Maintaining social distancing is crucial in all aspects of life in this pandemic situation. Stock updation is an important aspect in pharmacy to provide effective supply chain with the use of sensors. The availability of appropriate stock ensures that the product can be delivered to customer on time. In the advent of Enterprise Management System (ERM), most hospitals are integrating these software systems. However, manual effort is required for the updation of stocks in the ERM software which results in overhead costs such as payroll. In the existing model, if some of the old stocks are stocked for long time, it may lead to the waste of stocks. In order to avoid all of above problems, a Kanban planning based model has been built to implement the stock updation in pharmacy where all the hospitals can be benefited by reducing the manual work. This system also reduces the inadvertent errors caused by the workers and the number of employees and provide more effective supply chain to the hospital. It can enable designed to maintain the social distancing of pharmacists also enabling better storage their oxygen cylinders and vaccines in a particular place.

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Customers can submit reviews for numerous products on websites like Amazon and Flipkart. As e-commerce grows in popularity, so does the quantity of consumer reviews that a product receives. A single product may have hundreds of thousands of reviews, each of which may be lengthy and repetitious. As a result, computerised review summarization offers a lot of potential for assisting buyers in making quick selections about certain items. Because a single manufacturer may sell a variety of items. It is also beneficial for manufacturers to keep track of customer feedback and comments. The process of creating a summary from review sentences is known as review summarising.In this project, given a product review, a shorter version of the review is created while the sentiment and points are preserved. The tone of the review will also be determined, and a summary of sample favourable and bad product reviews will be generated. Web scraping is used to collect reviews from popular ecommerce websites. Natural Language Processing Toolkit and neural networks such as RNN (Recurrent Neural Network) are used to summarise. The RNN architecture is combined with the Seq2Seq model, which is an encoder-decoder architecture. The highest accuracy for sentiment analysis on Amazon Fine Food Reviews was found to be 91%.

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