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Manufacturing EngineeringTop 9 Best Process Flow Simulation Software of 2026
Top 10 Process Flow Simulation Software ranked for workflows, discrete-event modeling, and capacity planning, with tool comparisons for engineers.
How we ranked these tools
Core product claims cross-referenced against official documentation, changelogs, and independent technical reviews.
Analyzed video reviews and hundreds of written evaluations to capture real-world user experiences with each tool.
AI persona simulations modeled how different user types would experience each tool across common use cases and workflows.
Final rankings reviewed and approved by our editorial team with authority to override AI-generated scores based on domain expertise.
Score: Features 40% · Ease 30% · Value 30%
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Editor’s top 3 picks
Three quick recommendations before you dive into the full comparison below — each one leads on a different dimension.
AnyLogic
Experiment management that standardizes scenario runs and captures comparable performance metrics.
Built for fits when mid-size teams need controlled simulation automation with strong model governance..
Simio
Editor pickObject-based model constructs that tie networks, resources, and metrics to a single executable schema.
Built for fits when operations teams need scenario-driven simulation with controllable model structure..
Arena Simulation
Editor pickAutomation API for scenario provisioning and execution within a controlled RBAC environment.
Built for fits when mid-size teams need automated, governable simulation runs without manual reruns..
Related reading
- Manufacturing EngineeringTop 10 Best Process Flow Diagram Software of 2026
- Manufacturing EngineeringTop 10 Best Fluid Flow Simulation Software of 2026
- Manufacturing EngineeringTop 10 Best Dynamic Process Simulation Software of 2026
- Manufacturing EngineeringTop 10 Best Process Engineering Services of 2026
Comparison Table
This comparison table evaluates process flow simulation tools on integration depth, focusing on how each product connects to planning systems, data pipelines, and external optimization engines. It also compares the data model and schema design, plus automation and API surface for configuration, provisioning, throughput testing, and extensibility. Governance controls are assessed through RBAC, audit log coverage, and admin workflows for managing models across teams.
AnyLogic
simulation modelingAnyLogic provides process flow simulation with a model data layer, event-driven and agent-based execution, and model export options that support integration into engineering workflows.
Experiment management that standardizes scenario runs and captures comparable performance metrics.
AnyLogic executes simulations from a model data structure that includes entities, resources, process logic, and state transitions. It supports experiment configuration so teams can run multiple what-if scenarios and compare output measures such as cycle time distribution and queue behavior. Model governance is centered on configuration management inside the project, which helps standardize schema usage across runs.
A practical tradeoff is that deeper customization usually requires working within the model’s programming hooks rather than only dragging blocks. AnyLogic fits when teams need controlled simulation automation that can connect model inputs to external systems and maintain repeatable throughput studies across releases.
- +API-oriented automation surface for repeatable simulation runs
- +Unified process logic with experiment configuration
- +Clear simulation data model for metrics like WIP and utilization
- +Extensibility hooks for custom routing and transformation logic
- –Advanced automation often requires model-code adjustments
- –Large models can increase configuration overhead
- –Integration work may require data schema mapping effort
Operations engineering teams
Capacity and queue-time what-if studies
Improved throughput predictability
Supply chain analysts
WIP and lead-time sensitivity analysis
Lower lead-time uncertainty
Show 2 more scenarios
Systems integration teams
Automated model runs from external data
Faster release validation
API-driven execution supports importing input schemas and exporting results for downstream reporting.
Manufacturing process owners
Change control on process flows
Consistent change impact
Configured models standardize entity logic and metric collection for controlled comparisons across revisions.
Best for: Fits when mid-size teams need controlled simulation automation with strong model governance.
More related reading
Simio
discrete eventSimio delivers discrete-event process simulation with a built-in object and data model, plus extensibility through its scripting and API options for model automation.
Object-based model constructs that tie networks, resources, and metrics to a single executable schema.
Simio fits teams that need a simulation schema that stays consistent across iterations, because object definitions and network structure are captured in the model itself. Model execution can be driven by experiment configurations that capture input parameters and result outputs, which helps with repeatability and throughput measurement. The integration story is clearest when automation relies on repeatable configuration and external data exchange paths rather than one-off manual runs.
A key tradeoff is that deeper automation often requires building the surrounding orchestration, since Simio focuses on model execution and simulation logic rather than enterprise job scheduling. Simio is a good fit when a simulation team must govern model changes, support multiple scenarios, and produce auditable results from controlled inputs.
- +Data model maps processes, resources, and performance metrics in one schema
- +Experiment configuration enables repeatable throughput and queueing studies
- +Model reuse via libraries and parameterized definitions cuts rework
- +External automation works through scriptable execution and file-based interfaces
- –Deep end-to-end integration needs external orchestration beyond core simulation
- –Governance controls like RBAC and audit logging are not central to typical workflows
Manufacturing planning teams
Test routing and resource changes
Identifies throughput-limiting stations
Logistics and warehousing teams
Stress-test pick and replenishment flows
Defines staffing and capacity targets
Show 2 more scenarios
Operations analytics teams
Automate batch simulation studies
Produces repeatable scenario results
Drive model inputs from external files and compile experiment outputs for reporting pipelines.
Engineering simulation modelers
Build reusable process libraries
Reduces modeling time for variants
Standardize components and templates so future process variants share the same schema patterns.
Best for: Fits when operations teams need scenario-driven simulation with controllable model structure.
Arena Simulation
manufacturing DESArena supports manufacturing process flow simulation using flowchart-style modules mapped to a discrete-event engine, with automation options for parameter studies and model runs.
Automation API for scenario provisioning and execution within a controlled RBAC environment.
Arena Simulation is used to simulate process flows with explicit schema-driven entities, activity steps, and routing rules. Scenario configuration enables controlled what-if runs for capacity, timing, and bottleneck evaluation. Integration depth is geared toward automation, using an API surface for model setup and run orchestration.
A key tradeoff is that advanced behaviors require alignment with the platform data model, rather than free-form scripting. It fits best when teams need repeatable simulations linked to operational data pipelines, with controlled provisioning and RBAC-based access for multiple model authors.
- +Schema-driven process model improves repeatability across scenarios
- +Automation and API enable model provisioning and run orchestration
- +RBAC and audit log support controlled multi-author governance
- +Configuration supports throughput and queue analysis workflows
- –Complex custom logic can be constrained by the data model schema
- –Throughput modeling may require careful parameterization and validation
Operations analytics teams
Simulate queue capacity constraints
Clear capacity recommendations
Process engineering teams
Validate routing policy changes
Lower cycle time estimates
Show 2 more scenarios
Platform integration engineers
Automate model provisioning
Consistent automated experiments
Use the API to generate and execute simulation runs from upstream configuration and data sources.
Enterprise governance admins
Control shared model libraries
Traceable model governance
Apply RBAC permissions and review audit log entries for model changes and run executions.
Best for: Fits when mid-size teams need automated, governable simulation runs without manual reruns.
FlexSim
3D manufacturingFlexSim models manufacturing material flow with 3D visualization hooks, a simulation object model, and automation interfaces used to drive scenario runs and data exchange.
Scripting and configurable model parameters that coordinate automated scenario runs and throughput measurements.
FlexSim focuses on process flow and discrete-event simulation with a visual model editor tied to a structured simulation data model. It supports integration with external data sources through configurable import paths and model parameters, which helps keep experiments reproducible across runs.
Automation and extensibility are centered on scripting hooks and model configuration, which can coordinate scenario runs and throughput experiments. Governance depends on role-based access patterns around projects and assets, with auditability driven by the broader FlexSim environment and logging configuration.
- +Discrete-event process simulation with configurable entities and resource behaviors
- +Model editor maps directly to a structured data model for repeatable experiments
- +Scripting hooks support scenario automation and batch throughput studies
- +Model parameters enable external data mapping for repeatable data-driven runs
- +Asset-based project organization supports controlled sharing and scenario versioning
- –Automation surface relies more on scripting than a broad public REST API
- –External data integration often requires explicit mapping work per data schema
- –Large multi-team governance depends on environment setup and RBAC configuration
- –Automation workflows can become complex when many parameters drive logic
Best for: Fits when operations teams need visual simulation plus scripted automation for repeatable throughput studies.
PSM by Intersolve
process simulationIntersolve PSM focuses on process simulation with model execution and automation mechanisms that support repeatable what-if studies.
Schema-driven workflow definitions with API-based data mapping for scenario-driven simulation runs.
PSM by Intersolve performs process flow simulation using a configurable workflow data model and scenario inputs. It supports integration-focused execution by mapping external systems into simulation inputs and outputs through an API and automation hooks.
The configuration layer includes schema-driven definitions for steps and connectors, which supports repeatable runs and controlled throughput. Admin governance features like RBAC and audit logging help teams manage access and track configuration and run changes.
- +API-focused integration of simulation inputs and outputs
- +Schema-driven workflow model improves run repeatability
- +RBAC supports controlled authoring and execution
- +Audit logs track configuration and execution changes
- –Complex schema setup adds overhead for simple flows
- –Automation surface requires engineering time for advanced coupling
- –Scenario management can feel heavy without templates
Best for: Fits when teams need controlled process flow simulations integrated with external systems.
Rockwell Arena
industrial simulationRockwell's simulation offerings include process and discrete-event simulation workflows that connect to industrial data and engineering toolchains.
Arena’s entity-based process logic links model objects to metrics used in reports.
Rockwell Arena fits teams running plant and process simulations when governance, model reuse, and automation through APIs matter. It supports process-flow modeling with simulation logic, data collection, and output reporting inside a structured data model.
Rockwell Arena emphasizes integration depth with Rockwell Automation tooling and supports extensibility via automation and scripting hooks for repeatable runs. Admin controls focus on controlled access to projects and model artifacts, with change traceability for simulation inputs and results.
- +Process-flow simulation uses a structured data model for repeatable runs
- +Automation hooks support scripted configuration and batch execution
- +Integration with Rockwell Automation ecosystems supports model and data alignment
- +Reporting outputs are tied to model entities for traceable metrics
- –Complex model schemas increase setup effort for nonstandard flows
- –API surface for external model authoring is less obvious than simulation control
- –Large models can reduce iteration throughput during parameter sweeps
- –Governance depends on artifact management practices outside the simulator
Best for: Fits when plant teams need governed simulation workflows with automation and Rockwell integration.
AnyLogic Cloud
cloud simulationAnyLogic Cloud provides browser-based execution for models with an automation and deployment model intended for sharing simulation runs with controlled inputs.
Run orchestration via API with schema-bound inputs for repeatable, governed simulation experiments.
AnyLogic Cloud provides cloud-hosted process flow simulation models with a project-centric workflow built around model configuration and execution management. It distinguishes itself through integration depth options that include API-driven model orchestration and data mapping into simulation runs.
The data model centers on structured inputs, parameter sets, and run configurations that keep model versions and scenario outputs consistent across environments. Admin controls focus on governance for multi-user access, including role-based permissions, project scoping, and traceability via audit logging.
- +API-driven run orchestration supports automated scheduling and repeatable experiments
- +Structured input schemas reduce ad hoc data mapping errors in simulation runs
- +RBAC supports project-scoped access for shared teams and controlled collaboration
- +Audit log records administrative and model execution events for traceability
- –Automation depends on documented API patterns rather than embedded no-code workflows
- –Model versioning and migration workflows can add overhead for frequent schema changes
- –External system integration requires deliberate data mapping into the simulation input model
- –Admin governance features are strongest at project scope, not fine-grained resource scope
Best for: Fits when teams need API-driven simulation runs with schema-based inputs and governance over shared projects.
Simul8
process flowSimul8 models process flows and manufacturing systems with diagram-based logic and scenario automation for throughput and queue analysis.
Process model schema links routing, resources, and queues into one simulation-ready data model.
Simul8 is process flow simulation software focused on visual modeling, resource behavior, and performance metrics across flow variants. Its distinct workflow is built around a process data model that connects activities, resources, queues, routings, and controls into a runnable simulation.
Simul8 supports scenario-driven configuration and can integrate model inputs from external data sources while keeping model structure consistent. Extensibility and automation depend on a documented API and add-in surface that enable repeatable model runs and governance via controlled access.
- +Visual process model maps directly to a runnable simulation data model
- +Scenario configuration supports repeatable what-if runs without rebuilding models
- +API and automation hooks enable scheduled runs and parameterized experiments
- +Resource and routing controls capture throughput, downtime, and queue effects
- +Audit-friendly configuration patterns support change control in managed environments
- –Schema changes to model structure can require model refactoring
- –Complex orchestration needs more automation glue than built-in schedulers
- –Deep enterprise governance depends on external identity and access setup
- –High-volume batch runs can stress model preparation and instance throughput
Best for: Fits when teams need governed process flow simulations with automation and data-driven scenario inputs.
CASTOR 3D
production flowCASTOR 3D offers simulation for production flow with a model-driven approach that supports throughput analysis and configuration changes.
Configurable scenario parameters for step and transition models produce repeatable simulation outputs.
CASTOR 3D runs process flow simulation by executing defined workflow models against configurable parameters to produce output states. It supports a structured data model for steps, transitions, and simulation inputs so teams can keep scenarios consistent across runs.
CASTOR 3D emphasizes automation through import workflows and configurable execution settings, with an extensibility path that can be used to wire simulation runs into surrounding tooling. Governance is handled through project-level organization so model changes and simulation artifacts can be managed in a controlled environment.
- +Clear schema for steps and transitions supports repeatable simulation runs
- +Scenario configuration enables controlled throughput and parameter sweeps
- +Automation-oriented import flows reduce manual rework between model changes
- +Project-level organization helps keep simulation artifacts separate by use case
- –API surface details are limited, which constrains deeper system integration
- –RBAC granularity is not documented enough for strict multi-team governance needs
- –Audit logging and change traceability controls are not explicit for model edits
- –Automation hooks appear oriented to workflows, not high-frequency runtime orchestration
Best for: Fits when teams need repeatable process simulation with scenario controls and light automation wiring.
How to Choose the Right Process Flow Simulation Software
This buyer's guide covers Process Flow Simulation Software through nine named tools: AnyLogic, Simio, Arena Simulation, FlexSim, PSM by Intersolve, Rockwell Arena, AnyLogic Cloud, Simul8, and CASTOR 3D. It focuses on integration depth, data model design, automation and API surface, and admin governance controls that determine whether simulation work stays repeatable across teams and runs. Readers get concrete evaluation criteria tied to experiment management in AnyLogic, object-based schema in Simio, and API-driven orchestration in AnyLogic Cloud.
Simulation engines that execute workflow graphs against a defined process data model
Process Flow Simulation Software executes process logic such as routing, queuing, and resource behavior to produce throughput, WIP, utilization, downtime, and other performance outputs. Tools in this category reduce manual what-if reruns by driving consistent scenario inputs into a runnable model structure.
Arena Simulation uses flowchart-style modules mapped to a configurable data model for repeatable experiment runs, while Simul8 links activities, resources, queues, and routings into a simulation-ready data model. These products are used by operations, manufacturing, plant, and engineering teams that must test process changes with controlled parameters and traceable run results.
Evaluation criteria for integration, governance, and schema-driven repeatability
Process flow simulation projects break down when scenario inputs do not match the tool’s data model schema or when run orchestration cannot be automated end-to-end. Integration depth and a well-scoped automation surface determine whether simulations can be provisioned and executed consistently.
Admin governance controls then decide who can change model structure, run configurations, and shared assets without losing auditability. AnyLogic, Arena Simulation, and PSM by Intersolve are designed around repeatable execution patterns that depend on experiment configuration and schema-driven definitions.
Experiment management with standardized scenario runs
AnyLogic provides experiment management that standardizes scenario runs and captures comparable performance metrics, which supports repeatable throughput comparisons. Arena Simulation also ties schema-driven process models to repeatable experiment runs that reduce manual reruns.
Process data model that binds logic to metrics
Simio uses object-based model constructs that tie networks, resources, and metrics to a single executable schema, which keeps the model structure coherent. Simul8 similarly links routing, resources, and queues into one simulation-ready data model for throughput and queue analysis.
API and automation surface for scenario provisioning and execution
Arena Simulation emphasizes automation API support for scenario provisioning and execution inside a controlled RBAC environment. AnyLogic Cloud provides run orchestration via API with schema-bound inputs that supports automated scheduling and repeatable experiments.
Schema-driven workflow definitions with external data mapping
PSM by Intersolve uses schema-driven workflow definitions and API-based data mapping so external systems can feed scenario-driven simulation runs. CASTOR 3D uses structured steps and transitions with configurable scenario parameters so changes can remain consistent across runs.
Governance controls using RBAC and audit logging
Arena Simulation supports RBAC and audit log features that enable controlled multi-author governance for shared libraries. PSM by Intersolve includes RBAC for controlled authoring and execution and audit logs that track configuration and run changes.
Extensibility hooks for custom routing and transformation logic
AnyLogic provides extensibility hooks for custom routing and transformation logic, which helps teams implement domain-specific behavior inside the simulation model. FlexSim centers automation and scenario batch throughput coordination on scripting hooks and configurable model parameters.
Decision steps for matching model schema, automation, and governance to the workflow
Choosing a process flow simulation tool becomes straightforward when the evaluation starts with schema constraints and ends with governance and run orchestration. The right pick depends on whether scenario execution must be automated through an API and whether model changes must be controlled across shared teams. This guide sequences those checks using concrete tool capabilities such as AnyLogic experiment management, Simio schema cohesion, Arena Simulation API provisioning, AnyLogic Cloud API run orchestration, and PSM by Intersolve schema-driven data mapping.
Map the workflow to each tool’s simulation data model structure
Simio ties networks, resources, and metrics to one executable schema, so a first test should validate whether the intended process constructs fit that object model. Simul8 similarly binds activities, resources, queues, and routings into a runnable process data model, so complex schema changes that refactor model structure can become a gating factor.
Define the scenario automation path before building models
If scenario execution must be provisioned and triggered programmatically, Arena Simulation’s automation API and AnyLogic Cloud’s API-driven run orchestration should be evaluated early. If automation can be driven via scripting and configurable parameters instead of a broad REST-style surface, FlexSim scripting hooks and model parameters may fit the operational workflow.
Validate schema-bound inputs and data mapping for external systems
Teams integrating external data feeds should test PSM by Intersolve API-based data mapping with schema-driven workflow definitions. AnyLogic Cloud’s structured input schemas can also reduce ad hoc data mapping errors when schema changes are managed with deliberate versioning.
Confirm governance controls for multi-author model and run changes
Arena Simulation’s RBAC and audit log support controlled multi-author governance for shared libraries, so this should be verified against the actual authoring workflow. PSM by Intersolve also includes RBAC and audit logging for configuration and execution changes, while Simio flags that governance controls like RBAC and audit logging are not central to typical workflows.
Measure iteration throughput during parameter sweeps and scenario runs
If parameter sweeps are frequent, AnyLogic and Arena Simulation should be evaluated for how experiment configuration affects setup overhead in large models. Rockwell Arena notes that complex model schemas can reduce iteration throughput during parameter sweeps, so model complexity should be staged early to avoid slow cycles.
Choose extensibility mechanisms that match customization needs
AnyLogic supports extensibility hooks for custom routing and transformation logic, which can reduce the need for external pre-processing. FlexSim and CASTOR 3D both rely on configurable parameters and scripting or import workflows, so the evaluation should confirm that required logic can be expressed inside the simulation model rather than in brittle automation glue.
Who should use which process flow simulation tool based on execution and governance needs
Different tools fit different simulation governance and automation patterns. The best match depends on whether scenario runs need standardized experiment management, whether the data model must be object-based, and whether run orchestration must be API-driven. The segments below map to the named tools that fit specific operational profiles such as mid-size governed automation, plant integration, schema-driven external mapping, or controlled shared project execution.
Mid-size teams needing controlled simulation automation with strong model governance
AnyLogic is a strong fit because it pairs experiment management that standardizes scenario runs with comparable performance metrics and an API-oriented automation surface. Arena Simulation also fits teams that want automated, governable simulation runs without manual reruns using an automation API within RBAC.
Operations teams running scenario-driven studies where model structure must stay controllable
Simio is built around an object-based model construct that ties networks, resources, and metrics to one executable schema, which supports controllable scenario-driven studies. FlexSim fits when teams need visual process modeling plus scripting hooks for repeatable throughput studies.
Teams integrating simulation with external systems through schema-bound inputs and API mapping
PSM by Intersolve fits when teams need schema-driven workflow definitions and API-based data mapping for scenario-driven simulation runs. AnyLogic Cloud also fits when the execution layer must accept schema-based inputs via API-driven run orchestration.
Plant and industrial teams emphasizing governed workflows aligned with industrial ecosystems
Rockwell Arena fits plant teams that need process-flow modeling with structured data models and automation hooks aligned with Rockwell Automation tooling. Governance expectations should be validated because governance depends on controlled access to projects and artifact management practices beyond the simulator.
Teams needing repeatable scenario parameters with light automation wiring
CASTOR 3D fits teams that want structured steps and transitions with configurable scenario parameters that produce repeatable outputs. Simul8 fits teams that require governed process flow simulations with scenario configuration and data-driven inputs, while schema changes may require model refactoring.
Pitfalls that repeatedly derail process flow simulation implementations
Common implementation failures come from mismatches between the intended automation workflow and the simulator’s automation and governance mechanisms. Other failures come from underestimating schema mapping effort when external systems feed scenario inputs. The mistakes below tie directly to limitations observed across tools such as AnyLogic automation overhead for large models, Simio governance gaps, FlexSim scripting dependence, and CASTOR 3D limited API surface details.
Assuming automation exists without engineering support
FlexSim’s automation surface relies more on scripting hooks than a broad public REST API, so teams should plan for scripting-based orchestration when selecting it. AnyLogic Cloud can automate run orchestration via API, but automation depends on documented API patterns and schema design.
Overbuilding custom logic that fights the simulator’s schema constraints
Arena Simulation notes that complex custom logic can be constrained by the data model schema, so required domain logic should be tested against schema limits early. CASTOR 3D offers structured steps and transitions, so logic that requires deeper system integration may need external wiring.
Underestimating schema mapping effort for external data integration
AnyLogic highlights that integration work may require data schema mapping effort, so scenario input schemas should be aligned before scaling. Simio’s integration strength depends on scripts, file-based interfaces, and external orchestration beyond core simulation.
Treating governance as an afterthought for shared libraries and projects
Simio flags that governance controls like RBAC and audit logging are not central to typical workflows, so strict multi-team governance may require extra process controls outside the tool. Arena Simulation and PSM by Intersolve include RBAC and audit logging support that should be configured with the shared authoring workflow.
Ignoring iteration overhead during parameter sweeps
Rockwell Arena warns that complex model schemas can reduce iteration throughput during parameter sweeps, so model complexity should be staged and measured during pilot scenario runs. AnyLogic also notes that large models can increase configuration overhead, so experimentation should start with representative model scope.
How We Selected and Ranked These Tools
We evaluated AnyLogic, Simio, Arena Simulation, FlexSim, PSM by Intersolve, Rockwell Arena, AnyLogic Cloud, Simul8, and CASTOR 3D using the same criteria applied across product writeups: feature depth, ease of use, and value. Features carry the most weight because the ability to standardize scenario runs, bind logic to a data model, and support automation and API-driven execution determines whether teams can repeat results at scale. Ease of use and value each account for a smaller share of the overall scoring so teams can still plan for practical adoption effort.
AnyLogic stands apart in this ranking because it pairs experiment management that standardizes scenario runs with an API-oriented automation surface for repeatable simulation execution. That combination primarily lifted the score through feature depth for governed, comparable throughput and WIP outcomes and secondarily supported ease of automation versus tools that emphasize scripts or only partial integration.
Frequently Asked Questions About Process Flow Simulation Software
How do AnyLogic and Simio handle model data structure when simulating process flows?
Which tools support scenario automation through an API and what differs between them?
How do PSM by Intersolve and Rockwell Arena map external systems into simulation inputs and outputs?
What governance controls exist for shared libraries, projects, and shared run artifacts?
How do AnyLogic Cloud and Simul8 keep schema-based scenario inputs consistent across environments?
What extensibility options exist when teams need to wire simulation runs into broader automation pipelines?
How do CASTOR 3D and Simio support repeatable scenario execution when workflow definitions change?
What common technical issue breaks reproducibility, and how do these tools mitigate it?
Which tool is a better fit for multi-user orchestration where run configuration must be auditable?
Conclusion
After evaluating 9 manufacturing engineering, AnyLogic stands out as our overall top pick — it scored highest across our combined criteria of features, ease of use, and value, which is why it sits at #1 in the rankings above.
Use the comparison table and detailed reviews above to validate the fit against your own requirements before committing to a tool.
Tools reviewed
Primary sources checked during evaluation.
Referenced in the comparison table and product reviews above.
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