Machine learning and soft computing applications in textile and clothing supply chain: Bibliometric and network analyses to delineate future research agenda

Machine learning (ML) and soft computing (SC) techniques have contributed immensely towards improvisation of manufacturing, process and quality control, automation of operations, and decision making in the textile and clothing supply chain. The current study systematically reviews 312 research articles published in Scopus indexed journals in last two decades (2000-2020). Bibliometric analysis revealed a drastic rise in number of studies since 2015. Operations related to manufacturing and quality control of yarns and fabrics are found to be benefitted most from ML techniques, as deduced from keyword and thematic content analyses. On the other hand, with respect to ML and SC techniques, artificial neural network, genetic algorithm and fuzzy logic have preponderance over the others. Nevertheless, over the years, many novel ML and SC techniques have emerged in accomplishing diverse objectives of the textile and clothing supply chain. A detailed analysis of their evolution has been documented. Unfortunately, the extent of knowledge sharing and collaboration within the scientific community working in this domain has been very low. This review also unearths lack of integrated focus on complete value chain of textiles and clothing. Fibre development, fuzzy inference and control in textile machines, predictive analytics for clothing engineering, and fashion forecasting have been identified as areas for future research.

Automated summary

Machine learning and soft computing techniques have greatly advanced the manufacturing, process control, and decision making in the textile and clothing supply chain. However, there is still a lack of knowledge sharing and collaboration in this field, and more research is needed on the complete value chain of textiles and clothing.