Textual Data Science for Logistics and Supply Chain Management

Abstract

Background: Researchers in logistics and supply chain management apply a multitude of methods. So far, however, the potential of textual data science has not been fully exploited to automatically analyze large chunks of textual data and to extract relevant insights. Methods: In this paper, we use data from 19 qualitative interviews with supply chain experts and illustrate how the following methods can be applied: (1) word clouds, (2) sentiment analysis, (3) topic models, (4) correspondence analysis, and (5) multidimensional scaling. Results: Word clouds show the most frequent words in a body of text. Sentiment analysis can be used to calculate polarity scores based on the sentiments that the respondents had in their interviews. Topic models cluster the texts based on dominating topics. Correspondence analysis shows the associations between the words being used and the respective managers. Multidimensional scaling allows researchers to visualize the similarities between the interviews and yields clusters of managers, which can also be used to highlight differences between companies. Conclusions: Textual data science can be applied to mine qualitative data and to extract novel knowledge. This can yield interesting insights that can supplement existing research approaches in logistics and supply chain research.