As British economist Ronald Coase said: "Torture the data and it will confess to anything". Well, it's true when it comes to the business organization to make a better profit from the existing system by analyzing a historical data set and inculcating the knowledge gained in taking efficient business decisions. On the daily basis, lots of transactions are being done while procuring and storing materials in any industry which led to the generation of large datasets, which if analyzed can lead to impeccable insights and nail-biting inferences. In this research work, the aim is to avail KPIs to the stakeholders on the live dashboard to indicate the in-bound supply chain management performance. KPI are the key performance indicators that help the business heads to keep track of the organization's performance based on some crucial metrics and helps them to understand the core functionality of the system. The project also aims to predict the estimated time of arrival stock for better stock management in the enterprise. With the recent advancement in technology, it is easier to store and process such huge data. for this research work, we have used Hadoop infrastructure which has HDFS to store data and hive to query data. For analyzing and building data models, that will be the data set in our case for estimating the time of arrival of stock, Spark, and machine learning modules has been used. Since an organization which is entirely based on the buying and selling of raw materials, procuring it and converting it into a useful product is channelized by how well the inventory is managed, So it is important to make the data in terms of stock availability, space required for stock, date of arrival of individual stock should be transparent to everyone involved in the process to optimize the stock management system. Hence it is crucial to building a dashboard which can be presented to the stakeholders as a pamphlet to display the present scenario of stock and inventory system which will guide them to take better business decision