As an ever-increasing volume of data gets collected within industrial companies, existing data visualization methods become inadequate, and much of this data ends up just filling storage inside databases. This is particularly the case when interpreting data on the location of different resources, such as parts or machines, as it is difficult to represent intuitively with common options like tables or graphs. This paper explores how to effectively visualize this data and provide a solution that facilitates gaining visibility into lost items, production routes, and congestion. Specifically, a digital shadow dashboard is developed that maps production and resource data onto manufacturing companies’ floorplans. In addition, dynamic value stream mapping is also explored. Testing is done with industrial small-to-medium enterprises (SMEs), prioritizing low-cost software to ensure viability for them. A research through design approach is used to investigate the information needs of the SMEs, and to develop dashboard designs to address them. As a result, this project sits at a nexus between industrial engineering, UI/UX design, user research within industrial SMEs, and software prototyping on a low development budget. The contribution of the paper is the structured development of a digital shadow of factories’ floorplans, with a dashboard to convey part location data to users. The approach and steps taken also serve as a guide for the development of future digital shadow dashboards for industrial SMEs or research.

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Using a matching pursuit (MP) approach to solve least squares problems with a sparsity constraint sparse controllers are computed to compensate for atmospheric turbulence. The method has been validated by simulation of von Kármán turbulence with frozen flow for the case of prediction, reconstruction and control.