Cycle Time

Correct Answer: Analyze the underlying reasons for the longer cycle time.

To address the longer cycle time, it's important to first analyze the underlying reasons by comparing the different processes between the projects and identifying potential inefficiencies.

Correct Answer: Analyze and optimize the preparation phase workflow through value stream mapping

Value stream mapping is the most effective approach because: It allows the team to: - Identify wasteful activities and bottlenecks in the preparation phase - Visualize the entire workflow to spot improvement opportunities - Make data-driven decisions to optimize the process - Address root causes rather than symptoms The other approaches are less effective because: Adding more team members could lead to increased coordination overhead and complexity, potentially making the situation worse (Brooks' Law). Parallel processing might introduce dependencies and risks that could compromise quality and create additional complications in workflow management. Replacing the phase with automation and documentation is too extreme, may not address underlying issues, and could create new problems in knowledge transfer and team collaboration. Value stream mapping provides a systematic way to understand and improve the workflow, making it the most sustainable and effective solution for reducing cycle time in the preparation phase.

Correct Answer: Track cycle time separately for different types of technical work and create complexity-based baseline metrics

The best approach is to track cycle time separately for different types of technical work and create complexity-based baseline metrics. Here's why: Strengths of this approach: - Acknowledges that different types of work have different inherent complexities - Enables more accurate forecasting by categorizing work - Provides detailed insights for targeted process improvements - Allows teams to set realistic expectations based on work complexity Why other options are less effective: Using a standardized system across all features oversimplifies the reality that different work has different complexity levels, leading to inaccurate measurements and unrealistic expectations. Calculating average cycle time across all features masks the variability in complexity and provides misleading metrics for planning. Measuring cycle time based on story points introduces unnecessary complexity and potential bias, as story points are already an abstraction of complexity and effort. The chosen approach acknowledges the natural variation in work complexity and provides actionable data for continuous improvement while maintaining transparency about different work types.