| Abstract: |
Digitalization unlocks huge business potentials for products and services that create value based on the evaluation of data. Successful implementation depends on systematic procedures for managing and analyzing data to create insight with value for businesses. However, such procedures are typically not covered in today’s processes. From our experience, organizations start processing the data that happens to be available. In fact, this data often does not cover all relevant parameters in the situation of interest and typically lacks the quality to be processable. In industrial environments, the reliability and accuracy of results are critical for success. Therefore, an enormous responsibility comes with the development of digital products. Unless there are systematic procedures in place to guide data management and data analysis tasks in the development lifecycle, many promising digital industrial products will not meet expectations. We substantiate why Data Science and Engineering should be introduced as new engineering discipline in the PLM process, and outline the workflows and tasks of a systematic framework for developing digital products for industrial environments. |