A global exploration of Big Data in the supply chain

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.

Overcoming Barriers in Supply Chain Analytics—Investigating Measures in LSCM Organizations

“…Following the cycle of an adoption process, an early barrier to adopting analytics is the approachability of analytics. Efforts to adopt and employ analytics may be slowed down by a lack of consensus regarding the terms surrounding the concept of analytics and the subsequent confusion [13,14] as well as the perception of complexity and the difficulty of its management [11,12]. This is accompanied by a constant change of technologies and methods, which organizations would need to be able to keep up with [6,10].…”

“…Scholars and organizational reports alike have demonstrated a variety of barriers organizations face and the resulting effect of low implementation rates. Scholars have presented barriers ranging from management failures and lack of analytical knowledge to unwillingness to commit IT resources [9][10][11][12][13][14]. Organizational reports highlight the barriers of cost, insufficient data, and not achieving the aspired to benefits [15][16][17].…”

Big Data Research on the Green Internet of Things in New Smart-Logistics

“…Authors mentioning large data applications in logistics include [14] and [18][19][20] [14] assures that the BDA is particularly relevant for logistics and supply chain management, as it provides the tools for decision making in increasingly global and dynamic environments. However, this same relevance creates enormous challenges for organizations that want to take advantage of massive, massive data flows, such as: market trends, customer buying patterns, maintenance cycles, and ways to reduce costs and enable more targeted business decisions [20]. [19] defines mega-data in four (4) dimensions: volume, speed, variety and veracity.…”

Untitled

“…These shows big data and predictive analytics practical, relevant value. However, recently [Richey et al 2016] empirically explored the perception of Supply Chain Managers about big data, and practitioners have recognized the Volume, Variety, Velocity and Veracity characteristics of Big Data, but there is no harmony attained regarding its definition. This author defines, BDPA capabilities in the context of Supply Chain Management (SCM) as: "The ability of organizations to collect and organize supply chain data from heterogeneous systems, distributed across organizational boundaries, analyze it either batch-wise or real-time or near real-time, and visualize it intuitively to create proactive supply chain system and support decision making".…”

“…While a few literature review papers are linking BDPA and Supply Chain Addo-Tenkorang & Helo 2016;Arunachalam et al 2017;Richey et al 2016;Fawcett & Waller 2014], these reviews have not discussed the positive impact of BDPA on SCM performances in the innovation and sustainability context.…”