Harvest Kitchen Sales Analysis Report

This assignment presents a sales analysis report for Harvest Kitchen. It examines factors such as best-selling products (water, vegetables, fruits), the impact of seasons on sales, and customer purchasing habits (cash vs. credit). The report includes statistical analyses with p-values to support findings. Recommendations are provided for Harvest Kitchen's management based on the analysis.

Harvest Kitchen shop Report
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Contents
Introduction.............................................................................................................................................2
Definition of problem and necessary business intelligence......................................................................2
Results of Selected Business analytics.....................................................................................................3
Descriptive statistics............................................................................................................................3
Analytics one: Which products are best selling and worst selling?......................................................4
Analysis two: Is sales affected by different seasons?...........................................................................5
Analysis three: Does rainfall affect sales and profit?...........................................................................6
Analysis Four: Does gross profit between different months significant different?..............................8
Analysis five: Is there a difference in payments methods?..................................................................8
Results discussion and recommendations................................................................................................9
References.............................................................................................................................................10
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Introduction
Harvest kitchen is a small health food shop. It is located on the Sunshine Coast. It is a point
where customers come to collect their organic food stuffs. The harvest Kitchen is under a
business that is divided into retail, wholesale and box delivery system. The Harvest Kitchen
makes sale to retailers for different commodities that are organically produced. Most of the
customers prefer farm products that are chemical free to ensure that they prevent cancer of
different parts of the body. The business that Harvest kitchen is under has been in operation for
the last two years. These are the initial stage of a starting business and it is having some
challenges.
Major challenge that the Harvest kitchen has been undergoing through is inadequate capital.
Most of the customers don’t have information about the services that are being provided at
Harvest Kitchen. The sales made at Harvest Kitchen the profit are insufficient to run the
business. The cost of running the business is thus tough enough. Organic farming and products
are expensive to grow since they are naturally grown (Good Harvest, 2017).
Harvest Kitchen located at Sunshine coast has created opportunity through employment of six
employees, one delivery van, a retail outlet and a cold warehouse. Data entry is important and
important in making inference about the future of the business.
Definition of problem and necessary business intelligence
The study adopted inferential and descriptive business analytic method to answer challenges
facing Harvest Kitchen shop. Descriptive statistics and data presentation method were used to
describe the distribution of variable of interest while inferential statistics were used to answer
business problems under study such analysis of variance, regression analysis and confidence
intervals (Desaro S., 2011). The study problems were:
 Which products are best selling and worst selling?
 How does the location of products in shop affect profit?
 Does gross profit between different months differ?
 Does gross sales between different months significant different?
 Is sales affected by different seasons?
 Does rainfall affect sales and profit?
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Results of Selected Business analytics
Descriptive statistics
The first task was to check the variable on data set if they were properly scaled. There were no
missing cases in data set. Weekday, month, seasons and date were ordinal and the rest variables
were scale variable. Below is screenshot of data used in analysis of this report.
The nets sales of products were normally distributed. There were no outliers that very high
number of sales or too low number of sales in the data. The actual number of sales did not vary
much with mean net sales; the standard deviation of mean net sales was small. The mean net
sales were not significance different in different seasons of the year. Below is box plot of mean
net sales in different season of year.
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Analytics one: Which products are best selling and worst selling?
The best selling product is water, then vegetables and fruit. There is high sale of water and
vegetable and fruits this may be affected by the location and climate of Sunshine Coast. The
worst selling products include juicing, spices, snacks and herbal teas. The customers of Harvest
Kitchen prefer organic products.
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Analysis two: Is sales affected by different seasons?
ANOVA
Gross_Sales
Sum of
Squares
Df Mean Square F Sig.
Between
Groups 560240.410 3 186746.803 1.765 .153
Within Groups 38298267.52
0 362 105796.319
Total 38858507.92
9 365
Multiple Comparisons
Dependent Variable: Gross_Sales
Tukey HSD
(I) Season of the
year
(J) Season of the
year
Mean
Difference (I-
J)
Std. Error Sig. 95% Confidence Interval
Lower
Bound
Upper Bound
Summer
Autumn -22.938 48.089 .964 -147.06 101.18
Winter 58.785 48.089 .613 -65.33 182.90
Spring -46.442 48.220 .770 -170.90 78.02
Autumn
Summer 22.938 48.089 .964 -101.18 147.06
Winter 81.723 47.957 .323 -42.06 205.50
Spring -23.504 48.089 .962 -147.62 100.61
Winter
Summer -58.785 48.089 .613 -182.90 65.33
Autumn -81.723 47.957 .323 -205.50 42.06
Spring -105.226 48.089 .128 -229.34 18.89
Spring Summer 46.442 48.220 .770 -78.02 170.90
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Autumn 23.504 48.089 .962 -100.61 147.62
Winter 105.226 48.089 .128 -18.89 229.34
The p-value is large therefore gross sales between different seasons does not differ. The means
sales of different seasons are almost the same. Turkey multiple tests shows that the p-values of
interaction between different are greater than the level of significance (5%), this shows that there
is no statistical significance mean gross sales between different seasons.
The mean sales are not statistically significance difference. They are the almost the same, though
the data are not normally distributed they have extreme values. As show below by the box plot
Analysis three: Does rainfall affect sales and profit?
Regression analysis is use to test the causal relationship between two or more variables. The first
step is to check the linear relationship using scatter plot. The linear relationship between rainfall
and gross sales is weak relationship (Neuman W., 2014). As rainfall increase there is slight
decline in gross sale as shown below by a scatter plot
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The regression analysis at 5% level of significance revealed that rainfall is insignificant in
predicting gross sales. The R squared is 0.02 which means only 2% variation of average sales is
explained by the rainfall. Thus the model is poor fit and rainfall can be dropped as predictor of
average sales in Harvest Kitchen shop. The p-value of co efficient of rainfall is 0.360 greater
than 0.05, thus the co-efficient of rainfall is not significant and can be dropped from the
regression model. As below
Model Summary
Model R R Square Adjusted R
Square
Std. Error of
the Estimate
1 .049a .002 .000 3.991
a. Predictors: (Constant), Rainfall
ANOVAa
Model Sum of
Squares
df Mean Square F Sig.
1 Regression 13.373 1 13.373 .840 .360b
Residual 5654.154 355 15.927
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Total 5667.527 356
a. Dependent Variable: Average Sale
b. Predictors: (Constant), Rainfall
Analysis Four: Does gross profit between different months significant different?
The gross profit is same for different months in year. There is no significance different as shown
above by the box plot. The p-value is 0.222 with (11, 354) degrees of freedom greater than 0.05
so we fail to reject null hypothesis and conclude that the gross sales in different months is
statistically insignificance.
Analysis five: Is there a difference in payments methods?
ANOVA
Sum of
Squares
df Mean
Square
F Sig.
Cash Total
Between
Groups 313151.702 11 28468.337 1.214 .276
Within Groups 8303129.043 354 23455.167
Total 8616280.745 365
Credit Total Between
Groups
1788575.177 11 162597.743 3.322 .000
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Within Groups 17328958.23
6 354 48951.859
Total 19117533.41
3 365
The p-value of cash payments in different months is 0.276 and there no is statistical significance
difference of mean sale by cash. Those customers who bought products in cash did it
consistently. The p-value of credit sale is 0.00 which suggest statistical difference between more
than two mean sales by credit in different month. Some customers bought by credit in one month
and in cash in another month.
Results discussion and recommendations
1. The best selling commodity at Harvest Kitchen is water, followed by vegetables and
fruits. Most of the customers at Sunshine Coast tend to consume more water because of
the hot climate. Water, vegetables and fruits are of nutritional value for both young and
aged persons. The worst selling product is juices, snacks and spices. Most of these
products are fast foods and most tend to consider them as unhealthy. Harvest Kitchen
should ensure that water; vegetables and fruits are conveniently assessable to customers,
fresh and are of high quality.
2. The best sale is made at on the left side of the Harvest Kitchen. Water, vegetables and
fruits are mostly sold from this point thus more profit is obtained on these edge. Those
commodities that are found outside front are best selling as compared to those inside
don’t have a big sale. The management Harvest Kitchen should advertise those products
inside while selling outside.
3. Different months of the year have different gross profits. The gross profit doesn’t remain
cost across the year. Products sold at different times of the year have different prices and
different gross sales.
4. Gross sale across seasons is different. Some seasons have a higher gross sale as compared
to others. For instance, during summer the best selling product which water, vegetables
and fruits t end to have a higher sale while during winter the same products are in low
sales. Sale of different months of the year differs during the holiday months in a year
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Harvest Kitchen will have a higher sale compared working moths. Harvest Kitchen
should adjust according to seasons and months of the year.
5. During rainy seasons or not the gross sale remains constant. This signifies that the
Harvest Kitchen shouldn’t consider the rainy season in their product sale.
6. Customers tend to buy using their most convenient payment mode. Some of the
customers pay in cash whiles other pay credit cash. The management of Harvest Kitchen
should provide both means of payment. This way customer will buy by any means.
References
Good Harvest (2015) Harvest Kitchen [online]http://www.goodharvest.com.au
Neuman, W. L. (2014). Social Research Methods: Qualitative and Quantitative Approaches, 7th
Edition. Pearson Education Limited: UK.
The University of Queensland (2017). Business Management and Finance [online] Retrieved
from www.ua.edu.au/research.
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