# 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. supply chain risk management; big data and big data analytics; text mining; apparel industry; data mining techniques; future technologies +
Footnote †: 2021: 11, 2323. Bangladesh, for example, a multi-story garment factory called Rana Plaza collapsed in April 2013. Even with immediate local and global rescue efforts, 1133 people were killed and 2438 injured [6]. The stock market reaction to retailers on this particular day of this tragedy was negative and remarkable [7]. As a result, managing supply chain risks in general have received a lot of attention from researchers and practitioners, especially in the global context [8]. In the fashion industry, purchasing decisions for retailers are traditionally made based on various factors such as budget, sales target and interest rate [9]. As market demand is highly impulsive, risk is inherent and it is crucial to include risk consideration in the decision-making framework [9]. On the other hand, the high level of outsourcing in overseas garment manufacturing makes it more dependent on supplier performance, supply chain and associated external risks. In the meantime, however, IT-based inventions have generated and captured more data, changing the business atmosphere. The new generation of analytical tools and data management greatly helps to improve planning and operational performance [10]. Data mining predictive analytics is a new and growing topic in the industry. It offers many opportunities to improve supply chain design and operations in areas such as transportation modes, warehouse design and location, demand forecasting, supplier evaluation and selection. [11]. Data mining covers more than simple numerical analysis, and text mining extends information management to linguistic data [10]. Data mining is about viewing configurations in raw data. Similarly, text mining is related to observing patterns in text and mine useful information for analytical purpose [12]. In other words, text mining is a computational practice of deriving useful information from the data set and presenting the result in a readable form for the next application. This helps us to create a database of configured requirements via data crawling approaches, identify the pattern of a large amount of data and generate useful information and insights by consolidating various factors to make informed decisions [13]. Text mining can be used to derive information to provide summaries for the words that appear in the documents and allow companies to use the research base more in an effective way [14].