Purpose: Inconsistent research output makes critical literature reviews crucial tools for assessing and developing the knowledge base within a research field. Literature reviews in the field of supply chain management (SCM) are often considerably less stringently presented than other empirical research. Replicability of the research and traceability of the arguments and conclusions call for more transparent and systematic procedures. The purpose of this paper is to elaborate on the importance of literature reviews in SCM.Design/methodology/approach: Literature reviews are defined as primarily qualitative synthesis. Content analysis is introduced and applied for reviewing 22 literature reviews of seven sub‐fields of SCM, published in English‐speaking peer‐reviewed journals between 2000 and 2009. A descriptive evaluation of the literature body is followed by a content analysis on the basis of a specific pattern of analytic categories derived from a typical research process.Findings: Each paper was assessed for the aim of research, the method of data gathering, the method of data analysis, and quality measures. While some papers provide information on all of these categories, many fail to provide all the information. This questions the quality of the literature review process and the findings presented in respective papers.Research limitations/implications: While 22 literature reviews are taken into account in this paper as the basis of the empirical analysis, this allows for assessing the range of procedures applied in previous literature reviews and for pointing to their strengths and shortcomings.Originality/value: The findings and subsequent methodological discussions aim at providing practical guidance for SCM researchers on how to use content analysis for conducting literature reviews.

Purpose: Despite the variety of supply chain management (SCM) research, little attention has been given to the use of Big Data Analytics for increased information exploitation in a supply chain. The purpose of this paper is to contribute to theory development in SCM by investigating the potential impacts of Big Data Analytics on information usage in a corporate and supply chain context. As it is imperative for companies in the supply chain to have access to up-to-date, accurate, and meaningful information, the exploratory research will provide insights into the opportunities and challenges emerging from the adoption of Big Data Analytics in SCM.Design/methodology/approach: Although Big Data Analytics is gaining increasing attention in management, empirical research on the topic is still scarce. Due to the limited availability of comparable material at the intersection of Big Data Analytics and SCM, the authors apply the Delphi research technique.Findings: Portraying the emerging transition trend from a digital business environment, the presented Delphi study findings contribute to extant knowledge by identifying 43 opportunities and challenges linked to the emergence of Big Data Analytics from a corporate and supply chain perspective.Research limitations/implications: These constructs equip the research community with a first collection of aspects, which could provide the basis to tailor further research at the nexus of Big Data Analytics and SCM.Originality/value: The research adds to the existing knowledge base as no empirical research has been presented so far specifically assessing opportunities and challenges on corporate and supply chain level with a special focus on the implications imposed through Big Data Analytics.