Integrating Python Scripts into Tecnomatix Plant Simulation Models: Enhancing Plant Simulation with Real-Time Data, AI, and Advanced Analytics

Siemens Plant Simulation version 2404 unlocks a new era of simulation modeling by embedding native Python scripting capabilities into the digital factory environment. This integration revolutionizes the way engineers design, test, and optimize production and supply chain systems by connecting simulation models to powerful external tools such as APIs, data analytics platforms, and machine learning libraries [1].

This article explores how native Python scripting enhances simulation with automation, real-time data, AI, and optimization algorithms. It also highlights two practical use cases— supply chain scenario optimization and vehicle batching optimization— that demonstrate the transformative impact of this approach on manufacturing and logistics performance.

Key Features and Real-World Applications

As simulation models grow increasingly complex and data-driven, the need for enhanced flexibility, connectivity, and analytical capabilities becomes paramount. Integrating Python into Siemens Plant Simulation marks a significant leap forward in unlocking these capabilities. Here, key features and real-world applications of Python integration within simulation models are presented, highlighting how this capability enables users to surpass the limitations of old versions.

• Direct Invocation from SimTalk: Python functions can be called directly from SimTalk, Plant Simulation’s native scripting language. This enables smooth interaction between existing simulation logic and powerful Python routines. For example, triggering data processing or machine learning predictions mid-simulation.
• Access to Powerful Python Libraries: Users gain access to robust Python libraries like NumPy, Pandas, and Matplotlib, enabling advanced numerical computation, data manipulation, and dynamic visualization within simulation models.
• Automation and Customization: Python integration allows the automation of complex logic and the customization of simulation behaviors. Users can implement tailored algorithms for tasks such as custom dispatching rules, adaptive control systems, or AI-powered decision-making.
• Enhanced Connectivity and Real-Time Data Integration: Python’s extensive ecosystem enables direct communication with external APIs, databases, and live data sources. For instance, integrating Google Maps API for real-time transportation data adds real-world context and accuracy to logistics simulations.
• Advanced Analytics and Visualization: Python’s rich set of tools makes it easy to analyze simulation output, generate visual dashboards, and draw actionable insights—without leaving the Plant Simulation environment.
• Machine Learning and Predictive Modeling: With libraries like TensorFlow, users can train and deploy machine learning models directly in their simulation workflows for predictive maintenance, demand forecasting, or system optimization.

Use Case 1: Real-Time Supply Chain Simulation with Genetic Algorithms and Google Maps API (KISS Project) [2]

In this use case, we expand the traditional scope of factory-level Discrete Event Simulation (DES) to include the entire supply chain. By doing so, we acknowledge the complex interdependencies between internal production systems and external logistics in today’s globalized economy. Through our approach, we integrate transportation and logistics as essential parts of the production process, allowing us to optimize manufacturing and supply chain activities simultaneously. Figure 1 shows general framework at factory level and supply chain level.

A Dynamic, Platform-Driven Manufacturing Scenario

At the heart of this use case lies an online platform that connects a global network of manufacturers. Customers use this platform to order highly customized products that combine 3D-printed components with some purchasable parts, requiring post-processing, assembly, and quality checks across multiple suppliers. Each customer’s order dynamically creates a unique production scenario, tailored to:

• Product complexity,
• Available manufacturing technologies,
• Supplier capabilities,
• Geographic location of customers and providers.

This use case requires agile orchestration of production system and generating all possible production scenarios.  In this regard, Python scripts automate the generation of production scenarios by considering factors such as available resources, production constraints, and demand patterns. This automation enables the system to efficiently test and optimize numerous configurations without manual intervention, leading to more informed decision-making.

Real-Time Optimization with Integrated Simulation Framework

A pivotal advancement in this extended framework is the incorporation of real-time data into the simulation environment. Traditional DES tools often struggle to accurately model logistics and transportation dynamics. To address this, the framework utilizes real-world transportation data through the Google Maps API, dynamically calculating transportation times between locations. This integration allows the model to account for variables such as traffic conditions, road closures, and fuel consumption, thereby enhancing the simulation’s fidelity to real-world scenarios.​ ​Integrating external APIs into Tecnomatix Plant Simulation was previously a complex and challenging task. However, with the introduction of the Python Module object in Plant Simulation, this process has been significantly streamlined. This new feature allows users to utilize any API directly within the simulation environment, facilitating seamless integration and enhancing the framework’s overall capabilities.

Furthermore, the framework incorporates Genetic Algorithms (GAs) to evaluate various scenarios and identify optimal production and supply chain routes. This approach offers a holistic solution for supply chain optimization by bridging the gap between factory operations and broader network-level activities, thereby enhancing decision-making and operational efficiency.​

In summary, the KISS project’s integration of Python scripting within the simulation environment, coupled with real-time data incorporation and advanced optimization techniques, provides a robust and dynamic tool for comprehensive supply chain and production system analysis.

Use Case 2: Optimizing Automotive Paint Shop Batching with Gurobi Solver and Simulation Integration (ECOFACT Project) [3]

​In automotive manufacturing, optimizing the sequence of vehicles for painting is crucial to minimize color changeovers, thereby reducing paint waste and improving operational efficiency. To optimize the batch size of cars released to the paint shop at a car manufacturing facility, the “Batching with Looping” strategy was developed. This approach integrates mathematical optimization with simulation modeling to effectively tackle the challenge.

This approach reconfigures the vehicle staging platform into two primary zones:​

1. Looping Area: A dynamic buffer where vehicles circulate continuously, allowing flexible sequencing and temporary holding for batch formation.​
2. Buffer Area: Dedicated lanes operating on a First-In-First-Out (FIFO) basis, serving as temporary storage until vehicles are dispatched for painting.​

To determine the optimal sequence for vehicle release, a Mixed-Integer Linear Programming (MILP) model was formulated with objectives to minimize color changes and sequence alterations. Constraints were incorporated to reflect the capacities of the looping area, sequencing requirements, and batch size thresholds. We employed the Gurobi Solver, accessed via Python scripts, to solve this optimization problem efficiently.

The optimized sequencing decisions derived from Gurobi were then integrated into Siemens Tecnomatix Plant Simulation to validate the practicality of the proposed batching strategy. This integration was facilitated by Plant Simulation’s Python interface, allowing seamless interaction between the simulation model and the optimization algorithm. Figure 2 illustrates the flowchart of MILP tool integration into the Plant Simulation model.

The simulation model incorporated the looping and buffer areas, simulating vehicle movements, batch formations, and painting operations based on the optimized sequences. Key performance metrics, such as average batch sizes and color change frequencies, were analyzed to assess the effectiveness of the optimization strategy.​

In summary, the “Batching with Looping” approach, underpinned by the integration of Gurobi Solver and Tecnomatix Plant Simulation through Python scripting, demonstrates a robust solution for optimizing vehicle sequencing in automotive painting processes. This case exemplifies the synergy between mathematical optimization and simulation modeling in addressing complex manufacturing challenges, leading to tangible improvements in efficiency and resource utilization.

Autor: M.Sc. Omid Safari (Factory system design and production planning)

Titelbild und Beitragsbilder: Eigens von Omid Safari erstellt

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