Optimization of Order Scheduling in the Moroccan Garment Industry for Fast Fashion: A Clustering-Based Approach

Abdelfattah Mouloud, Yasmine El Belghiti, Samir Tetouani and Aziz Soulhi

Data and Metadata, 2024, vol. 3, .644

Abstract: The Moroccan garment industry plays a crucial role in the global fast fashion market, requiring efficient, flexible, and timely production to meet evolving consumer demands. However, the scheduling of small order batches presents significant challenges, as it demands skilled operators and strict adherence to On-Time Delivery (OTD) targets. Traditional scheduling methods based on product family groupings often result in frequent and time-consuming changeovers, increasing downtime and reducing operational efficiency by up to 15-20%. This paper introduces a novel clustering-based scheduling methodology that organizes production lines by technological times rather than product families. By grouping garments with similar operational requirements, this approach aims to minimize changeover times, streamline production transitions, and reduce downtime by an average of 30-35%. A case study conducted in a Moroccan garment factory validates the effectiveness of the proposed approach. The factory, with an average order size of 50-100 units per batch, achieved a 20% reduction in lead time and a 15% increase in operator productivity after implementing the clustering-based scheduling. Additionally, the use of clustering methods such as K-Means facilitated the grouping of garments with minimal operational variability, further enhancing planning flexibility and resource utilization. This study highlights how technological clustering enhances production scheduling in the garment industry. It emphasizes aligning production processes with operational needs to optimize resources and competitiveness in fast fashion. The methodology provides a framework for reducing changeover downtime, boosting productivity, and maintaining agility in a dynamic market.

Date: 2024
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:dbk:datame:v:3:y:2024:i::p:.644:id:1056294dm2024644

DOI: 10.56294/dm2024.644

Access Statistics for this article

More articles in Data and Metadata from AG Editor
Bibliographic data for series maintained by Javier Gonzalez-Argote ().