In this paper we proposed a new architectural model of the smart factory to allow production experts to make easier and more exact planning of new, smart factories by using all the key technologies of Industry 4.0. The existing complex reference architectural model of Industry 4.0 (RAMI 4.0) offers a good overview of the smart-factory architecture, but it leads to some limitations and a lack of clarity for the users. To overcome these limitations, we have developed a simple model with the entire and very simple architecture of the smart factory, based on the concept of distributed systems with exact information and the data flows between them. The proposed architectural model enables more reliable and simple modelling of the smart factory than the existing RAMI 4.0 model. Our approach improves the existing methodology for planning the smart factory and makes all the necessary steps clearer. At the end of the paper a comparison of the proposed architectural model LASFA (LASIM Smart Factory) with the existing RAMI 4.0 model was made. The developed LASFA model was already successfully implemented in the laboratory environment for building the demo centre of a smart factory.

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A digital twin of a manufacturing system is a digital copy of the physical manufacturing system that consists of various digital models at multiple scales and levels. Digital twins that communicate with their physical counterparts throughout their lifecycle are the basis for data-driven factories. The problem with developing digital models that form the digital twin is that they operate with large amounts of heterogeneous data. Since the models represent simplifications of the physical world, managing the heterogeneous data and linking the data with the digital twin represent a challenge. The paper proposes a five-step approach to planning data-driven digital twins of manufacturing systems and their processes. The approach guides the user from breaking down the system and the underlying building blocks of the processes into four groups. The development of a digital model includes predefined necessary parameters that allow a digital model connecting with a real manufacturing system. The connection enables the control of the real manufacturing system and allows the creation of the digital twin. Presentation and visualization of a system functioning based on the digital twin for different participants is presented in the last step. The suitability of the approach for the industrial environment is illustrated using the case study of planning the digital twin for material logistics of the manufacturing system.

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