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ABSTRACT
Fashion and apparel supply chains are complex and volatile. Thiscauses uncertainties for supply chain decisions. For reducing the uncertainty, decision-maker use different information sources. Typically, historical sales data is consulted for enhancing the decision base. Customer preferences are mostly publicly accessible via different social media channels. Motivated by the increased availability of customer preferences, this paper proposesa process model for exploiting social media data for fashion andapparel supply chain decisions. Characteristics of fashion andapparel supply chains and social media data are illustrated. Basedon these characteristics, functional requirements for a processmodel are elaborated. In particular, the veracity feature is takeninto consideration. Following these requirements, a process modelconsisting of the three main layers: process, social media and textmining is developed. A case study is framed around conducted following the proposed process model. Two years of fashion blogdata is extracted, and dictionary-based keyword extraction, rulebased classification, as well as automatic frequency analyses, areconducted. The proposed process model enables structural andtargeted exploitation of social media data for fashion and apparel supply chain decisions.
Disclosure statement
No potential conflict of interest was reported by the author.