01 / FLOW
How does work move?
Lead time, cycle time, time in status, touch time, throughput, waiting, and workflow constraints.
The framework
Evidence in. Understanding out.
TDF diagnoses delivery systems through four connected lenses—Flow, Predictability, Quality, and Value / Work Mix—while preserving the confidence and context of the evidence behind them.
Four connected diagnostic lenses
No single metric tells the truth about delivery. TDF reads related signals together and keeps their interpretation bounded by the evidence available.
02 / PREDICTABILITY
How consistently does it move?
Variation, historical distributions, throughput stability, trends, and delivery pressure.
03 / QUALITY
What quality pressure affects delivery?
Defect patterns, escaped defects, resolution behaviour, rework indicators, and related quality signals.
04 / VALUE & WORK MIX
What kind of work reaches completion?
Work composition, configured classifications, defects, operational demand, and evidence about value-bearing delivery.
An important boundary
Workflow data can describe work composition. It cannot manufacture realised business value.
The wider framework can incorporate customer or business outcome evidence when that evidence exists. The Jira product should not claim that a completed feature created adoption, revenue, satisfaction, or another realised outcome from workflow data alone.
Confidence without ambiguity
Delivery Confidence is not Measurement Confidence.
They answer two different questions, and keeping them separate is essential to trustworthy interpretation.
SYNTHESIS / DELIVERY CONFIDENCE
What do the delivery signals collectively suggest?
Delivery Confidence is a synthesis view of what flow, predictability, quality, constraints, and related evidence suggest about the delivery system’s ability to deliver with reasonable consistency.
EVIDENCE / MEASUREMENT CONFIDENCE
How much should we trust the measurement?
Measurement Confidence considers workflow configuration, data coverage, measurement boundaries, fallback assumptions, and other evidence conditions that affect interpretation.
Why it matters
A strong-looking signal built on weak evidence should not be presented with the same certainty as one supported by complete, well-configured measurement data.
The diagnostic loop
01
Observe
Build a consistent view of workflow history, configuration, and the available delivery context.
02
Measure
Calculate stable, defined metrics from a canonical measurement context.
03
Diagnose
Connect related signals, constraints, trends, and the confidence of the evidence.
04
Improve
Use the evidence to focus an investigation or experiment, then observe what changes.
What TDF is—and what it deliberately is not.
These boundaries protect the framework from becoming another mechanism for performance theatre.
TDF IS
Decision support for delivery systems
- A delivery-system diagnostic framework
- Evidence-led and context-aware
- Framework-agnostic
- Focused on investigation and improvement
- Designed to keep conclusions traceable
TDF IS NOT
A performance-management system
- An individual productivity monitor
- A maturity certification
- A universal benchmarking league table
- An automatic management answer
- Another Agile methodology
TDF is diagnostic, not prescriptive. It helps teams ask better questions; it does not pretend there is one universal answer.