Detailed Business Analysis and Statistics Report for Harvest Kitchen

This report analyzes Harvest Kitchen's business performance, focusing on sales, profit, and operational challenges. The study employs descriptive statistics, ANOVA, and regression analysis to address key business questions, such as the top-selling products, the impact of location and seasons on sales, and the relationship between sales and profit. The findings reveal insights into payment method preferences (Visa being most used), product performance (water being the best-selling), and location-based sales variations. The analysis includes graphical representations and statistical tests to support the conclusions. The report offers recommendations for the CEO to make informed decisions, including optimizing product placement and managing sales strategies. Overall, the report provides a comprehensive overview of the business's current state and suggests actionable strategies for improvement.

BUSINESS ANALYSIS AND
STATISTICS

TABLE OF CONTENTS
INTRODUCTION...........................................................................................................................1
Problem statement and selection of tools...............................................................................1
VISUALIZING DESCRIPTIVE STATISTICS..............................................................................2
Graphical presentation............................................................................................................3
RESULTS .......................................................................................................................................5
Question 1...............................................................................................................................5
Question 2...............................................................................................................................6
Question 3...............................................................................................................................6
Question 4...............................................................................................................................6
DISCUSSIONS................................................................................................................................7
Recommendation.............................................................................................................................9
REFERENCES .............................................................................................................................10
APPENDIX ...................................................................................................................................11
Appendix 1:..........................................................................................................................11
Appendix 2...........................................................................................................................11
Appendix 3...........................................................................................................................11
Appendix 4...........................................................................................................................16
Appendix 5...........................................................................................................................18

INTRODUCTION
In today’s period, companies have to take number of decisions and devise plan for sales
and profit maximization at minimum of cost. The process of decision-making involves critical
analysis of the data and statistical software are extensively useful for the managers in data
analysis. Harvest Kitchen is an intimate restaurant that serves quality food services to the people
using high-quality ingredients and sustainable sourcing from domestic suppliers. Currently,
business is experiencing several operational difficulties due to high increase in cost of goods sold
(COGS) consistently with the organic industry. Such occurrence arise challenges for the
company to maximize their sales with an adequate margin on COGS. Thus, the target of the
report is to present considerable insight to the restaurant CEO for making profitable business
decisions. Owing to this, necessary analysis including descriptive & inferential statistics such as
one-way anova, t-test etc. along with visualize presentation will be performed and communicated
to the CEO. The main purpose of performin such analytical tools is to answer specific business
problem and issues such as examining sales considering product location & seasons, relationship
between sales & gross profit, differences in the payment methods and many others.
Problem statement and selection of tools
Managing revenue in business and earning of good amount of profit is the one of the
main problem that firm is currently facing in its business. In order to solve this problem some
research questions are prepared which are given below.
11 What are the top/worst selling products in terms of sales?11 Is there a difference in payments methods?11 Does location have any impact on sale of the product?11 Is there a difference in sales and gross profits between different months of the year?11 Are their differences in sales performance between different seasons?
In order to answer first question descriptive statistics will be used and by doing best and
worst products will be identified by considering mean values. In second question ANOVA will
be used to identify whether there is significant difference between sales that is earned across
different locations of retail store.
In order to identify whether there is significant difference in sales and profit in previous
months regression analysis approach will be used. This is because sales and profit are those
1

variables that are associated with each other. Hence, by applying regression approach change
that comes in gross profit with deviation in sales will be identified. In fourth question sales
performance across different regions is identified by using ANOVA table. This is because by
using ANOVA significant mean difference can be identified in sales across different regions.
VISUALIZING DESCRIPTIVE STATISTICS
Descriptive statistics are used to present general characteristics of the dataset through
statistical measures such as mean, median, mode and standard deviation (Haughton and Kelly,
2015). Mean showcase average value for a given data while standard deviation provides useful
and meaningful insight about data variability from the mean.
 The average sales of the fruit shop is $369.96 10 (SD =1014.719)
 Average cost of goods sold by company at is reported to $205.22 (SD = 561.072)
 Average net profit earned by fruit shop is reported to $164.74 (SD = 482.106)
2

Graphical presentation
3

4

RESULTS
Question 1
From the statistical data it has been resulted that, there are two products identified which
are at the best and the worst condition. Further, product like water is at the best position where
mean value in data set is 733.706. Apart from this, snacks are at the worst condition in context
to selling where average is 0.525.
When looking at the payments methods used by the company then differences come up
to the moderate to high level. Mean value of different payments methods like Visa, Mastercard,
Cash and Credit is 555.85, 67.823, 153.643 and 228.860 respectively.
5

Question 2
H0: There is no significant difference in the sales performance at different locations of the
product.
H1: There is no significant difference in the sales performance at different locations of the
product.
Test statistics: 5%
Statistical Test performed: One –Way Anova test is applied to determine that sales performance
at different location shows significant difference or not.
Appendix 3
Sig. value as per one-way anova test is found (F= 37.176, P= 0.000<0.05) shows significant
differences in the products sales amount at different locations.
From further analysis, it is found that outside front location reported maximum average sales
value with $3384.37 whereas front location generate sales worth $ 572.75, left at $218.22, rear
with $536.07 and right at $239.89.
Question 3
H0: There is no significant mean difference between sales and profit of the business firm.
H0: There is significant mean difference between sales and profit of the business firm.
It is one of important tool that reflect relationship that exist between multiple variables.
By using this tool it is identified if there is big difference in terms of rate of change among
variables (What is linear regression, 2017). Value of level of significance is [P= 0.00<0.05] and
this reflect that there is significant mean difference between sales and profit. This means that
with change in sales significant change comes in profit. Value of [R square = 0.200] which
means with change in independent variable 20% comes in dependent variable. Correlation value
0.44 which reflect that there is moderate relationship between both variables. It can be said that
alternative hypothesis is accepted.
Question 4
H0: There is no significant mean difference between sales performance and different seasons.
6

H1: There is significant mean difference between sales performance and different seasons.
From the ANOVA test applied on data set of sales and seasons it has been identified
that, level of differences is at the lower level. The reason is that result generates in mean
significant difference is 0.176 only which lower as compared to standard value i.e. 0.5. In the
present case condition of significant difference is P < 0.5. Further, there is no mean significant
difference between sales performance and different.
In the company or workplace, among data set when relationship among two independent
variables required to identify then correlation tool is applied. Further, as per the present scenario
value of correlation between rainfalls as well as profit level is -0.35.
DISCUSSIONS
It can be discussed from the output generated that, fastly selling product in the company
is water due to having the highest value of average tool. Apart from this, water product is
extremely consumed by majority of customers from the firm as compared to other provided by it.
When looking at the opposite condition then, snacks are very slowly purchased by the customers
from selected enterprise. Further, selling of snacks from the organisation is very slow which is
unfavourable condition for it. Therefore, it can be assessed that product of the firm i.e. water is at
the best or top condition in the workplace. However, snacks are at the worst position among all
the goods and services in context to sales.
The selected organisation considering different kinds of methods and ways which help to
the customers in order to make payments of product purchasing. Those methods involved in the
business entity include Visa, Mastercard, Cash and Credit. Among them some are used at the
frequent basis and some ways used at the rare time. From the data set it has been discussed that
Visa card are used at the majority level as compared to other stated ways. When looking at the
rarely considered payment then it is Mastercard. The reason is that Visa card provide better
services and has easy procedure in comparison to Mastercard. Further, at the second and third
position two methods incurred which are like Credit and Cash respectively. Hence, Visa card is
highly used by the consumers for making payments of the products purchased.
7

As per the descriptive statistical result, it is necessary to report that average sales of the
business is above the cost incurred which derive net profit for the fruit shop (Burch and et.al.,
2015). However, according to the standard deviation, sales of various products significantly
differ from each other and items which fall in the product category of snacks, Tinned G, stocks S
were generated the lowest turnover, in contrast, water products generate the highest sales.
In examination of sales results at different product location, it is determined that products
and items that are placed outside front generate consumer attention and therefore, their sales
performance is extremely high in comparison to the other locations. However, on the other side,
products that are presented at front location generate sales worth $ 572.75, left at $218.22, rear
with $536.07 and right at $239.89. The result clearly demonstrates that outside front location
generates maximum sales value whereas rear location generate the lowest turnover. Thus, as per
the discussion made, it seems clear that no doubt, yes, there is difference between sales
performances among various locations. It affects both the profitability and revenue, because,
different products which have been kept outside front location bring maximum revenue to the
fruit shop results in higher profitability. However, on the other hand, other location generates
comparatively fewer revenues, henceforth; contribute less to maximize overall profitability.
According to the findings, company can be advised to maintain adequate arrangement
considering location aspect. Firm must disclose all the items outside front, so that, maximum
number of buyers can be attracted towards the business, which in turn, contribute towards
boosting total sales and net profitability as well.
In order to assess level of mean significant difference on a particular variable then
ANOVA statistical tool is applied on data set. In the present scenario this specific tool is
implemented on the sales performance which differ because of changing in seasons. As per the
rule of ANOVA test when P value is higher than 0.5, then it can be said that there is significant
difference and vice-versa. In the current case study, value is only 0.716 which clearly shows that
due to changing in seasons of country sales not affected. In opposite to this, if P value is greater
than in the present scenario then revenue generation position will affect. Hence, it can be
suggested to the firm that it should continue with production and selling even season changes in
the country.
The method or statistical tactic which reflects relation between interrelated data set or
variables is correlation. Value of this method always remains among -1 to +1 which shows
8

positive and negative relation respectively (Janssen and Laatz, 2017). On the basis of correlation
value it has been discussed that, there is negative relationship among rainfalls and profit level.
Therefore, one variable will create highly negative impact on another. In the present data set,
rainfall is independent variable as well as another depended. It can be interpreted that, if level of
rainfall enhance in the country then profit will decline with 35% in the company.
Sales and profit are closely inter-related to each other and due to this reason with change
in one variable huge change comes in other one. It must be noted that profit is computed after
subtracting entire expense from sales. Thus, sales are base of entire calculation and due to this
reason if it changes slightly big variation come in dependent variable. Hence, in order to earn
sufficient amount of profit it is necessary to maintain stiff control on sales and expenses amount.
By doing so profitability can be increased in business.
Recommendation
On the basis of above discussion it is recommended that focus must on controlling sales
and profit in the business. In this regard attractive schemes must be prepared which make
different image of business firm among its competitors. Cost control strategy must be prepared in
the business and by doing cost must be controlled and curtailed in the business. It is also
recommended that managers must focus on most profitable products and must purchase less
quantity of those products that are generating less profit for the firm. By doing the best use of
cash can be done in business and profit can be increased. Location of products must be chosen
carefully so that sales can be maximized.
9

REFERENCES
Burch, G.F. and et.al., 2015. An Empirical Investigation of the Conception Focused Curriculum:
The Importance of Introducing Undergraduate Business Statistics Students to the “Real
World”. Decision Sciences Journal of Innovative Education. 13(3). pp.485-512.
Haughton, J. and Kelly, A., 2015. Student Performance in an Introductory Business Statistics
Course: Does Delivery Mode Matter?. Journal of Education for Business. 90(1). pp.31-43.
Janssen, J. and Laatz, W., 2017. Schneller Einstieg in SPSS. In Statistische Datenanalyse mit
SPSS (pp. 5-51). Springer Berlin Heidelberg.
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APPENDIX
Appendix 1:
Descriptive
Statistics
N Minimum Maximum Mean Std. Deviation
Total Sales ($) 1034 0 17276 369.96 1014.719
Cost of Goods ($) 1034 0 8573 205.22 561.072
Net Profit ($) 1034 0 8703 164.74 482.106
Valid N (listwise) 1034
Appendix 2
Descriptiv
e Statistics
N Minimu
m
Maximu
m
Mean Std.
Deviation
Visa_Total 366 0 1407 555.85 244.870
Mastercard_Tot
al 366 0 399 22.09 67.823
Valid N
(listwise) 366
Descriptiv
e Statistics
N Minimu
m
Maximu
m
Mean Std.
Deviation
Cash_Total 366 0 1195 404.29 153.643
Credit_Total 366 0 1407 584.80 228.860
Valid N
(listwise) 366
Appendix 3
ANOVA
Total Sales
($)
11

Sum of
Squares
df Mean Square F Sig.
Between
Groups
134299725.0
24 4 33574931.25
6 37.176 .000
Within Groups 929333380.8
17 1029 903142.255
Total 1063633105.
841 1033
Descriptives
Location of product in shop Statistic S
t
d
.
E
r
r
o
r
Total Sales ($) Front
Mean 572.75
1
1
4
.
9
1
3
95% Confidence Interval
for Mean
Lower Bound 345.74
Upper Bound 799.76
5% Trimmed Mean 317.18
Median 125.10
Variance 2046779.018
12

Std. Deviation 1430.657
Minimum 7
Maximum 11910
Range 11904
Interquartile Range 341
Skewness 4.859
.
1
9
5
Kurtosis 29.445
.
3
8
7
Left
Mean 218.22
2
2
.
0
5
3
95% Confidence Interval
for Mean
Lower Bound 174.86
Upper Bound 261.58
5% Trimmed Mean 143.57
Median 76.75
Variance 182853.721
Std. Deviation 427.614
Minimum 0
13

Maximum 3300
Range 3300
Interquartile Range 170
Skewness 4.252
.
1
2
6
Kurtosis 21.523
.
2
5
1
Outside Front
Mean 3384.37
1
3
6
2
.
3
5
8
95% Confidence Interval
for Mean
Lower Bound 385.84
Upper Bound 6382.90
5% Trimmed Mean 2776.48
Median 1735.80
Variance 22272239.937
Std. Deviation 4719.347
Minimum 435
14

Maximum 17276
Range 16841
Interquartile Range 3992
Skewness 2.675
.
6
3
7
Kurtosis 7.903
1
.
2
3
2
Rear
Mean 536.07
7
9
.
9
1
4
95% Confidence Interval
for Mean
Lower Bound 378.38
Upper Bound 693.77
5% Trimmed Mean 366.10
Median 224.50
Variance 1149511.551
Std. Deviation 1072.153
Minimum 4
Maximum 10814
15

Range 10810
Interquartile Range 563
Skewness 5.960
.
1
8
1
Kurtosis 49.038
.
3
6
0
Right
Mean 239.89
3
1
.
3
5
8
95% Confidence Interval
for Mean
Lower Bound 178.19
Upper Bound 301.59
5% Trimmed Mean 137.53
Median 66.36
Variance 305813.096
Std. Deviation 553.004
Minimum 2
Maximum 4236
Range 4235
Interquartile Range 133
16

Skewness 4.469
.
1
3
8
Kurtosis 22.323
.
2
7
6
Appendix 4
Variables
Entered/Re
moveda
Model Variables
Entered
Variables
Removed
Method
1 Gross_Salesb . Enter
a.
Dependent
Variable:
Profit Total
b. All
requested
variables
entered.
Model
Summary
Model R R Square Adjusted R
Square
Std. Error of the
Estimate
1 .448a .200 .198 26.91551
17