The above considerations have to be carefully factored in while selecting the approach to model different subsystems and modules, hardware or physics-based twins in the digital twin.
Data recording and logging are crucial components of any digital twin project. This data not only serves as the basis for simulation and testing but also facilitates debugging, system optimization and performance analysis. Effective data recording strategies can also assist in the validation of model assumptions, further enhancing system accuracy and reliability.
Digital twins are not merely simulation tools; they represent a fundamental shift in the way we can plan, design, deploy and optimize robotic automation systems in warehouses. A well-designed digital twin, factoring in the aspects outlined in this article, empowers reliable, predictable and efficient order fulfillment, catalyzing innovation and progress in customer satisfaction.
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