on the application of best practices. This article presents a wide range of software estimation techniques to help broaden the perspective on estimation from traditional techniques to the new agile techniques.
Project Percent Complete – AC/PMEAC: An Improved Way to Measure Project Progress
- by Rob Seiler, MBA, PE, PMP and Darrel Raynor, MBA, PMP
PM-adjusted project Estimate at Completion (PMEAC) is the project manager’s best estimate of the final cost of a project based on both quantifiable data and the application of professional judgment of future costs on a project. The author explains how the actual costs from an accounting system and the developed PMEAC value can be used to calculate an accurate picture of project status.
Expectation versus Reality: Reducing the Estimation Gap
- by Hrishikesh Karekar, PMP
The benefits to be accrued from having realistic estimates of project costs and timelines cannot be overemphasized. This article explains how optimism bias and strategic misrepresentation can lead to forecasting errors. The author recommends proactive strategies that can be employed by both the buyer and the seller of project services to limit their negative impact.
The Flaw of Averages in Project Management
- by Philip Fahringer, John Hinton, Marc Thibault, and Sam Savage
The common practice of reducing an uncertainty to a single best guess eliminates a lot of information, which leads to the flaw of averages, a set of systematic errors that occur when a single number, usually an average, is substituted for a distribution. Interactive simulation provides intuitive risk dashboards that can be used to detect and manage hidden risks, even for those with no statistical training. This article is accompanied by an interactive simulation model in Excel.
Sizing Software Applications as an Input to Estimation
- by Vajee Uddin, CFPS, PMP
This article will review the estimation of engineering activities in software projects/applications using function points. IT project managers with solid experience in function point analysis are helpful in understanding and substantiating the effort required to complete a software project and have better control over the duration and schedule of the project.
Project Cost Estimating: Caught Between a Rock and a Hard Place
- by Neil Berman, PMP
We will always feel pressure to deliver estimates as quickly as possible; however, although it may seem quicker and cheaper to bypass expert analysis in cost estimation, we may end up costing the organization more during the project than the time and money we would have saved upfront. Instead, start moving away from that hard place; soften the rocks by negotiating with the project’s Activator and Drivers; and, if possible, chisel a pricing catalog to help with future cost estimation.
Dynamic Planning — Bringing the Project Plan to Life
- by J. R. Galati, PMP
Thanks to the introduction of modern, off-the-shelf simulation tools, we can now take our static plans and make them come alive so that we can better model how the plan will execute. Using simulation in your planning process is exciting and can provide rigorous motivation to improve based on the “what-if” nature of simulation. This article describes the benefits of adopting simulation to enhance the planning process.
Estimating Extract, Transform, and Load (ETL) Projects
- by Ben Harden, PMP
In the consulting world, project estimation is a critical component required for the delivery of a successful project. If you estimate correctly, you will deliver a project on time and within budget; get it wrong and you could end up over budget, with an unhappy client and a burned out team. Project estimation for business intelligence and data integration projects is especially difficult, given the number of stakeholders involved across the organization as well as the unknowns of data complexity and quality. In this article, I share my thoughts about the best way to approach a project estimate for an extract, transform, and load (ETL) project.
An Approach to Information Technology Resource Estimation and Scheduling
- by Jim Beinlich, MBA, PMP
The success of any project (particularly information technology projects) hinges on the skills, abilities, and, most importantly, the availability of the resources that will work on the project. Accurately estimating and scheduling these resources are challenging at best and very difficult to near impossible at worst. Resource estimation is difficult, time consuming, and sometimes the most challenging aspect of project management.
Overcoming Behavioral Anomalies of Project Estimation
- by Ravi Raj Kishore, PMP, CSM, ITIL, Six Sigma Greenbelt. Keane, Inc.
Estimation is a critical part of the budgeting process. Most organizations rely on subject matter experts and historical data and deploy various other methods to arrive at accurate estimations. We expect experienced and mature professionals to give accurate estimates, but they can inflate safety margins and buffers in their estimates. Project managers must understand the psychological reasons for a faulty estimate and develop a solution for fixing it.
Prototyping for Effective Estimation and Planning
- by Muddassar Sayed, PMP
Project estimation and planning in the absence of historical data are always challenging. This article is based on an actual fixed-price software development, called project “X,” for a large telecommunications company in Germany, which was executed between June 2009 and February 2010. The project involved a total of 25 people in two different geographic locations. Because the technology mix involved was new (J2EE/Swing), the project organization did not have any historical data to support the estimation process. The actual results were very close to the estimates, with 98% accuracy, and the project was delivered on time and within budget.