With Zena Wood and Tim Williams
We describe an open-source approach to the smart literature review (SLR) to demonstrate the use of machines in academic research projects. The method uses data pipelines for search breadth across multiple databases. Code and human checks are identified and tested throughout the stages of the smart literature review such as data cleaning, testing for false positives, unsupervised machine learning for topic identification and summarising topic areas. We will demonstrate the SLR applied to supply chain and logistics research and we use python litstudy, natural language processing and dashboards.