Harvest Kitchen Sales Analysis

This assignment involves analyzing sales data for a business named Harvest Kitchen. The analysis focuses on determining the most popular products, understanding sales patterns across different seasons, and investigating customer purchasing behavior. The report includes insights into factors influencing sales, such as product variety, pricing strategies, and seasonal demand.

Report on Harvest Kitchen Shop
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INTRODUCTION
Harvest kitchen is small health food shop located on Sunshine Coast. It is divided into retail
sector; harvest kitchen and box delivery system. As small health food shop in second year of
operation since it opening may be affected by different season. One of main challenges facing
start up business is negative perception, people view them as not sufficient to provide quality
services and goods, and this led to low sales (Amyx, 2005). Understanding the trend of profit and
sales is important tool of management. Human capital and money is one of big challenges
affecting Harvest Kitchen. The management of Harvest Kitchen therefore needs to make proper
decisions on when to require more human capital, expect rise in sales and require high delivery.
This will help to optimize available resources for maximum profit. To achieve this use of
historical of the business is necessary.
PROBLEM DEFINITION AND BUSINESS INTELLIGENCE
Historical data was collected for the last one year of trading and descriptive and inferential
statistics were used to answer the following research questions:
 What are top and worst selling health food products in sunshine coast?
 Where is the product with highest revenue located?
 Do the sales differ within different months of year?
 Is there a difference in payments method?
 Is there difference in number of sales between different seasons?
 Does rainfall affect sales?
The report adopted both inferential statistics and descriptive statistics. Descriptive statistics are
used to describe the data and the distribution of data. In this report histogram, bar graph and
measure of central tendency such as mean and mode were used. Inferential statistics are used to
infer about the data test hypothesis under study. In order to answer study questions. The report
made use of analysis of variance, which is used for continuous data which is normally
distributed. Correlation analysis which measure association between two variables of interest, the
Pearson Moment correlation coefficient is given by (r) = {n ∑x y ─∑x ∑y √ [n∑x2 ─ (∑x)^2 ]

}÷ { [n∑y2 ─ (∑y)^2] }. Regression analysis was also used in the report analysis. The level of
significance was 0.05 (Neuman W., 2014).
Data analysis
Descriptive statistics
Data analysis was done on IBM SPSS version 20. All variables in the data set were properly
scaled and with no missing cases. Date, weekday, month and seasons were ordinal and the rest of
the variables were scale variables.
Graph 1; the box plot of mean net sales of different season
There were no outliers of mean net sales of different season in a year. The data was normally
distributed. The sale was not affected by extreme values, where there are either vey low sales of
very high number of sales.

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.
Where is the location of the product with highest profit?
The product with highest profit was found to be outside front location of the shop. The products
found at this place had a mean profit of $1809.63 while those located at left of the shop had
lowest mean profit of $99.55.

Is there significance difference of gross sales between different months of year?
ANOVA
Gross Sales
Sum of
Squares
Df Mean Square F Sig.
Between
Groups 1508892.474 11 137172.043 1.300 .222
Within Groups 37349615.45
5 354 105507.388
Total 38858507.92
9 365
The p-value is 0.222 which is greater than the significance level 0.05 therefore we fail to reject
null hypothesis that the mean gross of monthly sale is insignificance different and conclude that
there is no statistical evidence of difference in gross monthly sales of products in Harvest
Kitchen. The gross monthly sales are almost the same for all months some may have slight.
Is there any difference in number of average sales between different months?
ANOVA
Average Sale
Sum of
Squares
df Mean Square F Sig.
Between
Groups 335.651 11 30.514 1.979 .030
Within Groups 5333.831 346 15.416
Total 5669.483 357
The p-value is 0.03 at 0.05 levels of significance we conclude that there is significance
difference of mean sale between different months. The mean sale of different months is

statistically significance. They are not homogenous. The mean sales from January to December
the same, the number of customer are different depending on climate, season and nature of
month.
Is there any correlation between rainy day and different seasons?
Correlations
Season of the
year
Rainfall
Season of the
year
Pearson
Correlation 1 -.067
Sig. (1-tailed) .100
N 366 365
Rainfall
Pearson
Correlation -.067 1
Sig. (1-tailed) .100
N 365 365
Using Pearson correlation test season and season has Pearson factor of 1 meaning that there are
uncorrelated. The correlation factor between season of the year and rainfall is -0.067, there exist
negative correlation between season and rainfall though it is week correlation. As season change
rainfall decrease or increase or vice versa.
Is there difference in number of sales between different seasons?
Report
Gross Sales
Season of the
year
Mean N Std.
Deviation
Summer 1042.44 91 349.184
Autumn 1065.37 92 341.391
Winter 983.65 92 264.131
Spring 1088.88 91 339.446

Total 1044.97 366 326.285
ANOVA Table
Sum of
Squares
df Mean Square F
Gross Sales * Season of
the year
Between Groups (Combined) 560240.410 3 186746.803 1.765
Within Groups 38298267.520 362 105796.319
Total 38858507.929 365
The p-value is 0.153 which is greater than 0.05 the significance level therefore we conclude that
there is no significance difference of gross sales of product in different seasons of the year. There
exist a positive weak association between gross sales of product and different seasons of a year.
Is there difference in gross profit between different seasons?
ANOVA
Sum of
Squares
df Mean Square F Sig.
Between
Groups 28591.757 3 9530.586 11.456 .000
Within Groups 301149.197 362 831.904
Total 329740.954 365
Multiple Comparisons
Dependent Variable: Profit Total
Bonferroni

(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 11.68574* 4.26430 .039 .3733 22.9982
Winter 3.78530 4.26430 1.000 -7.5272 15.0978
Spring -12.79330* 4.27593 .018 -24.1366 -1.4499
Autumn
Summer -11.68574* 4.26430 .039 -22.9982 -.3733
Winter -7.90043 4.25263 .384 -19.1820 3.3811
Spring -24.47903* 4.26430 .000 -35.7915 -13.1666
Winter
Summer -3.78530 4.26430 1.000 -15.0978 7.5272
Autumn 7.90043 4.25263 .384 -3.3811 19.1820
Spring -16.57860* 4.26430 .001 -27.8911 -5.2661
Spring
Summer 12.79330* 4.27593 .018 1.4499 24.1366
Autumn 24.47903* 4.26430 .000 13.1666 35.7915
Winter 16.57860* 4.26430 .001 5.2661 27.8911
*. The mean difference is significant at the 0.05 level.
The p-value is 0.00 which is less than 0.05 (level of significance) therefore we reject null
hypothesis and conclude that there is significance difference of gross profit in different seasons.
Summer and autumn have significance difference (p-value is 0.033 and 0.016) but summer and
spring has no significance difference (p-value is 0.811).
Is rainfall a good predictor of average sales?
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
Total 5667.527 356
a. Dependent Variable: Average Sale
b. Predictors: (Constant), Rainfall
Coefficientsa
Model Unstandardized
Coefficients
Standardized
Coefficients
t Sig.
B Std. Error Beta
1 (Constant) 18.441 .228 80.902 .000
Rainfall .020 .021 .049 .916 .360
a. Dependent Variable: Average Sale
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.
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
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. The p-value of credit sale is 0.00 which suggest statistical
difference between more than two mean sales by credit in different month.
RESULT
From our analysis we conclude that,

The Harvest kitchen has its most selling product as water. Water is a requirement for all living
creators and thus to humans. The Harvest kitchen should increase the order the make for the
water sale. The increase the points in which water sales are made so as to make it convenient.
They also check the fashion that water has been packed to increase the sales. The taste of the
water should be considered to improve sales. Most of the customers consider the convenience of
the points of sale of water, the fashion of package and the taste of water. Though in some case
some customers tend to buyer to acquire the bottle then they refill from their taps. The second
highest selling product is vegetables followed by fruits. Vegetables and fruits have prevention to
chronic diseases. The vegetables and fruits have been attributed to a longer life. The flesh
vegetables and fruits should be a target to increase the sales at Harvest kitchen. The different
varieties of vegetables and fruits should be available at all points to increase sales. The largest
profit products are those located outside the shop. This is the positioning of the products where
they are assessable to customers. The sales representatives should encourage the customers to
assess more products of Harvest kitchen.
The rainy days cannot be used to estimate the average sales of the shop. The rainy days we have
customer attending the Harvest kitchen to acquire fresh commodities as well as other days. It’s a
clear indication that all days sale are not affected being rainy or not.
Gross profit differs significantly among different seasons of the year. The mean sale of spring
was 1088 while the least was winter with mean of 983. The Harvest kitchen should adjust its
sales as per the seasons to increase their sales. During the winters production should be reduced
since most of the customers remain indoors while spring they are eager after a long duration of
cold season.
Gross sales do not differ significantly among different seasons of the year. The commodities that
are sold during the different seasons do not differ thus the gross sale being equivalent all through
the seasons. Most of the customers tend to buy similar good through out the year. The Harvest
kitchen should attain all products being required by the customer at all seasons.
There is a negative correlation between rainy days and different seasons. During the rainy days
the sale is different in seasons. A negative correlation indicates being rainy day does not affect

the season and sale of that specific day. The Harvest kitchen should not be worried about the day
being rainy and season since the sale of the day remains the same.
References
Alderman A. & Salem B. (2010). Survey design. Plastic Reconstruction Surgery, vol 4, pg 9.

Fisher R.A. 1925. Methods For Research Work; Macmillan Publishers: London.
Good Harvest (2015) Harvest Kitchen [online]http://www.goodharvest.com.au
Kothari, C. R, (2004), Research Methodology Methods and Techniques New Age International
(P) Limited, Publishers New Delhi
Neuman, W. L. (2014). Social Research Methods: Qualitative and Quantitative Approaches, 7th
Edition. Pearson Education Limited: UK.
Perry, J. & Perry, E. (2014). Contemporary Society: An Introduction to Social Science, 12th
Edition. Pearson Education, Inc.: Singapore