Matching Items (2)
Description
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.
ContributorsUribes-Yanez, Diego (Co-author) / Liu, Jessica (Co-author) / Taylor, Todd (Thesis director) / Gentile, Erica (Committee member) / Department of Economics (Contributor) / Department of Information Systems (Contributor) / Department of Marketing (Contributor) / School of International Letters and Cultures (Contributor) / School of Life Sciences (Contributor) / Department of Supply Chain Management (Contributor) / Barrett, The Honors College (Contributor)
Created2017-05
Description
The goal of this thesis was to provide in depth research into the semiconductor wet-etch market and create a supplier analysis tool that would allow Company X to identify the best supplier partnerships. Several models were used to analyze the wet etch market including Porter's Five Forces and SWOT analyses. These models were used to rate suppliers based on financial indicators, management history, market share, research and developments spend, and investment diversity. This research allowed for the removal of one of the four companies in question due to a discovered conflict of interest. Once the initial research was complete a dynamic excel model was created that would allow Company X to continually compare costs and factors of the supplier's products. Many cost factors were analyzed such as initial capital investment, power and chemical usage, warranty costs, and spares parts usage. Other factors that required comparison across suppliers included wafer throughput, number of layers the tool could process, the number of chambers the tool has, and the amount of space the tool requires. The demand needed for the tool was estimated by Company X in order to determine how each supplier's tool set would handle the required usage. The final feature that was added to the model was the ability to run a sensitivity analysis on each tool set. This allows Company X to quickly and accurately forecast how certain changes to costs or tool capacities would affect total cost of ownership. This could be heavily utilized during Company X's negotiations with suppliers. The initial research as well the model lead to the final recommendation of Supplier A as they had the most cost effective tool given the required demand. However, this recommendation is subject to change as demand fluctuates or if changes can be made during negotiations.
ContributorsSchmitt, Connor (Co-author) / Rickets, Dawson (Co-author) / Castiglione, Maia (Co-author) / Witten, Forrest (Co-author) / Simonson, Mark (Thesis director) / Hertzel, Michael (Committee member) / Department of Finance (Contributor) / Department of Economics (Contributor) / Department of Information Systems (Contributor) / Department of Supply Chain Management (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / School of Accountancy (Contributor) / WPC Graduate Programs (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12