How Many People in the World are PMP Certified & How many in other Certifications

Project Management Institute (PMI) Certifications became so popular and accredited around the world, each year the number of the certified people is increasing due to the reputation and the well-recognition of PMI, below are a statistical data for the main PMI certifications' holders up to 1st January 2018:

PMP® – There are 825,413 (Project Management Professional Certified around the World)

** Top countries that have PMP holders are:
1. United States .. more than 250,000 Holder
2. China .. more than 100,000 Holder
3. India .. more than 50,000 Holder
4. Japan .. more than 40,000 Holder
5. Canada .. more than 30,000 Holder


CAPM® –  Around 35,000 (Certified Associate in Project Management)

PMI-ACP® – Around 18,000 (PMI® Agile Certified Practitioner)

PMI-RMP® – Around 5,000 (PMI® Risk Management Professional)

PMI-SP® – Around  2,000 (PMI® Scheduling Professional)

PgMP® – Around 2,000 (Program Management Professional)

PMI-PBA® – Around 2,000 (PMI® Professional in Business Analysis)

PfMP® – Around 500 (Portfolio Management Professional)

Critical Success Factors fro Risk Management Processes- Important Topic for PMI-RMP Exam

Critical Success Factors (CSF's) is an Important Topic you will face in PMI-RMP Exam, at least 6-7 Questions will ask you directly/Indirectly about them.

The most useful way to get all of these questions on this topic is to understand deeply these factors and their effect on the risk management in general and their effect on the six processes of the risk management.

These factors are mentioned in PMI Standard Practice of Risk Management Book and they were discussed in deep details thru the book.

Below are these factors for each process and for risk management in General;

1. CSF for Project Risk Management• Recognizing Risk Management Value — Awareness of the value for the risk.
• Individual Commitment/Responsibility — All Stakeholders in the project should be responsible about the risk
• Open and Honest Communication — All stakeholders should be involved in the Project Risk Management
process. 
• Organizational Commitment — Organizational commitment can only be established if risk management is aligned with the organization’s goals and values. Project Risk Management may require a higher level of managerial support than other project management disciplines becausehandling some of the risks will require approval of or responses from others at levels above theproject manager.• Risk Effort Scaled to Project — Project Risk Management activities should be consistent with the value
of the project to the organization and with its level of project risk, its scale, and other organizationalconstraints. In particular, the cost of Project Risk Management should be appropriate to its potentialvalue to the project and the organization.• Integration with Project Management — Project Risk Management does not exist in a vacuum,
isolated from other project management processes. Successful Project Risk Management requiresthe correct execution of the other project management processes.

2. CSF for the Plan Risk Management Process•  Identify and Address Barriers to Successful Project Risk Management•  Involve Project Stakeholders in Project Risk Management•  Complying with the Objectives of the Organization, Rules, Policies, and Practices   

3. CSF for the Identify Risks Process•  Early, Iterative, Comprehensive and Emergent    Identification.•  Explicit Identification of Opportunities•  Multiple Perspectives•  Risks that Linked to Project Objectives•  Complete Risk Statement•  Ownership and Level of Detail•  Objectivity 

4.  CSF for the Perform Qualitative Risk Analysis Process

•  Use Agreed Upon Definitions and Approach for the risk terms and Methodology.
•  Collecting High-Quality Information about Risks•  Iterative Qualitative Risk Analysis 

 5. CSF for the Perform Quantitative Risk Analysis Process

•  Unbiased Data and collecting High-Quality data related to    Risk.

•  Prior Risk Identification and Qualitative Risk Analysis

• Appropriate Project Model

•  Overall Project Risk Derived from Individual Risks

•  Interrelationships Between Risks in Quantitative Risk Analysis

 6. CSF for the Plan Risk Responses Process

• People  

• Planning 

• analysis
  
7. CSF for the Monitor and Control Risks Process •  Integration between Risk Monitoring and Control with Project Monitoring and Control•  Continuously Monitor Risk Trigger Conditions•  Maintain Risk Awareness 

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Latin Hypercube Sampling

Most risk analysis simulation software products offer Latin Hypercube Sampling (LHS). It is a method for ensuring that each probability distribution in your model is evenly sampled which at first glance seems very appealing.

The technique dates back to 1980 when computers were very slow, the number of distributions in a model was extremely modest and simulations took hours or days to complete. It was, at the time, an appealing technique because it allowed one to obtain a stable output with a much smaller number of samples than simple Monte Carlo simulation, making simulation more practical with the .

Computing tools available at the time.

What is Latin Hypercube sampling

** The Main Principle is to divide the Area Into Strata 

Latin Hypercube Sampling (LHS) is a type of stratified sampling. It works by controlling the way that random samples are generated for a probability distribution. Probability distributions can be described by a cumulative curve, like the one below. The vertical axis represents the probability that the variable will fall at or below the horizontal axis value. Imagine we want to take 5 samples from this distribution. We can split the vertical scale into 5 equal probability ranges: 0-20%, 20-40%, …, 80-100%. If we take one random sample within each range and calculate the variable value that has this cumulative probability, we have created 5 Latin Hypercube samples for this variable:

This methodology is used to generate more more accurate simulation results in general when comparing it with other simulation sampling methods, with lower standard errors level, with lower or fewer sampling trials.

this method was established in 1979 by the governmental research of statistical mathematics in united kingdom and set the reference to many developed methodologies in this field around the world, the idea was derived from developing the normal distribution changing under certain conditions by setting a limit of fixed data and facultative results.

The optimal Latin hypercube option generates a large number of random Latin hypercube designs, then iteratively improves each of them and uses the best of the improved designs. This is very effective at finding a good design, but requires additional processing time. For example, a 20-variable 300-evaluation design takes about five minutes of CPU time. Optimal Latin hypercube uses centered L2 discrepancy (a measure of uniformity) as the optimality criteria. If a time limit is specified in the study definition, HEEDS will stop improving the sampling design when the specified wall-clock-time limit is reached. HEEDS will use the best design found to that point.

New Topics in PMI-RMP Exam

Risk Management - New Topics 2016

The below new topics are widely used and mentioned in PMI-RMP Exam, and in order to pass the exam you have to know them well.

1. Neuro Linguistic Programming

 Neuro-linguistic programming (NLP) is an approach to communication, personal development, and psychotherapy created by Richard Bandler and John Grinder in California, United States in the 1970s.

2. Latin Hepercube Sampling LHS

(LHS) is a statistical method for generating a sample of plausible collections of parameter values from a multidimensional distribution.

3. Correlation coefficient

A correlation coefficient is a coefficient that illustrates a quantitative measure of some type of correlation and dependence, meaning statistical relationships between two or more random variables or observed data values.

4. Agile Project Management

Agile project management is an iterative approach to planning and guiding project processes. 

Just as in agile software development, an agile project is completed in small sections called iterations. Each iteration is reviewed and critiqued by the project team, which may include representatives of the client business as well as employees. Insights gained from the critique of an iteration are used to determine what the next step should be in the project. Each project iteration is typically scheduled to be completed within two weeks.

5. Utility Function

the utility function measures welfare or satisfaction of a consumer as a function of consumption of real goods, such as food, clothing and composite goods rather than nominal goods measured in nominal terms. Utility function is widely used in the rational choice theory to analyze human behavior.

6. Pre-Mortem Analysis

also known as a premortem — is a managerial strategy in which a manager imagines that a project or organization has failed, and then works backward to determine what potentially could lead to the failure of the project or organization.

7.Crystal Ball Simulation

Crystal Ball is the easiest way to perform fast risk analysis and optimization in your own spreadsheets. With one integrated tool-set, you can use your own historical data to build accurate models, automate "what if" analysis to understand the effect of underlying uncertainty and search for the best solution or project mix.

Oracle Crystal Ball is the leading spreadsheet-based application for predictive modeling, forecasting, simulation, and optimization. It gives you unparalleled insight into the critical factors affecting risk. With Crystal Ball, you can make the right tactical decisions to reach your objectives and gain a competitive edge under even the most uncertain market conditions.

Monte Carlo Simulation

Monte Carol simulation is a practical tool used in determining contingency and can facilitate more effective management of cost estimate uncertainties. Given the right Monte Carlo simulation tools and skills, any size project can take advantage of the advancements of information availability and technology to yield powerful results.

  Monte Carlo sampling refers to the traditional technique for using random or pseudo-random numbers to sample from a probability distribution. The term Monte Carlo was introduced during World

War II as a code name for simulation of problems associated with development of the atomic bomb. Today, Monte Carlo techniques are applied to a wide variety of complex problems involving random

behavior. A wide variety of algorithms are available for generating random samples from different types of probability distributions.

Monte Carlo sampling techniques are entirely random — that is, any given sample may fall anywhere within the range of the input distribution. Samples, of course, are more likely to be drawn in areas

of the distribution which have higher probabilities of occurrence. In the cumulative distribution shown earlier, each Monte Carlo sample uses a new random number between 0 and 1. With enough iterations,

Monte Carlo sampling "recreates" the input distributions through sampling. A problem of clustering, however, arises when a small number of iterations are performed

In PMI-RMP Exam Expect to See between 4-7 questions that ask you to interpret the Monte Carlo simulation diagram, see the below example;

As you see, the diagram above shows histogram and a cumulative distribution, in Exam, Expect that you will be asked for example to find P-45 or P50, it is simply means probability of 50% for achieving a specific cost, which as illustrated from  the cumulative distribution above equal to 74,753,000$

Also expect to find questions such as, if the owner has a budget of the project equal to 60,000 $ and he wants to have a confidence level of 50%, what is the contingency reserve that the project manager needs to allocate to achieve this confidence level? the answer is the difference between 60,000 and 74,753,00 $

the characteristics of Monte-Carlo simulation is also a big topic in the exam and you have to understand them well 

Note that the above diagram was for a cumulative distribution given, you have to distinguish with the same histogram but with a Normal distribution given as the below diagram

Here the summation of the vertical bars starting from the left side will give the cumulative distribution as shown on the table at the right side

PMI-RMP Exam Tips

PMI-RMP Exam is considered as one of the most important

exams related to Risk Management Field around the world, the exam is designed and prepared bu Project Management Institute PMI and is conducted in accredited online training centers in most countries in the world.

The Exam is testing your knowledge, experience and your skills in applying and conducting Risk   Management in your Organization/Company and how much you can apply the Risk Management Methodology in your organization.   

The Exam contains 170 Multiple choice Questions, 20 Questions will be anonymously chosen and their results will not be counted in the last score, you have to get 76 % right answers to pass the exam as per most of training centers and those who got the exams, knowing that PMI did not declare the amount of questions that you have to answer them correctly to pass the exam.

Preparing for PMI-RMP Exam will not be easy, it will take a time to guarentee that you will pass the exam, the most useful references will be discussed and the topics will be clearly illutrated in our  Blogger, just follow us and passing the exam will just be a manner of time

The main reference for preparation for the PMI-RMP exam still the last issued book from PMI for the risk which is complying with PMBOK revision 4, knowing that PMI issued PMBOK after that but for the risk it is still the old reference, it is expected that project management institute PMI will issue a new revision of risk standards book within 2017 which will contain new aspects in risks and a new methodologies and field in risk management .

Risk Management by Rita Mulcahy is also a main useful resource to prepare for the exam, however it does not contain many tools and aspects in PMI Risk management standard book such as Monte-carlo simulation.