# A Five-Step Approach to Planning Data-Driven Digital Twins for Discrete Manufacturing Systems
## 5 Conclusions  
There are a variety of different software solutions (e.g., Siemens Tecnomatix Plant Simulation) for developing digital models that are used to improve real manufacturing systems and processes. The improvements are mainly related to manufacturing process planning, order scheduling and also to simplification of the visualization for various participants. To obtain a digital twin, it is necessary to capture process parameters and synchronize the real system with the digital model. The paper presents a five-step approach to planning the data-driven digital twin for discrete manufacturing systems: (i) Definition of manufacturing system. The approach is based on breaking down the manufacturing system into building blocks belonging into four groups: \"Fabrication\", \"Logistics\", \"Storage\", and \"Inspection\". (ii) Determining the sequence of processes in the manufacturing system. Classification from (i) forms the basis of identifying relevant parameters for planning the digital model (iii). The proposed approach shows which parameters are required for each group. In addition to the generalized data of the groups, each building block has its own specific data and specifications that need to be considered when planning a digital model. (iv) The digital model is upgraded to a digital twin with a feedback control loop to the real manufacturing system. The real manufacturing system sends initial data (the number of parts in buffers, WBPFP and WRG) to the digital model. Then the digital model sends control information back to the real system. The digital twin is able to generate a suboptimal manufacturing plan, check the current state of the real manufacturing system before the process starts, correct the real system/process according to the simulation of the digital twin, etc. (v) A visualization is introduced for different participants. By using a digital twin, they can make decisions based on the parameters they need (data-driven decisions). The approach is validated in a case study of Demo Center Smart Factory. It contains at least one example for each group of building blocks and presents the assembly of different products. Based on the case study, each step of the presented approach is clearly described and allows for the step-by-step guidance of the user. The digital model (for logistics) in our case study was created using the Siemens Tecnomix Plant Simulation software tool, but the approach is useful for any simulation software tool. Preparing the traditional manufacturing systems and processes, including the software equipment and visualization for digitization is still a significant challenge for the companies. In our experience, companies are struggling to define the right parameters that should be collected from the real systems and processes, ready as inputs to the digital models (digital twins) capable of performing real-time data analysis, process simulation and feedback control. Practitioners can use the results of our work as a guideline for defining and preparing the right parameters for different manufacturing systems and processes. Each system or process has its own specific characteristics and therefore requires different essential parameters. The presented approach guides practitioners through five crucial steps that contain the important information about which parameters are necessary for the digital twins and consequently where to install the sensors to collect this data from the real system. Knowing this is important to avoid later modifications of the systems, which are sometimes difficult to accomplish. Finally, our study helps experts and researchers to design modern manufacturing systems and processes that are ready to be upgraded with the digital twin. By using our proposed five-step approach, the experts and researchers are able to develop the specific data-driven digital twins that cover other systems and processes, which will be useful for the practitioners.