Problem
Business Context
You are the Engineering Manager for a Mercedes-Benz Group team building a customer-facing MB.OS feature used in the Mercedes-Benz app and in-vehicle digital experience. In the last two quarters, stakeholders say the team is "busy" and shipping often, but product partners are unsure whether delivery quality and business impact are actually improving.
Metric Scenario
Your team of 9 engineers, 1 QA engineer, and 1 engineering analyst supports one major quarterly release and weekly production deployments. Last quarter, the team completed 48 story tickets, shipped 14 production releases, and closed 91% of planned sprint scope. However, post-release defects rose from 11 to 19, average lead time increased from 6.2 days to 8.4 days, and the share of roadmap items delivered on the committed quarter slipped from 82% to 68%. Meanwhile, MB.OS feature adoption improved from 420k to 470k monthly active users.
The Director of Engineering asks: "What metrics would you use to know whether this team is delivering well, and how would you separate output from actual delivery health?"
Requirements
- Define a compact delivery KPI set for this team, including both leading and lagging indicators.
- Explain which 1-2 metrics should be primary and why they best represent healthy delivery.
- Show how you would interpret the data above: is the team delivering well or masking issues with high output?
- Describe how you would decompose the metrics to diagnose whether problems come from planning, execution, quality, or release process.
- Identify guardrails so the team does not optimize for speed at the expense of reliability or customer experience.
Data Available
- Jira sprint and issue history for epics, stories, bugs, and carryover
- GitHub and CI/CD logs for PRs, build times, deployments, and rollback events
- Incident and defect tracking from production support systems
- MB.OS feature usage logs from the Mercedes-Benz app and in-vehicle telemetry
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