When the data set is related to global supply chain, it is very important to have an analytical study to find out the key causes that lead to profit making or incurring losses by exporting nations. It can identify known risks and can predict future by spotting patterns and trends, and as mathematical models and data infrastructures have improved predictive modeling and machine learning has become powerful in deriving insights through visuals. Over the last three decades, global supply chains (GSCs) have increasingly gained importance in linking developing countries to international markets. Today a substantial share of the production processes of GSCs is taking place in developing countries. For developing countries and their enterprises, GSCs offer opportunities as well as challenges.

1. The factors that affect the performance in a GSCS.
2. Countries which fare better as a export nation.
3. Trend of global supply chain country wise, major factors and dependencies that affect the logistic process.

Supply Chain is a tricky business. One missing entity or a lack of synchronization can break the entire chain and mean millions in losses for a company. However, the use of analytics in this domain is resolving several pain points in supply chain management at the strategic, operational, and tactical levels. 

Tools Used:

1. R studio
2. R shiny
3. Tableau
4. MS word