Physics-Enabled
Digital Twin
A Guide to Procuring a
Whether you’ve invested in building an advanced digital twin ecosystem or you’re just starting to build one out, you’ve probably identified that one critical layer is missing: the physical twin of your network. Also called a physics-enabled digital twin, it enables you to advance beyond probability models and assumptions, gaining forward-looking insight into how your assets will perform under real-world stress, across any timescale, with engineering-grade certainty. When combined with existing technology, this layer of structural ground truth, which mirrors how assets behave in the physical world, will form the backbone of an integrated digital twin ecosystem.
This guide is designed to help utility leaders create an effective RFP to source a physics-enabled digital twin. It outlines the key requirements and evaluation criteria to include whether you are looking to invest in a solution to complement your existing tech stack or develop your digital twin strategy, ensuring you choose a solution capable of delivering on the full value of a physics-enabled digital twin.
Defining the right
requirements for your next RFP
The following RFP template is intended
as both a reference to guide vendor responses and a template you can adapt directly within your own procurement process.
Background
The core
need:
This RFP seeks proposals for a physics-enabled digital twin that provides a context-rich understanding of how assets behave and react in the physical world in response to any real-world condition, from extreme weather to network expansion. The solution must go beyond the limitations of current data aggregation tools or visualization twins, which fail to consider the physical interconnectedness between a network, its assets, and its surrounding environment, delivering engineering-grade analysis at both the asset and network level, in a single, spatially accurate model. It should incorporate environmental factors, such as vegetation, terrain, and weather, to provide a full contextual understanding of the network and sufficient detail to guide effective investment, operations, and resiliency planning.
EVALUATION
Scoring rubric for evaluation:
Primary Objectives
The solution
should meet
the following objectives:
Physics-enabled modeling
Risk assessment and intervention prioritization
Data normalization and enrichment
Questions
Physics-enabled modeling
Risk assessment and prioritization
Data normalization and enrichment
System integration and configurability
Customization and rules-based modeling
Transparency and auditability
Credibility
Physics-enabled modeling
Simulate any scenario across any timescale, from near-term high-consequence events to 100-year storm conditions.
Credibility
Transparency and auditability
Customization and rules-based modeling
System integration and configurability
Data normalization and enrichment
Risk assessment and prioritization
Physics-enabled modeling
How long does it take to build the network?
Is it possible to run an analysis or report on the entire network?
How long do they take to run?
How does your solution handle full-network modeling?
3.
Does the model dynamically update surrounding factors when you test an intervention?
Do recommendations change as you model different interventions and see how different workflows interact?
Can this be done across any timescale?
Describe what interventions your solution can model.
2.
Can you perform FEA in compliance with industry standards such as NESC and GO-95, in addition
to operator-defined variables?
Does your solution support finite element analysis?
1.
Credibility
Transparency and auditability
Customization and rules-based modeling
System integration and configurability
Data normalization and enrichment
Risk assessment and prioritization
Can your solution rank risks by custom factors, i.e., risk type, consequence, geographic region, or cost?
3.
Do you use customer risk models, or does your solution have its own risk thresholds and algorithms?
Can you calculate asset fragility, failure likelihood, and consequence analysis into risk scoring?
Describe how your solution incorporates different risk levers.
2.
Can this be done at the individual asset level and at network scale?
Describe how your solution identifies, quantifies, and prioritizes risks.
1.
Physics-enabled modeling
How do you support collaboration and consistency across a range of different teams and workflows i.e. vegetation management to asset management?
4.
How does it work, and does it enable users to test various interventions to prevent a cascading failure?
How many poles or assets can be tested at once?
Can your solution identify cascading failure risks (i.e., if one pole fails, what happens to the poles around it)?
4.
Credibility
Transparency and auditability
Customization and rules-based modeling
System integration and configurability
Data normalization and enrichment
How does your solution ensure accuracy and reliability?
4.
Can your solution automatically enrich existing data sets? Describe how.
3.
Are there any limitations by data source?
Describe how your solution normalizes disparate data into a single source of truth for all asset records.
2.
Do you have any limitations on the types of data that you can accept?
Can your solution ingest incomplete, inconsistent, or unstructured data?
1.
Risk assessment and prioritization
Physics-enabled modeling
What to look for
What to look for
Credibility
Transparency and auditability
Customization and rules-based modeling
System integration and configurability
What does the implementation process look like, and does this change depending on the number
of data sources/tools we have?
3.
Describe how your architecture enables alignment between existing tools and a physics-enabled
digital twin.
2.
Does your solution support open APIs for integration with other digital twins, external data systems,
and financial, logistics, and asset management software?
1.
Data normalization and enrichment
Risk assessment and prioritization
Physics-enabled modeling
Please provide indicative time scales where possible.
Credibility
Transparency and auditability
Customization and rules-based modeling
System integration and configurability
Data normalization and enrichment
Risk assessment and prioritization
How does the rules engine tie into asset fragility models, failure likelihood, and consequence analysis?
How much of this functionality, if any, relies on third-party vendors or services?
Can multiple rule sets be compared and adapted over time to reflect evolving regulatory or operational needs?
2.
What to look for
Can this be customized and configured to our specific standards, thresholds, and compliance requirements?
Does your solution support rules-based modeling?
1.
Physics-enabled modeling
Credibility
Transparency and auditability
How do you support collaboration and consistency across a range of different teams and workflows
i.e. vegetation management to asset management?
3.
Are these tested with regulators?
What safeguards are in place to ensure accuracy and complete confidence in the results?
1.
Customization and rules-based modeling
System integration and configurability
Data normalization and enrichment
Risk assessment and prioritization
Physics-enabled modeling
Are the results exported into a report-ready format, complete with full assumptions and calculations?
2.
Credibility
What demonstrated results do you have in risk reduction and resiliency efforts?
1.
Transparency and auditability
Customization and rules-based modeling
System integration and configurability
Data normalization and enrichment
Risk assessment and prioritization
Physics-enabled modeling
Please provide customer references where possible.
Have you deployed a physics-enabled digital twin for a utility of similar size and complexity?
2.
Please provide real results and accuracy measures.
For example, you could evaluate how wind forecasts affect both structural integrity and vegetation encroachment in the context of wildfire risk, and quickly identify areas with high ignition potential in different wind directions and speeds to guide proactive repairs and vegetation trimming.
None
Minimal
AdEquate
Exceptional
No simulation capability
Can simulate one factor in isolation (e.g., vegetation clearance only)
Can simulate multiple factors separately (e.g., wind, vegetation, terrain)
Simulates multiple interacting stressors
at once (e.g., high winds + vegetation + terrain), producing holistic fragility models
Fixed workflows, no customization
Limited configurability for certain asset workflows
Can configure thresholds, risk tolerances, and workflows per utility
Fully configurable architecture; supports customer-built custom risk models, assumptions, and workflows without vendor dependence; ability to integrate with AI models and agents
Black-box outputs, no auditability
Limited logging, some outputs traceable but incomplete
Clear outputs with supporting assumptions available for review
Full audit trail of all inputs, assumptions, and calculations that is understandable to engineers and regulators; direct integration with work management software
No integrations
Point-to-point integrations with limited systems (e.g., GIS only)
Integrates with multiple enterprise systems, whether homegrown or external
Not only integrates but ingests and centralizes your asset data and imagery so you can view and analyze everything in one comprehensive asset record. Connects seamlessly with the systems and platforms you already use, like Osmose, AWS, GCP, Azure, Oracle, Technosylva, and others.
No cloud capability, on-prem only
Cloud-enabled but limited speed,
batch runs only
Can process large datasets in the cloud with turnaround in days to weeks
Cloud-native with edge computing, delivering network-wide analysis in
hours to days
Visualization only; approximate asset representation with points or lines
Basic visualization with approximate geometry, suitable for mapping and clearance checks, but lacks structural integrity
Provides detailed representation at the network level sufficient for planning and visualization, but not validated for component-level engineering analysis
Supports network-wide analysis that links directly to distribution engineering design, enabling detailed assessment of each asset down to individual components
Can only analyze a small string of assets
at a time; large-scale asset modeling is time-consuming and manual
Can analyze subsets of assets or circuits in hours/days
Can analyze an entire network in days with reasonable accuracy
Can analyze millions of assets across an entire network in hours or days, with near-real-time updates
No ability to change design configurations or design changes are for a single pole only, with no external context.
Can change pole and attachment design on a single pole or a short string; pole loading calculations only change on the pole currently being analyzed
Can change pole configurations and test outcomes in static, pre-defined scenarios. May be able to analyze more than a few poles at once
Can test design changes as interventions at the individual asset level and network scale to effectively prioritize risk by total network impact and cost of consequence
No ability to configure or apply rules for modeling asset risk
Supports basic rule definition (e.g., if X condition, then Y risk score). Rule logic is limited to predefined/ out-of-the-box templates and often disconnected from asset-specific data
Allows for custom rule-building and risk scoring logic, with some integration of asset attributes and environmental inputs
Enables physics-informed, rules-based modeling that dynamically incorporates structural, environmental, and operational data. Integrates rule logic with asset fragility models, failure likelihood, consequence analysis, and investment timing
No modeling capability beyond static views
Can model basic short-term scenarios (e.g., storm response in the next 24 hrs)
Can run mid- and long-term simulations (e.g., storm season planning, 10-year investment outlook), but doesn’t consider the physical asset characteristics or total network impact
Perform finite element analysis and custom ‘what-if’ scenarios across all timescales and environments, from near-real-time to 10-year planning cycles
Cannot handle incomplete or inconsistent data, manual preprocessing
Accepts only clean, structured datasets with limited preprocessing
Can ingest multiple data sources (e.g., inspection logs, sensor feeds, LiDAR, imagery) and perform automated enrichment of common fields (e.g., missing pole height, conductor type)
Automatically ingests, normalizes, and enriches structured and unstructured data into a single view, applying engineering-grade validation
For example, identify GIS location discrepancies and conflate them with LiDAR survey data to validate ground truth, or validate conductor clearance based on actual field conditions.
For example, if a pole exceeds a defined loading threshold, then it is automatically flagged for replacement.
Structural and environmental simulations (terrain, vegetation, weather)
Configurability and flexible architecture
Audit-ready transparency and records
Integration with existing systems
Cloud-based scalability and high-speed processing
Proven utility and design engineering capabilities
Scalability and speed to model entire networks with asset-level fidelity
Ability to dynamically test interventions and design changes in-platform
Rules-based
risk modeling
Structural and environmental simulations (terrain, vegetation, weather)
Configurability and flexible architecture
Audit-ready transparency and records
Centralization of data
Integration with existing systems
Cloud-based scalability and high-speed processing
Proven utility and design engineering capabilities
Scalability and speed to model entire networks with asset-level fidelity
Ability to dynamically test interventions and design changes in-platform
Rules-based
risk modeling
Physics-enabled scenario modeling
Physics-enabled
scenario mdoeling
Physics-enabled
scenario modeling
Structural and environmental simulations (terrain, vegetation, weather)
Configurability and flexible architecture
Audit-ready transparency and records
Centralization of data
Integration with existing systems
Cloud-based scalability and high-speed processing
Proven utility and design engineering capabilities
Scalability and speed to model entire networks with asset-level fidelity
Ability to dynamically test interventions and design changes in-platform
Rules-based
risk modeling
Physics-enabled
scenario modeling
Rules-based
risk modeling
Structural and environmental simulations (terrain, vegetation, weather)
Configurability and flexible architecture
Audit-ready transparency and records
Centralization of data
Integration with existing systems
Cloud-based scalability and high-speed processing
Proven utility and design engineering capabilities
Scalability and speed to model entire networks with asset-level fidelity
Ability to dynamically test interventions and design changes in-platform
Scalability and speed to model entire networks with asset-level fidelity
Rules-based
risk modeling
Physics-enabled
scenario modeling
Structural and environmental simulations (terrain, vegetation, weather)
Configurability and flexible architecture
Audit-ready transparency and records
Centralization of data
Integration with existing systems
Cloud-based scalability and high-speed processing
Ability to dynamically test interventions and design changes in-platform
Scalability and speed to model entire networks with asset-level fidelity
Ability to dynamically test interventions and design changes in-platform
Rules-based
risk modeling
Physics-enabled
scenario modeling
Structural and environmental simulations (terrain, vegetation, weather)
Configurability and flexible architecture
Audit-ready transparency and records
Centralization of data
Integration with existing systems
Cloud-based scalability and high-speed processing
Proven utility and design engineering capabilities
Proven utility and design engineering capabilities
Cloud-based scalability
and high-speed processing
Scalability and speed to model entire networks with asset-level fidelity
Ability to dynamically test interventions and design changes in-platform
Rules-based
risk modeling
Physics-enabled
scenario modeling
Structural and environmental simulations (terrain, vegetation, weather)
Configurability and flexible architecture
Audit-ready transparency and records
Centralization of data
Integration with existing systems
Proven utility and design engineering capabilities
Configurability and flexible architecture
Proven utility and design engineering capabilities
Scalability and speed to model entire networks with asset-level fidelity
Ability to dynamically test interventions and design changes in-platform
Rules-based
risk modeling
Risk assessment, quantification, and prioritization by cost vs. benefit in any condition
Structural and environmental simulations (terrain, vegetation, weather)
Cloud-based scalability
and high-speed processing
Integration with existing systems
Cloud-based scalability
and high-speed processing
Proven utility and design engineering capabilities
Scalability and speed to model entire networks with asset-level fidelity
Ability to dynamically test interventions and design changes in-platform
Rules-based
risk modeling
Physics-enabled
scenario modeling
Structural and environmental simulations (terrain, vegetation, weather)
Configurability and flexible architecture
Audit-ready transparency and records
Centralization of data
Integration with existing systems
Centralization of data
Integration with existing systems
Cloud-based scalability
and high-speed processing
Proven utility and design engineering capabilities
Scalability and speed to model entire networks with asset-level fidelity
Ability to dynamically test interventions and design changes in-platform
Rules-based
risk modeling
Physics-enabled
scenario modeling
Structural and environmental simulations (terrain, vegetation, weather)
Configurability and flexible architecture
Audit-ready transparency and records
Centralization of data
Audit-ready transparency and records
Centralization of data
Integration with existing systems
Cloud-based scalability
and high-speed processing
Proven utility and design engineering capabilities
Scalability and speed to model entire networks with asset-level fidelity
Ability to dynamically test interventions and design changes in-platform
Rules-based
risk modeling
Risk assessment, quantification, and prioritization by cost vs. benefit in any condition
Structural and environmental simulations (terrain, vegetation, weather)
Configurability and flexible architecture
Audit-ready transparency and records
Model real-life asset behavior, down to the component, and the effects on the entire network using finite element analysis (FEA).
Dynamically calculate structural and environmental load and see the upstream and downstream effects on individual assets, their surroundings, and disparate workflows.
Keep models up to date as the network and data sources change.
Risk assessment and intervention prioritization
Prioritize and compare interventions by risk type, severity, and cost, quantifying which choice delivers optimal risk-spend efficiency.
Support custom analysis and workflow creation, for both near-real-time operational insights and long-term planning simulations without compromising accuracy.
Dynamically calculate structural and environmental load and see the upstream and downstream effects on individual assets, their surroundings, and disparate workflows.
Data normalization and enrichment
Unify network data into a single, structurally accurate foundation that reflects how the entire network and its surroundings behave.
Automatically infer missing or incorrect asset attributes from incomplete, inconsistent, or unstructured data.
Reconcile enterprise system records against enriched and validated field data for improved accuracy.
Process data and classify results at the individual asset level to system-wide scale, regardless of size.
Centralization of data
Ensure that every report is fully auditable, tracing back to its data sources, assumptions, and calculations.
Embed structural, spatial, and engineering context into every analysis using AI/ML.
Adapt to specific requirements and risk models without extensive customization.
Enable threshold, assumption, and workflow configuration from the same data set, allowing teams across asset management, planning, and operations to apply the same asset-centric foundation in different contexts.
Apply out-of-the-box or custom business rules and risk-scoring logic based on specific utility standards (NESC and GO-95), thresholds, and compliance requirements.
Compare outcomes across different rule sets and understand how changes in requirements will impact network-wide risk prioritization.
Integrate with any existing data or tool, including GIS, LiDAR, design drawings, inspection logs, imagery, other digital twins, ERP systems, and enterprise AI platforms.
Detect and correct spatial misalignments across datasets.
Transparency and auditability
System integration and configurability
Customization and rules-based modeling
Data normalization
and enrichment
The solution should meet the following technical requirements:
Example requirements:
Technical Requirements
Learn more
Procuring a physics-enabled digital twin is the core pillar of any advanced digital twin strategy. It provides the missing layer of precision you need to unify your broader digital twin ecosystem and anchor every decision in the same structural reality.
By crafting a well-informed RFP, you can capture all the right requirements up front, giving you confidence that the solution you choose will deliver high-velocity, engineering-grade analysis at the asset and network level.
Ready to take the next step?
Credibility
Transparency and auditability
Customization and rules-based modeling
System integration and configurability
Data normalization
and enrichment
Risk assessment and prioritization
Physics-enabled modeling
Credibility
Transparency and auditability
Customization and rules-based modeling
System integration and configurability
Data normalization
and enrichment
Risk assessment and prioritization
How long does it take to build the network?
Is it possible to run an analysis or report on the entire network?
How long do they take to run?
How does your solution handle full-network modeling?
3.
Does the model dynamically update surrounding factors when you test an intervention?
Do recommendations change as you model different interventions and see how different workflows interact?
Can this be done across any timescale?
Describe what interventions your solution
can model.
2.
What to look for
Can you perform FEA in compliance with industry standards such as NESC and GO-95, in addition
to operator-defined variables?
Does your solution support finite element analysis?
1.
Physics-enabled modeling
Credibility
Transparency and auditability
Customization and rules-based modeling
System integration and configurability
Data normalization
and enrichment
How does it work, and does it enable users to test various interventions to prevent a cascading failure?
How many poles or assets can be tested at once?
Can your solution identify cascading failure risks (i.e., if one pole fails, what happens to the poles around it)?
5
How do you support collaboration and consistency across a range of different teams and workflows i.e. vegetation management to asset management?
4.
Can your solution rank risks by custom factors, i.e., risk type, consequence, geographic region, or cost?
3.
Do you use customer risk models, or does your solution have its own risk thresholds and algorithms?
Can you calculate asset fragility, failure likelihood, and consequence analysis into risk scoring?
Describe how your solution incorporates different risk levers..
2.
Can this be done at the individual asset level and at network scale?
Describe how your solution identifies, quantifies, and prioritizes risks.
1.
Risk assessment and prioritization
Physics-enabled modeling
Credibility
Transparency and auditability
Customization and rules-based modeling
System integration and configurability
How does your solution ensure accuracy and reliability?
4.
Can your solution automatically enrich existing data sets? Describe how.
3.
Are there any limitations by data source?
Describe how your solution normalizes disparate data into a single source of truth for all asset records.
2.
Do you have any limitations on the types of data that you can accept?
Can your solution ingest incomplete, inconsistent, or unstructured data?
1.
Data normalization
and enrichment
Risk assessment and prioritization
Physics-enabled modeling
Credibility
Transparency and auditability
Customization and rules-based modeling
What does the implementation process look like, and does this change depending on the number
of data sources/tools we have?
3.
Please provide indicative time scales where possible.
Describe how your architecture enables alignment between existing tools and a physics-enabled
digital twin.
2.
Does your solution support open APIs for integration with other digital twins, external data systems, and financial, logistics, and asset management software?
1.
System integration and configurability
Data normalization
and enrichment
Risk assessment and prioritization
Physics-enabled modeling
Credibility
Transparency and auditability
How does the rules engine tie into asset fragility models, failure likelihood, and consequence analysis?
How much of this functionality, if any, relies on third-party vendors or services?
Can multiple rule sets be compared and adapted over time to reflect evolving regulatory or operational needs?
2.
Can this be customized and configured to our specific standards, thresholds, and compliance requirements?
Does your solution support rules-based modeling?
1.
Customization and rules-based modeling
System integration and configurability
Data normalization
and enrichment
Risk assessment and prioritization
Physics-enabled modeling
Credibility
How do you support collaboration and consistency across a range of different teams and workflows
i.e. vegetation management to asset management?
3.
Are the results exported into a report-ready format, complete with full assumptions and calculations?
2.
Are these tested with regulators?
What safeguards are in place to ensure accuracy and complete confidence in the results?
1.
Transparency and auditability
Customization and rules-based modeling
System integration and configurability
Data normalization
and enrichment
Risk assessment and prioritization
Physics-enabled modeling
Please provide real results and accuracy measures.
Please provide customer references where possible.
Have you deployed a physics-enabled digital twin for a utility of similar size and complexity?
2.
What demonstrated results do you have in risk reduction and resiliency efforts?
1.
Credibility
Transparency and auditability
Customization and rules-based modeling
System integration and configurability
Data normalization
and enrichment
Risk assessment and prioritization
Physics-enabled modeling
Questions
Learn more
Procuring a physics-enabled digital twin is about more than acquiring another tool or technology; it is the core pillar of any advanced asset management strategy and the backbone of a well-defined and integrated ecosystem. It provides the missing layer of precision you need to unify your broader digital twin ecosystem and anchor every decision in the same structural reality
By crafting a well-informed RFP, you can capture all the right requirements up front, giving you confidence that
the solution you choose will deliver high-velocity, engineering-grade analysis at the asset and network level.
Ready to take the
next step?
Integrate with any existing data or tool, including GIS, LiDAR, design drawings, inspection logs, imagery, other digital twins, ERP systems, and enterprise AI platforms.
Detect and correct spatial misalignments across datasets.
Apply out-of-the-box or custom business rules and risk-scoring logic based on specific utility standards (NESC and GO-95), thresholds, and compliance requirements.
Compare outcomes across different rule sets and understand how changes in requirements will impact network-wide risk prioritization.
Adapt to specific requirements and risk models without extensive customization.
Enable threshold, assumption, and workflow configuration from the same data set, allowing teams across asset management, planning, and operations to apply the same asset-centric foundation in different contexts.
Transparency
and auditability
Ensure that every report is fully auditable, tracing back to its data sources, assumptions, and calculations.
Embed structural, spatial, and engineering context into every analysis using AI/ML.
Adapt to specific requirements and risk models without extensive customization.
Enable threshold, assumption, and workflow configuration from the same data set, allowing teams across asset management, planning, and operations to apply the same asset-centric foundation in different contexts.
System integration and configurability
Apply out-of-the-box or custom business rules and risk-scoring logic based on specific utility standards (NESC and GO-95), thresholds, and compliance requirements.
Compare outcomes across different rule sets and understand how changes in requirements will impact network-wide risk prioritization.
Customization and rules-based modeling
Integrate with any existing data or tool, including GIS, LiDAR, design drawings, inspection logs, imagery, other digital twins, ERP systems, and enterprise AI platforms.
Detect and correct spatial misalignments across datasets.
Data normalization
and enrichment
Transparency
and auditability
Ensure that every report is fully auditable, tracing back to its data sources, assumptions, and calculations.
Embed structural, spatial, and engineering context into every analysis using AI/ML.
Adapt to specific requirements and risk models without extensive customization.
Enable threshold, assumption, and workflow configuration from the same data set, allowing teams across asset management, planning, and operations to apply the same asset-centric foundation in different contexts.
System integration and configurability
Apply out-of-the-box or custom business rules and risk-scoring logic based on specific utility standards (NESC and GO-95), thresholds, and compliance requirements.
Compare outcomes across different rule sets and understand how changes in requirements will impact network-wide risk prioritization.
Customization and rules-based modeling
Integrate with any existing data or tool, including GIS, LiDAR, design drawings, inspection logs, imagery, other digital twins, ERP systems, and enterprise AI platforms.
Detect and correct spatial misalignments across datasets.
Data normalization
and enrichment
Transparency
and auditability
Ensure that every report is fully auditable, tracing back to its data sources, assumptions, and calculations.
Embed structural, spatial, and engineering context into every analysis using AI/ML.
Adapt to specific requirements and risk models without extensive customization.
Enable threshold, assumption, and workflow configuration from the same data set, allowing teams across asset management, planning, and operations to apply the same asset-centric foundation in different contexts.
System integration and configurability
Apply out-of-the-box or custom business rules and risk-scoring logic based on specific utility standards (NESC and GO-95), thresholds, and compliance requirements.
Compare outcomes across different rule sets and understand how changes in requirements will impact network-wide risk prioritization.
Customization and rules-based modeling
Integrate with any existing data or tool, including GIS, LiDAR, design drawings, inspection logs, imagery, other digital twins, ERP systems, and enterprise AI platforms.
Detect and correct spatial misalignments across datasets.
Data normalization
and enrichment
The solution should meet the following technical requirements:
Technical Requirements