Predictive Sales Report, Essay Example

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Words: 1750

Essay

Introduction

For the retail sector, unemployment rates have had significant effects on customers, consumer spending and inventory in particular. With changing economic conditions, it is important to note the key data, in terms of decision-making and predictive sales for the future. Unemployment rates, on a state and national level, along with analysis and discussion for future sales levels, and advice for the next three years, will be discussed herein.

Part I

As the data shows, there has been a varying trend in relation to unemployment rates from 1948 to 2013, especially in relation to the mean yearly value, as calculated and shown below. However, over time, the average unemployment rate is seen to increase, ending with an average unemployment rate of approximately 8% (see Table 1).

This shows that the average unemployment rate is set to increase, and this will have a negative impact on projected sales, especially in the context of the retail industry. Future trends should also be taken into account, so as to find strategic ways of offsetting loss of sales and increasing the value of customer relations in the retail store.

Year Mean Yearly Value (Annual)
1948 3.8
1949 6.1
1950 5.2
1951 3.3
1952 3.0
1953 2.9
1954 5.6
1955 4.4
1956 4.1
1957 4.3
1958 6.8
1959 5.5
1960 5.5
1961 6.7
1962 5.6
1963 5.6
1964 5.2
1965 4.5
1966 3.8
1967 3.8
1968 3.6
1969 3.5
1970 5.0
1971 6.0
1972 5.6
1973 4.9
1974 5.6
1975 8.5
1976 7.7
1977 7.1
1978 6.1
1979 5.9
1980 7.2
1981 7.6
1982 9.7
1983 9.6
1984 7.5
1985 7.2
1986 7.0
1987 6.2
1988 5.5
1989 5.3
1990 5.6
1991 6.9
1992 7.5
1993 6.9
1994 6.1
1995 5.6
1996 5.4
1997 4.9
1998 4.5
1999 4.2
2000 4.0
2001 4.7
2002 5.8
2003 6.0
2004 5.5
2005 5.1
2006 4.6
2007 4.6
2008 5.8
2009 9.3
2010 9.6
2011 8.9
2012 8.1
2013 7.9

Table 1: Mean Yearly Value 1948-2013

In addition, the data provided was used to form a scatter plot of the average unemployment rate, from 1948 to 2013. Certain metrics were used to analyse and highlight selected points of data, which are shown below (see Figure 1). This is explained as by the figures that accompany it, that will be discussed later.

Average Unemployment Rate Scatter Plot

Figure 1: Average Unemployment Rate Scatter Plot

Using the data, the slope of the linear regression line was calculated as approximately 4.8, as shown below (see Table 2). Although the linear regression line varies over time, it can be seen to stabilise towards the end of 2013, albeit the unemployment rate is still higher than certain years previously as shown by the data.

Regression Analysis with Slope and Y-Intercept

Table 2: Regression Analysis with Slope and Y-Intercept

Furthermore, the Y-Intercept, as also identified using the linear regression line, is approximately 1952.6. This also shows that the unemployment rate on average was consistently high throughout the time period shown by the regression statistics and the scatterplot above. In regards to the equation of regression line in slope-intercept form, it is calculated as below:

slope-intercept form

Based on the same linear regression line, the slope and y-intercept have been calculated, as aforementioned. Additionally, the other metric that can be used to calculate the unemployment rate for 2016. Utilizing the data as provided, the projected average unemployment rate for 2016 is slightly above 6%. Although this is significantly less than the current rate for 2013, it is still a higher rate than selected years shown in the scatterplot above.

Lastly, the residuals of each year are shown below. Though most are negative values, the residual values steadily increase and are seen to become positive by observation 39 onwards (see Table 3). This data is also confirmed by the regression statistics above, and are according to the data provided.

RESIDUAL OUTPUT
Observation Residuals
1 -32.8
2 -28.0
3 -18.3
4 -16.2
5 -14.7
6 -25.8
7 -19.2
8 -17.1
9 -16.9
10 -27.4
11 -20.1
12 -19.5
13 -23.7
14 -17.6
15 -17.0
16 -13.8
17 -9.9
18 -5.6
19 -4.9
20 -2.6
21 -1.3
22 -7.0
23 -10.4
24 -7.8
25 -3.4
26 -6.0
27 -17.8
28 -13.3
29 -9.3
30 -3.9
31 -1.9
32 -6.9
33 -7.9
34 -16.3
35 -14.8
36 -4.4
37 -2.0
38 -0.1
39 4.6
40 8.7
41 10.7
42 10.1
43 5.6
44 3.7
45 7.3
46 12.0
47 15.2
48 17.1
49 20.2
50 23.2
51 25.4
52 27.6
53 25.1
54 21.4
55 21.4
56 24.5
57 27.5
58 30.7
59 31.6
60 27.3
61 12.6
62 12.1
63 16.2
64 21.0
65 22.8

Table 3: Residual Value Yearly

As research shows, Florida currently has a state unemployment rate of 7.1 (BLS, 2013). In comparison with the data as shown above, it can be seen that it is in congruence. Therefore, the average unemployment rate for the State of Florida is within the linear regression line of the data given for the unemployment rate between the years 1948 to 2013.

This data is quite significant in relation to decision-making, especially in relation to the retail industry. As unemployment rates can be projected and analysed for possible trends and estimations, it can also be used to make better business decisions. In retail, when the unemployment rate is high, it can be seen that inventory needs to be minimized along with costs. This in turn will drive up profits in economically unstable times. On the other hand, when the unemployment rate is low, profits can be invested into consumers and staff, who are key stakeholders in retail stores.

Part II

According to the data and its statistical analysis as shown above, the data shows that the annual mean trend over time, as based on the average unemployment rate. In particular, a number of different analytic tools were used, including linear regression, slope and y-intercept tools, as well as yearly residuals. These showed critical indicators as to what future trends were likely to result, as based upon current data, and how such projections are able to identify decision-making drivers, future unemployment rates, and significant impacts on economic conditions.

The analysis shows that the current trend will show a slight reduction in the average unemployment rate, as exemplified by the metrics aforementioned. This will have an impact on economic conditions, including the retail sector. However, these impacts will generally be positive, and as reflected by current statistics provided by the Bureau of Labour Statistics, the trend will continue for at least three years.

Findings show the data can be compared with the Job and Labor statistics, as currently collated by the U.S. Department of Labor.  As aforementioned, Florida currently has a state unemployment rate of 7.1; yet has the third-largest over-the-year job increase (BLS, 2013).

According to the scatterplot of the data shown above, the average unemployment has begun to decrease, or become somewhat stationary. U.S. state unemployment rates are stationary, and may continue this trend (Stephton, 2012; Towers, 2013). As a result, the retail sector may experience increased sales, and this will reflect in consumer savings and retail profitability. The data is therefore quite accurate, in terms of congruence and similarity with the current trends.

In regards to the results identified as a result of the data analytics, the changing unemployment rate does have a significant impact on the retail store, in particular the projected sales, consumer spending, and inventory management. Firstly, projected sales can be seen to drop if unemployment rates are on the rise, especially if sales figures have been significantly high in the past. Staff are also key individuals who maintain sales, as interaction with customers will decrease if redundancy becomes an apparent influencer, or customers themselves engage in limited expenditure in such retail stores.

Secondly, consumer spending will decrease as the unemployment rate increases, especially as consumers are often at the receiving end of negative impacts surrounding unemployment. As the consumers continue to curb spending, this will have an adverse impact on the management of the retail store, forcing the store to cut costs and increase prices, leading to other internal and external problems, surrounding the build-up of inventory.

Thirdly, inventory management plays a key role in the cost effectiveness of the retail store. If the store keeps a large amount of inventory, especially in periods when the unemployment rate is high, they could experience loss of sales and backlog. However, if the retail store takes advantage of the unemployment rate by limiting stocked inventory, the resulting cost effectiveness will be evident by the continuing trend of projected sales, albeit small drops in sales figures may result. According to research, the real economical and logistic benefits from the point of view of both the retail store and the whole supply chain, follow the adoption of a consignment stock inventory policy, in which stock is only ordered on a needs-basis (Battini et. al., 2010).

Although another global financial crisis or similar depression on a worldwide scale may result in a change in the linear regression line, and as a result, a change in the predicted sales, this will not alter the projections dramatically. This is because the retail market takes into consideration such economic fluctuations, and can cater for these changes on a rapid basis. Therefore, if the advice provided above is followed, there will not be any adverse effects as a result of changing economic conditions, changing unemployment rates, or any other change in the market.

Conclusion

In summary, is important to note the key data, in terms of decision-making and predictive sales for the future, has significant impacts on the retail sector. As the data shows, the average unemployment rates from 1948 to the present time, and until 2016, will continue in the current trend, although the rate is seen to decrease. Therefore, retail stores should take such changes into consideration by minimising inventory, cutting costs, and by doing so, can increase predicted sales for the present time and the future.

References

Battini, D., Gunasekaran, A., Faccio, M., Persona, A., & Sgarbossa, F. (2010). Consignment stock inventory model in an integrated supply chain. International Journal of Production Research48(2), 477-500.

Bureau of Labor Statistics. (2013). Regional and State Unemployment – June 2013. U.S. Department of Labor. Retrieved July 2013, from http://www.bls.gov/news.release/pdf/laus.pdf

Sephton, P. S. (2009). Persistence in US state unemployment rates. Southern Economic Journal76(2), 458-466.

Towers, N. (2013). International Journal of Retail and Distribution Management Editorial Volume 41 Issue 10 Editor name: Professor Neil Towers. International Journal of Retail & Distribution Management41(10), 1-12.

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