Design of a digital twin in low-volume, high-mix job allocation and scheduling for achieving mass personalization

Abstract

The growing consumer demand for unique products has made customization and personalization essential in manufacturing. This shift to low-volume, high-mix (LVHM) production challenges the traditional paradigms and creates difficulties for small and medium-sized enterprises (SMEs). Industry 5.0 emphasizes the importance of human workers and social sustainability in adapting to these changes. This study introduces a digital twin design tailored for LVHM production, focusing on the collaboration between human expertise and advanced technologies. The digital twin-based production optimization system (DTPOS) uses an intelligent simulation-based optimization model (ISOM) to balance productivity and social sustainability by optimizing job allocation and scheduling. The digital twin model fosters a symbiotic relationship between human workers and the production process, promoting operational excellence and social sustainability through local innovation and economic growth. A case study was conducted within the context of a printed circuit board assembly (PCBA) using surface mount technology to validate the digital twin model’s efficacy and performance. The proposed DTPOS significantly improved the performance metrics of small orders, reducing the average order processing time from 19 days to 9.59 days—an improvement of 52.63%. The average order-to-delivery time for small orders was 19.47 days, indicating timely completion. These findings highlight the successful transformation from mass production to mass personalization, enabling efficient production capacity utilization and improved job allocation and scheduling. By embracing the principles of Industry 5.0, the proposed digital twin model addresses the challenges of LVHM production, fostering a sustainable balance between productivity, human expertise, and social responsibility.