Zimbabwe's economic crisis has spun over decades since the late 90s. The ordeal began with hyperinflation between 1999 and 2008. During this time, the country was in debt and the government kept printing money to pay off the debt and take care of fiscal obligations. As a result, the economy has undergone some unstable phases which have made it difficult for citizens to comprehend how money works around the world. This project identifies the key events leading to Zimbabwe's economic struggles; describes the challenges of inadequate financial education; analyzes the role of financial literacy in alleviating Zimbabwe's economic crisis; acknowledges that financial literacy is a necessary foundation for creating financial independence amongst citizens, but is ineffective in the absence of financial inclusion; suggests blockchain technology as a sustainable means to mobilize both financial education and inclusion; provides recommendations for successful development of financial education and inclusion and introduces our plan to start an initiative that promotes financial independence amongst young Zimbabweans. It is without a doubt that public financial education and inclusion programs become a top priority for Zimbabweans in order to revive the economy. The conclusion is drawn from the idea that when individuals are empowered to be self-sufficient, they can intentionally or unintentionally contribute to economic growth by improving their standards of living and that of those around them.

The purpose of this project is to provide our client with a tool to mitigate Company X's franchise-wide inventory control problem. The problem stems from the franchises' initial strategy to buy all inventory as customers brought them in without a quantitative way for buyers to evaluate the store's inventory needs. The Excel solution created by our team serves to provide that evaluation for buyers using deseasonalized linear regression to forecast inventory needs for clothing of different sizes and seasons by month. When looking at the provided sales data from 2014-2016, there was a clear seasonal trend, so the appropriate forecasting model was determined by testing 3 models: Triple Exponential Smoothing model, Deseasonalized Simple Linear Regression, and Multiple Linear Regression.The model calculates monthly optimal inventory levels (current period plus future 2 periods of inventory). All of the models were evaluated using the lowest mean absolute error (meaning best fit with the data), and the model with best fit was Deseasonalized Simple Linear Regression, which was then used to build the Excel tool. Buyers can use the Excel tool built with this forecasting model to evaluate whether or not to buy a given item of any size or season. To do this, the model uses the previous year's sales data to forecast optimal inventory level and compares it to the stores' current inventory level. If the current level is less than the optimal level, the cell housing current value will turn green (buy). If the currently level is greater than or equal to optimal level or less than optimal inventory level*1.05, current value will turn yellow (buy only if good quality). If the current level is greater than optimal level*1.05 current level will be red (don't buy). We recommend both stores implement a way of keeping track of how many clothing items held in each bin to keep more accurate inventory count. In addition, the model's utility will be of limited use until both stores' inventories are at a level where they can afford to buy. Therefore, it is in the client's best interest to liquidate stale inventor into store credit or cash In the future, the team would also like to develop a pricing model to better meet the needs of the client's two locations.