Purpose The value of big data in supply chain management (SCM) is typically motivated by the improvement of business processes and decision-making practices. However, the aspect of value associated with big data in SCM is not well understood. The purpose of this paper is to mitigate the weakly understood nature of big data concerning big data’s value in SCM from a business process perspective. Design/methodology/approach A content-analysis-based literature review has been completed, in which an inductive and three-level coding procedure has been applied on 72 articles. Findings By identifying and defining constructs, a big data SCM framework is offered using business process theory and value theory as lenses. Value discovery, value creation and value capture represent different value dimensions and bring a multifaceted view on how to understand and realize the value of big data. Research limitations/implications This study further elucidates big data and SCM literature by adding additional insights to how the value of big data in SCM can be conceptualized. As a limitation, the constructs and assimilated measures need further empirical evidence. Practical implications Practitioners could adopt the findings for conceptualization of strategies and educational purposes. Furthermore, the findings give guidance on how to discover, create and capture the value of big data. Originality/value Extant SCM theory has provided various views to big data. This study synthesizes big data and brings a multifaceted view on its value from a business process perspective. Construct definitions, measures and research propositions are introduced as an important step to guide future studies and research designs.

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.

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Purpose While big data creates business value, knowledge on how value is created remains limited and research is needed to discover big data’s value mechanism. The purpose of this paper is to explore value creation capabilities of big data through an alignment perspective. Design/methodology/approach The paper is based on a single case study of a service division of a large Danish wind turbine generator manufacturer based on 18 semi-structured interviews. Findings A strategic alignment framework comprising human, information technology, organization, performance, process and strategic practices are used as a basis to identify 15 types of alignment capabilities and their inter-dependent variables fostering the value creation of big data. The alignment framework is accompanied by seven propositions to obtain alignment of big data in service processes. Research limitations/implications The study demonstrates empirical anchoring of how alignment capabilities affect a company’s ability to create value from big data as identified in a service supply chain. Practical implications Service supply chains and big data are complex matters. Therefore, understanding how alignment affects a company’s ability to create value of big data may help the company to overcome challenges of big data. Originality/value The study demonstrates how value from big data can be created following an alignment logic. By this, both critical and complementary alignment capabilities have been identified.

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