A Project Manager in a modern organization will need to rely on an array of concepts and practices to manage a project efficiently. As a project manager, you will also need to use a range of tools that will help you in decision-making and get a clear idea about the health of the project as it progresses. If you are planning to appear for the PMP exam, preparing for the online PMP certification is a guaranteed way to ensure that you are well versed in all the tools that are used in project management.
Control charts in PMP are widely used tools to denote the health of a project or how well the project is performing when compared to estimated values and targets. So. what is a control chart in PMP and why is it so important?
A Brief History of Control Chart
Before getting into the details of a control chart in PMP, let’s explore how it came into practice. A control chart was first used more than a century ago. It was invented by Walter Shewhart in the early 1920s at Bell Labs. The objective of the control chart is to establish measures to alert you when a process is going out of control.
Most processes see a certain amount of variation in practice. A standard mean is the average value you can expect. This is plotted as a line on a graph. Using standard deviation two more lines are plotted on the graph which represents an upper limit and a lower limit. A variation within these limits is not considered a cause for concern. The moment one of these lines is crossed, you will need to step in and find out the reason for this change.
In control chart project management, these variations are closely monitored to ensure quality and consistency. As technology and data collection have advanced, these charts can be generated automatically and will alert you when there is a significant variation. You could add further lines within the limit when you want to exert tighter control over a process.
Purpose of Control Chart
A control chart in PMP is used to keep track of events that signal something going wrong. You cannot manually check all numbers in a complex project to make sure that everything is going according to plan. Processes do not yield the same results every day. A certain degree of variation is to be expected. These variations are part of minor factors that do not deserve attention. These variations may not have an impact on project completion or make any significance to any stakeholder.
When the variation goes beyond a certain extent, there is an extraordinary factor at play. This needs to be investigated and corrected to make sure that the project stays on track. Our certificate course in Project Management equip professionals with the best practices and concepts that are used in project management.
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Basic Procedure in Control Chart
You can create a control chart in PMP and start using it if you have project management software that allows you to do that. In case you do not have that option, it is easy enough to do it on an excel sheet. You can create a line chart based on the average values you have collected.
The mean of the values would become on a straight line. This is the control line. Ideally, all your values should be close to the line. The next step is to calculate the standard deviation to see how much the values fluctuate during the normal course of business.
Based on the standard deviation you will create an upper limit and a lower limit.
This needs to be significantly higher than the standard deviation. We are looking for events where there is a major and unexpected change quite different in magnitude from the normal fluctuations you expect.
Any value falling outside the control limits will need to be investigated. In the normal course, the values should fall on either side of the control line (mean). If there are seven continuous values on the same side of the line, then this also deserves an investigation, as this is a key principle in a control chart PMP methodology.
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Benefits of Using Control Charts in PMP
Let us explore the key benefits of using control charts in project management and how they contribute to successful project outcomes.
- Improves Process Control: Control charts help project managers monitor and control the quality of processes by detecting variations early. This enables proactive adjustments to keep the process stable.
- Identifies Trends and Patterns: Control charts can show trends, patterns, and changes in performance over time by continually showing data points. This aids in seeing any problems before they get out of hand.
- Stabilizes Processes: By monitoring process variation over time, control charts help to maintain process stability and consistency. This can improve product or service quality and reduce customer complaints.
- Makes Decisions Easier: Data-driven insights from control charts help make wise decisions. They help project managers take more targeted remedial action by helping them distinguish between common and unique causes of variation.
- Increases Productivity: Control Charts can help streamline processes and cut down on waste by spotting and correcting discrepancies early on, which improves overall project execution efficiency.
- Supports Continuous Improvement: Control charts offer a visual tool for tracking and fine-tuning processes over time, guaranteeing constant delivery of high-quality results and fostering a culture of continuous improvement.
- Facilitates Communication: Control charts provide a quick and easy means of informing stakeholders about process performance, which helps to maintain everyone's focus on the project's quality goals.
- Reduces Costs: By identifying issues before they escalate, control charts help reduce rework and resource wastage, ultimately leading to cost savings for the project.
Control Chart Types in PMP
You could learn about the diverse types of control charts that are used in different contexts to build on your knowledge. There are two categories of control charts Variable and Attribute-based.
1. Variable-based Control Charts
Variable Control charts work by calculating the average values and measuring variations.
- X-bar and R Chart: This chart is used for monitoring the mean (X-bar) and range (R) of small sample groups over time. This type of chart is ideal for tracking continuous data where sample sizes are consistent.
Six-Sigma-Material.com- X-bar and S Chart: Tracks average values and standard deviation of sample data, ideal for larger sample sizes.
Support - Minitab2. Attribute Control Charts
It is used in situations where quality is checked with a pass/fail attribute.
- P Chart (Proportion Chart): P chart is designed for tracking the proportion of defective items in a sample. It is useful when monitoring categorical data, such as pass/fail or defective/non-defective products.
Six Sigma Study Guide- np Chart: This control chart is similar to the P chart, but it monitors the number of defective items, rather than the proportion. It works well when the sample size remains constant.
Six Sigma Study Guide- C Chart: This control chart is used for counting the number of defects per unit in a sample. It is applicable when tracking the occurrence of defects in consistent, fixed units, like errors per page or scratches on a surface.
Support - Minitab- U Chart: This chart is used to monitor defects per unit, similar to the C chart, but it allows for variable sample sizes. This makes it ideal for processes where the size of the sample or inspection unit changes.
Six Sigma Study GuideThere are further types of control charts within these subcategories that you should explore while deciding what kind of control chart works for you.
How to Create a Control Chart?
Let us take the case of a customer service center where there are callers handling customer queries. You are looking at the average call time and using that as a control parameter. You notice that the average call duration is 90 seconds (one and a half minutes). This is now your control line. You calculate the standard deviation and find it to be 10 seconds.
Multiplying the standard deviation by 3, you establish limits. The control chart in PMP would alert you if the average call time went beyond 120 seconds or comes down under 60 seconds. Since this is the average call time individual calls, and an exceptionally long or short call occasionally will not have much of an impact on the number.
We look at the values for the entire month of January. Then we plot the values on a chart.
| Day | Average Call Duration (in seconds) | Mean (Control Line) | Upper limit | Lower Limit |
|---|
| 1 | 95 | 90 | 120 | 60 |
| 2 | 107 | 90 | 120 | 60 |
| 3 | 90 | 90 | 120 | 60 |
| 4 | 99 | 90 | 120 | 60 |
| 5 | 99 | 90 | 120 | 60 |
| 6 | 86 | 90 | 120 | 60 |
| 7 | 91 | 90 | 120 | 60 |
| 8 | 85 | 90 | 120 | 60 |
| 9 | 88 | 90 | 120 | 60 |
| 10 | 71 | 90 | 120 | 60 |
| 11 | 91 | 90 | 120 | 60 |
| 12 | 78 | 90 | 120 | 60 |
| 13 | 100 | 90 | 120 | 60 |
| 14 | 99 | 90 | 120 | 60 |
| 15 | 86 | 90 | 120 | 60 |
| 16 | 88 | 90 | 120 | 60 |
| 17 | 89 | 90 | 120 | 60 |
| 18 | 137 | 90 | 120 | 60 |
| 19 | 93 | 90 | 120 | 60 |
| 20 | 98 | 90 | 120 | 60 |
| 21 | 93 | 90 | 120 | 60 |
| 22 | 102 | 90 | 120 | 60 |
| 23 | 89 | 90 | 120 | 60 |
| 24 | 86 | 90 | 120 | 60 |
| 25 | 88 | 90 | 120 | 60 |
| 26 | 80 | 90 | 120 | 60 |
| 27 | 72 | 90 | 120 | 60 |
| 28 | 55 | 90 | 120 | 60 |
| 29 | 73 | 90 | 120 | 60 |
| 30 | 86 | 90 | 120 | 60 |
| 31 | 96 | 90 | 120 | 60 |
For this month you will receive three alerts.
- On 18th the average call duration is above the upper limit
- On 28th the value is below the lower limit
- On 30th there are 7 consecutive values below the control line. This is also an example of the ‘rule of 7’ in project management.
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Control Chart Example
Control chart in PMP is a crucial tool used across several projects, especially where quality control is concerned. It has proven to be a useful device over a century, and you can see the principle of application everywhere. Various stock markets across the world have measures in place where they suspend trading when the index moves beyond a certain level in either direction. This is done so that the regular can check for foul play.
During the pandemic, city administrations have been closely monitoring the rate of infections, patients requiring hospitalization and the number of beds available. When numbers shift quickly in either direction it is a cause for concern. While numbers dropping is a good thing a sudden unexplained drop is still a cause for concern. Has there been a reduction in testing? Are we testing in the right clusters? Did we change the type of tests that we were using? Understanding these factors could help in identifying issues and rectifying them.
Control charts are most extensively used in manufacturing and more specifically in quality control. It has been a mainstay in several industries helping project leaders identify anomalies and make timely decisions.
When you’re figuring out the best sources for project management training, you should also pay attention to which program covers all the project management tools. The KnowledgeHut online PMP certification preparation program works by training candidates in understanding both old and new approaches in project management with real-world examples.
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Conclusion
Widely in use, control chart in PMP have evolved into distinct types to be used for different contexts depending on the type of data you are dealing with. Control charts are also popular with six sigma practitioners.
Control charts form one of the many tools at the disposal of a project manager to ensure that the project stays on track and the output matches what was promised. Using and implementing tools that are both old and new will help make the job of managing a project much easier. It will also give more transparency to all the stakeholders and increase credibility and trust across the board.
