This article explores the transformative power of digital twins for small and medium enterprises (SMEs), highlighting how AI-driven simulations can optimize complex processes and reduce risks associated with strategy execution.
Digital twins allow businesses to repeatedly simulate and optimize complex, multivariable problems, reducing the learning costs associated with experimentation in the physical world. Previously confined to large corporations, small and medium enterprises (SMEs) can now leverage AI to create advanced digital twins . These twins facilitate repeated simulations and optimizations of intricate, multivariable issues.
Building these twins involves a five-step process: defining a clear business objective; outlining a detailed flowchart of the process being twinned; identifying and structuring the required data; constructing the digital model based on the flowchart; and finally, testing, implementing, and iterating the model. Strategy execution is inherently risky as any miscalculations incur tangible costs. One way to mitigate this risk is to repeatedly execute a process in a controlled experimental setting, gradually refining it each time. This mirrors the approach renowned for Toyota's continuous improvement systems. However, this iterative process demands continuous data monitoring and numerous micro-decisions in response.
Digital Twins AI Simulation Optimization Smes
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Digital Twins: Optimizing Strategies with AIDigital twins are empowering businesses, including SMEs, to simulate and optimize complex processes using AI, reducing risks and costs associated with physical experimentation. This five-step process involves defining objectives, mapping workflows, structuring data, building digital models, and iteratively testing and refining them.
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