Every commissioning organization measures something. Far fewer measure readiness. The Commissioning (Cx) Readiness Maturity Model maps the distance between the two. Five tiers describe how a program's measurement capability evolves from counting work performed to autonomously orchestrating the successful execution of Cx operations across an entire facility.
A maturity model is useful for one reason. You cannot improve what you cannot locate. Place your program on these tiers honestly and two things become clear, how much of your current confidence rests on activity rather than readiness, and the single most valuable move available to you next. The tiers are cumulative. Each builds on the data and discipline of the one below. Most of the industry today operates at Tiers 1 and 2.
The climb is cumulative, but it is not even. Tiers 1-3 describe the present and past with rising structure. The decisive step is Tier 3 to Tier 4, where a program stops seeing dependencies and starts computing readiness from them.
| Tier | Name & defining question | Characteristics | Metrics it relies on | Limitations | Outcomes |
|---|---|---|---|---|---|
| 1 | Activity Tracking How much have we done? | Work is logged as discrete items, scripts, issues, documents, milestones. Reporting is a roll-up of counts and percentages. Readiness is inferred informally, if at all. | Scripts completed/executed, percent complete, issues opened/closed, documents submitted, milestones achieved. | Cannot see dependencies, severity, or integration state. Systematically overstates readiness. The Readiness Gap is invisible and unmanaged. | False confidence. Readiness surprises at integrated testing. Reliance on individual heroics and instinct for go/no-go. |
| 2 | Status Reporting Where do we stand against plan? | Activity is organized into structured status, RAG ratings, dashboards, schedule and earned-value views by system and area. Trends are visible. | Tier 1 metrics plus schedule variance, earned value, RAG status, burndown trends. | Still item-based and backward-looking. RAG colours encode judgment, not computed readiness. Dependencies remain implicit. | Better visibility and coordination, with faster detection of slippage. Readiness is still inferred, though, and the Gap persists behind green status. |
| 3 | Dependency Visibility What depends on what, and what is blocking? | The dependency and prerequisite structure between systems and tests is made explicit. Blocking relationships and critical prerequisite chains are mapped and tracked. | Dependency completeness, prerequisite satisfaction, blocking-issue counts on critical paths, turnover/prerequisite status. | Shows where readiness is constrained but does not yet quantify probability of success. Still largely descriptive of the present, not predictive. | Constraints become visible and actionable. Sequencing improves. The causes of the Readiness Gap are exposed even if not yet measured. |
| 4 | Readiness Intelligence What is the probability the next execution succeeds, why, and where will readiness fall short before it does? | Readiness is computed continuously as a multidimensional state from dependencies, prerequisites, integration, resources, quality, and risk. The model also forecasts where the Gap will open and recommends the constraint-resolution sequence that closes it most efficiently. Go/no-go becomes a measured, predictive judgment. | A composite readiness measure per activity/system/facility, priority-weighted open issues, evidence freshness, schedule feasibility and float, calibrated probability-of-success forecasts and predicted Gap trajectory, completion-velocity forecasting, and contractor/supplier reliability scoring. | Requires disciplined, structured data and an agreed readiness model. Forecasts calibrate further as outcome history accumulates across programs. | Readiness assessed and forecast before execution. Fewer failed tests. Defensible, shared go/no-go decisions. Effort directed proactively at the binding constraint before it bites. |
| 5 | Readiness Orchestration Can readiness be orchestrated across the whole program? | Closed-loop. The system continuously senses program state, predicts the Gap, and autonomously orchestrates constraint resolution, sequencing work, routing prerequisites, re-planning, with people supervising exceptions rather than computing readiness by hand. The model learns across facilities and improves itself. | Orchestrated Gap-closure rate. Share of system-recommended actions accepted vs. overridden. Readiness gained per unit effort, optimized automatically. Cross-program learning and calibration. | Demands mature, trusted data, strong governance, and human oversight of the orchestration. The frontier, beyond today's practice. | Readiness managed forward continuously and largely autonomously. People supervise rather than assemble the picture. The program optimizes itself for probability of success. |
How to read the model. The jump that matters is Tier 3 to Tier 4, the transition from seeing dependencies to computing readiness from them. Tiers 1 through 3 are, in the end, increasingly well-structured descriptions of the present and the past. Tier 4 is the first tier that produces a forward-looking measure, a probability of success.
That is the threshold at which an organization crosses from activity tracking into Readiness Intelligence. Tiers 1 to 3 make the Readiness Gap progressively more visible. Tier 4 is the first that measures, manages, and forecasts it. Tier 5 closes the loop, orchestrating readiness across the program.
Mapping organisational maturity to readiness measurement
The tiers describe a capability, but they do not by themselves explain why an organisation sits where it does, or why two teams running the same software land on different tiers. For that, the maturity model has to be mapped onto the organisational capabilities that enable it. We call this conceptual framework the Readiness Measurement Map. It ties the readiness-measurement tier an organisation can sustain to its maturity across five enabling dimensions.
From loose item-level records (scripts, issues, documents) to structured, relational data that can be reasoned over. You cannot compute what you have not structured.
From relationships held in someone's head to an explicit map of prerequisites and blocking ties between systems and tests.
From milestone sign-offs to defined readiness gates with pre-agreed deferral rules, and the discipline that keeps the evidence behind them trustworthy, independent witnessing, and re-validation when conditions change, so a sign-off is never treated as permanently true.
From siloed tools (Cx software, project controls, issue trackers) to joined data that an interpretation layer can read across.
From instinct-and-hallway go/no-go to evidence-based, probabilistic judgment the organisation trusts and acts on.
The framework has one governing rule, and it mirrors how readiness itself behaves. The tier an organisation can sustain is capped by its weakest enabling dimension, not its strongest. A team with excellent tooling but no explicit dependency model cannot rise above Tier 2, however good its dashboards look.
A team with a strong dependency map but siloed data and an instinct-driven culture stalls at Tier 3. And a team computing readiness on stale, self-reported evidence is producing confident fiction, not measurement, however advanced its model. Readiness measurement gates on the weakest dimension exactly as readiness gates on the weakest layer.
Used as a quick diagnostic, the Map turns the maturity model from a picture into a move. Rate each of the five dimensions honestly, item-level, relational, or computed, take the lowest, and that is the readiness-measurement tier you can actually sustain today. The dimension you scored lowest is your next investment. Take the 5-question self-assessment →
How to climb, without new field work
Moving up the tiers is not a leap in field technology. It is a new layer of interpretation over data disciplined programs already generate. Five concrete moves take a program from Tier 1-2 toward Tier 4.
- Make the dependency and prerequisite structure between systems and tests explicit, this is the Tier 3 foundation everything else builds on.
- Replace raw open-issue counts with a priority-weighted view tied to blocking status and the next test.
- Track completion velocity against the velocity required to hit committed dates, not just cumulative percent-complete.
- Score contractor and vendor reliability from the closure history you already hold.
- Treat schedule feasibility, compression, float, look-ahead reliability, as a readiness input, not a separate report.
