Showing posts with label pareto diagram. Show all posts
Showing posts with label pareto diagram. Show all posts

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.

Histograms

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.

Trivia:
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.


Prev: SWOT Analysis

Next: Section Summary

Thursday, July 7, 2011

Chapter 79: Controlling Quality

Quality is controlled by using the Perform Quality Control process, a process of monitoring and recording results from the execution of projects, including quality activities. The goal is to evaluate quality performance and recommend necessary changes.

The picture below shows the Perform Quality Control process.


Input to Quality Control

The goal of quality control is to ensure that the performance from the project execution meets the planned quality standards. Therefore, the outputs from project execution and from quality planning are the obvious inputs to the Perform Quality Control process. The inputs to this process are:
• Quality planning output - The following items from the output of the quality planning process, are the input to the Perform Quality Control process:
o The quality management plan lets you know what quality standards are supposed to be implemented and how.
o Quality metrics specify which quality features to monitor and how to measure them.
o Quality checklists show which quality steps need to be performed.
• Output from the Direct and Manage Project Execution process - The following items from the output of the Direct and Manage Project Execution process, are input to the Perform Quality Control process:
o A list of deliverables from the project execution to ensure that all the required deliverables are produced before project completion
o Work performance measurements to monitor performance. These measurements are made from the work performance information received from project execution by processing it through scope control, schedule control, and cost control.
• Approved change requests - You need this list to ensure that all the approved changes are implemented in a timely fashion. The list of approved change requests also includes modifications, such as revised work methods and a revised schedule.
• Organizational process assets - These include quality policies and procedures, work guidelines, change control procedures, communication policies, and quality-related historical data. These items will be used in the quality control process.

Tools and Techniques for Quality Control

The tools and techniques used for quality control include inspection, defect repair reviews, and the so-called seven basic tools of quality.

Seven Basic Tools of Quality

The table below shows the seven kinds of charts and diagrams used in quality control, also known as the seven basic tools of quality.

Charts Purpose
Flowchart To anticipate what quality problems might be and where they might occur
Run chart To perform trend analysis; i.e., to predict future results based on past performance
Scatter diagram To find the relationship between two variables, such as cause and effect, or two causes
Histogram To display the relative importance of different variables
Pareto diagram To identify and rank errors based on the frequency of defects caused by them
Control chart To monitor whether the variance of a specified variable is within the acceptable limits dictated by quality control
Cause and effect diagram To explore all the potential causes of a problem, not just the obvious ones
Let us look at each of them one by one.

Flowcharts

A flowchart is a diagram that depicts inputs, actions, and outputs of one or more processes in a system. Flowcharts, commonly used in many disciplines of knowledge, show the activities, decision points, and order of processing. They help to understand how a problem occurs. You can also use flowcharts to anticipate what quality problems might be, where they might occur, and how you might deal with them.

Run Charts

A run chart is a chart that shows the history and pattern of variations. Therefore, run charts are used to perform trend analysis, which is the science of predicting future performance based on past results. In quality control, trend analysis can be used to predict such things as the number of defects and the cost to repair them. You can use the results of trend analysis to recommend preventive actions if needed. Run charts or trend analysis can be used to monitor cost performance, schedule performance, and technical performance. An example of technical performance is how many defects have been identified and how many of them remained unrepaired.

Scatter Diagrams

A scatter diagram is used to show the pattern of the relationship between two variables; an independent variable and another variable that depends on the independent variable. The dependent variable is plotted corresponding to the independent variable. For example, a variable representing a cause can be the independent variable, and a variable representing the effect can be a dependent variable. The closer the data points are to a diagonal line, the stronger the relationship (called the correlation) is between the two variables.

Histograms

A histogram is a bar chart that shows a distribution of variables. Each bar can represent an attribute, such as defects due to a specific cause, and its height can represent the frequency of the attribute, such as number of defects. This tool helps to identify and rate the causes of defects. You also use a histogram to identify and illustrate the most common cause of a problem, such as a cause behind defects.
You might wonder how defects can be repaired efficiently. Pareto diagrams, which are examples of histograms, have the answer for you.

Pareto Diagrams

A Pareto diagram is used to rank the importance of each error (problem) based on the frequency of its occurrence over time in the form of defects. A defect is an imperfection or deficiency that keeps a component from meeting its requirements or specifications. A defect is caused by an error (problem) and can be repaired by fixing the error. An error in a product can give rise to multiple defects, and by fixing the error you repair all the defects caused by that error.
However, all errors are not equal. Some errors cause more defects than others. According to Pareto’s law, which is also known as the 80/20 rule, 80 percent of project defects are caused by 20 percent of errors. Qualitatively, it means that most defects are caused by a small set of errors. The Pareto diagram lets you rank errors based on the frequency of defects they cause. You begin by having the error that causes most of the defects fixed and make your way to other errors that cause lesser numbers of defects. This way, the efforts of the project team are optimized: You get the maximum number of defects repaired with minimal effort.

The advantages of a Pareto diagram are twofold:
• It ranks errors according to the frequency of defects they cause.
• It optimizes efforts to repair the defects by working on the errors that cause most of the defects.

Below is a sample Pareto Diagram.


In this example, 200 defects are caused by seven errors, and Error A alone causes 75 defects, which is 37.5 percent of all the defects. The Pareto diagram tells you that you should address Error A first, Error B second, and so on in order to address the most number of defects quickly.


Control Charts

Control charts are used to monitor whether the variance of a specified variable is within the acceptable limits dictated by quality control. A variance is a measurable deviation in the value of a project variable, such as cost from a known baseline or expected value. This is a way to monitor the deviations and determine whether the corresponding variable is in or out of control. The values are taken at different times to measure the behavior of a variable over time. The mean value in the control chart represents the expected value, and a predetermined spread from the mean value (usually ±3 σ) is used to define the limits within which an acceptable value can fall.

Control charts can be used to monitor the values of any type of output variables. To illustrate their main features, consider the example of a control chart shown below:


In this example, assume that a manufacturer produces 100 units of a product each day, and it is expected that 95 out of 100 units should have no defect; i.e., the expected number of defective units is equal to five. The control limits are set to ±3. In other words, 95 units out of 100 must be correct, give or take three. That puts the lower limit at 92 and the upper limit at 98. Crossing the lower limits is not acceptable to the customer, and crossing the upper limits might require an unjustifiable cost.

Controlling quality includes dealing with defects and problems that cause them. So, studying causes of a problem is critical to quality control.

Cause and Effect Diagram

A cause and effect diagram is used to explore all the potential causes (inputs) that result in a single effect (output), such as a problem or a defect. This type of diagram is the brainchild of Kaoru Ishikawa, who pioneered quality management processes in the Kawasaki shipyards, and therefore these diagrams are also called Ishikawa diagrams. Due to the shape of these diagrams, they are also known as fishbone diagrams. To construct and use cause and effect diagrams effectively, perform the following simple steps:

1. Identify the problem - Write down the problem in a box drawn on the right side of a large sheet of paper. This represents the head of the fish. Starting from the box, draw a horizontal line across the paper. This represents the spine of the fish.
2. Identify the possible areas of causes - Identify the areas or factors from where the potential causes of the problem might come. Environment, people, materials, measurements, and methods are some examples of areas (factors) of causes. For each factor relevant to the problem under study, draw a line off the spine and label it with the name of the factor. These lines represent the fish bones.
3. Identify the possible causes - For each factor, identify possible causes. Represent each possible cause with a line coming off the bone that represents the corresponding factor.
4. Analyze the diagram - Analyzing the diagram includes narrowing down the most likely causes and investigating them further.

To understand this better, look at a sample diagram below:


The above is a simple example of a cause and effect diagram. The problem in this example is the delay in the release of a website. The factors considered are environment, methods, people, and time. Of course, the diagram is incomplete in the sense that more factors and related causes can be explored, and causes for each factor can be explored further. But this is just a simple example and you need to improvise based on the situation…

A cause and effect diagram offers a structured way to think through all possible causes of a problem. You can use these diagrams to carry out a thorough analysis of a problematic situation. This kind of analysis is useful in complex situations when, to discover the real causes, you need to explore all the potential causes and not just the obvious ones.
In addition to the seven quality tools we have discussed, there are some other tools that you can use for quality control.

Other Quality Control Tools

In addition to the seven quality tools, the following tools can also be used for controlling quality:
• Statistical sampling - Statistical sampling involves randomly selecting a part of the population for study. In quality control, you can select a subset of features for inspection. This can save a substantial amount of resources.
• Inspection - This is a technique to examine whether an activity, component, product, service, or result conforms to specific requirements. Inspections can be conducted at various levels of project execution. For example, you can inspect the results of a single activity, or you can inspect the final product of the project. Nevertheless, inspection generally includes measurements. There are various forms of inspections, such as reviews, peer reviews, audits, and walkthroughs.
• Review of approved defect repairs - This review is conducted by the QC department or body to ensure that the defects approved for repair are actually repaired to bring the defective product, service, or results in conformance with the specified requirements.

These tools can be used to make quality control measurements, which in turn can be used to recommend preventive and corrective actions: the output of quality control.

Output of Quality Control

The quality control measurements and the recommendations based on those measurements are the obvious output items of the quality control process.

Quality control measurements - These are the results of the QC activities and are fed back to the QA process. They are also used to make recommendations for corrective and preventive actions. These measurements function as feedback for the quality assurance process.
Validated items - These are the items that have been validated through the QC process:
• Validated defect repair - Once a component has been repaired from a defect, it needs to be inspected so the repair will be accepted or rejected. The rejected items might need to be repaired again. The accepted repair is a validated defect repair.
• Validated deliverables - This refers to verifying the correctness of project deliverables. A deliverable accepted through a QC process is a validated deliverable.
Change requests - The quality control process can generate the following kinds of change requests:
• Recommended corrective actions - These actions are recommended as a result of the QC process to meet the established quality goals.
• Recommended preventive actions - These actions are recommended as a result of the QC process to avoid future failure to meet the established quality goals.
• Recommended defect repair - A defect is an imperfection or deficiency that keeps a component from meeting its requirements or specifications. Such a component needs to be repaired or replaced.
• Other change requests - These requests may arise from recommended corrective or preventive actions that might require changes to the project, or they may be needed independent of these recommendations. The changes are requested and processed through the integrated change control process.

Updates - The quality control process might generate updates to the following items:
• Organizational process assets - The completed checklists become part of the project record. Furthermore, you can update the lessons-learned database and documentation. These might include the causes of variances, the reasons for corrective and preventive actions, and the actions that worked and those that did not.
• Project management plan - The project management plan should be updated to reflect changes to the quality management plan resulting from the QC process. The quality baseline might need to be updated to reflect changes to the quality plan resulting from the QC process.

Prev: Big Picture of Quality Management

Next: Monitoring & Controlling Risk
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