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

Abstract

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.

Explaining the competitive advantage generated from Analytics with the knowledge-based view: the example of Logistics and Supply Chain Management

“…In accordance, LSCM is considered as an early adopter of Analytics (Davenport 2009) and is considered as a data-rich field with promising returns from Analytics (Kiron et al 2012;Jeske et al 2013). Despite these circumstances, many organizations in LSCM show reluctance towards Analytics adoption (Thieullent et al 2016;Kersten et al 2017;Brinch et al 2018), although this audience might develop greater interest based on research insights into Analytics associated with their own field, and may eventually be persuaded regarding the value and benefits.…”

Traceability in Textile and Clothing Supply Chains: Classifying Implementation Factors and Information Sets via Delphi Study

“…(See detailed explanation in [18]). Delphi has been adapted in numerous research studies designed for needs assessments, policy determinations, and forecasting and program planning [19][20][21]. In the area of supply chain management, studies highlighting applications of the Delphi technique for investigating factors influencing decision making can be found in [22][23][24][25].…”

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

“…However, with the variety of tasks requiring support come a variety of applications that are hyper-specialized and also provide a challenge for organizations in LSCM [10]. Further, adoption of SCA in organizations in LSCM is slow, unwillingness to share data with supply chain partners is widespread, and organizations experience barriers [12,13,35].…”

“…As opposed to the paralyzing complexity described above, the missing literacy concerning data and analytics is expressed in missing knowledge and creativity. The lack of knowledge about how to approach and utilize data has been repeatedly stressed in the literature [10,[12][13][14]35,36]. This includes the identification of data most suitable for analytics, understanding which data is useful and which is useless, experience with relevant technologies, ideas about what to do with the available data, knowledge on how to transform data into information for decision-making, and how to drive the supply chain with data.…”