Industries are in the midst of significant, compelling transformation regarding technology innovation and the ways products are manufactured. This is largely impacted by Industry 4.0, which is mainly featured by digitalization. New and relatively low-cost technologies for smart sensing and operation, fast data communication among sensor node, database, internet, and cloud services in manufacturing sites have started to be used. These provide a variety of opportunities for advancing engineering sustainability research and practice. In industry, sustainability assessment is commonly conducted annually and improvement strategies are developed accordingly. It has been questioned whether sustainability problems could be identified, assessed, analyzed and predicted in time so that solutions could be derived and actions be taken quickly. With abundant data accessible in the digital age, traditional sustainability assessment could be performed dynamically. This renders a need to study methods for dynamic sustainability assessment.
In this paper, we introduce a concept of dynamic sustainability assessment and discuss data accessibility, data generation using Digital Twin technology, and data spatial-temporal classification and utilization. Then we analyze the features of sustainability indicators in the economic, environmental, and social categories and classify them into groups that demand different data acquisition frequencies. We will also describe what sustainability aspects should be assessed more often as compared with others for annual evaluation. The method for dynamic sustainability assessment has been applied to a study on electroplating system sustainability. The assessment results will be compared with those by conventional annualized sustainability assessment. It shows a potential to open new areas of study for industrial sustainability as a whole.
