Business Analytics And Statistics

Business Analytics And Statistics

 

Introduction

Good Harvest grows and offers organic food products across Australia. The business operates through different areas including wholesale a box delivery system, Harvest Kitchen and retail.

The company is operating its business in the second year as it is in introduction phase of the product life cycle (Good Harvest, 2017). The company has different resources including cold store warehouse, retail outlet, delivery van and team of 6 persons.

It is reported that there is an issue of increasing Cost of Goods Sold (COGS) within the organic industry. The key issues in the business are related to the revenue i.e. lead generation, COGS margins and average sales.

The presented business report provides suggestions to the CEO of Harvest Kitchen to address the identified issues through data analysis.

Problem Identification and Business Intelligence Required

The following questions will be addressed:

  1. What are the top/worst selling products in terms of sales?
  2. Is there a difference in payments methods?
  3. Are the differences in sales performance based on where the product is located in the shop? How does this effect both profits and revenue?
  4. Is there a difference in sales and gross profits between different months of the year?
  5. Are their differences in sales performance between different seasons?
  6. How does this relate to rainfall and profits?

Additional questions:

  1. Are the differences in total orders obtained in different days of the week? How does this effect total orders?
  2. Is there difference in average sales between different seasons?

For addressing these questions, product mix data set and sales data set are collected and presented with appropriate scales of the variables as below:

In order to analyze these data sets, SPSS 17 is used to present the statistical analysis of the given data and address the research problem. Through this, regression analysis, correlation analysis and Pareto curve and descriptive statistics are presented (Newbold et al. 2012).

Visualise the descriptive statistics

Descriptive of Sales mix dataset:

Descriptive Statistics
N Minimum Maximum Sum Mean Std. Deviation
Day of the Year 366 1 366 67161 183.50 105.799
Month of the year 366 1 12 2384 6.51 3.456
Season of the year 366 1 4 915 2.50 1.117
GST Inclusive 366 0 271 41876 114.42 48.723
GST Exclusive 366 0 2492 340583 930.56 303.827
Gross Sales 366 0 2642 382460 1044.97 326.285
Net Sales 366 0 2370 371220 1014.26 313.986
Cash Total 366 0 1195 147969 404.29 153.643
Credit Total 366 0 1407 214036 584.80 228.860
Visa Total 366 0 1407 203441 555.85 244.870
Mastercard Total 366 0 399 8086 22.09 67.823
House Account 366 -264 1113 13684 37.39 113.204
Total Orders 366 0 129 20327 55.54 15.844
Average Sale 358 8 61 6631 18.52 3.985
Staff Cost 366 170 351 91022 248.69 52.418
Weekday 366 1 7 1463 4.00 1.998
Rainfall 365 0 63 1452 3.98 9.811
Profit Total 366 -33.98 271.97 1.12E4 30.7098 30.05661
Valid N (listwise) 357

Descriptive of Product mix dataset:

Descriptive Statistics
N Minimum Maximum Sum Mean Std. Deviation
Product Class (number) 1034 1 30 15464 14.96 8.515
Quantity 1034 1 3769 74348 71.90 212.400
Weight 209 0 2913 16156 77.30 242.323
Total Sales ($) 1034 0 17276 382540 369.96 1014.719
Cost of Goods ($) 1034 0 8573 212203 205.22 561.072
Net Profit ($) 1034 0 8703 170338 164.74 482.106
Location of product in shop 1034 1 5 3218 3.11 1.526
Total Profit 1034 .00 8702.93 1.70E5 1.6473E2 482.10651
Valid N (listwise) 209

Results of the selected analytics methods and technical analysis

  1. What are the top/worst selling products in terms of sales?

The below plotted Pareto curve between the total sales and the product class is shown below that also depict the top/worst selling products in the context of sales:

The above Pareto curve (80/20 rule), demonstrated that the y axis at 80% is intersecting with the x axis which is parallel to it depicting the top/worst selling products. The top/worst selling products are also shown separately on the intersection point at x axis. Further, the left side of x axis point demonstrates the products which are top selling such as the vegetables, dairy items, dry goods, chocolates, and bakery products, meats Smallgoods, spreaders, sauces and sweeteners as well (Weiers, 2010). On the other hand, it is seen that the right side depicts the products which are worst selling. This signifies that the top selling products (20%) mentioned above out of the total products consist 80% of the total sales.

Business Analytics And Statistics

  1. Is there a difference in payments methods?

For this, one sample t-test is been conducted that help to find the variation that exists in the payment methods:

One-Sample Test
Test Value = 0
t df Sig. (2-tailed) Mean Difference 95% Confidence Interval of the Difference
Lower Upper
Cash Total 50.340 365 .000 404.287 388.49 420.08
Credit Total 48.885 365 .000 584.798 561.27 608.32
Visa Total 43.427 365 .000 555.849 530.68 581.02
Mastercard Total 6.232 365 .000 22.094 15.12 29.07
House Account 6.318 365 .000 37.388 25.75 49.02

From the above results obtained, it can be discovered that the p-value is < 0.05. This shows that the alternative hypothesis is accepted that a significant difference does exist. It depicts that a significant difference lies in the payment methods.

 

  1. Are the differences in sales performance based on where the product is located in the shop? How does this effect both profits and revenue?

The Pareto curve below depicts the relationship that exists between total sales of the company and the location.

It can be concluded from the above Pareto curve that there exist variations in the sales performance based on where does the product is lying in the shop. The product located at rear position is sold mostly ($96493) in the shop. At the same time, product which is located outside front of the shop is sold not much (Weiers, 2010).

The Pareto curve between the total profit and the location of the product in the shop is shown below:

From the above graph, it can be observed that the products which are most profitable ($39073.98) are located in the front in the shop, while those which are less profitable ($21715.52) are located on the outside front of the shop.

  1. Is there a difference in sales and gross profits between different months of the year?

The regression analysis test is done to identify the variations in the gross profits and sales between the different months of the year:

ANOVAb
Model Sum of Squares df Mean Square F Sig.
1 Regression 201321.056 1 201321.056 1.896 .169a
Residual 3.866E7 364 106201.063
Total 3.886E7 365
a. Predictors: (Constant), Month of the year
b. Dependent Variable: Gross_Sales

From the above regression table, it can be illustrated that the p-value which is 0.169 is > 0.05. It depicts that the null hypothesis is accepted and it cannot be stated that there is a significant difference in the net sales between the different months of the year (Newbold et al. 2012). Also, there is no significant difference in sales between different months of the year.

The table below of regression depicts the analysis of regression for the profit in different methods:

ANOVAb
Model Sum of Squares df Mean Square F Sig.
1 Regression 17979.279 1 17979.279 20.992 .000a
Residual 311761.675 364 856.488
Total 329740.954 365
a. Predictors: (Constant), Month of the year
b. Dependent Variable: Profit Total

The ANOVA table presented above demonstrates that the p-value which is 0.00 is < 0.05. It states that the null hypothesis is not accepted. It further depicts that there lies no significant difference (Groebner et al. 2011). At the same time, it can be depicted that there is a statistical difference in the profits in different months in the year.

  1. Are their differences in sales performance between different seasons?

ANOVA test is conducted for this and shown below:

ANOVAb
Model Sum of Squares df Mean Square F Sig.
1 Regression 14613.958 1 14613.958 .137 .712a
Residual 3.884E7 364 106713.994
Total 3.886E7 365
a. Predictors: (Constant), Season of the year
b. Dependent Variable: Gross_Sales

The ANOVA table presented above demonstrates that the overall p-value which is 0.712 is > 0.05. It shows that there is no significant difference in the sales performance between different seasons.

  1. How does this relate to rainfall and profits?

For showing this a correlation table is been showed below which provide a relationship between the rainfall and the profits:

 Correlations
Rainfall Profit Total
Rainfall Pearson Correlation 1 .008
Sig. (2-tailed) .885
N 365 365
Profit Total Pearson Correlation .008 1
Sig. (2-tailed) .885
N 365 366

From the table presented above, it can be observed that there is no significant relation between the rainfall and the profit. It shows that the change in the rainfall has no effect on the profits of the firm (Black, 2009).

Additional Questions:

  1. Are the differences in total orders obtained in different days of the week? How does this effect total orders?

For this, Pareto curve is plotted as below:

On the basis of the above curve, it can be determined that on Thursday, there are more orders as compared to other days. At the same time, on Sunday, people are likely to give orders in less number.

  1. Is there a difference in average sales between different seasons?

The ANOVA table is presented below:

ANOVAb
Model Sum of Squares df Mean Square F Sig.
1 Regression 4.713 1 4.713 .296 .587a
Residual 5664.770 356 15.912
Total 5669.483 357
a. Predictors: (Constant), Season of the year
b. Dependent Variable: Average_Sale

The ANOVA table presented above shows that the overall p-value which is 0.587 is > 0.05. It depicts that there is no significance difference in the average sales between different seasons that means that the null hypothesis is accepted.

Discussion of the results and recommendations

After analysing the data sets, it can be concluded that the top selling products of the company are vegetables, dairy items, dry goods, chocolates, and bakery products, meats Smallgoods, spreaders, sauces and sweeteners.

The firm should focus on these products and try to sell the less selling products through effective marketing practices like discounts, etc. Products located at front and rear locations are effective for the company to generate higher profits and revenues for the company.

It can be recommended to the company that it should merchandise its products at these locations to increase sales. Moreover, it is also found that rainfall and profit do not relate as it will not be essential for the firm to focus on rainfall season to increase sales and profits.

Seasonal aspects are not significant for the firm in terms of sales as it will be good decision for the firm to invest more on seasonal promotion. In addition, the firm should focus on increasing sales in weekends as there is less sales in weekends.

References

Good Harvest 2017. [Online] Available at: http://www.goodharvest.com.au (Accessed: 26 September 2017)

Black, K., 2009. Business statistics: Contemporary decision making. USA: John Wiley & Sons.

Groebner, D.F., Shannon, P.W., Fry, P.C. and Smith, K.D., 2011. Business statistics: A decision making approach. UK: Prentice Hall/Pearson.

Newbold, P., Carlson, W. and Thorne, B., 2012. Statistics for business and economics. UK: Pearson.

Weiers, R.M., 2010. Introduction to business statistics. USA: Cengage Learning.

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