Simulation-Based POLCA Integrated QRM Approach for Smart Manufacturing
Issued Date
2023-01-01
Resource Type
ISSN
21954356
eISSN
21954364
Scopus ID
2-s2.0-85135015237
Journal Title
Lecture Notes in Mechanical Engineering
Start Page
421
End Page
434
Rights Holder(s)
SCOPUS
Bibliographic Citation
Lecture Notes in Mechanical Engineering (2023) , 421-434
Suggested Citation
Kumar S., Petchrompo S., Ahmed T., Jain A.K. Simulation-Based POLCA Integrated QRM Approach for Smart Manufacturing. Lecture Notes in Mechanical Engineering (2023) , 421-434. 434. doi:10.1007/978-981-19-0561-2_37 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/81739
Title
Simulation-Based POLCA Integrated QRM Approach for Smart Manufacturing
Author(s)
Other Contributor(s)
Abstract
Digitalisation and Industry 4.0 have enabled real-time interaction of customers and suppliers with manufacturing systems. However, this requires a high level of reactivity in the value chain to sustain in a competitive economy. As a result, Quick Response Manufacturing (QRM) in dynamic conditions such as wide product variety, change in demand, volatile market conditions, uncertainty in supply, and machine failures is critical in capturing the benefits of digitalisation in industries. To empower QRM, Paired-cell Overlapping Loops of Cards with Authorization (POLCA) is suitable for a manufacturing environment with a wide variety and highly dynamic conditions. The combination of QRM and POLCA provides a significant competitive advantage through their quick response to dynamic conditions. Such a combination is not reported in the literature. Accordingly, this paper proposes a simulation-based POLCA integrated with the QRM approach for smart manufacturing. The offered approach evaluates a dynamic sequence of jobs at each machine to meet the due date and at the same time control the material flow to improve system performance. The approach is illustrated with the help of a complex manufacturing scenario. A simulation-based technique is used to evaluate the system performance. Further, the performance of the approach under dynamic conditions is analysed. Results show that improved system performance can be achieved by considering QRM and POLCA in dynamic conditions.