Busby Manufacturing Company's Choice to Invest or Not to Invest - Example

BUSBY MANUFACTURING COMPANY Busby Manufacturing Company and Section # of Busby Manufacturing Company Findings: Invest or Not to Invest The model suggests that sales revenue will continue to increase over the period of 5 years at a constant rate of 15.5% while variable costs will increase by 8%. Moreover, it also demonstrates the returns that will continue to increase throughout the given period. Therefore, we believe that the investment is worth making. There is more to the findings, which also serves as a reason to invest in the project. As it is apparent from the model that the sale of each unit will contribute £22 gross profit to the Busby Company, this will keep on increasing till it will reach the 21st year of the operations, from where it will eventually decline. Moreover, based on the same assumptions and information, the profits will continue to rise up to £289 thousand till the 23rd year of the operations from a mere £32 thousand in the first year. After the 23rd year, the profits will go down rapidly, thus suggesting the life of the project not more than 27 or 28 years. However, the project will only start making losses in the 30th years of operations, a finding, which once again supports the execution of the project (Brigham & Houston, 1998). As the feasibility of the project is based on certain assumptions, one may feel that the assumptions are too optimistic. In order to remove this bias, we also evaluated the model under a conservative scenario, in which we plugged the growth rate of sales as 7% while the growth of variable costs was increased to 10% per year. Although the margins declined, which is natural in conservative scenarios, the project was found to be a feasible one. In the conservative scenario, the unit profit maximized in year 5, but the life of the project was not more than 15 years; however, in the case the project will start making losses in the 17th year of operations and onwards (Van Horne, 2008). Forecasting Sales The scatter diagrams for sales and each of the three advertisement methods shows a weak relationship between sales and billboard advertising while a very strong relationship can be found between sales and print ads. There was also a positive relationship between sales and advertisement on the TV, whose relationship was not very strong. In order to obtain correlation coefficient of each with respect to sales, regression analysis was ran through the data sets separately for all the methods, and it was found out that correlation coefficient of TV ads with respect to sales was 32%, which shows a weak relation between the sales; the correlation coefficient for the print ads was 92% showing a highly strong correlation between the two. However, the correlation for the billboard ads was found to be mere 7%, which shows its very weak but positive correlation to sales (Walpole, 1968). Moreover, we also applied the data with respect to multiple variables and found the relation of sales with the three advertisement platforms as following: S=7.348+0.127T+1.4025N+0.1045B (Budnick, 1979) Where S stands for Sales T stands for Television ads expense N stands for Newspaper & Magazines ads expense B stands for Billboards ads expense Since company has planned to spend advertising expense by allocating 14,500 on TV, 18,750 on newspapers, and 2,000 on billboards, sales have been forecasted to be 26697.85, i.e. 26,700 approximately. Due to small allocation on billboards whose influence on sales is negligible, combined with 96% coefficient of correlation, we are quite confident about the forecasting unless an extraordinary event occurs (Francis, 2004). Human Resource Issues In order to investigate the level of absences across the factories, statistical analyses were performed, and it was found out that an employee remains absent for 7.9 days on average. However, if we go deep down, although Carlisle and Manchester experience low level of absentee rate with 4.8 days and 4.6 days in a year respectively, the absenteeism rate faced in Newcastle is beyond average at 12.6 days in a year. Moreover, one can observe the consistency of these figures by looking at standard deviation, which is 2.5 for the Carlisle, 1.7 for Manchester while Newcastle has once again been the area of concern where standard deviation has hovered around 4.0. As the figures clearly depict the disorganized nature of Newcastle factory, it is therefore recommended that the HRM department shall start from Newcastle factory; this can be done by increasing accountability, discouraging absences and at the same time reinforcing the attitude that encourages attendance. In order to increase accountability, one can, for instance, ensure monitoring checks such as submitting relevant documents that can include the reason with supportive evidence that can state the reason of one’s absence (Walpole, 1968). Although there is no evidence of gender discrimination or an analysis regarding gender discrimination, if we look at the overall data of the company, where average salary of males roam around 17,860 while for females it roamed around 16797, the Carlisle factory seems to be discriminating women. The factory pays 17,638 to men on average as compared to 13,914 that it pays to women on average. Moreover, there has been lesser consistency among the pays of females, whose standard deviation is 3,258 as compared to that of males of 2,403. Therefore, it is recommended that HR department shall reconsider pay packages at the factory (Walpole, 1968) (Brigham & Houston, 1998). At the moment, there are 10.78% chances that a male picked at random earns salary above 20,000 while there are only 5.04% chances that the female picked at random was absent for more than 5 days. Moreover, there remained a probability of 12.75% that a male picked at random will earn more than 17,000 as well as remained absent for more than 9 days. Such shows that the absentees not only earn good salaries, but they are also part of the top management. (Walpole, 1968; Brigham & Houston, 1998) Production Department Given that minimum 200 units of Product X must be produced, the department can minimize costs by minimizing the production of Product X while it shall maximize the production of Product Y. Thus, 200 units of Product X and 300 units of Product Y shall be produced in order to minimize costs under the given conditions, which will amount to 31,000 that include extra cost of 4,000 to do extra time (Budnick, 1979). Production Department 1) Index of Yearly Average Paid (WAP) Year WAP BASE 1994 100.00 1995 103.75 1996 104.47 1997 109.52 1998 112.27 1999 100.72 2000 105.05 2001 125.83 2002 129.29 2003 137.37 2004 134.92 2005 145.31 2006 158.15 2007 163.64 2008 161.62 2009 176.05 2010 185.86 2011 193.94 (Turvey, 2004) 2) Graph of the WAP Index 3) Calculate a chain base index for the WAP. Give your answers to two decimal places. Year WAP CHAIN BASE 1994   1995 103.75 1996 100.70 1997 104.83 1998 102.50 1999 89.72 2000 104.30 2001 119.78 2002 102.75 2003 106.25 2004 98.21 2005 107.70 2006 108.84 2007 103.47 2008 98.77 2009 108.93 2010 105.57 2011 104.35 (Turvey, 2004) 4) Comment on what these indices tell you about recent changes in Weekly Average Paid. The base of the recent years depicts the fact that the employees are being paid well. In the recent years employees’ salary has grown by 6% on average, which is considerably higher than pre-2009 growth rate of 3%; thus, one can say that the employee demand is beyond what is just. (Budnick, 1979; Van Horne, 2008) 5) Calculate a simple index for Cost per Unit (CpU) and Sales per Unit (SpU) using 2010 as the base period. Give your answers to two decimal places. Year CPU BASE SPU 1994 25.00 47.06 1995 40.23 52.89 1996 40.23 58.82 1997 49.43 58.82 1998 51.72 58.82 1999 52.59 58.82 2000 58.62 64.71 2001 64.66 70.59 2002 71.84 70.59 2003 74.57 76.47 2004 79.60 82.35 2005 83.76 84.59 2006 85.20 85.37 2007 87.50 88.24 2008 87.93 94.12 2009 89.80 95.33 2010 100.00 100.00 2011 93.10 111.76 (Turvey, 2004) 6) Graph these indices. 7) Calculate a chain base index for the CpU and SpU. Give your answers to two decimal places. Year Chain CPU Chain SPU 1994     1995 160.92 112.40 1996 100.00 111.21 1997 122.86 100.00 1998 104.65 100.00 1999 101.67 100.00 2000 111.48 110.00 2001 110.29 109.09 2002 111.11 100.00 2003 103.80 108.33 2004 106.74 107.69 2005 105.23 102.72 2006 101.72 100.92 2007 102.70 103.36 2008 100.49 106.67 2009 102.12 101.29 2010 111.36 104.90 2011 93.10 111.76 (Turvey, 2004) 8) Comment on what these indices tell you about recent changes in cost per unit, and Sales per unit. Identify when the biggest year-on-year change occurred. The indices reveal the gloomy picture of the Busby when it comes to controlling costs and matching them with equal or more growth in the revenue. Not only the occurrence has of the growth rate of cost per exceeding the growth rate of sales per unit, has been more pervasive, but the changes such as that occurred in 1995 has been very severe. In 1995, Busby recorded the maximum change in the cost per unit price of 60.92% while the maximum change for sales per unit growth has also been recorded in 1995, which was just 12.4% if we compare to the growth of Cost per unit (Brigham & Houston, 1998). 7) Based on your results, discuss whether an increase of 7% in salary is reasonable. Given the gloomy scenario of the sales increase growth that has lagged behind the growth of cost per unit, it seems unfeasible to increase the salary of the employees. Moreover, the employees have already received a considerable growth of 6% in their salaries for the last 3 years, which confirms the unjust demand of the employees (Budnick, 1979; Van Horne, 2008). References Brigham, E. F., & Houston, J. F. (1998). Fundamentals of financial management. Fort Worth: Dryden Press. Budnick, F. S. (1979). Applied mathematics for business, economics, and the social sciences. New York: McGraw-Hill. Francis, A. (2004). Business mathematics and statistics. London: Thomson Learning. Turvey, R., & International Labour Office. (2004). Consumer price index manual: Theory and practice. Geneva: International Labour Office. Van Horne, J. (2008). Van Horne: Fundamentals Of Financial Management. Pearson Education UK. Walpole, R. E. (1968). Introduction to statistics. New York: Macmillan. Read More