Quality Management Tools for Your Projects | Velopi
For Certified Associate in Project Management (CAPM)® and Project Management Professional (PMP)® exam students, the Project Quality Management knowledge area is full of many and varied tools and techniques. In this article, we will describe only a handful of these, but this should give you a feel for what these tools are created to achieve.
1. Cause and Effect Diagrams (a.k.a. Ishikawa or Fishbone Diagrams)
These diagrams are useful when conducting root cause analysis and are Data Representation tools used by both the Manage and Control Quality processes.
They allow the analyst to explore all the different possible causes with a view to determining which caused the effect. This sort of analysis benefits from the 5 Whys approach – keep asking “why?” until you arrive at the real root cause. For instance, an error is detected and traced back to a step in the process not being followed. Often, companies will adjust the process, when the real root cause is the operator who had not followed the process correctly.
Flowcharts are popular in the software world to describe computer algorithms. However, the corporate world uses flowcharts to describe business processes. These often use the “swim-lane” format, where the process is shown entering and leaving different departments.
A variation of this, the Supplier, Input, Process, Output, Customer (SIPOC) diagram, is used in Six Sigma analysis. It is also given in the PMBOK® Guide, so CAPM® and PMP® exam students should be aware of it. Flowcharts are also Data Representation tools that are used in Plan Quality Management and Manage Quality.
3. Checksheets (a.k.a. Tally Sheets)
In manufacturing, items are often taken off the assembly line for inspection. But what is inspected? A checksheet is really helpful here. It lists the features of the item that need to be checked. For instance, a device like a water pump needs to be connected to standard sized pipes, so an important check is to ensure the inside and outside diameters of its openings are within tolerances. This Data Gathering tool is emplyed by the Control Quality process.
4. Pareto Diagrams
The Pareto principle is that 20% of causes generate 80% of effects. A root cause analysis of all defects encountered in your process can be collated into a Pareto Diagram that shows which causes we should tackle to eliminate most of our defects.
Interestingly, the Guide to the Project Management Body of Knowledge (PMBOK® Guide) no longer mentions the Pareto Chart, but it is a useful form of the Data Representation tool called the Histogram.
This is a bar chart that shows the distribution of a set of samples based on frequency. For instance, we might measure every single part that comes off our assembly line and reject any that deviates from the required size by over one millimetre. A histogram will show us just how difficult this target is to achieve. Are most of our products exactly the right size, or are most of them just about inside the acceptance thresholds? A typical histogram would look like:
The goal of any process improvement initiative is to lower the numbers near the thresholds and increase the numbers nearer the target size. This Data Representation tool is used by both the Manage and Control Quality processes.
6. Control Charts
A control chart is a Data Representation tool used by the Control Quality process and can be regarded as a histogram that shows variation over time. The target value is represented by the middle line and the acceptance thresholds are given by the upper and lower control limits:
While the histogram shows totals, the control chart allows us to observe the variations as they happen. We might see our outputs get close to the thresholds at certain times of the day. Such a clustering effect can help our investigation. Maybe the products drift off the target value as a machine approaches a maintenance window? If so, we can increase the maintenance frequency and see if this changes the pattern.
A simpler type of control chart, called a “Run Chart” is the same thing, but it does not have the threshold, or control limit lines. This is no longer mentioned in the PMBOK® Guide.
7. Scatter Diagrams
In our root-cause analysis studies, project managers need to be very careful that one effect causes another. We can be fooled by apparent correlations. Correlations do not, necessarily, mean causality. However, we can confidently state that no correlation means no causality.
As an example, let us return to our assembly line. We have seen from our histogram that too many of our products are near the limits of our acceptance. But why is this happening? We might suspect a variety of causes, but we need hard facts before taking action. We might suspect that ambient temperature has something to do with it. If so, we should plot the product size against the temperature when it was made. If the temperature changes, does our product size change? If it does, then we can investigate further; if not, we can dismiss temperature from our study and move onto another factor.
When trying to establish cause and effect, the effect is called the “dependent variable” and the supposed cause the “independent variable”. The idea is that the effect is “dependent” on the cause. If we vary the cause, we should see an impact on the effect. So to test our theory that temperature is causing the problem we can adjust the temperature and see what happens. As this is unlikely to be allowed on a live assembly line, we need to measure the temperature along with the size of each product (i.e. we will add temperature readings to our inspection checksheet). Then the two values are plotted using a scatter diagram.
In this case, it is clear that the size of the product correlates with temperature – as the temperature falls, the product drifts towards the upper control limit. On the basis of this analysis, it is worth controlling the temperature around the assembly line. However, we still need to continue measuring. If a more stable temperature results in more compliant product, only then can we assert that temperature caused product variation.
Scatter Diagrams are Data Representation tools used by both Manage and Control Quality.
The majority of the quality management tools are very intuitive. They assist in understanding processes and teasing out issues with them. They should not be considered as being suitable solely for manufacturing. As a PMP®, you can use control charts, for instance, to illustrate how your earned value management is faring. Use a Schedule (or Cost) Variance of zero as the centre line and plot your actual variance throughout the project. If you are working to an unrealistic schedule, this will highlight it.
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