A decision-making methodology for selecting digital twin applications in the product service phase considering value and effort

The digital twin holds great potential for manufacturing using time series data in statistical models and simulations. Despite the recognised benefits of digital twins, many companies fail to achieve satisfactory value from their data due to a disconnect between data collection and its application in data-driven use cases. A "data-to-value" strategy is lacking, which would enable companies to select effective applications to achieve specific goals. This publication introduces a methodology that allows for the quantification of suitability and targeted selection of data-driven applications based on a value-effort analysis and the underlying time series data. This makes it easier for manufacturing companies to select the most suitable application for their individual needs. After identifying value aspects and their interactions, the value of each data-driven application is evaluated using the analytical network process. Subsequently, the implementation effort of each application is assessed from both a data and technological perspective. The results of the quantifications are then compared using the TOPSIS method. The methodology is demonstrated using a grinding process example before final discussions. Assuming that the economic value and effort are initially unknown, the methodology contributes to decision-making in selecting the most suitable digital twin application.