Purpose The purpose of this paper is to share the experiences and perspectives of three practitioners from two continents on the subject of Lean Six Sigma (LSS) from both academic and industrial viewpoints. The authors of the paper have each been working on the topic of LSS over the past 15 years and have contributed over 150 journal and conference papers to the topics of lean and Six Sigma. Design/methodology/approach The approach is to synthesize the practical experiences and research conducted by three authorities on the topic of LSS. In addition, relevant secondary data have also been used in the sections where and when appropriate. Findings The authors initially present the history of LSS emphasizing the importance of integration of the two most effective process excellence methodologies over the past 30 years. The authors also report the current trends of LSS in organizations as well as the emerging future trends. They argue that LSS will continue to grow and evolve across the globe for several years. Practical implications The paper is intended to be equally useful to both academics and practitioners who are interested on the topic of LSS. From a pure practical standpoint, the paper provides an overview of the past, present and future trends of LSS as a powerful business strategy and problem-solving methodology for all industrial sectors, irrespective of their size and nature. The documentation of the history and recent developments in LSS should be useful to researchers in academia. Originality/value In authors’ best knowledge, there are no recent journal articles which cover all the three of these aspects; the past, the present and the future of LSS. This paper presents the above three aspects in a unique manner and addresses the gap between the current state and future directions of LSS.

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A growing number of students are completing undergraduate degrees in statistics and entering the workforce as data analysts. In these positions, they are expected to understand how to utilize databases and other data warehouses, scrape data from Internet sources, program solutions to complex problems in multiple languages, and think algorithmically as well as statistically. These data science topics have not traditionally been a major component of undergraduate programs in statistics. Consequently, a curricular shift is needed to address additional learning outcomes. The goal of this paper is to motivate the importance of data science proficiency and to provide examples and resources for instructors to implement data science in their own statistics curricula. We provide case studies from seven institutions. These varied approaches to teaching data science demonstrate curricular innovations to address new needs. Also included here are examples of assignments designed for courses that foster engagement of undergraduates with data and data science.

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