HKSM

Structured documents that compile and analyze quality data to show how the project and product meet agreed standards and acceptance criteria. They inform decisions on corrective and preventive actions and support stakeholder communication.

Key Points

• Summarize quality performance using metrics, trends, defects, and audit results.
• Compare actual results to thresholds and acceptance criteria to highlight gaps.
• Provide evidence for decisions on acceptance, rework, prevention, and process improvements.
• Should be clear, visual where possible, and tailored to stakeholder needs.
• Must be timely, accurate, and traceable to requirements, tests, and standards.
• Become part of project records and feed lessons learned and governance reviews.

Purpose of Analysis

Analyze these documents to understand quality performance, detect patterns, and decide what actions to take to meet or maintain the required level of quality.

• Validate whether deliverables meet specifications and acceptance criteria.
• Identify trends, recurring issues, and potential root causes.
• Prioritize corrective and preventive actions based on impact and urgency.
• Demonstrate compliance with organizational and regulatory standards.
• Support risk management, change control, and stakeholder communication.

Method Steps

• Collect the latest quality data and prior reports for context.
• Verify data completeness, integrity, and alignment with defined metrics.
• Group findings by product area, process, release, or iteration for clarity.
• Compare results to thresholds, baselines, and acceptance criteria to flag variances.
• Analyze trends and distributions using visual aids (charts, Pareto, control views) as appropriate.
• Document insights, suspected causes, and the magnitude of impacts.
• Recommend actions with owners, timelines, and expected outcomes.
• Review with stakeholders, agree next steps, and publish the approved report.

Inputs Needed

• Quality management plan, metrics definitions, and thresholds.
• Test results, inspection records, and acceptance criteria.
• Audit and assessment findings, checklists, and compliance requirements.
• Defect and nonconformance logs with severity and status.
• Process performance data and measurement system information.
• Change requests, issue log, and risk register entries related to quality.
• Cost of quality data and prior quality reports for trend comparison.

Outputs Produced

• Consolidated quality report and evaluation summary.
• Dashboards or scorecards highlighting key metrics and trends.
• Recommendations for corrective and preventive actions with owners.
• Quality-related change requests and updates to the issue and risk logs.
• Updates to the quality management plan, metrics, or test approach.
• Records for compliance and lessons learned repositories.

Interpretation Tips

• Focus on meaningful variation and trends, not single data points.
• Distinguish product defects from process issues to target the right fix.
• Check sample sizes and measurement methods to avoid biased conclusions.
• Consider context such as scope changes or new team members when reading spikes.
• Link findings to customer impact and business value to prioritize actions.
• Verify that recommended actions have clear closure criteria and evidence.

Example

A project team compiles a monthly quality report showing test pass rate, escaped defects, and audit findings. The report highlights a rising trend in rework tied to a specific component and notes that the audit found incomplete checklists. The team recommends strengthening peer reviews for that component, retraining on the checklist, and adding a new in-process check. The sponsor approves the actions, and the next report shows reduced defects and improved first-pass yield.

Pitfalls

• Reporting too late for actions to influence the current work.
• Cherry-picking metrics that present only favorable results.
• Mixing raw data and conclusions without showing traceability.
• Overloading reports with jargon or visuals that obscure the message.
• Ignoring measurement system errors or inconsistent definitions.
• Failing to track action execution and verify effectiveness.

PMP Example Question

A monthly quality report shows a sudden increase in defect density after a process change. What should the project manager do first?

1. Submit a change request to roll back the process change.
2. Notify the sponsor that the variance will be accepted.
3. Validate the data and analyze the report to identify likely root causes.
4. Update the lessons learned register and close the issue.

Correct Answer: C — Validate the data and analyze the report to identify likely root causes.

Explanation: First confirm the integrity of the data and use the report to investigate causes before proposing changes. Acting without analysis may lead to incorrect or costly decisions.