Artificial intelligence (AI) is gaining ever more traction with the latest trends of large language models, generative AI, and autonomous agents bringing capabilities to the attention of millions outside of its classic computer science domain. While new algorithms and services (e.g., ChatGPT, DeepSeek, or Groq) receive ample attention, the crucial role that simulation techniques play in creating mature and safe applications in domains such as Production is often overlooked. This article sheds light on this role by reviewing applications and use cases in the domains of Production and Logistics, and new concepts such as cyber-physical production systems and digital twins.
Use of Simulation in Production and Logistics
Simulation refers to creating virtual environments that model real-world scenarios. It enables businesses to optimize operations, reduce costs, and improve efficiency. Simulations help, for example, manufacturers to model production lines, test workflow changes, and identify bottlenecks before implementing costly modifications. This leads to improved resource utilization, reduced downtime, and higher productivity.
Simulations can take various forms, including discrete-event simulation (where events occur at specific time points), continuous simulation (where changes happen in a continuous flow over time), and agent-based simulation (where autonomous entities interact within a defined system). These methods are crucial for replicating real-world dynamics.
Simulations further provide a safe, cost-effective way to experiment with various conditions and scenarios that might be rare, dangerous, or expensive to recreate. Thus, they can play a vital role in AI development by providing controlled environments for training, testing, and refining AI models. They allow AI systems to learn from synthetic yet realistic data, improve decision-making through repeated experimentation, and adapt to a wide range of conditions before real-world deployment.
Cyber-Physical Production Systems (CPPS) and Digital Twins
Within Cyber-Physical Systems information from all related perspectives is closely monitored and synchronized between the physical factory floor and the cyber computational space (Lee et al. 2014 [2]). These systems leverage sensors, actuators, and computational resources to create smart manufacturing environments that can adapt to changing conditions and requirements.
Digital twins complement this concept. A digital twin is a digital model of an intended or actual real-world physical product, system, or process (a physical twin) that serves as a digital counterpart of it for purposes such as simulation, integration, testing, monitoring, and maintenance (Moi et al. 2020 [1]).
These digital replicas enable real-time monitoring, predictive analysis, and optimization of physical systems. In manufacturing contexts, digital twins can represent individual machines, entire production lines, or complete factories, providing unprecedented visibility into operations and enabling data-driven decision-making.
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Simulations provide safe, repeatable environments for evaluating the performance of AI systems. This is crucial in production and logistics, where the cost of errors can be substantial.
Simulated Data Generation
One of the most significant contributions of simulation to training AI algorithms is the generation of synthetic data. AI models, particularly deep learning systems, require vast amounts of data for effective training. In many production and logistics applications, collecting sufficient real-world data can be challenging, time-consuming, or impossible.
Simulations address this challenge by generating synthetic data that mirrors real-world conditions. For example, when developing autonomous mobile robots for production environments, simulations can generate diverse traffic patterns and obstacle scenarios, creating comprehensive training datasets that would be impractical to collect in real warehouses.
Controlled Environments for Testing AI Models
Simulations provide controlled environments where AI models can be tested across a wide range of scenarios, including those that would be impractical or dangerous to recreate in the real world. This is particularly valuable in Industry 5.0 applications, where AI systems work alongside human operators.
In these “humans in the loop” scenarios, simulations allow developers to test how AI systems interact with simulated human operators under various conditions, ensuring safe and effective collaboration before testing and deployment in real production environments.
Reinforcement Learning and Simulation
Reinforcement learning, where AI agents learn by interacting with an environment and receiving feedback, particularly benefits from simulation. In production scheduling, for instance, initial AI-generated plans might be far from optimal. Allowing AI systems to learn through trial and error in real production settings would be prohibitively expensive and disruptive.
Instead, simulated production environments enable AI systems to explore various scheduling strategies, learn from mistakes, and optimize their approaches without affecting real operations. AI can experience thousands of production days in simulation before being deployed to make decisions in actual facilities.
Performance Assessment
Simulations provide safe, repeatable environments for evaluating the performance of AI systems. This is crucial in production and logistics, where the cost of errors can be substantial.
For example, simulations can recreate management scenarios to assess AI decision-making capabilities under normal operations and extreme situations, such as supply chain disruptions or equipment failures.
By running thousands of simulated scenarios, developers can comprehensively evaluate AI performance across a broad spectrum of conditions, identifying strengths and weaknesses before deployment.
Edge Cases and Stress Testing
Another highly valuable aspect of simulation for AI validation is the ability to test edge cases and rare events. In real-world operations, infrequent but critical situations might occur only once every few years, making it difficult to train AI systems to handle them appropriately.
Simulations can generate these rare scenarios on demand. For instance, when developing autonomous mobile robots for warehouse environments, simulations can create unusual accident scenarios, allowing developers to test and refine the AI safety protocols without putting actual equipment or personnel at risk.
Autonomous Vehicles in Production
Automated ground vehicles (AGVs) in manufacturing and warehouse settings benefit tremendously from simulation-based development. Simulated factory floors and warehouse environments allow developers to test navigation algorithms, obstacle avoidance systems, and traffic management protocols across countless scenarios.
As highlighted by Schwarz and Wang (2022 [3]), these simulations enable comprehensive testing of the AI decision-making processes in different applications, from material transport to inventory management, ensuring reliable and safe operation in real environments.
Industrial Automation and CPPS
In the context of cyber-physical production systems, simulation plays a vital role in optimizing AI for factory automation. Virtual factory models allow developers to test how AI-driven control systems respond to changing production requirements, equipment failures, and other variables.
These simulations facilitate the development of adaptive production systems that can automatically reconfigure themselves in response to changing conditions, maximizing efficiency and resilience.
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Simulation technologies are widely considered indispensable tools in the development and deployment of AI applications for production and logistics. From (faster) training and testing to validation, simulations provide safe, controlled environments where AI systems can learn, be evaluated, and refined before deployment in real-world settings.
Predictive Maintenance
Predictive maintenance represents one of the most valuable applications of AI in production environments. By analyzing patterns in equipment performance data, AI systems can predict when machinery is likely to fail, enabling maintenance to be performed before breakdowns occur.
Simulations are instrumental in developing these predictive capabilities (Carvalho et al, 2019 [4]). By simulating various failure modes and equipment degradation patterns, developers can train AI systems to recognize early warning signs of potential problems. The benefits include reduced downtime, extended machinery life, and significant cost savings.
Conclusion
Simulation technologies are widely considered indispensable tools in the development and deployment of AI applications for production and logistics. From (faster) training and testing to validation, simulations provide safe, controlled environments where AI systems can learn, be evaluated, and refined before deployment in real-world settings.
As simulation and AI continue to evolve, becoming more sophisticated and more widely implemented, the role of simulation in AI development will only grow in importance. The integration of simulation with emerging technologies like digital twins and cyber-physical systems promises to further accelerate innovation in production and logistics, enabling more adaptive, efficient, and resilient operations.
There is a symbiotic relationship between AI and thorough testing and refinement in virtual environments of that AI before it can face the complexities, and the reliability demands of real-world production and logistics systems.
References
[1] Moi, T., Cibicik, A., & Rølvåg, T. (2020). Digital twin-based condition monitoring of a knuckle boom crane: An experimental study. Engineering Failure Analysis, 112, 104517.
[2] Lee, Jay & Bagheri, Behrad & Kao, Hung-An. (2014). A Cyber-Physical Systems architecture for Industry 4.0-based manufacturing systems. SME Manufacturing Letters. 3. 10.1016/j.mfglet.2014.12.001.
[3] Schwarz, Chris & Wang, Ziran. (2022). The Role of Digital Twins in Connected and Automated Vehicles. IEEE Intelligent Transportation Systems Magazine. 10.1109/MITS.2021.3129524.
[4] Carvalho, Thyago & Soares, Fabrizzio & Vita, Roberto & Francisco, Roberto & Basto, João & G. Soares Alcalá, Symone. (2019). A systematic literature review of machine learning methods applied to predictive maintenance. Computers & Industrial Engineering. 137. 106024. 10.1016/j.cie.2019.106024.
