A global exploration of Big Data in the supply chain. Issue 8 (5th September 2016)

• Record Type: Journal Article
• Authors: Richey, Robert Glenn; Morgan, Tyler R.; Lindsey-Hall, Kristina; Adams, Frank G.
• Is Part Of: International journal of physical distribution & logistics management. Volume 46:Issue 8(2016)
• Page Start: 710
• Page End: 739
• Publication Date: 2016-09-05
• Subjects: Big Data -- Governance -- Effectiveness -- Relationships -- Efficiency -- Integration -- Global -- Transparency; Physical distribution of goods -- Management -- Periodicals; Business logistics -- Periodicals; Materials management -- Periodicals; 658.788
• DOI: 10.1108/IJPDLM-05-2016-0134
• Languages: English
• ISSNs: 0960-0035

Abstract

Abstract : Purpose: Journals in business logistics, operations management, supply chain management, and business strategy have initiated ongoing calls for Big Data research and its impact on research and practice. Currently, no extant research has defined the concept fully. The purpose of this paper is to develop an industry grounded definition of Big Data by canvassing supply chain managers across six nations. The supply chain setting defines Big Data as inclusive of four dimensions: volume, velocity, variety, and veracity. The study further extracts multiple concepts that are important to the future of supply chain relationship strategy and performance. These outcomes provide a starting point and extend a call for theoretically grounded and paradigm-breaking research on managing business-to-business relationships in the age of Big Data. Design/methodology/approach: A native categories qualitative method commonly employed in sociology allows each executive respondent to provide rich, specific data. This approach reduces interviewer bias while examining 27 companies across six industrialized and industrializing nations. This is the first study in supply chain management and logistics (SCMLs) to use the native category approach. Findings: This study defines Big Data by developing four supporting dimensions that inform and ground future SCMLs research; details ten key success factors/issues; and discusses extensive opportunities for future research. Research limitations/implications: This study provides a central grounding of the term, dimensions, and issues related to Big Data in supply chain research. Practical implications: Supply chain managers are provided with a peer-specific definition and unified dimensions of Big Data. The authors detail key success factors for strategic consideration. Finally, this study notes differences in relational priorities concerning these success factors across different markets, and points to future complexity in managing supply chain and logistics relationships. Originality/value: There is currently no central grounding of the term, dimensions, and issues related to Big Data in supply chain research. For the first time, the authors address subjects related to how supply chain partners employ Big Data across the supply chain, uncover Big Data's potential to influence supply chain performance, and detail the obstacles to developing Big Data's potential. In addition, the study introduces the native category qualitative interview approach to SCMLs researchers.