As Big Data has undergone a transition from being an emerging topic to a growing research area, it has become necessary to classify the different types of research and examine the general trends of this research area. This should allow the potential research areas that for future investigation to be identified. This paper reviews the literature on 'Big Data and supply chain management (SCM)', dating back to 2006 and provides a thorough insight into the field by using the techniques of bibliometric and network analyses. We evaluate 286 articles published in the past 10 years and identify the top contributing authors, countries and key research topics. Furthermore, we obtain and compare the most influential works based on citations and PageRank. Finally, we identify and propose six research clusters in which scholars could be encouraged to expand Big Data research in SCM. We contribute to the literature on Big Data by discussing the challenges of current research, but more importantly, by identifying and proposing these six research clusters and future research directions. Finally, we offer to managers different schools of thought to enable them to harness the benefits from using Big Data and analytics for SCM in their everyday work.

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Purpose The purpose of this paper is to illustrate the systematic role played by Six Sigma methodology in improving the quality of healthcare. The literature review identifies the relevant opportunities for successful introduction and development of Six Sigma approach in healthcare sector. Design/methodology/approach A systematic methodology to identifying literature on Six Sigma in healthcare is presented. Web of Science, Medline, Emerald Insight, ASQ and ProQuest databases (1998-2016) were searched, and 68 papers of fair methodological quality were identified. Findings The findings of the systematic review reveal a growing interest in research on Six Sigma adoption in healthcare. The findings indicate that Six Sigma applications in healthcare have been focused on the entire hospital with no real focus on a particular department or function. The key findings on benefits, success factors, challenges and common tools of Six Sigma from the existing literature are also presented in the paper. Research limitations/implications The papers included in the systematic review were peer-reviewed papers available in English. Due to these limitations, relevant papers may have been excluded. Moreover, the authors have excluded all conference and white papers for their inclusion in this study. Originality/value This paper can serve as a guide on how Six Sigma approach can be applied to improve the quality of healthcare. The authors also believe that this is possibly the most comprehensive systematic literature review on the topic and will set the foundation for various research avenues based on the key findings of this study.

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Purpose The purpose of this paper is to review the existing literature on supply chain performance measures and metrics (PMMs). It provides a critical evaluation of 234 articles published in past 24 years. Design/methodology/approach The paper examines the studies published from 1991 to 2014 by adopting the bibliometric technique of citation and co-citation analysis. Findings The analysis of the results indicate that the number of articles on supply chain PMMs is increasing at its fastest pace in the past few years. Furthermore, the study identifies some of the most influential articles on performance measurement and metrics. Finally, it concludes that there has been a transition from traditional to more sophisticated performance measurement system. Research limitations/implications This study focuses only on supply chain performance measurement and metrics and excludes research on performance management and control. Thus, researchers may explore and extend this area of research. Originality/value To the knowledge of the authors, this is the first study to review the literature on supply chain PMMs by using citation and co-citation analysis. The study includes 234 articles over the time of 24 years (1991-2014).