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Friday, July 6, 2012

Other Tools & Techniques we can use in Risk Identification

In this section we set out with the idea of looking into the Risk Identification process. We saw all the inputs used in this process and then on the chapter on tools and techniques we saw that 7 different tools will be used and we covered them all one by one. At this point in time, you must be wondering that, we listed down 7 tools and we covered them all. So what is the purpose of this chapter?

Do you remember that I used the term “7 basic tools of quality” a few chapters ago? We use these tools in the “Perform Quality Control” process of the PMBOK guide and we covered them in the chapter on Controlling Quality in our PMP Exam Prep series. All of these 7 tools, though primarily used in quality control can also help us in Risk Identification as well as Risk Management. That is exactly why we have this chapter. These 7 basic tools of quality are:

1. Cause & Effect Diagrams
2. Control Charts
3. Flow Charts
4. Histograms
5. Pareto Charts
6. Run Charts and
7. Scatter Diagrams

I repeat – these are called the 7 basic tools of quality and are featured here because they are relevant from both an overall understanding of risk management perspective as well as from the RMP Exam perspective. We have already seen the cause & effect diagrams and flowcharts. So, let us quickly cover the basics of the rest of these tools.

Control charts

Control charts gather data to show when a process experiences an out of control condition. These are called “Special Cause Variations”. Basically we will use the control chart to determine whether a process is stable with predictable performance or not. It helps us understand how a process performs/behaves over time. Ideally we would expect a process to be stable and consistent and the control chart can help us confirm the same.

A typical control chart has 3 lines:
a. The Mean or Median line
b. The Upper Control Limit or UCL
c. The Lower Control Limit or LCL

Data points are gathered and plotted within these 3 lines. If all your data points lie between the UCL and the LCL then you can safely assume that your process is “In Control”. Every process will always have some variance and it is inevitable but, as long as all our data points lie within the control limits then we are good. If you see no variance and all points lie on the median, then either the data collection was wrong or something else is majorly wrong which is yet to be identified. There is no way that all data points lie on the median line.

The difference between the UCL/LCL and the mean is called the sigma value of the project. In other words it signifies the level of quality in the project. Don’t worry too much about sigma right now. Just know that higher the quality the better it is. If you are really keen on understanding about Sigma values and better quality, to visit the blog on Lean Six Sigma quality improvements.

Spotting a process that is out of control (cases where the data points are either above the UCL or below the LCL) is critical. There could also be cases where all data points are within the UCL and LCL but still the process is out of control. Those cases are identified using the “Rule of 7” wherein if 7 consecutive points fall on the same side of the control chart then it suggests that something is wrong and needs to be investigated.

Look at the sample control chart above. There is a data point above the UCL and one below the LCL which signifies that this process id definitely out of control. By analyzing the control chart we can identify potential risk points. If a process is in control then practically speaking we need not do anything. But, if it is out of control then we definitely need to take remedial actions.


A Histogram shows a distribution of variables in a bar-chart format. Each column represents an attribute or characteristic of an issue. The histogram usually shows a frequency of distribution for a set of measurements. Look at the sample histogram below:

As you can see, the frequency of occurrence of various issues is plotted in a bar-graph format. The frequency of occurrence is visible in the height of each of the bar. A sub-set of histograms known as the “Pareto Charts” are widely used in risk management which is what we are going to cover next.

Pareto Chart

A Pareto chart is a type of histogram where the bar-chart is ordered by the frequency of occurrence. It can easily help us identify those issues that have the highest frequency. Look at the sample Pareto chart below:

Here as you can see, the occurrences due to the “Slow Page Load” problem result in “Page Errors” the most number of times and then the other outcomes too are plotted based on the number of occurrences. So, if I were in-charge of quality or risk for this project I would probably concentrate on the first one or two problems which should help be address a bulk of the issues.

Pareto’s law is something that is commonly used in quality parlance. It is also called the 80-20 rule which says that 80% of the problems are because of 20% of the causes. So, if we address those 20% causes, we can eliminate 80% of the problems.

The idea behind Pareto Chart is similar. We want to concentrate on those issues that have the highest frequency so that we can nail those that can give us the most benefit.

Run Charts

A run chart shows a pattern or trends of variation over time. It also shows a history of declines and/or improvements within the process. The Run chart is nothing but a line graph that has data points plotted in the order of occurrence. It is used to monitor two main things:
1. Technical Performance – Ex: How many defects were identified & fixed in a time-period
2. Cost & Schedule Performance – Ex: Total no. of activities completed per time period without significant variances

Run charts are very useful in trend analysis which is a mathematical technique that is used to forecast the future outcome of the process based on historical information. These are extremely useful in risk analysis. Look at the sample run chart below:

Here we have plotted the number of times we had to perform an activity every month and seeing this we can safely say that trend here is improved performance and that the number of instances has steadily declined and is at a much lower level when compared to what it was at the beginning.

Scatter Diagrams

Scatter Diagrams show the pattern of relationship between two different variables. The purpose of these diagrams is to analyze the relationship between identified changes within the two variables. The dependent and independent variables are plotted on a graph with a diagonal line passing through. The closer the variables are to each other, the closer they are linked/related to one another. Look at the sample scatter diagram below:

Before we wrap up this chapter let me reiterate the fact that, you need not be an expert in these tools in order to pass the RMP examination. But, understanding them well is vital in being a good risk manager. So, take some time and understand them before proceeding further.

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