A bibliometric study using 1992 to 2021 database of the Science Citation Index Expanded was carried out to identify which are the current trends in textile wastewater treatment research. The study aimed to analyze the performance of scholarly scientific communications in terms of yearly publications/citations, total citations, scientific journals, and their categories in the Web of Sciences, top institutions/countries and research trends. The annual publication of scientific articles fluctuated in the first ten years, with a steady decrease for the last twenty years. An analysis of the most common terms used in the authors’ keywords, publications’ titles, and KeyWords Plus was carried out to predict future trends and current research priorities. Adsorbent nanomaterials would be the future of wastewater treatment for decoloration of the residual dyes in the wastewater. Membranes and electrolysis are important to demineralize textile effluent for reusing wastewater. Modern filtration techniques such as ultrafiltration and nanofiltration are advanced membrane filtration applications.

The goal of this article is to examine opportunities and show the approach of using big data analytics to boost productivity in the case of clothing manufacturing factories in a sustainable way. The Bangladeshi manufacturing industry is mainly dominated by the apparel and textile sector for a long time now, and this has seen a large growth over the years. However, this industry is still far from using the latest technologies to improve productivity even further and bring sustainability. Usually, manufacturing operations involve the generation of a large amount of structured or unstructured, useful or non-useful data on a daily basis. This huge amount of information is known as big data, which is difficult to handle by using traditional data management and analysis tools. However, with the help of big data analytics used in a proper method, the collected information can be used to track insufficiencies in different areas of manufacturing operations. This research is conducted based on a similar idea where problems are identified, and production data collected from a garments manufacturing plant in Bangladesh are analyzed. Based on real factory data, several hypothetical frameworks were developed to implement and analyse the production data with the help of big data analytics, computerized sewing machines, radio frequency identification (RFID) tags and passive infrared sensors. The paper also shows an estimated implementation cost and return on investment of the suggested approach.