Metric Hierarchy Template
This template helps you design metric hierarchies -- systems of metrics where one metric decomposes into components. Use it to define the OEE (Overall Equipment Effectiveness) hierarchy and identify leading and lagging indicators for Verdant Packaging.
1. OEE Decomposition
OEE (Overall Equipment Effectiveness) is a standard manufacturing metric that measures how effectively a production line is being used. It decomposes into three components:
OEE = Availability x Performance Rate x Quality Rate
Each component captures a different dimension of production effectiveness:
| Component | What it measures | Formula |
|---|---|---|
| Availability | How much of the planned time the line was actually running | (Planned Time - Unplanned Downtime) / Planned Time |
| Performance Rate | How fast the line ran compared to its maximum rated speed | Actual Output / (Maximum Possible Output at Rated Speed) |
| Quality Rate | What proportion of the output was good (passed quality testing) | Good Units / Total Units |
Why decomposition matters: An OEE of 72% tells you the line is underperforming, but not why. Is it shutting down too often (low availability)? Running slowly (low performance rate)? Producing too many defective units (low quality rate)? The decomposition turns a single diagnostic number into a targeted investigation.
Your task
Define each component for Verdant Packaging using the data sources available. For each component:
- Write the precise definition (what counts, what doesn't)
- Identify the data source (which file, which columns)
- Calculate the current value per production line
- Document what "good" looks like (target values based on industry standards or client expectations)
2. Leading and Lagging Indicators
Leading indicators predict future outcomes. Lagging indicators confirm past performance.
Example from a different industry (restaurant chain):
- Leading: health inspection scores, staff training completion rate, ingredient supplier quality ratings
- Lagging: customer complaint rate, food poisoning incidents, revenue per seat
The leading indicators predict the lagging ones. A restaurant with falling health inspection scores will eventually see more complaints. The value of leading indicators is catching problems before they show up in the lagging numbers.
Your task
Identify leading and lagging indicators for Verdant Packaging. Use this template:
| Indicator name | Type (leading / lagging) | Data source | Threshold (if applicable) | What it predicts / confirms |
|---|---|---|---|---|
For each leading indicator, ask: "If this number moves, which lagging indicator will follow?" For each lagging indicator, ask: "Which leading indicator would have warned us?"
3. Hierarchy Documentation
Metric hierarchies have dependencies -- changing one metric's definition affects others. Document these relationships so the team knows what breaks when a definition changes.
Your task
For each metric in the hierarchy, document:
| Parent metric | Component metrics | Cascade effect (if component changes) | Data sources | Owner (who maintains this definition) |
|---|---|---|---|---|
The "cascade effect" column is the most important. When you redefine a component, what happens upstream? If you change how "quality rate" is calculated (say, including rework as "good" instead of "defective"), every metric that depends on quality rate shifts. The documentation prevents silent breakage.