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
NMinimumMaximumSumMeanStd. Deviation
Day of the Year366136667161183.50105.799
Month of the year36611223846.513.456
Season of the year366149152.501.117
GST Inclusive366027141876114.4248.723
GST Exclusive36602492340583930.56303.827
Gross Sales366026423824601044.97326.285
Net Sales366023703712201014.26313.986
Cash Total36601195147969404.29153.643
Credit Total36601407214036584.80228.860
Visa Total36601407203441555.85244.870
Mastercard Total3660399808622.0967.823
House Account366-26411131368437.39113.204
Total Orders36601292032755.5415.844
Average Sale358861663118.523.985
Staff Cost36617035191022248.6952.418
Weekday3661714634.001.998
Rainfall36506314523.989.811
Profit Total366-33.98271.971.12E430.709830.05661
Valid N (listwise)357

Descriptive of Product mix dataset:

Descriptive Statistics
NMinimumMaximumSumMeanStd. Deviation
Product Class (number)10341301546414.968.515
Quantity1034137697434871.90212.400
Weight209029131615677.30242.323
Total Sales ($)1034017276382540369.961014.719
Cost of Goods ($)103408573212203205.22561.072
Net Profit ($)103408703170338164.74482.106
Location of product in shop10341532183.111.526
Total Profit1034.008702.931.70E51.6473E2482.10651
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
tdfSig. (2-tailed)Mean Difference95% Confidence Interval of the Difference
LowerUpper
Cash Total50.340365.000404.287388.49420.08
Credit Total48.885365.000584.798561.27608.32
Visa Total43.427365.000555.849530.68581.02
Mastercard Total6.232365.00022.09415.1229.07
House Account6.318365.00037.38825.7549.02

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
ModelSum of SquaresdfMean SquareFSig.
1Regression201321.0561201321.0561.896.169a
Residual3.866E7364106201.063
Total3.886E7365
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
ModelSum of SquaresdfMean SquareFSig.
1Regression17979.279117979.27920.992.000a
Residual311761.675364856.488
Total329740.954365
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
ModelSum of SquaresdfMean SquareFSig.
1Regression14613.958114613.958.137.712a
Residual3.884E7364106713.994
Total3.886E7365
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
RainfallProfit Total
RainfallPearson Correlation1.008
Sig. (2-tailed).885
N365365
Profit TotalPearson Correlation.0081
Sig. (2-tailed).885
N365366

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
ModelSum of SquaresdfMean SquareFSig.
1Regression4.71314.713.296.587a
Residual5664.77035615.912
Total5669.483357
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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