{"id":{"repo_id":"regina","oai_identifier":"oai:uregina.scholaris.ca:10294/16040"},"canonical_url":"https://search.dev.ndltd.org/etd/regina/oai:uregina.scholaris.ca:10294/16040","repository":{"repo_id":"regina","name":"University of Regina","base_url":"https://uregina.scholaris.ca/server/oai/request"},"display":{"title":"Digital twin framework development for apparel manufacturing industry","abstract":"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.","abstract_html":"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.","abstract_has_math":false,"creators":["Alam, Mohammed Didarul"],"institution":"Faculty of Graduate Studies and Research, University of Regina","degree_name":"Master of Applied Science (MASc)","degree_level":"Master&apos;s","degree_discipline":"Engineering - Industrial Systems","degree_department":null,"school":null,"contributors":[],"advisors":["Kabir, Golam"],"committee_chairs":[],"committee_members":["Khan, Sharfuddin","Khondoker, Mohammad"],"year":2023,"date_issued":"2023-01","date_published":"2023-01","updated_at":"2026-07-24T04:03:30Z","subjects":[],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier.doi","label":"DOI","values":["https://doi.org/10.82465/4033"],"render_values":[{"text":"https://doi.org/10.82465/4033","href":"https://doi.org/10.82465/4033","code":true}]}]},"links":{"outbound_url":"https://hdl.handle.net/10294/16040","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Kabir, Golam"]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Khan, Sharfuddin","Khondoker, Mohammad"]},{"key":"dc:creator","label":"Author","values":["Alam, Mohammed Didarul"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2023-07-17T19:54:15Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2023-07-17T19:54:15Z"]},{"key":"dc:date.issued","label":"Date","values":["2023-01"]},{"key":"dc:publisher","label":"Institution","values":["Faculty of Graduate Studies and Research, University of Regina"]},{"key":"dc:type","label":"Dc Type","values":["master thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Engineering - Industrial Systems"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Master&apos;s"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Applied Science (MASc)"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Faculty of Graduate Studies and Research, University of Regina"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.doi","label":"DOI","values":["https://doi.org/10.82465/4033"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10294/16040"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["A Thesis Submitted to the Faculty of Graduate Studies and Research In Partial Fulfillment of the Requirements for the Degree of Master of Applied Science in Industrial Systems Engineering, University of Regina. vii, 113 p."]},{"key":"dc:description.abstract","label":"Abstract","values":["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."]},{"key":"dc:title","label":"Title","values":["Digital twin framework development for apparel manufacturing industry"]}]}],"canonical_facts":{"dc:contributor.advisor":["Kabir, Golam"],"dc:contributor.committeemember":["Khan, Sharfuddin","Khondoker, Mohammad"],"dc:creator":["Alam, Mohammed Didarul"],"dc:date.accessioned":["2023-07-17T19:54:15Z"],"dc:date.available":["2023-07-17T19:54:15Z"],"dc:date.issued":["2023-01"],"dc:description":["A Thesis Submitted to the Faculty of Graduate Studies and Research In Partial Fulfillment of the Requirements for the Degree of Master of Applied Science in Industrial Systems Engineering, University of Regina. vii, 113 p."],"dc:description.abstract":["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."],"dc:identifier.doi":["https://doi.org/10.82465/4033"],"dc:identifier.uri":["https://hdl.handle.net/10294/16040"],"dc:language.iso":["en"],"dc:publisher":["Faculty of Graduate Studies and Research, University of Regina"],"dc:title":["Digital twin framework development for apparel manufacturing industry"],"dc:type":["master thesis"],"thesis:degree_discipline":["Engineering - Industrial Systems"],"thesis:degree_level":["Master&apos;s"],"thesis:degree_name":["Master of Applied Science (MASc)"],"thesis:institution_name":["Faculty of Graduate Studies and Research, University of Regina"]},"updated_at":"2026-07-24T04:03:30Z"}