Introduction
A Key Performance Parameter (KPP) is a measurable objective that quantifies how well a system meets its intended purpose. Think of it as a specific number or metric that tells you if a system is working as expected.
Overview
Key Performance Parameters (KPPs) are foundational elements within the Integrated Systems Engineering Decision Management (ISEDM) framework. They represent quantifiable metrics that can be measured and tracked throughout the system development lifecycle. KPPs are essential for evaluating system performance against mission objectives and requirements.
KPPs serve as the bridge between high-level mission objectives and specific system performance characteristics. They enable systems engineers to make data-driven decisions by providing concrete, measurable criteria for evaluating system performance.
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KPPs are not just random metrics - they are carefully selected to directly support mission success and are aligned with stakeholder priorities. A well-chosen KPP should be specific, measurable, achievable, relevant, and time-bound (SMART). |
Position in Knowledge Hierarchy
Broader concepts: - ISEDM (is-a)
Narrower concepts: - MoE (is-a)
Details
KPPs are defined as measurable objectives that represent critical aspects of system performance. They are distinct from other concepts in the ISEDM framework:
Concept |
Relationship to KPP |
MoE (Measure of Effectiveness) |
KPP is a parent concept to MoE (is-a relationship) |
Value Function |
Sibling concept sharing parent ISEDM |
Swing Weight |
Sibling concept sharing parent ISEDM |
KPPs are typically defined in the context of a specific mission or system. For example, in the Catapult system case study, the KPPs included:
KPP |
Description |
Impact Angle |
Angle at which the projectile impacts the target |
Flight Time |
Time from launch to impact |
Impact Velocity |
Speed of the projectile at impact |
Range |
Distance traveled by the projectile |
Circular Error Probability (CEP) |
Measure of accuracy in hitting the target |
KPPs are linked to requirements in the SysML model using specific ontology tags. This allows them to be integrated into the IoIF framework for automated analysis and decision support.
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In the IoIF framework, KPPs are connected to the Assessment Flow Diagram (AFD) which defines how different analysis models interact to produce these key performance metrics. The AFD shows the flow of data from the system model through various analysis models to the KPPs. |
Practical applications and examples
Catapult System Example
In the Catapult case study, KPPs were used to evaluate different design alternatives. The system was modeled using SysML with the following KPPs:
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Impact Angle
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Flight Time
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Impact Velocity
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Range
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Circular Error Probability (CEP)
These KPPs were linked to the AFD (Assessment Flow Diagram) which showed how the system parameters flowed through different analysis models (geometry, fire simulation, error analysis) to produce the final KPP values.
The Decision Dashboard in IoIF used these KPPs to visualize trade-offs between different design alternatives. For example, a user could compare the "Range" (X-axis) against "CEP" (Y-axis) to see how increasing range might affect accuracy.
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The Catapult example demonstrates how KPPs can be used to support decision-making by providing clear, quantifiable metrics for comparing different system designs. |
Implementing KPPs in SysML
To implement KPPs in a SysML model, follow these steps:
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Create a Block Definition Diagram (BDD) for your system
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Add a "Key Performance Parameter" block to the model
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Define the specific KPPs as value properties of the block
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Tag the KPPs with appropriate ontology stereotypes (e.g., [kpp])
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Connect the KPPs to the appropriate analysis models in the AFD
Here’s a Python code example showing how to create a KPP in a SysML model using the IoIF framework:
= Define KPPs for the Catapult system
kpps = {
"Impact Angle": {
"unit": "degrees",
"min": 0,
"max": 90,
"description": "Angle at which projectile impacts target"
},
"Flight Time": {
"unit": "seconds",
"min": 0.5,
"max": 5.0,
"description": "Time from launch to impact"
},
"Range": {
"unit": "meters",
"min": 10,
"max": 1000,
"description": "Distance traveled by projectile"
}
}
= Add KPPs to the IoIF triplestore
for kpp_name, kpp_data in kpps.items():
# Create a new KPP instance in the ontology
kpp_iri = f"http://example.org/kpp/{kpp_name.replace(' ', '_')}"
# Add properties to the KPP instance
triplestore.add((kpp_iri, RDF.type, ISEDM.KPP))
triplestore.add((kpp_iri, RDFS.label, Literal(kpp_name)))
triplestore.add((kpp_iri, ISEDM.unit, Literal(kpp_data["unit"])))
triplestore.add((kpp_iri, ISEDM.min_value, Literal(kpp_data["min"])))
triplestore.add((kpp_iri, ISEDM.max_value, Literal(kpp_data["max"])))
triplestore.add((kpp_iri, RDFS.comment, Literal(kpp_data["description"])))|
When defining KPPs, ensure they are truly critical to mission success. Avoid defining too many KPPs - focus on the most important metrics that will drive decision-making. |
Related wiki pages
References
Systems Engineering Research Center - Digital Engineering Overview
Driving Digital Engineering Integration and Interoperability Through Semantic Integration of Models with Ontologies
SysML v2 Specification
Protégé Ontology Editor
OWL 2 Web Ontology Language
NAVSEA - Naval Sea Systems Command
NAVAIR - Naval Air Systems Command
Knowledge Graph
Visualize the relationships between KPP and related concepts