The Flow and Reuse of Data Capabilities of AutomationML in the Production System Life Cycle

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. © 2007-2011 IEEE.