Analyzing the Future Economic State of South Carolina - Case Study Example

? Predictive Sales Report of the Table of Contents Table of Contents 2 PART I 3 Part II 12 References 15 PART I Introduction The economic satiation of a country or state significantly impacts its business environment. A number of factors are directly associated with the economic scenario of a state (Wongswan, 2006). For example consumer spending behavior, the operations of a company and even the marketing aspects are directly related to the financial situation. Irrespective of the size of business, the impact of financial scenario in business is almost identical. However, large companies generally have better capacity in dealing with credit crunches. The upswings in the economy usually offer a rush of expanding the business; on the contrary, downward economy results in low business transaction. In strong economies, business enjoys greater prosperity. Along with that, it is also necessary to consider the other economical indicators of a state in order to exactly determine the scenario (Albuquerque & Vega, 2009). In this report, the economic condition of South Carolina will be highlighted and on the basis of that the retailer will be recommended to stock the inventory. In addition to recommend the appropriate stock inventory, the study also intends to formulate a predictive sales report. In order to shed light on the economic condition of the state, the most important factor that will be considered is the unemployment rate of the state. The unemployment rate will be the factor that will be stressed more. Studies have shown that the unemployment rate of a country has significant impact on the overall trade of a country. Unemployment Rate as an Economic Indicator During recessionary phases, the unemployment rate rises to its maximum level. For example, the unemployment rate during booming and normal phases is far below in comparison with the unemployment rate during recession. The 1975 depression saw global unemployment rate reaching 8.9%. The unemployment scenario has been aggravated by the surge in unemployment rate during the recent months as well. Furthermore, due to weakening recovery, the pressure of modifying the policies pertaining to unemployment reduction is intensifying (Yahoo, 2010). The unemployment rate of a state simply represents the percentage of people who are yet to be employed and is actively seeking to work. The unemployment rate is also considered as the relative measure of the jobs available for employment and the tightness or slack of the labor market. Therefore, the unemployment rate of a state plays imperative roles in presenting the economic scenario of state and is acting as an alarm for the administration and government (Balduzzi, Elton & Green, 2001). Nowadays, business houses have also taken immense interests in the latest unemployment rates of a country or state so as to determine the chances of success for the business. This is principally because the unemployment rate is directly associated with the consumer spending. Thus, more unemployment rate will obviously lead to low customer traffic and reduced business transaction. Since, this report seeks to throw light on the economic scenario of South Carolina so as to recommend an overall inventory stock to the client, involved in physical retailing business, stressing more on the unemployment rate is fully justified. Presentation of the Data The data will be gathered from the website of the Job and Labor Statistics. In order to analyze the requirements of the inventory; the study will mainly consider the unemployment rate of South Carolina and along with that overall economic scenario of the state. A trend will be developed and on the basis of that inventory stock will be recommended. The findings of data are presented below: - Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 2003 6.4 6.4 6.5 6.6 6.8 6.9 6.9 7.0 6.9 6.8 6.8 6.7 2004 6.7 6.8 6.8 6.8 6.9 6.9 6.9 6.9 6.9 6.9 6.9 6.9 2005 6.9 6.9 6.8 6.7 6.6 6.6 6.7 6.8 6.9 6.9 6.8 6.7 2006 6.6 6.4 6.4 6.4 6.4 6.5 6.5 6.5 6.4 6.3 6.2 6.1 2007 5.9 5.7 5.6 5.5 5.5 5.5 5.5 5.5 5.6 5.6 5.6 5.5 2008 5.5 5.5 5.5 5.7 6.0 6.3 6.7 7.1 7.5 8.0 8.6 9.3 2009 9.9 10.6 11.1 11.4 11.6 11.7 11.8 11.8 11.8 11.8 11.9 11.9 2010 11.9 11.7 11.6 11.4 11.2 11.0 11.0 10.9 10.9 10.9 10.8 10.7 2011 10.6 10.5 10.5 10.5 10.5 10.6 10.7 10.6 10.5 10.2 10.0 9.7 2012 9.5 9.4 9.3 9.3 9.3 9.3 9.2 9.1 8.9 8.8 8.6 8.6 (Source: Bureau of Labor statistics, 2013) Mean Unemployment Rate The mean unemployment rate of the state is presented below. Mean Unemployment Rate Year Rate 2003 6.725 2004 6.85833 2005 6.775 2006 6.39167 2007 5.58333 2008 6.80833 2009 11.4417 2010 11.1667 2011 10.4083 2012 9.10833 (Source: Bureau of Labor statistics, 2013) Data Analysis and Interpretation The above table represents the monthly breakup of the unemployment rate of South Carolina. It has been clearly identified that the unemployment rate of the state has increased juts after the global credit crunch. However, since then the state has been able to recover its position but has not succeeded to fully recover. The trend shows that the city is recovering at a constant pace and hence it will consume some more time to get back to original state and simultaneously reduce the unemployment rate. The mean unemployment rate of the state for the year 2013 further shows improvements. Hence, it is expected that the consumer spending will increase and hence is a positive sign for the retailers. Mean Unemployment Rate 2013 (Jan - Aug) 2013 8.2375 The report will now consider certain statistical calculation so as to accurately predict the scenario of retail business and its impact for unemployment rate. SUMMARY OUTPUT Regression Statistics Multiple R 0.710331921 R Square 0.504571438 Adjusted R Square 0.442642868 Standard Error 2.260333392 Observations 10 ANOVA   df SS MS F Significance F Regression 1 41.62714 41.62714 8.147635843 0.021334042 Residual 8 40.87286 5.109107 Total 9 82.5         Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 1999.505752 2.890443 691.7646 2.13563E-20 1992.84038 2006.171 1992.84 2006.171 X Variable 1 0.98370564 0.344627 2.854406 0.021334042 0.188994249 1.778417 0.188994 1.778417 Residuals of Each Year RESIDUAL OUTPUT Observation Predicted Y Residuals 1 2006.121173 -3.12117 2 2006.252333 -2.25233 3 2006.170358 -1.17036 4 2005.793271 0.206729 5 2004.998109 2.001891 6 2006.203148 1.796852 7 2010.760984 -1.76098 8 2010.490465 -0.49047 9 2009.744488 1.255512 10 2008.465671 3.534329 The first three residual is negative and then the next three residual is positive. Again. Hence, this random pattern clealrly indicates that a linear model will provide decent fit to the data and will therefore suuports a linear model. x Y xy x2 (Ex)2 Year Rate       1 (2003) 6.725 6.725 1   2 6.858333 13.71667 4   3 6.775 20.325 9   4 6.391667 25.56667 16   5 5.583333 27.91667 25   6 6.808333 40.85 36   7 11.44167 80.09167 49   8 11.16667 89.33333 64   9 10.40833 93.675 81   10 (2012) 9.108333 91.08333 100             55 81.26667 489.2833 385 3025 Slope of Linear Regression Line: - (Y) = A + Bx (Slope(B) = (N?XY - (?X)(?Y)) / (N?X2 - (?X)2)) N =10 (Number of Years) ?XY = 489.2833333 ?X = 55 ?Y = 81.26666667 ?X2 = 385 (?X)2 = 3025 (Slope(B) = (N?XY - (?X)(?Y)) / (N?X2 - (?X)2)) = [(10*489.2833333 – (55) (81.26666667)] / [10*385 – 3025] B= 0.51. Hence th slope of the linear regression line is 0.51. Y-intercept of the linear regression line: Y = A+Bx ?Y = 81.26666667 B= 0.51. ?X = 55 N= 10 Y-Intercept = [(81.26666667 – (0.51*55)]/10 Y=5.3216. Hence, Y-intercept of the linear regression line is 5.3216. Equation of the linear regression line in slope-intercept form: - Y= A+Bx Y= 5.3216+0.51x. Unemployment rate in 2016: - Now in order to find the unemployment rate of 2016 the same formula will be used. Y is the rate o unemeployment. Hence, Y = A+Bx (Here x= 14; because 2003 has been taken as 1 and thus 2016 will be 14). Y = 5.32+0.51(14) Y = 12.46. Therefore, from the above trend it is evident that the unemployment rate in the year 2016 may reach 12.46%. Hence, the interpretaion and consequences of the same is portrayed in the next half of the study. Moroever, it is also within the range of the regresion and not an outlier. Part II Introduction of the Project and its Significance to the Retail Store The modern demand forecasting situation has provided new opportunities and dimension for the retailers to improve their overall performance. Although it is impossible to replace the art of individual merchant, it can be augmented by an objective, efficient and scientific approach to forecast the demand. With the availability of large scale systems and technology, mass retail transaction data can now be easily handled. This in turn is generally used for the purpose of mining, organizing and understanding the consumer behavior. A number of scholars believe that this new approach of forecasting will contribute heavily towards the accuracy of the future plans, consumer satisfaction, profitability and overall efficiency of the retail operations. In the similar way of demand forecasting, the importance of estimation of inventory has become equally important. This is because accurately predicting the required inventory will greatly help the company to remain cost effective. Therefore, from these facts it is obvious that such projects are highly essential and is significant for the retail stores. General Economic Scenario of South Carolina The economy of South Carolina is dominated by Livestock and its associated products. Broilers accounts for 27% of the total agricultural functions (Netstate, 2013). Hence, from this fact it is evident that a majority of the people is employed in the agricultural sector. Furthermore, the state also has flourishing manufacturing, fishing and mining industries. In the recent past, the activities of the government have also intensified its service sector. Although, in the previous decades the state had to remain dependent on individual sectors, but of late the scenario changed drastically. Around 22% of the economy is dependent on the services, while manufacturing, construction accounts for 19% and 6 % respectively (Thestate, 2013). According to reports, the state’s rural places have been the sufferer for the reducing number of jobs. However, the scenario in the state’s urban place is better. Thus, from an overall point of view the economic scenario of the state is not according to the desired situation. The per capita income of $23,854 and the mean unemployment rate of the last 8 months in 2013, 8.23% also signify the same (Quickfacts, 2013). The state is an important contributor to the prosperity of the national economy. However, its significance has been reduced to a certain extent. Data Collection and Type of Analysis The data have been collected from the website of the Bureau of Labor statistics. From the website, unemployment rate of South Carolina was discovered. Regression analysis was used to identify the data. Findings and Data Accuracy The results have shown that the unemployment rate in South Carolina has constantly remained between 6-7%. However, after the credit crunch of 2008, the unemployment rate of the state reached around 10- 11%. The same rate continued till the year 2012. According to the analysis, the rate may also increase in the future (BLS, 2013). One of the major reasons for the same is the slow recovery rate. Therefore, it is evident that every business house around South Carolina is expected to face a tough situation. Thus, for retailing business also the consumer spending will be low and may result in low volume of business transaction. Regarding the accuracy of data, it is expected that the forecast is nearly accurate considering the current scenario and trend. However, changes may take place in the future and slight variation may be observed. Conclusion and Future Prediction The study was meant for analyzing the future economic state of South Carolina and on the basis of that recommendation to be made to the retailer. The analysis found that the unemployment rate of the state has remained within the control till the year 2008 and it lay between 6-7%, but after the credit crunch it reached around 11%. Further analysis has shown that considering the present trend and external situation, the year 2016 is expected to get worse and the unemployment rate may reach 12.46%. Hence, the company is strongly recommended to limit the stock of inventory. Thus keeping a flat inventory and SKU proliferation will greatly help the company matching the demands. References Albuquerque, R. & Vega, C. (2009). Economic news and international stock market co-movement. Review of Finance, 13, pp. 401-65. Balduzzi, P., Elton, E. J. & Green, T. C. (2001). Economic news and bond prices: evidence from the US treasury market. Journal of Financial and Quantitative Analysis, 36, pp. 523-43. Bureau of Labor statistics. (2013). Local Area Unemployment Statistics. Retrieved from http://data.bls.gov/timeseries/LASST45000003. Netstate. (2013). South Carolina's Economy. Retrieved From http://www.netstate.com/economy/sc_economy.htm. Quickfacts. (2013). South Carolina. Retrieved from http://quickfacts.census.gov/qfd/states/45000.html. Thestate. (2013). Declining income, rising poverty plague SC economy. Retrieved from http://www.thestate.com/2013/09/19/2988225/declining-income-rising-poverty.html. Wongswan, J. (2006). Transmission of information across international equity markets. Review of Financial Studies, 19, 1157-89. Yahoo. (2010). Unemployment as an Economic Indicator. Retrieved from http://voices.yahoo.com/unemployment-as-economic-indicator-5403431.html. Read More