Digital twin and big data-driven sustainable smart manufacturing based on information management systems for energy-intensive industries

•A digital twin and big data-driven sustainable smart manufacturing are proposed.•The operation mechanism is presented from the product lifecycle perspective.•The strategy of data cleansing and integration is designed and explained.•The effectiveness of the proposed strategy is discussed for companies in China. Internet of Things (IoT) technology, which has made manufacturing processes more smart, efficient and sustainable, has received increasing attention from the industry and academia. As one of the most important applications for IoT, sustainable smart manufacturing enables lower cost, higher productivity and flexibility, better quality and sustainability during the product lifecycle management. Over the years, numerous enterprises have promoted the implementation of both sustainable and smart manufacturing. In the Industry 4.0 context, a ‘digital twin’ is widely used to achieve smart manufacturing, although this approach often ignores sustainability. This study aims to simultaneously consider digital twin and big data technologies to propose a sustainable smart manufacturing strategy based on information management systems for energy-intensive industries (EIIs) from the product lifecycle perspective. The integration of digital twin and big data provides key technologies for data acquisition in energy-intensive production environments, prediction and mining in uncertain environments as well as real-time control in complex working conditions. Moreover, a digital twin-driven operation mechanism and an overall framework of big data cleansing and integration are designed to explain and illustrate sustainable smart manufacturing. Two case studies from Southern and Northern China demonstrate the efficacy of the strategy, with the results showing that Companies A and B achieved the goals of energy saving and cost reduction after implementing the proposed strategy. By applying an energy management system, the unit energy consumption and energy cost of production in Company A decreased by at least 3%. In addition, the ‘cradle-to-gate’ lifecycle big data analysis indicates that the costs of environmental protection in Company B decrease significantly. Finally, the effectiveness of the proposed strategy and some managerial insights for EIIs in China are analysed and discussed.