2233 Words9 Pages

Executive Summary:
Mark X Company is the maker of farm and specialty trailers. The company has fallen on difficult times and wants their bank, Wells Fargo, to extend a loan for working capital and an expansion of their current facilities. Mark X feels this will help the company increase sales and therefore improve their financial situation. Wells Fargo is leery of extending any additional funds to Mark X based on their current financial situation and their most recent liquidity and debt ratios.
For the background of this case we have used anticipated ratios to complete forecast tables and also some pro forma financial statements. These tools should help us to analyze the position and sensitivity of the company. We also will use*…show more content…*

If the scale is not accurately represented then the graphs can be misleading and would easily lead an analyst to be more critical to changes and also influence the decisions based on inaccurate information. The first change that we demonstrated was the percentage increase in sales. For 1993 we looked at a range of sales increases from 5% up to 15%, with 10% being the amount that the company forecasted. Once we completed this table we modeled a graph from the results and found that the results from changing the sales growth percentage we not all that sensitive when computing the effect on net income: As you can see from the graph above, the effect on net income from changing the sales growth by a percent at a time is not as sensitive as people might believe. The line on the graph is nearly flat which would lead us to believe that the information is not extremely sensitive to possible increases and decreases. We used the same type of data table for 1994 and varied the sales growth from 10% to 20%, with 15% being the projected percentage, and modeled the data that we received from the analysis in a graph which showed the following results: From the graph above we have determined that the changes in the sales growth rate in 1994 are slightly more sensitive that those in 1993, but the graph over all is not very steep which shows that

If the scale is not accurately represented then the graphs can be misleading and would easily lead an analyst to be more critical to changes and also influence the decisions based on inaccurate information. The first change that we demonstrated was the percentage increase in sales. For 1993 we looked at a range of sales increases from 5% up to 15%, with 10% being the amount that the company forecasted. Once we completed this table we modeled a graph from the results and found that the results from changing the sales growth percentage we not all that sensitive when computing the effect on net income: As you can see from the graph above, the effect on net income from changing the sales growth by a percent at a time is not as sensitive as people might believe. The line on the graph is nearly flat which would lead us to believe that the information is not extremely sensitive to possible increases and decreases. We used the same type of data table for 1994 and varied the sales growth from 10% to 20%, with 15% being the projected percentage, and modeled the data that we received from the analysis in a graph which showed the following results: From the graph above we have determined that the changes in the sales growth rate in 1994 are slightly more sensitive that those in 1993, but the graph over all is not very steep which shows that

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