Mapping the market for remanufacturing: An application of “Big Data” analytics

Abstract

Remanufacturing is one of the most examined topics in the closed-loop supply chain (CLSC) literature. However, we still have limited knowledge on the characteristics of the market for remanufactured products. This study addresses this gap by using a big data analytics framework. We employ off-the-shelf, pre-trained vectors created with the Global Vectors for Word Representation (GloVe) word embedding method from a data set crawled from the Internet. The Louvain method subsequently provides us with clusters based on remanufacturing and related terms, without requiring human interactions. Our findings provide the following main insights. First, remanufacturing and related terms are associated with specific industries and products, among which printing equipment, automobiles and car parts, treadmills, consumer electronics, and household appliances. Among the terms capturing remanufacturing activity, remanufactured, reconditioned, and rebuilt are strongly associated with business-to-business and slow clockspeed products, while refurbished is mostly associated with business-to-consumer and fast clockspeed products. Second, original equipment manufacturers (OEMs) are much more salient than independent remanufacturers, and Japanese OEMs are especially well represented as players in the market for remanufacturing. Third, environmental concerns only appear weakly in the discourse surrounding product recovery, while consumers do seem to place emphasis on quality and price. In a final part of the study, we contrast the CLSC academic literature with the clusters obtained through our big data analysis, thereby identifying industries, products, and brands that are understudied. We also outline the practical implications of our work for managers involved in setting up a remanufacturing strategy, as well as regulators.