InTRODUCTION

Hello, fellow project managers! Today, I am going to talk about the crucial aspect of project management: project quality management. In particular, I want to share insights into the areas I've studied extensively - Tools and Techniques for Quality Control. As you may know, there are three main sections: Statistical Sampling, Six Sigma, and Testing. It might seem a bit overwhelming at first glance, but fear not. I've got your back. Are you ready to dive in? Let's start this learning journey together!

Tools and Techniques for Quality Control

Have you heard of the seven basic tools of quality? According to Kathy Schwalbe [1], these tools make sure projects run smoothly. Let’s look deeply into each tool.

• Cause-and-effect diagrams: They are also known as fishbone or Ishikawa diagrams and what they do is help trace quality issues back to their source. Imagine it like detective work, finding the root cause of a problem by repeatedly asking "why." Control Charts, another tool, visually display if our processes are behaving or misbehaving over time. Think of it like a health monitor for our projects.

• Control charts: They help us see if our project processes are within the expected limits. If seven points in a row misbehave, it's like a red flag saying, "Hey, check what's going wrong!"

• Checksheets: They are organized lists that help us keep track of issues. For instance, we could use a checksheet to tally up where complaints about a system are coming from.

• Scatter Diagrams: They are visual friends that show relationships between things. For example, we might use one to see if age affects how much people like a system.

• Histograms: They are bar graphs that tell stories about how often things happen. We could use one to see how many complaints we get each week.

• Pareto Charts: They help us focus on the big issues. They follow the idea that 80% of our problems might come from 20% of the causes. It's like putting a spotlight on the most important things to fix.

• Flowcharts: They are maps for our processes. They show how things move from one step to another. Think of it as drawing a roadmap to understand how we handle project deliveries.

Statistical Sampling

Ever wondered how we decide which part of a big group to inspect? That's where statistical sampling comes in. Imagine you're building a new system for handling invoices from suppliers. If you've got thousands of invoices, checking each one would be a huge task. So, statisticians have a cool trick.

First off, in Kathy Schwalbe’s [1] view, there's the certainty factor. It's about how sure you want to be that your sample really represents the whole batch. Let's say you're good with 95% certainty. Then, there's an acceptable error, which is just 1% minus your certainty (in this case, 5%). Plug these into a formula, and voila, you know how many invoices to check! For example, aiming for 95% certainty might mean inspecting 384 invoices. Need a quicker check with 90% certainty? That's 68 invoices. It's all about balancing confidence and efficiency in understanding what your system needs.

Six Sigma

Now, let’s talk about Six Sigma. It's a way to supercharge business success. Imagine aiming for just 3.4 defects per million opportunities—that's the Six Sigma goal. Picture it as a superhero for quality, cost reduction, and customer satisfaction. The secret sauce? As Rachel Nizinski [2] mentioned, understanding customers, using facts, data, and a five-step process: DMAIC (Define, Measure, Analyze, Improve, Control).

• Define: Understand the problem, process, and customer needs. Tools like project charters and Voice of the Customer data come in handy. 

• Measure: Set up measures and collect data. Think of defects per opportunity. 

• Analyze: Dig deep into processes for improvements, using tools like the trusty fishbone diagram. 

• Improve: Cook up solutions and pilot test them. 

• Control: Keep an eye on improvements and solution stability using tools like control charts. It's Six Sigma magic!

How is Six Sigma Quality Control Unique?

So, what makes Six Sigma different from other quality control methods? Unlike past initiatives like Total Quality Management and Business Process Reengineering, Six Sigma is a total commitment. From CEOs to every level of staff, everyone dives in. It's like a martial arts class, where you earn different-colored belts with training levels. 

Kathy Schwalbe [1] argues that Yellow Belts, Green Belts, Black Belts—each level gets specific training, even project managers. Successful Six Sigma organizations juggle seemingly opposite goals, aiming for creativity and rationality, focusing on big and small details, reducing errors while speeding things up, and keeping customers happy while making a profit. It's not just a program; it's an operating philosophy—customer-focused, waste-eliminating, quality-raising, and financially boosting. It's the secret sauce for achieving extraordinary quality improvements.

Six Sigma and Project Selection and Management

In the Six Sigma world, success begins with project selection and management. According to quality guru Joseph M. Juran, all improvement happens project by project. Selecting the right projects is crucial. Pande, Neuman, and Cavanagh found that poor project selection is a common pitfall. Well-chosen projects mean better and faster results, while poorly selected ones lead to delays and frustration.

Not every project is a candidate for Six Sigma. It's about addressing quality gaps and problems. Building a house or merging companies might not fit the bill. Kathy Schwalbe [1] highlights that the key is having a quality issue, an unclear problem, and no predetermined solution. Once chosen, Six Sigma projects follow standard project management practices, with charters, schedules, budgets, and dedicated teams. They align with the Six Sigma philosophy—customer-focused, waste-cutting, quality-raising, and financially empowering.

Six Sigma and Statistics

In Six Sigma, we aim to enhance quality by minimizing variation. Sigma, or standard deviation, measures how spread-out data is. In the words of Kathy Schwalbe [1], a small sigma means less variability, while a large sigma indicates more variability. Figure 9 shows a typical bell-shaped curve of a normal distribution, a statistical concept used in Six Sigma.

The target for Six Sigma is 3.4 defects per million opportunities, even though pure statistics might allow two defects per billion at six sigma. Why the difference? Six Sigma uses a scoring system that considers more variation over time. Table 1 illustrates sigma's relationship with defect percentages and the number of defective units.

Table 2 provides a conversion for Six Sigma projects. A process at six sigma allows a maximum of 3.4 defects per million opportunities. This method focuses on defects per opportunity, reflecting the number of chances for a defect to occur in a product or service. While the telecommunications industry aims for "six 9s of quality" (99.9999% service availability), achieving such high quality requires continuous testing or robust backup systems.

Testing

When we hear "testing," it's easy to think it's the final check before a product ships. However, Kathy Schwalbe [1] states that testing is more effective when woven into various phases of the systems development life cycle (SDLC).

In Figure 10, the SDLC is portrayed with 17 tasks. Testing isn't just a box at the end; it's part of tasks like unit testing, which checks individual components. Integration testing ensures grouped components work together, and system testing checks the entire system. User acceptance testing, done by end users, focuses on business fit.

Conclusion

So, we've learned the tools and techniques for quality control of the project quality management today. These insights are not just theoretical; they're tools for success. I trust this knowledge equips you for your future projects, underlining the critical role of quality control. Until next time, when we explore another topic of project management, have a fantastic day and may your projects be nothing short of outstanding!

References

[1] Schwalbe, Kathy. Information Technology Project Management, 9th Edition.

[2] Nizinski, Rachel. “What Is a Continuous Improvement Process and How Is It Implemented?”.
      appian.com, link to the article.

Graphic References

[Figure 1] Illustration of project quality management, link to the graphic.

[Figure 2] Sample cause-and-effect diagram
  Page 337, Schwalbe, Kathy. Information Technology Project Management, 9th Edition.

[Figure 3] Sample control chart
  Page 338, Schwalbe, Kathy. Information Technology Project Management, 9th Edition.

[Figure 4] Sample checksheet
  Page 339, Schwalbe, Kathy. Information Technology Project Management, 9th Edition.

[Figure 5] Sample scatter diagram
  Page 339, Schwalbe, Kathy. Information Technology Project Management, 9th Edition.

[Figure 6] Sample histogram
  Page 340, Schwalbe, Kathy. Information Technology Project Management, 9th Edition.

[Figure 7] Sample Pareto chart
  Page 341, Schwalbe, Kathy. Information Technology Project Management, 9th Edition.

[Figure 8] Sample flowchart
  Page 341, Schwalbe, Kathy. Information Technology Project Management, 9th Edition.

[Figure 9] Normal distribution and standard deviation
  Page 347, Schwalbe, Kathy. Information Technology Project Management, 9th Edition.

[Figure 10] Testing tasks in the software development life cycle
    Page 349, Schwalbe, Kathy. Information Technology Project Management, 9th Edition.

[Table 1] Sigma and defective units
Page 347, Schwalbe, Kathy. Information Technology Project Management, 9th Edition.

[Table 2] Six Sigma conversion table
Page 348, Schwalbe, Kathy. Information Technology Project Management, 9th Edition.