Abstract
Supply chain finance (SCF) is dynamic approach in banks’ proprietary platform and is becoming more flexible and transparent through ingenious technological solutions of effectively integrating the flows of logistics and capital into the financial service provider industry. The paper aims to utilize the TF-IDF technique in order to make greater contributions to future SCF researches and discusses different scopes of SCF and their relation to roll out SCF solutions. In efforts to demonstrate the importance role that frequency-inverse document frequency (TF-IDF) plays in retrieving information using various keywords within various document (otherwise known as text mining), this study will attempt to showcase the research findings from more than 250 academic database which focuses on supply chain finance between seller and buyer. In presenting the two leading components that impact the analysis of text mining, namely the mechanism and technological innovation of SCF. Through systematic review of the SCF is concerned with financial liquidity and the viability of SCF, this research will analyze the keyword frequencies and assess the significance of terms (or words) within this document collection separately. Finally, this report explores possible solutions for future research based on the current framework and data analysis in order to achieve capital gain, sustainability and viable replenishments.
Highlights
Text mining is an emerging field of study based on the statistical and the “natural language processing” (NLP) analytical systems where one would extract and analyze keywords in various articles (Dias et al, 2011)
The applications of Supply Chain Finance (SCF) has added to the gradually growing database of systematic literature reviews which allow for the opportunities for various perspectives to address most, if not all the technical and social science challenges associated with using text mining and SCP
This paper aims to utilize the that frequency-inverse document frequency (TF-IDF) technique in order to make greater contributions to future SCF researches
Summary
Text mining is an emerging field of study based on the statistical and the “natural language processing” (NLP) analytical systems where one would extract and analyze keywords in various articles (Dias et al, 2011). We utilized the text mining field of study to handle the raw data in various articles. SCF plays an essential part in optimizing and managing the working capital and liquidity investment within the supply chain “process and transactions” (Kwon and Kim, 2018). The purpose of the study is using TF-IDF to evaluate why these terms (or words) are so critical and valuable in document collection. This paper aims to utilize the TF-IDF technique in order to make greater contributions to future SCF researches. Through different scopes of SCF and their relation to roll out SCF solutions, providing an integral scope, with clear requirement for supply chain collaboration and a comprehensive purpose
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