Practitioners understanding of big data and its applications in supply chain management

Purpose: Big data poses as a valuable opportunity to further improve decision making in supply chain management (SCM). However, the understanding and application of big data seem rather elusive and only partially explored. The purpose of this paper is to create further guidance in understanding big data and to explore applications from a business process perspective. Design/methodology/approach: This paper is based on a sequential mixed-method. First, a Delphi study was designed to gain insights regarding the terminology of big data and to identify and rank applications of big data in SCM using an adjusted supply chain operations reference (SCOR) process framework. This was followed by a questionnaire-survey among supply chain executives to elucidate the Delphi study findings and to assess the practical use of big data. Findings: First, big data terminology seems to be more about data collection than of data management and data utilization. Second, the application of big data is most applicable for logistics, service and planning processes than of sourcing, manufacturing and return. Third, supply chain executives seem to have a slow adoption of big data. Research limitations/implications: The Delphi study is explorative by nature and the questionnaire-survey rather small in scale; therefore, findings have limited generalizability. Practical implications: The findings can help supply chain managers gain a clearer understanding of the domain of big data and guide them in where to deploy big data initiatives. Originality/value: This study is the first to assess big data in the SCOR process framework and to rank applications of big data as a mean to guide the SCM community to where big data is most beneficial.