One of the scheduling tools at the project manager’s disposal is the Critical Path Method, the outcome of which would be:
1. A list of all activities required to complete the project
2. The time (duration) that each activity will take
3. The dependencies between the activities.
With the above, the longest path of planned activities can be identified, and the “critical” activities (i.e those activities whose combined duration dictate how long the duration is planned to be) are identified.
There are a number of methodological issues with the Critical Path Method, one of which is that the view used to identified the critical path and the tasks it includes is a static view, correct at a point in time, and does not take into account the likelihood of any task on the project schedule taking longer (or shorter) than initially planned.
Let’s look at the following example:
Simply viewing at this schedule, without taking into account other supporting information could be misleading. One issue that could substantially affect the accuracy of this view is the availability (or lack) of resources (see elaboration on this issue in my earlier post “The Critical Path Does not Tell You Everything You Need to Know About Your Project Constraints“). Another factor that needs to be examined is the level of comfort, or the likelihood of achieving the planned duration.
Having examined the above schedule, would you change your view of this project given the following additional information?
The table above introduces further information regarding the Optimistic, Pessimistic and Most Likely duration for each of these tasks. It clearly demonstrates that although tasks 2 – 5 share a Most Likely Duration, the level of risk associated with each is gradually increasing, from 5 days in task1 to 9 days in task 5.
Utilizing the above added information to better understand the risk dimension associated with the schedule requires the execution of a sensitivity analysis.
Sensitivity Analysis is defined as a “Simulation analysis in which key quantitative assumptions and computations (underlying a decision, estimate, or project) are changed systematically to assess their effect on the final outcome. Employed commonly in evaluation of the overall risk or in identification of critical factors, it attempts to predict alternative outcomes of the same course of action. In comparison, contingency analysis uses qualitative assumptions to paint different scenarios. Also called what-if analysis“.
In other words, a greater insight into the risks hidden behind the project schedule can be derived by adjusting the various tasks’ durations (within their PERT parameters) and confirming, for each such variation, whether a task is included or excluded from the critical path. Performing such adjustments a large number of times would result in an indication of how many times, or in what percentage of times, did any particular task appear on the critical path. The larger the frequency, the greater the need to examine the task and put in place the mechanism to ensure that the task does not negatively deviate from its scheduled duration.
Just to wrap this up, let’s look at the schedule from two additional, extreme perspectives:
The following is a Critical Path view of the project schedule should all tasks durations happen to match the best case duration estimates:
Next is the view of the project schedule should all tasks durations happen to match the worst case duration estimates:
Performing Sensitivity Analysis on the above project would result in a result similar to the one outlined below:
What the above diagram suggests is that when running a simulation with a large number of samples (lets say 1,000 times), then Task1 will appear on the Critical Path 100%, Task4 and Task5 – 39% of the times, etc.
Wouldn’t this be useful to know?
A 2007 article by Young Hoon Kwak and Lisa Ingall, titled “Exploring Monte Carlo Simulation Applications for Project Management”, examines the Monte Carlo Simulation method and its uses in the field of Project Management.
Apart from being a good reference document, where a brief history of this technique is being discussed and explained, this article provides a good review of various studies published around the benefits as well as the potential complexities associated with implementing this technique in real life situations.
The article points out that one of the limitations of using this technique as being project managers’ discomfort with statistical approaches, as well as lack of thorough understanding of the method (see also my earlier post discussing this issue in “Some Risk Management Related Thoughts“.
Discussing the process for utilizing Monte Carlo Simulation in the context of Time Management, the article suggests the following steps are commonly used:
Outlining the advantages of utilizing Monte Carlo Simulation applications in Project Management, the article points out that its primary advantage is in being an “extremely useful tool when trying to understand and quantify the potential effects of uncertainty in projects“. Clearly, not utilizing this technique, project managers lack a powerful tool that can result in not meeting the project’s schedule and cost targets. Better quantification of the necessary schedule and cost reserves can substantially reduce such risks.
The article highlights the importance of having access to expert knowledge and prior experience and detailed data from previous projects in order to mitigate the inherent issues of estimates uncertainly which would ultimately affect the quality of the simulation results. This is correct not just with respect to the three-point-estimates but also with respect to choosing the correct probability distributions with which to model these estimates.
An interesting point is raised when referring to an earlier study published by Grave R. (2001. “Open and Closed: The Monte Carlo Model,” PM Network, vol. 15, no. 2, pp. 48-52) which discusses the merits of using different types of probability distributions for project task duration estimates. Grave suggests the use of open-ended distribution (the lognormal distribution) instead of using closed-ended distributions (such as the triangular distribution) when performing the Monte Carlo Simulations.
His logic is as follows: A closed-ended distribution (e.g. triangular distribution) does not consider the possibility of a task duration completing BEFORE the best-case or AFTER the worst-case duration estimate. However, in real life projects, due to various constraints, it is possible for a task to complete before or after the best or worst scenarios.
When an open-ended distribution is used, the possibility for exceeding the upper limit of the task duration is recognized, thus making the simulation more realistic.
The article touches (although very briefly) on one of the aspects used within the context of Monte Carlo Simulation which is the Criticality Index (I will endeavor to provide a more detailed discussion about this feature in a future post). In a nutshell, the Criticality Index is a reflection of the rate at which the task appears on the critical path of the project through the simulation iterations.
Overall an interesting article. If you are already using Monte Carlo Simulation as part of your portfolio of project tools, this article will encourage you to keep doing so. And if you’re not – you’ll need to ask yourself – WHY?
Want to know more what this thing is all about, check for more info in my “Monte Carlo Simulation – Project Risk Analysis Report”
Ok, I’m having a bit of a fun with this one.
Submit a copy of your project plan (in Microsoft Project format only at the moment) and I will send back to you a high level risk assessment of your project schedule’s based on a Monte Carlo Simulation.
There’s absolutely no catch. The reason I’m doing it is because it seems to me that many don’t yet understand the benefits of using this technique to better understand the risks that lie dormant within their project schedules.
If you are interested in this FREE offer fill in and submit the details below.
And by the way, I give you my guarantee that once I process your project schedule I will not make any further use of it and it will be erased/discarded and will not be used for any purpose other than attempting to provide you with this service.
To learn / read more about this topic check out the Related Posts listed below.
Over a week ago I came up with an offer to provide readers with the unique opportunity to run their project plans through a Monte Carlo Simulation and provide them with a high level summary of the results.
The response exceeded my expectations and up until now I was approached by 30 readers to provide them with an assessment of their plan.
Having gone through this experience I thought it would be appropriate to summarize my findings from this exercise, without, obviously, revealing the individual nature of the plans that have come under my hands.
All plans provided were in a Microsoft Project format and none was larger than 500 lines. To my surprise, none of the plans provided had the basic scheduling distribution populated (Most Likely, Optimistic and Pessimistic estimates) for each of the tasks. In order to overcome this issue I have applied an across the board triangular distribution rule of 75%, 100%, 125% (i.e. assuming that the Optimistic Estimate will also be 75% of the Most Likely Estimate and that the Pessimistic Estimate will always be taken as 125% of the Most Likely Estimate).
Not surprisingly and as expected, in all cases, the 80% likelihood of finishing the project on or before a certain date was well after the plans’ deterministic date (i.e. the date predicted by the software as being the project’s completion date).
Not all project schedules provided had costing information provided but in those who had an expected project cost, this as well has shown a deterministic cost well below the 80% likelihood mark.
As I’m excited by what I’ve seen I intend to run with this activity for few more days so the opportunity to use this option is still open (but not for much longer).
To ensure the successful completion of a project, it is of utmost importance for the project manager to find ways to handle uncertainties that can pose potential risks for a project. Risk management is an iterative process. Risks can relate to any aspect of the project – be it the cost, schedule, or quality. The key to managing risks is to identify them early on in the project and develop an appropriate risk response plan.
To develop a Risk Response Plan, you need to quantify the impact of risks on the project. This process is known as quantitative risk analysis wherein risks are categorized as high or low priority risks depending on the quantum of their impact on the project. The Project Management Body of Knowledge (PMBOK) advocates the use of Monte Carlo analysis for performing quantitative risk analysis.
Monte Carlo analysis involves determining the impact of the identified risks by running simulations to identify the range of possible outcomes for a number of scenarios. A random sampling is performed by using uncertain risk variable inputs to generate the range of outcomes with a confidence measure for each outcome. This is typically done by establishing a mathematical model and then running simulations using this model to estimate the impact of project risks. This technique helps in forecasting the likely outcome of an event and thereby helps in making informed project decisions.
While managing a project, you would have faced numerous situations where you have a list of potential risks for the project, but you have no clue of their possible impact on the project. To solve this problem, you can consider the worst-case scenario by summing up the maximum expected values for all the variables. Similarly, you can calculate the best-case scenario. You can now use the Monte Carlo analysis and run simulations to generate the most likely outcome for the event. In most situations, you will come across a bell-shaped normal distribution pattern for the possible outcomes.
Let us try to understand this with the help of an example. Suppose you are managing a project involving creation of an eLearning module. The creation of the eLearning module comprises of three tasks: writing content, creating graphics, and integrating the multimedia elements. Based on prior experience or other expert knowledge, you determine the best case, most-likely, and worst-case estimates for each of these activities as given below:
| Tasks | Best-case estimate | Most likely estimate | Worst-case estimate |
| Writing content | 4 days | 6 days | 8 days |
| Creating graphics | 5 days | 7 days | 9 days |
| Multimedia integration | 2 days | 4 days | 6 days |
| Total duration | 11 days | 17 days | 23 days |
The Monte Carlo simulation randomly selects the input values for the different tasks to generate the possible outcomes. Let us assume that the simulation is run 500 times. From the above table, we can see that the project can be completed anywhere between 11 to 23 days. When the Monte Carlo simulation runs are performed, we can analyse the percentage of times each duration outcome between 11 and 23 is obtained. The following table depicts the outcome of a possible Monte Carlo simulation:
| Total Project Duration | Number of times the simulation result was less than or equal to the Total Project Duration | Percentage of simulation runs where the result was less than or equal to the Total Project Duration |
| 11 | 5 | 1% |
| 12 | 20 | 4% |
| 13 | 75 | 15% |
| 14 | 90 | 18% |
| 15 | 125 | 25% |
| 16 | 140 | 28% |
| 17 | 165 | 33% |
| 18 | 275 | 55% |
| 19 | 440 | 88% |
| 20 | 475 | 95% |
| 21 | 490 | 98% |
| 22 | 495 | 99% |
| 23 | 500 | 100% |
This can be shown graphically in the following manner:
What the above table and chart suggest is, for example, that the likelihood of completing the project in 17 days or less is 33%. Similarly, the likelihood of completing the project in 19 days or less is 88%, etc. Note the importance of verifying the possibility of completing the project in 17 days, as this, according to the Most Likely estimates, was the time you would expect the project to take. Given the above analysis, it looks much more likely that the project will end up taking anywhere between 19 – 20 days.
Whenever you face a complex estimation or forecasting situation that involves a high degree of complexity and uncertainty, it is best advised to use the Monte Carlo simulation to analyze the likelihood of meeting your objectives, given your project risk factors, as determined by your schedule risk profile. It is very effective as it is based on evaluation of data numerically and there is no guesswork involved. The key benefits of using the Monte Carlo analysis are listed below:
Now that you are aware of the Monte Carlo analysis and its benefits, let us look at the steps that need to be performed while analysing data using the Monte Carlo simulation.
The series of steps followed in the Monte Carlo analysis are listed below:
Each of the above listed steps of the Monte Carlo simulation is detailed below:
Sensitivity analysis is used for determining the most critical variables in a project. To identify the most critical variables in the project, all the variables are subjected to a fixed deviation and the outcome is analysed. The variables that have the greatest impact on the outcome of the project are isolated as the key project risk variables. However, sensitivity analysis in itself might give some misleading results as it does not take into consideration the realistic nature of the projected change on a specific variable. Therefore it is important to perform uncertainty analysis in conjunction with the sensitivity analysis.
Uncertainty analysis involves establishing the suitability of a result and it helps in verifying the fitness or validity of a particular variable. A project variable causing high impact on the overall project might be insignificant if the probability of its occurrence is extremely low. Therefore it is important to perform uncertainty analysis.
Monte Carlo simulation is a valuable technique for analyzing risks, specifically those related to cost and schedule. The fact that it is based on numeric data gathered by running multiple simulations adds even greater value to this technique. It also helps in removing any kind of project bias regarding the selection of alternatives while planning for risks. While running the Monte Carlo simulation, it is advisable to seek active participation of the key project decision-makers and stakeholders, specifically while agreeing on the range values of the project risk variables and the probability distribution patterns to be used. This will go a long way in building stakeholder confidence in your overall risk-handling capability for the project. Moreover, this serves as a good opportunity to make them aware of the entire risk management planning being done for the project.
Though there are numerous benefits of the Monte Carlo simulation, the reliability of the outputs depends on the accuracy of the range values and the correlation patterns, if any, that you have specified during the simulation. Therefore, you should practice extreme caution while identifying the correlations and specifying the range values. Else, the entire effort will go waste and you will not get accurate results.
Want more? Check out my special offer here.
The first part of this series (Project Management – Statistics for dummies (part 1)) focused on the meaning of the Mean, Median and the Mode.
Today’s installment will try to make sense of the Variance and the Standard Deviation.
The variance is a measure of how spread out a data distribution is. It is computed as the average squared deviation of each number from its mean. Sounds a bit complicated so let’s try and work it out.
Using mathematical symbols, the above equation will look as follows:
Where:
is the VarianceFor example:
A project schedule consists of 10 tasks, with the following estimated durations:
| Task ID | Estimated Duration (days) |
| 1 | 3 |
| 2 | 4 |
| 3 | 5 |
| 4 | 6 |
| 5 | 3 |
| 6 | 7 |
| 7 | 4 |
| 8 | 5 |
| 9 | 2 |
| 10 | 4 |
Based on the above:
Now let’s calculate the Variance:
The sum of the squares of the difference between the individual values and the Mean =
(3 – 4.3)2 + (4 – 4.3)2 + (5 – 4.3)2 + (6 – 4.3)2 + (3 – 4.3)2 + (7 – 4.3)2 + (4 – 4.3)2 + (5 – 4.3)2 + (2 – 4.3)2 + (4 – 4.3)2 = 22.333
With N = 10 the Variance = 22.33 / 10 = 2.2333
The Standard Deviation (
– pronounced Sigma) is the square root of the Variance which, in the example above will be 
= 1.494434
So what does it actually mean?
Like the Variance, the Standard Deviation is a measure of how spread out a data distribution is around the mean (average) of the set. While the mean only provides an indication of the average result, it lacks the ability to indicate how widely spread all items in the set are. The Standard Deviation provides this additional dimension by indicating how spread the data items are from the average. A set of values that are closely clustered near the mean will have a low standard deviation, a set of numbers that are widely separated will have a higher standard deviation and a set of numbers that are all the same will have a standard deviation of zero.
The Program Evaluation and Review Technique (PERT) stipulates the use of Standard Deviation as a reflection of each tasks estimation’s uncertainty as it is calculated as the difference between the pessimistic and optimistic duration divided by six. A small Standard Deviation would be interpreted as a smaller uncertainty compared with a larger Standard Deviation. It should be noted, however, that although it would be theoretically correct to determine the level of uncertainty for each task by determining the tasks’ duration Standard Deviation; determining the project’s standard deviation require a more rigor approach which will involve the use of “Monte Carlo Simulation“.
The mantra for clearing the PMP certification exam is practice and even more practice. The more you reinforce your learning by attempting mock tests, the better prepared you will be for the exam. Various researchers have emphasized on the importance of reflective learning. Learning is an ongoing process and reflecting on whatever has been learnt by means of practice is one of the best ways of perfecting the learning. After you have completed an in-depth study of a particular process group or a knowledge area of the PMBOK, attempt as many practice questions as you can. The practice will not only highlight your strengths and weaknesses, but it will also go a long way in boosting your morale for the exam. Set every score you attain as a benchmark and try to improvise on it the next time. Practicing reinforces your learning.
While preparing for the PMP exam, make sure that you have a complete understanding of the PMBOK – all the knowledge areas, process groups, tools and techniques, inputs and outputs. Even if you consult other books for your preparation, the PMBOK Guide should still be your first reference material. Some highly recommended resources that you could refer to for your preparation (and which I myself used as part of my preparation for the PMP exam) are:
PMP Exam Prep, Sixth Edition: Rita’s Course in a Book for Passing the PMP Exam
The PMP Prepcast by Cornelius Fichtner, PMP
No doubt all your preparation and hard work will come in handy while preparing for the exam, but here is a list of do’s and don’ts that you should adhere to:
Get enough sleep prior to the exam and go for the exam with a relaxed mind, confident of getting PMP-certified by the end of it. All the best!
“I know you’ve heard it a thousand times before. But it’s true — hard work pays off. If you want to be good, you have to practice, practice, practice. If you don’t love something, then don’t do it.” Ray Bradbury – 1920-, American Science Fiction Writer.
Affiliate program disclosure: Links to external products and/or services published in this post could earn me (should you decide to purchase them) some referral commission. Please note that I take my referrals seriously and under no circumstances would I recommend something I don’t genuinely believe is of high quality and of high professional value.
Note: PMI®, PMP®, and PMBOK® are registered trademarks of the Project Management Institute, Inc.
Although, generally speaking, project managers are not expected to demonstrate complicated mathematical and / or statistical capabilities, there are some aspects of both these disciplines where basic knowledge and understanding of some basic concepts can enhance the project managers’ ability to perform fundamental project management duties – primarily around risk management.
The Project Management Body of Knowledge advocates the use of “Monte Carlo Simulation” within the context of performing quantitative risk assessment analysis. Although in most cases, executing such analysis will require the invocation of some sort of automated software tools, it is important for the project manager to understand the key principles behind the mathematical and statistical analysis performed by this sort of tools.
Today’s post will focus on three basic concepts (all of which, funnily enough, start with the letter ‘M’):
The mean (or average) of a set of data values is the sum of all of the data values divided by the number of data values. That is:
Using mathematical symbols, the above equation will look as follows:
Where:
is the sum of all x values in the setFor example:
A project schedule consists of 10 tasks, with the following estimated durations:
| Task ID | Estimated Duration (days) |
| 1 | 3 |
| 2 | 4 |
| 3 | 5 |
| 4 | 6 |
| 5 | 3 |
| 6 | 7 |
| 7 | 4 |
| 8 | 5 |
| 9 | 2 |
| 10 | 4 |
Based on the above:
The median of a set of data values is the middle value of the data set after it has been arranged in an ascending order.
Median = ½ (n + 1)th value in a set, where: n is the number of data values in the set
Note: If the number of values in the set is even, the median is calculated as the average of the two middle values.
For example:
The above task list, ordered in an ascending order, will look as follows:
| Task ID | Estimated Duration (days) |
| 9 | 2 |
| 1 | 3 |
| 5 | 3 |
| 2 | 4 |
| 7 | 4 |
| 10 | 4 |
| 3 | 5 |
| 8 | 5 |
| 4 | 6 |
| 6 | 7 |
Given that there are 10 tasks in this list, the then ½(10+1) = 5.5.
Given that in this case n = 10, the median will be calculated as the average between the two middle values (being tasks 7 & 10) = (4 + 4) / 2 = 4 days.
The mode represents a data value that appears most frequently within a set of values. Obviously if one or more values appear in exactly the same frequency, all such values will be considered to be part of the set Mode.
For example:
Given the following set of numbers: 1, 2, 3, 2, 3, 4, 1, 3; the number 3 appears the most times and is therefore the Mode.
Stay tuned for the next installment as things will get slightly spicier.
The Project Management Body of Knowledge (PMBOK)
The PMBOK, published by the PMI, is a compilation of the project management guidelines to be adopted as a best practice for the successful execution of a project. It serves as a guiding principle for achieving the scope, cost, schedule and quality constraints of a project and is rigorously followed by numerous organizations throughout the world. The PMBOK establishes five process groups for any project, irrespective of the type of industry. These process groups are: Initiating, Planning, Executing, Monitoring and Controlling, and Closing. Each of these process groups has its own inputs, tools and techniques, and outputs. The inputs for a process group include the list of documents that are required to be in place before starting off a particular process.
For instance, before starting the scope planning process, you need to have the project charter and the list of assumptions and constraints for the project. The tools and techniques for each process group include the mechanism or the procedure that should be applied to the inputs to attain the desired outputs. During the scope planning process, you need to perform a benefit/cost analysis and use expert judgment to derive the scope statement for the project. The benefit/cost analysis and expert judgment in this example are the tools and techniques to be applied whereas the scope statement is the output of the scope planning process.
There are nine knowledge areas recognized by the PMBOK. These include the processes that need to be completed for the successful execution of a project. The nine knowledge areas are: Project Integration Management, Project Scope Management, Project Time Management, Project Cost Management, Project Quality Management, Project Human Resource Management, Project Communications Management, Project Risk Management, and Project Procurement Management. Each of these knowledge areas go through the initiation, planning, execution, monitoring and control, and closure phases.
The following table represents the matrix of the knowledge areas vs. the process groups:
check out the final article (The Secret to Clearing the PMP Certification Exam (Part 3)) which will include practical ideas on how to prepare and what resources to use in preparation for the exam.
Note: PMI®, PMP®, and PMBOK® are registered trademarks of the Project Management Institute, Inc.
You have read enough about the PMP certification and the benefits associated with it. You have also been toying with the idea of getting PMP-certified for quite some time but do not know where to begin with. You have often pondered how to clear the PMP certification exam. This article aims to throw some light on the PMP certification, its benefits, the eligibility requirements, the examination structure, reference material, and some practical tips for clearing the exam.
Before you resolve to get PMP-certified, you must understand that the secret for clearing the exam (apart, obviously, from having good practical experience in Project Management) is lots and lots of revisions and exam practices. If you are game for it, then half the battle is already won. So, let’s get cracking!
Project Management is increasingly becoming popular as a profession. More and more organizations have realized the benefits of following a disciplined methodology for executing their projects successfully. To become a successful project manager, it is imperative to have a thorough understanding of the different processes to be followed at each and every stage of the project. The Project Management Professional (PMP) certification is a universally recognized benchmark that validates an individual’s expertise and knowledge in the field of project management. The PMP certification is offered by the Project Management Institute (PMI) (http://www.pmi.org). It demonstrates your knowledge of all the aspects of project management. Once obtained, the certification will become a testimony to your understanding of how to manage and lead projects and project teams.
While hiring a project manager, most organizations are increasingly opting for people who are PMP-certified. The certification is becoming a distinguisher against the ever-growing competition and is a valuable tool for increasing your market value. With the PMP certification to add to your resume, you do not only stand a better chance of getting the coveted job, but are also in a position to negotiate for a higher pay package. Another key advantage of getting PMP-certified is that you establish a common language of communication with the project management fraternity. Organizations hiring a PMP-certified project manager are confident that the individual is well-versed with the various process groups and knowledge areas as prescribed by the PMI in its Project Management Body of Knowledge (PMBOK). Therefore, there is a higher probability that such an individual will easily gel with the organization’s project management methodology.
Considering the numerous benefits of getting PMP-certified, you would be wondering as to how to get PMP-certified? Well, to get PMP-certified, you need to combine substantial practice with a thorough understanding of the PMP examination structure and the concepts included in the PMBOK.
The PMP certification exam is conducted by the PMI. To be able to apply for the PMP certification examination, you need to have a bachelor’s degree with a minimum of three years (4500 hours) of demonstrable project management experience. In addition to this, you need to have 35 hours of project management education (which are referred to as Professional Development Units (PDUs)). More details about the PMP certification eligibility and the process for filling up the online application form can be obtained from http://www.pmi.org/PDF/pdc_pmphandbook.pdf. You must remember that filling up the application form is a time-consuming process and adequate care should be taken while filling up the form. At times, the application might be rejected, or audited to verify the correctness of the information provided by the applicant. On successful submission and approval of the application form, the PMI will send a notification for the same. After this, you can schedule your exam.
The PMP certification exam uses the computer-based testing method and you can schedule the exam at your convenience depending on your preparation. You have to appear for the exam within one year of successfully submitting the online application form.
The examination comprises 200 multiple choice questions that need to be completed within 4 hours. Out of these 200 questions, 25 questions are pretest questions which do not affect your overall score. The current passing score for the PMP certification exam is 61%.
The exam comprises a specific percentage of questions for each project management process group. The percentages of questions for each process group are: Initiating: 11%, Planning: 23%, Executing: 27%, Monitoring and Controlling: 21%, Closing: 9%, and Professional and Social Responsibility: 9%.
A visual representation of the distribution of the PMP questions across various process groups is given below:
All the PMP examination questions are based on the content included in the latest edition of the PMBOK (4th edition as of date). Therefore, it is important that you have a thorough understanding of all the concepts included in the PMBOK.
Click here for the second article in this series, titled ”The Secret to Clearing the PMP Certification Exam (Part 2)“. This article provides a closer look into the PMBOK.
Click here for the third article in this series, titled “The Secret to Clearing the PMP Certification Exam (Part 3)” which provides practical advise as to how to prepare and what resources to use in preparation for the exam.
As usual, your comments and suggestions will be appropriated.
Note: PMI®, PMP®, and PMBOK® are registered trademarks of the Project Management Institute, Inc.
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quantmleap: My weekly contribution to Project Management related debates http://is.gd/dOh24 #pmot #ftpm #pm - posted on 28/07/2010 09:51:10
