An empirical investigation on Big data in Supply chains: Case from the Smart Connected Products

Data has always been at the centre of planning activities, while recent advancement in computing capacity presents further opportunities to the supply chain context. Despite the research community holds high expectations on the potential of big data and advanced analytics, little empirical evidences is brought to support the conceptual development in this field. In this regard, building on the context of “smart connected products”, our paper aims at shedding light on how big data will reshape supply chains and supply chain management in the manufacturing industry. This paper is based on a single-embedded case study, where multiple sources of evidence are granted by collecting data from semi-structured interviews and secondary materials. Result shows that manufacturing companies can benefit from the use of big data from smart connected products mainly leveraging on data timeliness (i.e. velocity) and data richness (i.e. variety and volume), that, in turn, affect supply chain planning, as well as supply chain configuration and relationship. We further highlight that, such influence is dependent on data quality (i.e. veracity, value) and data ownership determined by the type of product (i.e. component or final product). Our manuscript enriches empirical evidence to literature on how the characteristics and management of big data from smart connected product will affect supply chain management. The resulted conceptual framework can be used as the basis for future studies towards theory testing.