It is a widely accepted fact that almost every research or business revolves around Data. Data from various business sectors has been growing sharply and the management of this massive amount of data is the biggest professional crunch these days. The notion of Big Data Analytics (BDA) is a prominent facet that delivers the best possible solution to decision-makers for efficiently handling the problems related to huge data. The key role of BDA in the area of Supply Chain Management (SCM), Logistics Management (LM), and Inventory Management (IM) is of utmost significance as it optimises the business operations by analyzing customer behaviour. Motivated with the promising paybacks of the BDA, a recent review from the year 2015–2019 is presented in this paper. Further, the significance of BDA in SCM, LM, and IM has been highlighted by studying 58 papers, which have been sorted after a detailed study of 260 papers, collected through the Web of Science (WoS) database. The findings and observations give state-of-the-art insights to scientists and business professionals by presenting an exhaustive list of the progress made and challenges left untackled in the field of BDA in SCM, LM, and IM.

Every industrial sector emits carbon emissions during production, which is always a major concern for environmental practitioners. To resolve this, innovative and smart ways are required that are not only cost-effective but also environmentally savvy. Moreover, the production processes are also prone to various imperfections, and thus the production of defectives is quite pragmatic. The handling of such defective products varies from industry to industry and type of products. The present study constructs an inventory model to handle the defective products via a proficient rework strategy that makes the product fit to be sold at the primary price. Here, the demand for the product is assumed to be dependent on the selling price and advertisements. The model also considers the energy usage during the production and the cost of carbon emissions while optimizing the production batch size and selling price. Optimality is proved graphically by using the software Mathematica 11.3.0. A comprehensive sensitivity analysis for all the parameters is performed to impart managerial insights and robustness to the model.