Harvest Kitchen Sales Analysis

This solved assignment analyzes sales data from Harvest Kitchen over a two-year period. It examines the best-selling and worst-selling products, investigates sales trends across different months and seasons, and compares cash and credit sales. The analysis provides recommendations for improving sales performance based on these findings.

Report on Harvest Kitchen Shop
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Introduction
Harvest Kitchen is located at Sunshine Coast. It is a small health food shop. The Harvest kitchen
is a business under retail sale, wholesale and a box delivery. The business started two years ago
and still under process of establishment. Harvest Kitchen has organic industry that passionately
able to feed the community, take care of the earth by being chemical free, farmer by creating
ready market and customer who is the consumer by ensuring they consume chemical free
products ( Good Harvest, 2017).
Harvest Kitchen being at its establishments stages like any other business has challenges. One
major challenge is finances. The business plan may have estimated amount required running the
business but some expenses come up. Harvest Kitchen experiencing competition from other
competitors. These cause low sales in the kitchen thus low profit margins and the average sales
of products through the year as well as seasons. The Harvest Kitchen requires management
strategies. These ensure that profit made can run the business, to ensure it is self sufficient.
The Harvest Kitchen management makes use of historic data to calculate profit, predict and
forecast on future days.
Statement of Problem
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 highest selling health food products in sunshine coast?
 Where is the product with highest profit located in the shop?
 Do different months of year affect sales?
 Does payments method differ?
 Does different season affect sales?
 Does rainfall affect sales?
Research Method
The report utilized both descriptive statistics and inferential statistics analysis (Desaro S., 2011).
Descriptive statistics are useful in explaining the distribution of data. In this study made use of
bar graph histogram and measure of central locations such as average and standard deviation.
Inferential statistics were adopted to test hypothesis under study such as analysis of variance and

correlation, a measure association given by (r) = {n ∑x y ─∑x ∑y √ [n∑x2 ─ (∑x)^2 ] }÷ { [n∑y2
─ (∑y)^2] } and regression analysis at 5% level of significance (Neumann W., 2014)
Data analysis
Descriptive statistics
IBM SPSS version 20 was used for analysis. The data sets had no missing cases and variables
were probably scaled. Ordinal variable were weekday, month, seasons and date and all other
were scale variables.
Graph 1; the box plot of mean net sales of different season
The distribution of mean net sale was normal distribution. The mean net had no extreme values,
with no very low sales nor very high sales. The observed value of sales does not vary much from
mean net sales.

Graph 2; a bar graph of mean sale of products
There is high sale of water and vegetable and fruits this may be affected by the location and
climate of Sunshine Coast. Water, vegetables and fruits have nutritional value and increase
immunity of the body. The worst selling products include juicing, spices, snacks and herbal teas.
The customers of Harvest Kitchen prefer organic products. Customers indicate to avoid citric
acid products despite them being organic.

Where is the location of the product with highest profit?
Graph 3; a bar graph of location of highest sale in Harvest Kitchen
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. Most of the customers tend to buy products that have been
displayed outside which are water, vegetables and fruits. Products inside are not highly since of a
negative perception. Such perception may include spices, juicy quality and herbal tea. These may
prevent most customers from accessing inside Harvest Kitchen.

Is there significance difference of gross sales between different months of year?
ANOVA
Gross Sales
Sum of Squares Degree of
freedom
Mean
Square
F Sig.
Between
Groups 1508892.474 11 137172.043 1.300 .222
Within Groups 37349615.455 354 105507.388
Total 38858507.929 365
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.
Despite different months throughout the year it is evident that gross sale tends to remain the
same. Factors like units being sold could differ but gross sale remain along average. These
means you can make one hundred units of water sale which is equivalent to twenty bakery and
dairy.
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 gross sales from January to December
is the same, the number of customer are different depending on climate, season and nature of
month. Irrespective of the gross sale being the same throughout the year, number of units sold
differs significantly thus average sale being different. In other word, the gross sale being the
numerator and units sold or customer being the denominator average will be different.
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
The correlation factor between season of the year and rainfall is -0.067, there exist negative
correlation between season and rainfall though it is weak correlation. As season change rainfall
decrease or increase and as rainfall increase or decrease the seasons are changing. The weak
negative correlation indicates that raining and season are attached opposite manner.
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.
Despite the change in the seasons the sale of products at remains the same. Products being sold at
Harvest kitchen serve purpose through out different season and that’s why gross sale across
seasons remains the same (Perry J. & Perry E., 2014)
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
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.
Products being sold have different profit margin. Products being sold during winter may tend to
have bigger margins compared to those of summer. Summer and autumn have significance
difference (p-value is 0.033 and 0.016) but winter 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
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. Rainfall
doesn’t contribute anything to the model thus it is insignificant.
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. 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.

Discussion and Recommendation
The Harvest Kitchen data collected for the last two years of operation indicated consistency and
proved good enough to be used to make conclusion. Despite the fact that Harvest Kitchen is
facing challenge at establishment it’s as well making recommendable progress. The most best
selling product is water then vegetable and fruits. These products at Harvest Kitchen shop have
proved essential to every customer who visits the Kitchen. The Harvest Kitchen should ensure
that water is conveniently available at all points of the Kitchen. These will definitely increase the
sale. The water taste should be upheld to maintain customers and get referrals to the Kitchen.
The packaging should be descent to make it impressive taking from the bottle. The vegetables
and fruits should be naturally grown. They should be fresh and of high quality to ensure nutrition
content is maintained.
The Harvest Kitchen worst selling product include juicy and spices. The management can hire an
expert in this field to increase quality of these products. The spices and juicy are essential for
appetite and digestion thus if done in better way sales would increase. The suppliers of products
in delivery shouldn’t observe the weather in any case. Being rainy will not affect the sales being
made at Harvest Kitchen. Consumption remains across average even during rainy days. Delivery
can increase deliveries on the rainy days.
The Harvest Kitchen shop the gross sales remain average along all seasons. Display of products
along the seasons should be upheld. The Harvest Kitchen should upgrade a portal where
customers can purchase products while still at home or work place. These will increase the sale
along the season. Since during winter most customers may avoid to attend the kitchen by they
can alternatively get the commodities.
Finally, the Harvest Kitchen should get a team not working by as marketers. The management
should market the kitchen and the products that are being sold. These will definitely increase the
sales being made. Some of the worst sales in Harvest Kitchen may be caused by customer’s lack
of knowledge that a particular product is on sale at that joint.