{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T21:22:44Z","timestamp":1777411364736,"version":"3.51.4"},"reference-count":37,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T00:00:00Z","timestamp":1776384000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Science and Technology Council of Taiwan","award":["111-2813-C-131-008-E"],"award-info":[{"award-number":["111-2813-C-131-008-E"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JTAER"],"abstract":"<jats:p>Upstream textile small and medium-sized enterprises (SMEs) frequently exhibit constrained supply chain resilience owing to persistent information latency and structural dependence on downstream orders. To address these challenges, this study develops and validates a customer-to-manufacturer (C2M) intelligence framework that enables data-driven production planning using publicly available e-commerce data. The framework incorporates ethically compliant acquisition of consumer demand signals, semantic translation of unstructured market data into textile engineering attributes, machine-learning-based demand forecasting, and human-centric decision support. Utilizing 3.87 million consumer comments from 127,846 product listings, a Neural Boosted Tree model with entity embeddings for textile attributes was constructed. This model achieved a mean R2 of 0.921 in cross-validation, surpassing benchmark methods. Consumer comment volume was validated as a proxy for sales activity, facilitating demand estimation. Forecasts were translated into production guidance using Monte Carlo simulation and a decision dashboard. In a 12-month field study at a Taiwanese dyeing SME, implementation resulted in a 28% reduction in inventory value, a 31% decrease in dye lot changeovers, and a 16% increase in capacity utilization. This research extends the C2M paradigm from downstream retail contexts to upstream textile SMEs, proposes an integrated and operationally feasible intelligence framework for resource-constrained manufacturers, and demonstrates how digital intelligence can enhance supply chain resilience while supporting, rather than replacing, human decision-making. The results indicate that upstream textile SMEs can leverage publicly visible e-commerce signals to enhance production planning responsiveness, minimize inventory exposure and dye-lot disruptions, and strengthen resilience to demand uncertainty through planner-centered digital decision support.<\/jats:p>","DOI":"10.3390\/jtaer21040123","type":"journal-article","created":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T14:24:51Z","timestamp":1776435891000},"page":"123","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Enhancing Supply Chain Resilience in Textile SMEs: A Human-Centric Customer-to-Manufacturer Framework Using Public E-Commerce Data"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7869-3965","authenticated-orcid":false,"given":"Chien-Chih","family":"Wang","sequence":"first","affiliation":[{"name":"Department of Industrial Engineering and Management, Ming Chi University of Technology, New Taipei City 243303, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu-Teng","family":"Hsu","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering and Management, Ming Chi University of Technology, New Taipei City 243303, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hsuan-Yu","family":"Kuo","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering and Management, Ming Chi University of Technology, New Taipei City 243303, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,4,17]]},"reference":[{"key":"ref_1","first-page":"105","article-title":"What is the right supply chain for your product?","volume":"75","author":"Fisher","year":"1997","journal-title":"Harv. 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