Digital twin framework development for apparel manufacturing industry

Apparel manufacturing, being a labor-intensive industry, is evolving into a more complicated, challenging, and dynamic business as a result of fast-changing fashion trends, increased variety, and increased personalization of product demands. Quick and optimized decision-making is extremely important to overcome these barriers. It is necessary to have a mechanism that can ensure real-time visibility of the production process and provide assistance in decision-making. Digital Twin (DT) provides real-time visibility. It keeps all production data sources in connection, allowing for fast analysis without affecting physical setup. Although most DT ‘research focused on the machine level and automated industries, this has the potential of applying to apparel manufacturing industries as well. This research contributes in several ways to the current literature. 1. This study identifies the research gap and demonstrates its application to apparel manufacturing industries. 2. It develops a methodology describing step-by-step guidance for applying apparel manufacturing plants. 3. A case study is instantiated to authenticate the methodology. Using the suggested idea and technique, the case study creates a DT of a sewing assembly line. It collects real-time data and carried out simulations dynamically to reduce bottleneck operations. It also communicated production downtime with users instantly to reduce downtime by responding quickly. Keywords: DT; Apparel Manufacturing; Sewing Assembly Line; Production Efficiency; Production Downtime.

Name thesis:degree_name

Master of Applied Science (MASc)

Level thesis:degree_level

Master's

Discipline thesis:degree_discipline

Engineering - Industrial Systems

Grantor dc:publisher

Faculty of Graduate Studies and Research, University of Regina

Year dc:date.issued

2023

Author dc:creator

• Alam, Mohammed Didarul

Advisor dc:contributor.advisor

• Kabir, Golam

Committee members dc:contributor.committeemember

• Khan, Sharfuddin
• Khondoker, Mohammad

Language dc:language.iso

en

DOI

https://doi.org/10.82465/4033

OAI identifier oai:identifier

oai:uregina.scholaris.ca:10294/16040