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Supplementary Material to "EDAS-Sort with central profiles: Novel sorting method applied to multi-criteria inventory classification in make-to-order manufacturing"

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Figshare2025-06-26 更新2026-04-08 收录
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https://figshare.com/articles/dataset/Supplementary_Material_to_EDAS-Sort_with_central_profiles_Novel_sorting_method_applied_to_multi-criteria_inventory_classification_in_make-to-order_manufacturing_/28902707/1
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Classifying inventory items in make-to-order (MTO) manufacturing environments finds increasing importance beyond traditional ABC classifications, primarily focusing on costs or consumption, where high customization, just-in-time procurement, and dynamic production requirements are prevalent. To address this limitation, Multi-Criteria Inventory Classification (MCIC) integrates additional factors such as lead time, supplier reliability, and production dependency. However, conventional MCIC approaches typically rely on rigid threshold-based sorting techniques, which can misclassify items that do not conform to clearly defined boundaries. Thus, this study introduces Evaluation Based on Distance from Average Solution Sorting (EDAS-Sort) adopted within the ABC framework, a novel classification method that integrates the EDAS framework with ABC logic using central profiles. Instead of employing fixed thresholds, the method constructs representative central profiles for each class (A, B, or C) based on average performance defined <i>a priori</i> across multiple criteria. Inventory are then evaluated based on their deviation from these profiles, enabling a more flexible, adaptive, and context-aware classification system. In addition to classification, inventory items are ranked within each class to identify the most critical alternatives per category. An actual case study was conducted in an MTO furniture manufacturing firm, using six criteria selected through a PRISMA-guided literature review and weighted via the entropy method. Results demonstrate that EDAS-Sort effectively prioritizes inventory items based on their production criticality. By aligning classification with operational relevance, the proposed method enhances inventory planning, improves resource allocation, and contributes a robust, data-driven solution to multi-criteria sorting in complex manufacturing systems.
提供机构:
Ocampo, Lanndon; Ubod, Bea
创建时间:
2025-04-30
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