According to a Powerpoint to Write a analysis paper.~! (about 6-8 pages)

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timer Asked: May 4th, 2017
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Question Description

According to PPT to write a paper.

Guideline:

Most use regression or forecasting. determining settings in a production process, analyzing state data to forecasting sales and predicting the profit and all related to Buckle In company!

The paper need

1. Introduction – includes a detailed description of the problem and motivation for the study.

2. Variables used in the study, like Independent Variable and Dependent Variable

3. Data Analysis – includes all necessary computer output.

4. Interpretation and limitations of study

5. Summary/Conclusions


File .

In the file, it gonna mentioned Dependent Variable will be Monthly Sales and Independent Variable like: collecting the data for Number of Stores, Unemployment, Consumer Sentiment Index, and Inflation. Also regression of each one of those variables against Net Sales. What's more, regression using all variables and generated a Correlation Matrix.

Unformatted Attachment Preview

Ben Freeman Joseph Orosz Nikolas Swett Shuangxing Xu OUR MISSION: "To create the most enjoyable shopping experience possible for our guests." Buckle’s History  A lot of people have heard of Buckle, but what you may not know is that it started as a single store selling men’s clothing in Kearney, Nebraska back in 1948.  It was originally named Mills Clothing by David Hirschfield.  In 1965 David’s son Dan took over the business and when he started a new store changed the name to Brass Buckle. Buckle’s History  In 1977 Buckle started selling women’s clothing and opened their first mall location.  In 1991 Buckle decided to begin developing their private clothing label  In 1992 they had over 100 locations in 18 states and became a publicly traded company on the NASDAQ where it is traded as BKLE Buckle’s History  When E-commerce started taking off, Buckle was soon to join by opening their first online store in 1999  In 2011 Buckle sold 5 million pairs of jeans which is the highest amount they have ever sold  Buckle currently has over 450 stores in 44 states. The Buckle Crisis  What draws our attention to Buckle, Inc. is its steady decline in sales and net profit.  After achieving over 5 millions sales in jeans in 2011, the data we have recently gathered shows over a 15% decline as of 2016.  Our research is based on the concern and source of this decline through regressions  We then finalized our research by forecasting two possible outcomes depending on Buckle, Inc.’s performance in 2017 Theories on Buckle’s Decline  One of the most well known perceptions of Buckle, Inc. is that their prices are considered too high, even for it’s exclusive brands. Consumers may be finding substitutions for what buckle offers in terms of denim and fashion  The Buckle also has over 450 stores, which could lead to a loss in sales each time they open and stock a new store. Location could also be an issue.  The economy itself has also been evaluated in terms of inflation and job availability. General Statistics: 2013 Net Income By Quarter  Quarter 1- $2697,12  Quarter 2- $232,529  Quarter 3- $28,761  Quarter 4- $338,999  Total for the Year- $1,128,001  Average per Quarter- $282,000 General Statistics: 2014 Net Income By Quarter  Quarter 1- $271,375  Quarter 2- $235,725  Quarter 3- $292,201  Quarter 4- $353,541  Total for the Year- $1,153 ,142  Average Per Quarter- $288,286  Total Increase of $25,141 or around 2% General Statistics: 2015 Net Income By Quarter  Quarter 1- $271,345  Quarter 2- $236,053  Quarter 3- $280,187  Quarter4- $332,031  Total for the Year-- $1,119,616  Average per Quarter - $279,904  Total Decrease from 2014 of $33,52 or -3% General Statistics: 2016 Net Income By Quarter  Quarter 1- $243,543  Quarter2- $212,157  Quarter 3- $239,213  Quarter 4- $279,960  Total for the Year- $974,873  Average per Quarter- $243,718  Total Decrease from 2015 of $144,743 or -15% The Buckle, Inc. Net Sales By Month The Buckle, Inc. Net Sales (M2M) from 2013 - 2016 250 Net Sales (Millions) 200 150 100 50 0 Oct-aa May-aa Nov-aa Jun-aa Dec-aa Months Jul-aa Jan-aa Aug-aa Mar-aa y = -0.0015x + 152.96 R² = 0.0004 Net Sales (M2M) Analysis  By looking at the graph it can be seen that months with steep increases happen around specific times of year, showing high seasonality.  For example:   March: spring/summer fashion  August: Back to school shopping  November/December: Holiday shopping Months with steep decreases are thus idle/slow seasons, showing that consumers are not actively purchasing clothing retail or see it as an immediate necessity. Variables Evaluated in 2013 Date Consumer Number of Unemployme Sentiment Stores nt Rate Index Net Sales Inflation Jan-13 78.8 440 8 73.8 0.3 Feb-13 89.3 441 7.7 77.6 0.82 Mar-13 106.6 442 7.5 78.6 0.26 Apr-13 73.8 443 7.6 76.4 -0.1 May-13 72.8 444 7.5 84.5 0.18 Jun-13 82.5 445 7.5 84.1 0.24 Jul-13 77.2 452 7.3 85.1 0.04 Aug-13 101.1 451 7.3 82.1 0.12 Sep-13 99 451 7.2 77.5 0.12 Oct-13 86.6 452 7.2 73.2 -0.26 Nov-13 101.2 452 6.9 75.1 -0.2 Dec-13 180.9 450 6.7 82.5 -0.01 Variables Evaluated in 2014 Data Consumer Number of Unemployme Sentiment Stores nt Rate Index Net Sales Inflation Jan-14 56.9 449 6.6 81.2 0.37 Feb-14 89.5 450 6.7 81.6 0.37 Mar-14 106.6 450 6.7 80.0 0.64 Apr-14 75.6 452 6.2 84.1 0.33 May-14 72 453 6.3 81.9 0.35 Jun-14 84.8 453 6.1 82.5 0.19 Jul-14 79 452 6.2 81.8 -0.04 Aug-14 103.6 457 6.2 82.5 -0.17 Sep-14 103.1 460 5.9 84.6 0.08 Oct-14 85.4 461 5.7 86.9 -0.25 Nov-14 104 463 5.8 88.8 -0.54 Dec-14 190.6 463 5.6 93.6 -0.57 Variables Evaluated in 2015 Date Net Sales Consumer Number of Unemployme Sentiment Stores nt Rate Index Inflation Jan-15 58.9 460 5.7 98.1 -0.47 Feb-15 88.6 460 5.5 95.4 0.43 Mar-15 108.5 462 5.4 93.0 0.6 Apr-15 74.3 463 5.4 95.9 0.2 May-15 75.2 463 5.4 90.7 0.51 Jun-15 87.1 464 5.3 96.1 0.35 Jul-15 73.8 464 5.2 93.1 0.01 Aug-15 101.4 464 5.1 91.9 -0.14 Sep-15 97.4 465 5 87.2 -0.16 Oct-15 81.4 469 5 90.0 -0.04 Nov-15 96.9 469 5 91.3 -0.21 Dec-15 182.1 469 5 92.6 -0.34 Variables Evaluated in 2016 Date Consumer Number of Unemployme Sentiment Stores nt Rate Index Net Sales Inflation Jan-16 53 468 4.9 92.0 0.17 Feb-16 81.8 468 4.9 91.7 0.08 Mar-16 96.6 468 5 91.0 0.43 Apr-16 65.2 467 5 89.0 0.47 May-16 67.4 467 4.7 94.7 0.41 Jun-16 78.3 467 4.9 93.5 0.33 Jul-16 66.5 470 4.9 90.0 -0.16 Aug-16 87.2 470 4.9 89.8 0.09 Sep-16 82.9 470 4.9 91.2 0.24 Oct-16 69.1 470 4.8 87.2 0.12 Nov-16 81.5 471 4.6 93.8 -0.16 Dec-16 154.6 471 4.7 98.2 0.03 Key Points Between Variables  From the data you can see that the number of stores increased every year with the largest increase happening in 2014 when The Buckle opened up 14 new stores.  The Unemployment rate for the US consistently fell from 2013-2016 with the lowest points happening in 2016 which means more people had more money to spend in a year that The Buckle had a 15% decrease in Net Sales  The Consumer Sentiment Index shows consumers confidence in the economy and in 2013 the average score for the year was 79.2 which increased to 92.94 in 2015 yet The Buckle still saw a decrease in Net Sales from 2014 to 2015 and an even bigger decrease from 2015 to 2016 when the Index score was an average of 91.84 Net Sales Vs. Number of Stores Net Sales vs. # of Stores Series1 Linear (Series1) 250 Net Sales 200 150 100 50 0 0 10 20 30 Number of Stores 40 50 60 y = -0.1895x + 90.348 R² = 0.0066 Net Sales Vs. Unemployment Rate Net Sales vs. Unemployment Rate Series1 Linear (Series1) 250 Net Sales 200 150 100 50 0 4 4.5 5 5.5 6 6.5 7 Unemployment Rate 7.5 8 8.5 y = -0.1895x + 90.348 R² = 0.0066 Net Sales Vs. Inflation Net Sales vs. Inflation Series1 Linear (Series1) 250 Net Sales 200 150 100 50 0 0 10 20 30 Inflation 40 50 60 y = -0.1895x + 90.348 R² = 0.0066 Regressions Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 0.382298239 0.146151944 0.070254339 28.97928105 50 Regressions ANOVA df Regression SS MS F 4 6468.621335 1617.155334 1.925646319 Residual 45 37790.94287 839.7987303 Total 49 44259.5642 Significance F 0.12256333 Regressions Coefficients Intercept Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% -143.9481922 977.4237873 -0.14727306 0.883574366 -2112.580755 1824.68437 -2112.580755 1824.68437 Number of Stores 0.311307249 1.899221982 0.16391304 0.870533604 -3.513922182 4.13653668 -3.513922182 4.13653668 Unemployment Rate 8.265280655 18.17312315 0.454807937 0.651434004 -28.33726826 44.86782957 -28.33726826 44.86782957 Consumer Sentiment Index 0.539154267 1.123353822 0.479950534 0.63358732 -1.723396473 2.801705007 -1.723396473 2.801705007 Inflation -35.75611295 14.71355929 -2.430147067 0.019144069 -65.39074258 -6.121483321 -65.39074258 -6.121483321 Relationship to the Graphs  All of these variables are relevant information towards the strength of the economy, but they don’t seem to play a huge part in the success of The Buckle  The most relevant would be the number of stores and when The Buckle had their most number of stores they still showed a decline  As said before when the unemployment rate was at it’s lowest they still continued to have a loss Forecasting the Outcomes  Depending on what Buckle, Inc. decides to do for the year 2017, two different forecasts have been created.  Forecast Number 1:   Decline in sales continue  Buckle Makes little changes to Business Strategy  Based on current 15% decline Forecast Number 2:  Recovery in Sales  Buckle changes their approach  Sales increase at a reasonable percentage (7%) Forecast 1: Net Income Loss The Buckle, Inc. Net Income Forecast if Sales Continue the Trend 2013 2014 2015 2016 QT. Avg QT Factor Forecast Quarter 1 269,712 271,675 271,345 243,543 264,069 0.96559766 200,034 Quarter 2 232,529 235,725 236,053 212,157 229,116 0.83778892 173,557 Quarter 3 286,761 292,201 280,187 239,213 274,591 1.00407164 208,004 Quarter 4 338,999 353,541 332,031 279,960 326,133 1.19254179 247,048 Forecast 1: Net Income Loss Average/Year 282,000 288,286 279,904 243,718 Total/Year 1,128,001 1,153,142 1,119,616 974,873 Overall Total 4,375,632 Overal Average 273,477 2017 Forecast Average/Quarter 207,161 Forecast 1: Net Income Loss  By following the trends and using average quarterly net income we forecasted a 18% decline if The Buckle doesn’t make a change.  This would lead Buckle, Inc. at a net income of:  Quarter 1: $200,034  Quarter 2: $173,557  Quarter 3: $208,004  Quarter 4: $247,048  The total net income for 2017 would be $828,642  This would be a decrease of $146,231 from 2016. Forecast 2: Net Income Gain The Buckle, Inc. Net Income Forecast if Increase Based on Avg Year 2013 2014 2015 2016 QT. Avg QT Factor Forecast Quarter 1 269,712 271,675 271,345 243,543 264,069 0.96559766 252,804 Quarter 2 232,529 235,725 236,053 212,157 229,116 0.83778892 219,342 Quarter 3 286,761 292,201 280,187 239,213 274,591 1.00407164 262,877 Quarter 4 338,999 353,541 332,031 279,960 326,133 1.19254179 312,221 Forecast 2: Net Income Gain 2013 Average 2014 2015 2016 282,000 288,286 279,904 243,718 Total 1,128,001 1,153,142 1,119,616 974,873 Overall Total 4,375,632 Overal Average 2017 Forecast Average 273,477 261,811 Forecast 2: Net Income Gain  By taking the averages of 2015 and 2016, we saw a reasonable potential for a 7% net profit goal for Buckle, Inc. to aim for.  This would lead Buckle, Inc. at a net income of:  Quarter 1: $252,804  Quarter 2: $219,342  Quarter 3: $262,877  Quarter 4: $312,221  The total net income for 2017 would be $1,047,245  This would be an increase of $72,372 from 2016 Conclusion of Results  Based on our regressions, it appears that the economy (aside from 2016) had little affect on Buckle’s performance.  This would lead us to conclude that Buckle may be suffering from internal variables which could include:   Management and Corporate issues  Hiring and recruiting process  Not holding true to their mission statement  E-commerce’s rapid growth  Not making consumers aware of what other services they offer Much of this, we would recommend that Buckle, Inc. Go consider taking surveys of their consumers as well as surveys within their field to see if these variables are potential threats Conclusion of Results  If Buckle, Inc. is to stay alive in the retail industry they need to reach the root of their decline.  (More conclusion to come – this is outline) - NS ...
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Proff_White
School: UIUC

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Diminishing sales revenue at Buckle
1. Introduction
Buckle as a company have been in the fashion business industry for long. Buckle was
started by David Hirschfield back in 1948 as a men’s clothing store in Kearney, Nebraska.
However, there is need to note that its original name was Mills Clothing. The name was later
changed to Brass Buckle in 1965 when Dan, David’s son, took over the business. Buckle’s
first mall was opened in 1977. It was during this time when the company started selling
women’s clothing. They continued selling women clothes until 1991 when they decided to
develop their own private clothing label. This was due to increase in their business hence they
wanted to privatize their business. This was a great step since the company started getting
more customers hence became more famous. As a result, the Buckle Company developed
more than 100 locations in 18 different states. In addition to this, the company became a
publicly traded company on the NASDAQ. During this time, the Buckle Company was
traded as a BKLE. The company’s operations continued to improve hence increase in profits
made. In addition, the company became more famous and...

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