With the mega trend of digitalizing industry [1], several new application scenarios have arisen, such as order-controlled production, adaptable factories, self-organizing adaptive logistics, value-based services, and circular economies [2]. These scenarios address manufacturers' concerns, including increased individualization and the demand for shorter innovation cycles of their products. However, for the digitalization and enabling of the application scenarios, data are crucial. How are data generated, processed, stored, and exchanged? The flow and reuse of data need to be considered throughout the manufacturer's company. However, what data must be taken into account? According to Lu et al. [11], the three manufacturing dimensions (i.e., product, production system, and business) of a company should each be analyzed along its life cycle. This analysis includes the data generated, processed, stored, and exchanged within and across these dimensions. Each dimension and life-cycle phase involves different stakeholders, disciplines, and software tools that come with a different view of the product, production system, or business. This view is typically reflected by or within the data, i.e., how the data are structured (syntax) and what is meant by each data object (semantics). A way of unifying and standardizing the data is, therefore, required to enable the flow of information and data reuse, i.e., consistent data exchange.

中文翻译：

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数据的流动和重用：AutomationML 在生产系统生命周期中的能力

随着行业数字化的大趋势[1]，出现了几个新的应用场景，例如订单控制生产、适应性工厂、自组织自适应物流、基于价值的服务和循环经济[2]。这些方案解决了制造商的担忧，包括增加个性化和缩短产品创新周期的需求。然而，对于应用场景的数字化和赋能，数据是至关重要的。数据是如何生成、处理、存储和交换的？整个制造商公司都需要考虑数据的流动和重用。但是，必须考虑哪些数据？根据 Lu 等人的说法。[11] 公司的三个制造维度（即产品、生产系统和业务）都应该在其生命周期中进行分析。该分析包括在这些维度内和跨这些维度生成、处理、存储和交换的数据。每个维度和生命周期阶段都涉及不同的利益相关者、学科和软件工具，它们对产品、生产系统或业务有不同的看法。该视图通常由数据或在数据内部反映出来，即数据的结构（语法）以及每个数据对象的含义（语义）。因此，需要一种统一和标准化数据的方法，以实现信息流和数据重用，即一致的数据交换。生产系统或业务。该视图通常由数据或在数据内部反映出来，即数据的结构（语法）以及每个数据对象的含义（语义）。因此，需要一种统一和标准化数据的方法，以实现信息流和数据重用，即一致的数据交换。生产系统或业务。该视图通常由数据或在数据内部反映出来，即数据的结构（语法）以及每个数据对象的含义（语义）。因此，需要一种统一和标准化数据的方法，以实现信息流和数据重用，即一致的数据交换。