Good Harvest Organic Farm: Sales and Seasonality Analysis Report

This report analyzes the sales performance of Good Harvest Organic Farm, a company specializing in organic produce. The analysis aims to identify challenges and propose data-driven solutions to improve business performance. The research addresses key questions regarding top-selling products, the impact of product location within the shop, monthly and seasonal sales variations, and their effects on profits and revenue. Employing statistical techniques, the report examines descriptive statistics, multiple comparisons, and ANOVA tests to evaluate the relationships between sales, location, and seasonality. Key findings reveal the highest-selling product, Banana Cavendish, and indicate that placing products at the outside front location yields higher sales. The analysis also concludes that there are no significant differences in sales across different months or seasons, and that rainfall does not affect profits. The report provides detailed conclusions after each analysis and recommends placing products at the outside front location to maximize revenue. The student used datasets, variable views, and statistical tests to reach conclusions. References are provided for the methodologies used.

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Table of Contents page
Introduction…………………………………………………………2
Problem definition and business intelligence.......................................2
Analytical techniques...........................................................................2
Variable view......................................................................................3
Sales and location analysis.................................................................6
Monthly Sales and gross monthly profits............................................8
Sales and different seasons..................................................................9
Discussion.........................................................................................10
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Introduction
Good harvest organic farm is a company, which specializes in fresh organic produce from
organic farms. Its mission is to connect local people with farms to support farmers who produce
safe produce at an affordable price (Good harvest ,2017).
This analysis seeks to deep in to the performance of the company by identifying the challenges
of the company and seeking to propose statistically proven solutions. The challenges in the
business are revenue, cost of goods and average sales.
Problem definition and business intelligence
Good Harvest as a business aims to maximize share holder wealth and therefore for decision
making purposes , the results of this analysis will accordingly inform the decisions of the
management.
The research will answer the following questions;
1. What are the top/worst selling products in terms of sales?
Are the differences in sales performance based on where the product is located in the
shop? How does this effect both profits and revenue?
2. Is there a difference in sales and gross profits between different months of the year?
3. Are their differences in sales performance between different seasons?
Analytical Techniques
The research employs appropriate statistical techniques to propose a solution for each of the
identified research questions. Looking at the two datasets , we conclude that the each measure is
set correctly .Below is a variable view of the two datasets used in our analysis.
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Dataset 2
Dataset 1
The variables in the dataset have been correctly been scaled. In dataset 4 , all the
variables :product class , product name , product category , weight ,total sales , COGS, net
profit ,location and profit total have been matched accordingly with their respective variable
types and measures. The same is true for dataset 3.
Descriptives
Descriptive statistics tell us how the data look, and what the relationships are
between the different variables in the data set.( Pallant, J. (2007)
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Analysis 1; Results of Descriptive analysis
N Minimum Maximum Mean
Std.
Deviation
Total Profit 1034 .00 8702.93 164.7338 482.10651
Total Sales ($) 1034 0 17276 369.96 1014.719
Net Profit ($) 1034 0 8703 164.74 482.106
Cost of Goods
($)
1034 0 8573 205.22 561.072
Valid N
(listwise)
1034
From the above output , the maximum for the total profit ,total sales ,net profit ,cost of goods
sold are 8702.93 ,17276 , 8703 and 8573 respectively. The mean for the same variables are
164.73, 369.96, 164.74 and 205.22 respectively. The above descriptive answer the first question
as to which product is highest selling .The product ,Banana Cavendish is the highest sold product
with 17276 sales.
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Multiple comparisons
Total Sales ($)
LSD
(I) Location of
product in
shop
(J) Location of
product in
shop
Mean
Difference (I-
J) Std. Error Sig.
95% Confidence Interval
Lower Bound Upper Bound
Front Left 354.531* 90.712 .000 176.53 532.53
Outside Front -2811.617* 284.761 .000 -3370.40 -2252.84
Rear 36.679 104.135 .725 -167.66 241.02
Right 332.860* 93.438 .000 149.51 516.21
Left Front -354.531* 90.712 .000 -532.53 -176.53
Outside Front -3166.148* 278.682 .000 -3713.00 -2619.30
Rear -317.851* 86.136 .000 -486.87 -148.83
Right -21.671 72.842 .766 -164.61 121.27
Outside Front Front 2811.617* 284.761 .000 2252.84 3370.40
Left 3166.148* 278.682 .000 2619.30 3713.00
Rear 2848.297* 283.336 .000 2292.31 3404.28
Right 3144.477* 279.582 .000 2595.86 3693.09
Rear Front -36.679 104.135 .725 -241.02 167.66
Left 317.851* 86.136 .000 148.83 486.87
Outside Front -2848.297* 283.336 .000 -3404.28 -2292.31
Right 296.181* 89.003 .001 121.53 470.83
Right Front -332.860* 93.438 .000 -516.21 -149.51
Left 21.671 72.842 .766 -121.27 164.61
Outside Front -3144.477* 279.582 .000 -3693.09 -2595.86
Rear -296.181* 89.003 .001 -470.83 -121.53
*. The mean difference is significant at the 0.05 level.
Conclusion
The front and rear locations do not show significant difference in sales (P-value=0.725)
Also , sales in the right and the left location are not significantly different statistically.(p-
value=0.766)
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The rest of the locations , front-left ,front-outside ,front-right and the other locations with p-value
less than 0.05 have significant differences in sales.
For outsidefront, the sales are significantly different across each location.(p-value=0.00)
It is therefore advisable to place the products on the outsidefront location.
Profits and revenues generated will be higher if the products are placed at the outside front of
the shop .
Analysis 3: Is there a difference in sales and gross profits between different months of the
year?
Group Statistics
Month of the
year N Mean Std. Deviation Std. Error Mean
Profit Total January 31 33.0187 41.52022 7.45725
December 31 37.2877 29.33486 5.26870
Independent Samples Test
Levene's Test for Equality of
Variances t-test for E
F Sig. T df Sig. (2-tailed) Mean Differ
Profit Total Equal variances assumed .055 .816 -.468 60 .642 -4.2
Equal variances not
assumed
-.468 53.976 .642 -4.2
Group Statistics
Month of
the year N Mean Std. Deviation Std. Error Mean
Gross_Sales >= 12 31 1082.74 413.527 74.272
< 12 335 1041.48 317.551 17.350
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Independent Samples Test
Levene's Test for Equality of
Variances t-test fo
F Sig. T df Sig. (2-tailed) Mean Di
Gross_Sales Equal variances assumed 2.337 .127 .673 364 .501
Equal variances not
assumed
.541 33.354 .592
Total profits on average do not vary between the various months of the year .The p-value is more
than 0.05.Gross sales also do not significantly vary between different periods of the year.
Analysis 4: Are their differences in sales performance between different seasons?
Running the anova test to test the:
Null hypothesis :exists no significant difference between gross sales between different seasons
Alternative hypothesis:exists significant difference
The ouput is below:
ANOVA
Gross_Sales
Sum of
Squares df Mean Square F Sig.
Between Groups 560240.410 3 186746.803 1.765 .153
Within Groups 3.830E7 362 105796.319
Total 3.886E7 365
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From the output above ,(F(3,1.765), p-value=.153) ,the p-value is greater than 0.05 ,hence we
fail to reject the null hypothesis thus the exist no significant difference in gross sales between
different seasons.
Relationship with rainfall and profits
Rainfall does not affect profits because no relationship was established between seasons and
sales.
Discussion on the Results and Recommendations
It is therefore advisable to place the products on the outsidefront location.This is because sales
tend to be higher if products are placed at this location.The companys business do not vary with
seasons .Conclusions in detail are explained at the explanations after each analysis.
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References
Pallant, J., 2007. SPSS survival manual: A step-by-step guide to data analysis using SPSS version 15. Nova
Iorque: McGraw Hill.
Good harvest. 2017. Sunshine Coast Organic Fresh Product Home Deliver, Available at:
<https://www.goodharvest.com.au">https://www.goodharvest.com.au> [Accessed 9 October 2017]
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