Simio 19 has arrived, bringing a suite of powerful new features that redefine what’s possible in simulation modeling. This major release marks Simio’s evolution from a traditional simulation tool into a comprehensive decision support platform. While the update includes several notable enhancements—Bi-PASS for faster experimentation, an enhanced Bill of Materials (BOM) framework, and Nvidia Omniverse user extension—the Python integration stands as the most transformative capability that changes everything for simulation professionals.
This release eliminates traditional constraints that have limited simulation applications for decades. Organizations can now leverage the combined power of Simio’s proven simulation capabilities alongside Python’s extensive computational ecosystem to address complex challenges that were previously beyond reach. For modelers, this integration significantly simplifies development by reducing custom coding requirements. From a business perspective, it enables more native blending of data transformation, optimization, and connectivity—resulting in more accurate models with faster delivery times. Let’s explore how Simio 19 breaks through these barriers and creates new possibilities for modeling, analysis, and decision-making.
Understanding Simio Python Integration: A Complete Revolution
The Simio Python integration in version 19 creates valuable opportunities for advanced simulation modeling by establishing a native bridge between Simio’s simulation engine and Python’s computational framework. This integration represents a significant enhancement that expands how simulation delivers value to organizations.
Architecture and Core Capabilities
At its foundation, the Python integration functions through an innovative process step that allows modelers to execute Python code directly within their simulation models. This architecture establishes a dynamic workflow where Simio can send current model states, entity attributes, and table data to Python for processing. Python then applies its extensive library ecosystem—including NumPy for numerical computing, Pandas for data manipulation, and scikit-learn for machine learning—before returning results that directly influence simulation behavior in real time.
This bidirectional data flow during runtime eliminates traditional barriers between simulation modeling and advanced data processing. Traditional approaches required modelers to export data, process it externally, and reimport results—a time-consuming process that often resulted in outdated information by the time simulations executed. The new architecture enables continuous, real-time interaction between simulation logic and advanced computational processes, creating a unified environment where complex algorithms and machine learning models operate alongside discrete event simulation.
The technical implementation leverages enhanced API frameworks that provide programmatic access to digital twin models while supporting real-time synchronization capabilities. This infrastructure enables seamless connectivity with enterprise systems including ERP and MES platforms, creating a comprehensive ecosystem for data-driven decision making.
Native Script Execution and Library Access
Integrating Python with Simio eliminates traditional barriers between simulation and advanced analytics, allowing modelers to leverage Python’s extensive ecosystem while maintaining Simio’s powerful simulation capabilities. Modelers can now execute Python scripts as standard process steps, accessing thousands of specialized libraries without leaving the Simio environment.
This capability adds another powerful tool for implementing complex logic in simulation models. Previously challenging algorithms—such as advanced routing optimization, predictive maintenance scheduling, or dynamic resource allocation—can now be more easily implemented using proven Python libraries and frameworks, particularly when system specifics are not readily available in standard simulation constructs. The integration supports everything from basic data manipulation with Pandas to sophisticated machine learning implementations with TensorFlow and PyTorch.
The Python integration provides flexibility and implementation simplification by connecting Simio’s simulation environment with Python’s computational capabilities. Python code works alongside the simulation context, maintaining access to current model states while leveraging the extensive library ecosystem of modern Python implementations. This integration enables modelers to implement complex decision logic that complements Simio’s native capabilities.
Enhanced Data Integration Framework
The enhanced data integration framework streamlines connections to enterprise systems and cloud storage, creating new possibilities for automated workflows and data processing. Simio 19 introduces native direct integration with Amazon S3 and Azure Blob Storage (formerly available only as user extensions), enabling organizations to import and export JSON and CSV files directly from cloud storage without requiring full-scale database infrastructure. These connections open up more modern workflows for data and reduce operating costs for customers storing large amounts of information.
This cloud integration capability addresses critical needs in modern simulation workflows. The framework supports automated data preparation workflows where Python scripts can clean, transform, and validate data before it enters Simio models. Organizations can query database tables directly, filter and transform data using Python, and import exactly what’s needed for simulation—all without manual export/import processes.
The data integration framework also enables direct connections to enterprise systems through Python packages. For example, organizations can use Python packages to connect directly to SAP systems, query relevant tables, and process the data during model execution. This capability eliminates traditional bottlenecks in simulation workflows while ensuring models always work with current, accurate information.
Real-Time Processing and Bidirectional Communication
One of the valuable aspects of the Python integration lies in its ability to simplify connectivity to diverse data sources. The integration enables improved data access and processing, allowing simulation models to incorporate information from various systems. This enhanced connectivity strengthens digital twin applications by improving synchronization between physical systems and their virtual counterparts.
Digital twin applications benefit from Simio’s enhanced data connectivity capabilities, allowing organizations to create more accurate representations of their systems. Enterprise systems can feed data into simulation models through simplified interfaces, while simulation results can be processed and shared with connected systems. This approach improves model accuracy and provides more actionable insights.
The bidirectional exchange creates powerful feedback loops where simulation models can adapt and respond to calculations performed in Python. For example, a manufacturing simulation could send production data to a Python script that applies algorithms to identify optimal scheduling decisions, then return those decisions to influence the simulation’s behavior.
Practical Applications Across Industries
The Python integration enables sophisticated applications across multiple industries that were previously difficult to implement with traditional simulation approaches.
In manufacturing environments, organizations can connect simulation models to production control systems, creating dynamic models that reflect factory conditions. Python algorithms can analyze production data, identify bottlenecks, and recommend scheduling decisions that get implemented through the simulation framework. This approach reduces gaps between simulation analysis and operational implementation, enabling systems to adjust production parameters, reschedule operations, and optimize resource allocation based on current conditions.
Healthcare organizations leverage these capabilities to optimize patient flow, resource allocation, and facility design. The integration enables connections to patient management systems for realistic patient flow modeling and implementation of custom triage algorithms based on data models. The enhanced capabilities enable healthcare organizations to respond more effectively to changing patient volumes, emergency situations, and resource constraints.
In logistics and transportation, the Python integration simplifies implementation of route optimization, fleet management, and supply chain coordination. Organizations can more easily implement algorithms that analyze performance data to predict fleet configurations and routing strategies. Simulation models validate these recommendations under various demand scenarios, providing comprehensive analysis of operational efficiency and cost optimization opportunities.
Future Possibilities and Advanced Applications
The integration simplifies artificial intelligence applications in simulation. Organizations can more easily train machine learning models on simulation outputs, use AI to optimize simulation parameters, and implement reinforcement learning for complex decision-making scenarios. These capabilities facilitate the development of adaptive simulations that improve over time, with machine learning algorithms analyzing simulation results to identify patterns and optimize model parameters.
The AI integration also supports predictive analytics applications where machine learning models analyze historical data to forecast future conditions, enabling proactive optimization and risk mitigation strategies. Advanced automation capabilities include scheduled simulation runs with automatic reporting, integration with development pipelines for simulation model development, and distributed simulation execution across multiple computing resources.
Python integration enables new levels of collaboration and automation in modeling workflows. Teams can develop shared Python libraries that implement common algorithms and processes, creating reusable components that accelerate model development across organizations. The integration supports automated validation and verification processes where Python scripts test model behavior, validate results against known benchmarks, and generate comprehensive reports.
Other Notable Features in Simio 19
While Python integration stands as the centerpiece of Simio 19, several other significant enhancements work together to create a truly next-generation simulation environment.
Bi-PASS for Accelerated Experimentation
Simio 19 introduces the Bi-PASS feature, implementing Parallel Adaptive Survivor Selection designed to accelerate large-scale experiments and optimizations. This feature dynamically evaluates means and variances of simulation outputs, identifying noncompetitive scenarios early in the experimental process.
The Bi-PASS capability allows models to skip over unpromising experimental runs, saving significant time and computational resources. This proves particularly valuable for organizations with limited computing capacity or tight project timelines, making experimentation more efficient and focused on promising scenarios. This feature is another valuable addition to Simio’s experimentation toolkit, joining the scenario generator, OptQuest, Subset Selection, and Select Best Scenario options to help users find the best method to optimize their model results. Early implementations demonstrate substantial reductions in total experiment runtime without sacrificing result quality, enabling more comprehensive exploration of solution spaces within practical time constraints.
Enhanced Bill of Materials (BOM) Framework
The reimagined Bill of Materials (BOM) framework in Simio 19 better supports complex production and supply chain scenarios. The new system is fully table-driven and tightly integrated with material elements, providing capabilities that mirror real-world planning logic in ERP systems.
Key enhancements include support for multiple BOMs per product, component-level substitutions, validity windows for time-based BOM selection, and prioritization rules for BOM selection. The framework also supports material mixing capabilities, enabling more accurate modeling of production environments with complex material requirements. These capabilities allow organizations to model sophisticated production scenarios that closely reflect actual manufacturing processes, including dynamic material substitutions, time-sensitive component selections, and complex assembly requirements.
Nvidia Omniverse User Extension
Perhaps the most visually impressive enhancement is Simio’s integration with Nvidia Omniverse, enabling high-fidelity, real-time visualization of simulation models. This extension creates bidirectional connections where Simio can send live updates to Omniverse, driving animations and visual representations, while Omniverse can send inputs back to Simio.
The result creates more immersive, intuitive digital twin experiences that prove particularly valuable for stakeholder communication and operational validation. This visualization capability helps bridge gaps between technical simulation results and business decision-making by making complex system behavior more accessible to non-technical stakeholders. The Omniverse integration supports real-time rendering of simulation scenarios, enabling stakeholders to observe system behavior as it unfolds and interact with models through visual interfaces that influence simulation behavior.
Strategic Impact and Getting Started
The introduction of Python integration in Simio 19 represents a significant enhancement to simulation modeling capabilities. By reducing barriers between simulation modeling and advanced analytics, organizations can leverage existing Python expertise while accessing robust simulation capabilities. Note that Simio 19 introduces the first part of the Python integration, with import/export functionality planned for future releases.
This integration creates unified platforms that support everything from basic process modeling to advanced data-driven optimization systems. The result enables organizations to develop more sophisticated simulations that accurately reflect real-world complexity while providing actionable insights for decision-making.
For organizations currently using simulation technology, Simio 19’s Python integration offers opportunities to enhance existing models with additional analytics capabilities and improved data integration. This feature is designed for users with simulation experience who want to extend their models with Python’s computational capabilities.
Implementation Approach
Begin with exploring the examples provided with Simio 19 to understand how Python integration can enhance your specific simulation scenarios. Review the documentation and training materials to gain familiarity with implementation approaches before applying them to your own models.
Experience the Future of Simulation Technology
The release of Simio 19 marks an important advancement in simulation technology. The Python integration provides valuable opportunities for organizations to develop more sophisticated simulation applications that directly support business objectives and operational excellence.
This advancement eliminates many constraints that limited simulation applications, opening new possibilities for innovation and value creation. Organizations can now leverage the combined power of Simio’s proven simulation capabilities alongside Python’s extensive computational ecosystem to address complex challenges more effectively than with traditional simulation approaches alone.
Ready to experience these new capabilities for yourself? Try Simio free for 30 days or, if you’re already a license holder, access the latest version through My Account. Discover how Python integration can transform your approach to modeling and decision-making.
For deeper technical insights and implementation guidance, watch these detailed Simio Sync presentations:
While this blog focused primarily on the Python integration, stay tuned for upcoming blogs that will explore Simio 19’s other powerful features in greater detail, including the enhanced BOM framework, Bi-PASS experimentation capabilities, and Nvidia Omniverse user extension.
The simulation barrier has been broken. The question now becomes: how will you leverage these capabilities to transform your organization’s approach to modeling, analysis, and decision-making?