Simulation Lifecycle Monitoring Module (SLMM)
OOF™ Origin Open Foundation™
Independent Methodological Authority
Architecture Ecosystem: Structured Reality Standards™
Architecture Family: SIMULOS® — Simulation Governance Architecture
Parent Standard: Simulation Lifecycle & Resimulation Standard (SLRS)
Module: SLMM — Simulation Lifecycle Monitoring Module
Operational Layer: Simulation Lifecycle Monitoring Layer
Category: Governance & Enforcement
Subcategory: Simulation Governance
Type: Core Module
Governed Space: Simulation Lifecycle Monitoring
Origin ID: SIMULOS-SLRS-SLMM-012.1
Version: 1.0
Status: Canonical · Open Standard
Effective Date: 20 August 2026
Compatibility: OOF® Methodology OS™ · GOA™ · ORA™ · VALIDOS® · INTEGROS® · ADIT® · AGA™ ·
AIG® · ASGA™ · RIS™
AI-Readable: Yes
Authority: OOF®
Protection: MIP® — Methodology Intellectual Property Protection
Canonical Language: English
Canonical Definition
Simulation Lifecycle Monitoring Module (SLMM) is the first Core Module of theSimulation Lifecycle & Resimulation Standard (SLRS) within SIMULOS® —
Simulation Governance Architecture.
SLMM governs the continuing observation of conditions capable of materially
affecting the relevance, applicability, representativeness, evidential
meaning, or continued use of an existing simulation and its governed results.
The Module establishes a structured relationship between the simulation basis
that existed when simulation evidence was produced and the conditions that
exist as the simulation continues through its lifecycle.
SLMM does not determine whether observed change constitutes Simulation Drift,
nor whether resimulation is required.
Its task is foundational:
to ensure that material changes capable of affecting simulation relevance can
become visible rather than allowing a simulation to remain operationally
influential after its governing basis has silently changed.
Core Question
Which conditions must remain monitored so that material changes affectingsimulation relevance can be detected throughout the simulation lifecycle?
Architectural Position
SLMM is the first module within SLRS.It follows the completed active simulation lifecycle:
Simulation Object → Purpose & Scope → Reality Representation → Assumptions →
Model & Environment → Inputs & Data → Scenario Design → Execution → Output
Governance → Simulation-to-Reality Correlation → Simulation Evidence &
Reliance Boundary
and begins lifecycle governance.
SLMM precedes:
SDRM — Simulation Drift Recognition Module.
The relationship is:
Lifecycle Monitoring → Drift Recognition.
SLMM observes and structures relevant change.
SDRM determines whether that change creates a material divergence between the
existing simulation basis and the conditions to which that simulation remains
applied.
Internal Flow Position
Governed Simulation Lifecycle Basis → Monitoring Context Definition →Change-Sensitive Condition Identification → Lifecycle Observation → Change
Signal Detection → Material Change Candidate Registration → Simulation
Relevance Review Input → SDRM — Simulation Drift Recognition Module
Operational Space
SLMM governs the space between:
an established governed simulation basis
and
subsequent changes capable of affecting that basis.
This includes monitoring relevant change affecting: Simulation Object
identity; object version; object configuration; object state; simulation
purpose; simulation scope; represented reality; abstraction; fidelity
requirements; assumptions; model applicability; model version; simulation
environment; parameterization; inputs; data distributions; temporal data
relevance; input uncertainty; scenario relevance; scenario coverage; observed
operational conditions; simulation-to-reality correlation; evidence
applicability; evidence weight; reliance conditions; known limitations;
previously unknown conditions.
Operational Scope
SLMM applies wherever simulation results, evidence, models, scenarios, orsimulation-derived conclusions continue to be retained, reused, referenced,
compared, relied upon, or maintained after their initial production.
It may govern lifecycle monitoring for: individual simulations; simulation
models; repeated simulation programs; scenario portfolios; Monte Carlo
studies; digital twins; engineering simulations; scientific simulations;
safety simulations; infrastructure simulations; mission simulations; economic
simulations; AI simulations; autonomous-system simulations; large-scale
simulation campaigns; long-duration simulation programs; future simulation
technologies.
The Module remains technology-neutral.
Its concern is not how frequently a simulator executes.
Its concern is whether the conditions supporting simulation relevance remain
sufficiently observable over time. Simulation Completion ≠ Lifecycle
Completion
SLMM establishes the distinction:
Simulation Execution Completion ≠
Simulation Lifecycle Completion
A simulation run may end.A simulation campaign may end.
The simulator may be switched off.
But the simulation results may continue influencing: engineering; design;
planning; scientific reasoning; safety assessment; mission preparation; system
development; validation; operational decisions; deployment; future simulation
campaigns.
Lifecycle governance therefore continues for as long as simulation-derived
information remains materially relevant or relied upon.
Lifecycle Monitoring Basis
SLMM requires a Simulation Lifecycle Monitoring Basis to identify which partsof the governed simulation lifecycle are capable of materially affecting
continuing relevance.
The monitoring basis may reference:
Simulation Object → what was simulated.
Purpose & Scope → why and within what boundaries it was simulated.
Reality Representation → which reality was represented and how.
Assumptions → what conditions were presumed.
Model & Environment → which model and simulation environment generated the
behavior.
Inputs & Data → which information basis entered the simulation.
Scenarios → which conditions were tested.
Execution → how the simulation was actually executed.
Outputs → what was produced.
Simulation-to-Reality Correlation → what relationship with reality was
established.
Evidence & Reliance Boundary → what the simulation-derived evidence was
permitted to support.
Lifecycle monitoring does not require every element to be monitored equally.
It requires monitoring to remain proportionate to the conditions capable of
materially changing simulation relevance.
Change-Sensitive Condition Principle
Not every characteristic of a simulation requires continuous monitoring.SLMM identifies Change-Sensitive Conditions:
conditions whose material change could alter the relevance, applicability,
representativeness, evidential meaning, or legitimate reliance of an existing
simulation.
Examples may include: object redesign; software update; hardware replacement;
environmental change; changed operational profile; new observed behavior;
changed data distribution; new failure mode; revised assumption; model
discovery; changed mission condition; new regulatory operating constraint; new
physical observation; changed scenario prevalence; newly discovered edge case;
degraded simulation-to-reality correlation.
The monitoring burden should therefore follow material relevance, not
indiscriminate observation.
Reality Change Monitoring
A simulation may remain unchanged while reality changes around it.SLMM therefore requires relevant changes in represented or relied-upon reality
to remain observable where those changes can affect simulation relevance.
Examples may include: environmental change; infrastructure change; human
behavioral change; operational practice change; traffic pattern change;
climate condition change; market structure change; mission environment update;
newly observed physical behavior; new operational interactions.
ORA™ continues to govern Operational Reality itself.
SLMM governs whether changes in relevant reality are being observed for their
potential effect on simulation relevance.
Simulation Object Change Monitoring
A simulation may become stale because the Simulation Object itself changes.SLMM may monitor: new object versions; configuration changes; component
replacement; software changes; hardware changes; changed interfaces; changed
capabilities; changed constraints; changed operational modes; changed
dependencies.
A simulation of Object Version V3 does not automatically remain representative
of Object Version V7.
Object evolution must therefore remain connected to lifecycle monitoring.
Assumption Change Monitoring
Assumptions that were reasonable when a simulation was constructed may laterbecome questionable, obsolete, contradicted, or unnecessary.
SLMM may therefore monitor: newly available evidence affecting assumptions;
observed contradiction; changed uncertainty; revised scientific understanding;
changed operational conditions; dependence on assumptions no longer
applicable.
SLMM does not govern the assumptions themselves.
That remains under SAS — Simulation Assumption Standard.
It monitors changes capable of affecting their continued applicability.
Model and Environment Change Monitoring
Simulation models and environments may evolve independently of the simulationevidence already produced.
SLMM may monitor: model replacement; model revision; environment updates;
parameterization changes; discovered model defects; corrected physical
relationships; changed solver behavior; updated simulation infrastructure;
changed environmental representations.
A newer model does not automatically invalidate earlier simulations.
But material differences must become visible for lifecycle assessment.
Input and Data Change Monitoring
Simulation relevance may degrade when the distributions or conditionsrepresented by simulation inputs materially change.
SLMM may monitor: new operational data; changed distributions; changed
populations; changed environmental data; temporal shifts; new sensor
characteristics; newly available empirical data; changed uncertainty; changed
input-generation methods.
SIDS continues to govern simulation inputs and data at the simulation-input
boundary.
SLMM monitors subsequent changes that may affect the relevance of previously
generated simulation results.
Scenario Relevance Monitoring
Scenario sets may become incomplete over time.SLMM may identify changes such as: new operational scenarios; newly observed
edge cases; changed scenario frequency; emerging stress conditions; newly
relevant interactions; previously unknown failure combinations; changed
environmental combinations.
The discovery of a new scenario does not automatically invalidate existing
simulations.
It may, however, reveal that existing scenario coverage no longer represents
the relevant problem space sufficiently.
Correlation Change Monitoring
Simulation-to-reality correlation is not necessarily permanent.New real-world observations may show: stronger correlation; weaker
correlation; systematic deviation; condition-specific divergence; temporal
degradation; newly discovered mismatch.
SLMM therefore monitors information capable of changing the existing
correlation basis established through SRCS.
It does not recalculate correlation.
That remains within Simulation-to-Reality Correlation Standard (SRCS).
Evidence Relevance Monitoring
Simulation-derived evidence may remain technically unchanged while itsrelevance changes.
SLMM may monitor conditions capable of affecting: evidence qualification;
evidential weight; applicability; reliance boundaries; use limitations;
temporal relevance.
Where a material lifecycle change affects simulation evidence, the relevant
SERBS governance may require reassessment.
SLMM does not independently redefine evidential weight or reliance.
Change Signal
SLMM introduces the Simulation Lifecycle Change Signal.A Change Signal is:
an observed or reported change that may materially affect the continuing
relevance of a governed simulation basis.
A Change Signal is not yet Simulation Drift.
It is an input for further assessment.
Possible sources include: operational observations; engineering change
records; new measurements; updated datasets; new scientific findings; system
version changes; mission updates; incident findings; model corrections; new
scenario discoveries; changed environmental conditions; changed correlation
results.
Material Change Candidate
Where a Change Signal has a plausible relationship to simulation relevance,SLMM may register it as a Material Change Candidate.
The distinction is:
Change Signal → something changed.
Material Change Candidate → the change may matter to the simulation.
Simulation Drift → the change has produced a material divergence affecting the
governed simulation basis.
The final determination of Simulation Drift belongs to SDRM.
Monitoring by Dependency
Simulation relevance may be affected indirectly.For example:
Component Change → changes system behavior → changes object configuration →
affects scenario behavior → changes simulation applicability.
SLMM therefore permits lifecycle monitoring across material dependency chains
rather than monitoring only direct changes to the simulator.
This is especially important for complex systems where a seemingly small
change may propagate into a simulation-relevant difference.
Event-Driven Monitoring
Lifecycle monitoring may be event-driven.Relevant events may include: object update; software release; hardware change;
new operational dataset; incident; newly observed failure; mission redesign;
environmental change; assumption challenge; model correction; correlation
deterioration; regulatory operating change.
An event may initiate a simulation relevance review without waiting for a
scheduled lifecycle review.
Periodic Monitoring
Where simulation relevance may degrade gradually or where material change isnot tied to a discrete event, organizations may use periodic monitoring.
The appropriate interval may depend upon: rate of object change; rate of
environmental change; consequence of stale simulation reliance; simulation
purpose; operational volatility; evidence importance; availability of new
reality information.
SLMM does not impose one universal review interval.
Continuous Monitoring
Certain simulation contexts may justify continuous or near-continuouslifecycle monitoring.
Examples include: digital twins; rapidly changing autonomous systems;
high-frequency operational environments; continuously updated models; adaptive
systems; dynamic infrastructure environments.
Continuous monitoring does not imply continuous resimulation.
It provides a higher-frequency basis for detecting potentially material
change.
Monitoring Proportionality Principle
SLMM rejects the assumption that all simulations require identical monitoringintensity.
Monitoring should be proportionate to: rate of relevant change; consequence of
outdated reliance; simulation reuse; operational importance; uncertainty;
dependence on volatile assumptions; dependence on changing reality; expected
lifecycle duration.
A one-time exploratory simulation may require minimal lifecycle monitoring.
A simulation supporting years of safety-critical operational reliance may
require substantially stronger monitoring.
Dormant Simulation Principle
A simulation may remain unused for a period and later return to active use.Dormancy must not create an assumption of continuing relevance.
Before material reuse, the simulation may require review against accumulated
lifecycle changes.
Therefore:
Stored Simulation ≠ Current Simulation Basis.
Monitoring at Scale
Large organizations may maintain thousands or millions of simulation artifactsand evidence objects.
SLMM must therefore support lifecycle monitoring at scale.
Monitoring may operate through: simulation families; object families; model
versions; scenario families; evidence classes; dependency relationships;
change-event propagation; lifecycle status registers.
A material change affecting one shared model may therefore identify many
dependent simulation results requiring review.
Lifecycle Monitoring State
SLMM may assign a lifecycle monitoring
state such as: 1. Active Monitoring
Relevant lifecycle conditions are
currently monitored. 2. Reduced Monitoring
Monitoring continues at a reduced level because active reliance or changeexposure is limited. 3. Dormant
The simulation is retained but not currently subject to active reliance. 4.
Review Required
A material Change Signal or accumulated lifecycle change requires assessment.
5. Monitoring Suspended
Required monitoring cannot currently be performed sufficiently. 6. Lifecycle
Exit Candidate
The simulation may no longer justify continued monitoring and may require
retirement assessment.
These are governance states rather than universal technical classifications.
Lifecycle Traceability
SLMM requires material lifecycle changes to remain traceable to the simulationassets potentially affected by them.
A lifecycle trace may connect:
Change Event → Affected Reality Condition → Affected Simulation Basis Element
→ Affected Simulation → Affected Outputs → Affected Evidence → Potential
Reliance Impact
This relationship enables downstream drift recognition and proportionate
resimulation decisions.
SLMM Boundary with SDRM
SLMM identifies and structures potentially relevant lifecycle change.SDRM determines whether the change constitutes material Simulation Drift.
Therefore:
Monitoring Signal ≠ Drift Determination.
SLMM Boundary with SRGM
SLMM does not determine whether resimulation must occur.That decision belongs to:
SRGM — Resimulation Trigger Module.
The lifecycle sequence is:
Monitoring → Drift Recognition → Trigger Determination.
SLMM Boundary with ORA™
ORA™ governs actual Operational Reality.SLMM may consume relevant reality-change information to determine whether
simulation relevance requires review.
It does not redefine, reconstruct, or govern Operational Reality itself.
SLMM Boundary with SERBS
SERBS governs simulation-derived evidence, evidential weight, relianceboundaries, and use limitations.
SLMM monitors changes capable of affecting those governed evidence conditions.
It does not independently redefine evidence qualification, weight, or
permitted reliance.
SLMM Boundary with VALIDOS®
VALIDOS® governs validation and revalidation.SLMM governs simulation lifecycle monitoring.
A lifecycle change detected by SLMM may eventually contribute to revalidation
processes, but:
Simulation Lifecycle Review ≠ Validation Reassessment.
SLMM Boundary with INTEGROS®
SLMM does not govern general lifecycle integrity.INTEGROS® continues to govern integrity properties of objects, states,
evidence, and processes.
SLMM governs only lifecycle monitoring specifically required to preserve the
continuing relevance of simulation.
SLMM Boundary with ADIT®
ADIT® may audit whether lifecycle records, change evidence, and monitoringactivities are observable, preserved, and verifiable.
SLMM owns the simulation-specific monitoring logic.
Lifecycle Monitoring ≠ Lifecycle Audit.
Governance Outputs
SLMM may produce: Simulation Lifecycle Monitoring Basis Change-SensitiveCondition Register Simulation Lifecycle Monitoring Profile Simulation
Lifecycle Change Signal Material Change Candidate Record Simulation Object
Change Link Reality Change Link Assumption Change Link Model & Environment
Change Link Input & Data Change Link Scenario Relevance Change Link
Correlation Change Link Evidence Relevance Change Link Simulation Dependency
Change Record Lifecycle Monitoring State Simulation Relevance Review Input
Simulation Lifecycle Monitoring Record
These outputs provide the governed input basis for:
SDRM — Simulation Drift Recognition Module.
Enterprise Application
SLMM enables organizations to answer a lifecycle question that conventionalsimulation programs frequently leave implicit:
How will we know when the simulation we trusted yesterday no longer represents
the problem we face today?
An organization may use SLMM to detect when: a simulated product has changed
configuration; new operational behavior has emerged; a mission environment has
been revised; new physical measurements challenge earlier assumptions; an
autonomous system receives a significant software update; operational data
distributions change; new edge cases appear; a model defect is discovered;
simulation-to-reality correlation deteriorates; simulation evidence remains in
organizational use long after its original basis has changed.
SLMM does not force the organization to rerun every simulation whenever
something changes.
Instead, it creates the governed observation layer necessary to determine
whether the change matters at all.
This allows later modules to distinguish between:
irrelevant change, material drift, targeted resimulation, broad resimulation,
replacement, and retirement.
Foundational Principles
Simulation completion is not simulation lifecycle completion.A simulation may remain computationally unchanged while becoming operationally
stale.
Lifecycle monitoring must follow the conditions that give simulation results
their meaning.
Not every change is material to simulation relevance.
A detected change is not automatically Simulation Drift.
Monitoring intensity should be proportional to the consequence and likelihood
of simulation obsolescence.
Stored simulation results do not automatically remain current simulation
evidence.
Reality change, object change, assumption change, model change, data change,
scenario change, and correlation change may all affect continuing simulation
relevance.
Continuous monitoring does not require continuous resimulation.
A simulation cannot remain governably relevant over time if material changes
to the basis supporting its relevance are allowed to remain invisible.