Using natural language processing for supply chain mapping: a systematic review of current approaches

Abstract

Purpose: The COVID-19 crisis has shown that the global supply chains are not as resilient as expected. First investigations indicate that the main contributing factor is a lack of visibility into the supply chain's lower tiers. Simultaneously, the willingness to share data in the supply chain is low as companies mainly consider their data as proprietary. However, large amounts of data are available on the internet. The amount of this data is steadily increasing; however, the problem remains, that this data is hardly structured. Therefore, this paper investigates current approaches to use this data for supply chain transparency and derives further research directions. Methodology: The paper uses a systematic review of the literature followed by content analysis. The research process further follows established frameworks in the literature and is subdivided into distinct stages. Findings: Descriptive and clustering results show a fragmented research field, where current approaches disconnect from prior research. We classify the methods using a simple taxonomy and show developments from rule-based to supervised techniques and horizontal to vertical mining approaches. The techniques with rule-based-matching procedures mainly suffer from low recall. The current approaches do not satisfy yet essential requirements on supply chain mapping based on natural language. Originality: To the best of the authors' knowledge, no prior research has been attempted to review textual data usage for supply chain mapping. Therefore, this paper's main contribution is to fill this gap and add further evidence to the use of data-driven supply chain management methods.