Day 1: Building a Reliable Progress Picture

Progress Measurement Rules

• Define clear evidence proving activity completion
• Use milestones or weighted completion rules
• Prevent unsupported subjective completion percentages
• Apply progress rules to work packages

Status Date and Baseline

• Use measurement date across project data
• Compare performance with correct reference date
• Identify approved changes affecting the baseline
• Prepare a project status snapshot

Data Collection and Validation

• Reconcile progress with execution records
• Check time-cost information inconsistencies
• Resolve differences across contributing data sources
• Run a concise project update cycle

Day 2: Time, Cost, and Forecast Analysis

Analyzing Schedule Variance

• Identify activities moving key milestones
• Analyze changes to path and float
• Distinguish local delay from completion impact
• Prepare a concise schedule variance analysis

Cost Variance and Earned Value

• Compare planned earned and actual value
• Calculate performance indicators appropriate to case
• Interpret variance against completed work
• Solve an integrated performance case

Trend and Forecast

• Use patterns over one reporting period
• Update completion date or final cost
• Test forecast sensitivity to performance improvement
• Build forecasts across three performance scenarios

Day 3: Cause, Corrective Action, and Management Reporting

Diagnosing Variance Causes

• Segment gaps by package/resource
• Separate primary causes from downstream effects
• Validate conclusions with accountable delivery owners
• Build cause tree for one variance

Corrective Action Planning

• Compare resequencing resourcing and scope alternatives
• Estimate intervention impact on remaining project
• Set owner, deadline, recovery evidence
• Draft a concise recovery plan

Progress Reporting Laboratory

• Present status forecast and highest variances
• Highlight the decision or support required
• Avoid hiding trends beneath excessive detail
• Deliver project progress briefing to management

• Progress updates built from changing project data
• Schedule cost and earned-value analyses across practical cases
• Cause scenarios requiring different recovery interventions
• Reporting laboratory connecting variance with management action