Project Quality Management

● Project Quality Management determine quality policies, objectives, and responsibilities so that the project will satisfy the needs for which it was undertaken and ensure that the project requirements, including product requirements, are met and validated.
● Project Quality Management is a continuous process within the project's context.
● Quality as a delivered performance or result is "the degree to which a set of inherent characteristics fulfill requirements".
Grade as a design intent is a category assigned to deliverables having the same functional use but different technical characteristics. Quality level that fails is always a Problems; Low grade of quality may not be a problem.
For example:

• It may not be a problem if a low-grade software product (lack of features) but high quality (no obvious defects/bugs, readable manual). 一個低等級（功能有限）、高質量（無明顯缺陷，用戶手冊易讀的軟件產品，也許不是問題。
• It may be a problem if a high-grade software product (many features) but low quality (many defects, poorly organized user documentation). It's resulting ineffective and/or inefficient due to its low quality.

Importance of quality management

• Understand requirements and customer needs.
• Prevention over inspection - The cost of preventing mistakes is less than the cost of correcting mistakes when they are found by inspection or during usage.
• Continuous Improvement
• Management Responsibility
• Cost of Quality (COQ) - COQ refers to the total cost of the conformance work and the nonconformance work.
  質量成本是指一致性工作和非一致性工作的總成本。一致性工作是為預防工作出錯而做的附加努力，非一致性工作是為糾正已經出現的錯誤而做的附加努力。質量工作的成本在可交付成果的整個生命周期中都可能發生。項目結束後，也可能因產品退貨、保養而發生“後項目質量成本”。(Post-project quality costs)
  投資通常用在一致性工作方面，以預防缺陷或檢查出不合格單元來降低缺陷成本。
  PMO should follow up the post-project COQ, e.g review, funding allocations etc.

8.1 Plan Quality Management
8.1.2.3 Seven Basic Quality tools
● The seven basic quality tools, also known in the industry as 7QC Tools, are used within the context of the PDCA Cycle to solve quality-related problems.

1. Cause-and-effect diagrams (因果圖，又稱魚骨圖或石川圖)
2. Flowcharts
3. Checksheets
4. Pareto diagrams
5. Histograms
6. Control charts
7. Scatter diagrams

●Design of Experiments (DOE)
PMBOK don't have clearly explain, but see Fundamental Concepts of DOE & Design of Experiments for Software Testing, it's most likely Unit Test, functional breakdown, test case and assert etc.


8.1.3 Plan Quality Management: OutPuts
Quality Metrics - A quality metric specifically describes a project or product attribute and how the control quality process will measure it. A measurement is an actual value. The tolerance defines the allowable variations to the metric. For example, if the quality objective is to stay within the approved budget by ± 10%, the specific quality metric is used to measure the cost of every deliverable and determine the percent variance from the approved budget for that deliverable.
Quality Checklists - A checklist is a structured tool, usually component-specific, used to verify that a set of required steps has been performed.


8.2 Perform Quality Assurance
Tools and Techniques - 8.2.2.1 Quality Management and Control Tools

1. Affinity diagrams
2. Process decision program charts (PDPC)
3. Interrelationship digraphs
4. Tree diagrams
5. Prioritization matrices
6. Activity network diagrams
7. Matrix diagrams

8.3 Control Quality
● Control Quality is the process of monitoring and recording results of executing the quality activities to assess performance and recommend necessary changes.
The key benefits of this process include:

1. Identifying the causes of poor process or product quality and recommending and/or taking action to eliminate them.
2. Validating that project deliverables and work meet the requirements specified by key stakeholders necessary for final acceptance.

● The Control Quality process uses a set of operational techniques and tasks to verify:

• Prevention(保證過程中不出現錯誤) and inspection(保證錯誤不落到客戶手中)
• Attribute sampling and variables sampling
  Explain:
  Attribute Sampling vs. Variable Sampling Attribute sampling: Discrete or attribute data can only measured by categories (like yes/no, true/false, pass/fail etc.) or intervals (like absolute rank, educational level, types etc.)
  Attribute data cannot be further divided, for example if I say I have 10 students who are either taking a math class or a science class, there will be no student who would be taking 50% of the match class and 50% of the science class. Variable sampling: Variable or continuous data can be further divided into more classifications and that will still have meaning. For example if I measure temperature for two rooms, 22F and 23F respectively, this does not mean that a temperature of 22.2F or 22.5F cannot be recorded.
• Tolerances (容錯率) and Control Limits (控制穩定界限)