Text Mining for Supply Chain Risk Management in the Apparel Industry

Abstract

Text mining tools are now widely used for the efficient management of information and resources in business, academic and research organizations. This paper provides a comprehensive overview of research articles on the application of text mining techniques in the field of Supply Chain Risk Management and the apparel industry. Research articles published between 2000 and 2020, were obtained from various journals through two online databases, i.e., SCOPUS and IEEE Xplore. Through a systematic approach following PRISMA guidelines, 370 research papers were screened, filtered and finally classified into three main areas: Supply Chain Risk Management and outsourcing in the apparel industry, application of text mining in Supply Chain Risk Management and application of text mining in the apparel industry. In this study, we have identified a comprehensive list of various available data sources for text mining, methodologies and risks associated with outsourcing in the apparel industry. We classify the gaps in expanding the application of text mining in the apparel industry’s Supply Chain Risk Management. Extracting useful information from online newspapers through text mining could vividly enhance the ability to monitor supply chain risks and provide the ability to link data to provide decision makers with the right information at the right time.

A thematic analysis–based model for identifying the impacts of natural crises on a supply chain for service integrity: a text analysis approach

“…Today, large amounts of SC crisis data are available as text-based information from social networks, open portals, and databases for academic purposes. In this regard, new research has been directed toward data-driven methods, a text analysis approach to monitoring SC crises and understanding risk patterns (Yan et al 2019;Chu et al 2019;Shah et al 2021;Kara et al 2020;Da Silva et al 2020).…”

What Causes the Virtual Agglomeration of Creative Industries?

“…Qualitative methods are not entirely suitable for this research. With the ongoing advancements in bigdata-related research, quantitative methods based on big data web crawling and text mining can provide excellent solutions [45,46], and researchers are increasingly obtaining results that better reflect reality using big data text mining methods [47][48][49]. Taking the Shenzhen Dafen Oil Painting Village as an example, Yuan et al [50] studied the convergence between creative industry parks and tourism using ROST Content Mining 6 and performing an analysis based on the network text.…”

Textual Data Science for Logistics and Supply Chain Management

“…To date, automated analyses of large chunks of textual data, usually labeled as text analysis or text mining, have been used only infrequently in logistics and supply chain management. Several notable exceptions, which also illustrate the wide range of possible uses, include their application for literature reviews [1][2][3], conference abstracts and summaries [4], demand planning [5], supply chain risk management [6], identification of key challenges and prospects for logistics services [7], and the creation of insights regarding drivers of change [8]. Meanwhile, extant research in other application fields has tapped deeply into the huge potential of textual data science and illustrated what kind of further insights can be gained from such applications.…”

“…colvec <-c(rep(1,8), rep(2,6), rep(4, 5)) x11() plot(fitmds, label.conf = list(col = colvec), col = colvec) legend("bottomleft", legend = c("C1", "C2", "C3"), pch = 20, col = c(1,2,4)) ## That's an interesting picture: the companies are nicely separated in the MDS ## hierarchical clustering: fitclust <-hclust(as.dist(cosine_dist_mat), method = "ward.D2") x11() plot(fitclust, hang = -1, ann = FALSE) title("Interviews Dendrogram") rect.hclust(fitclust, k = 3) ## this confirms what we have seen in MDS cutree(fitclust, k = 3) ## 3 cluster membership…”