Analyzing Mutual Fund Returns And Improving Harvest Kitchen’s Business Performance

Problem Definition

The harvest kitchen business is situated along the sunshine coast. It mainly deals with the farm products and it’s been doing this for the past one year. The business has got six employees, 1 delivery van, a retail outlet and a cold store warehouse. It deals on all levels/ chains of meeting customers; retail, wholesale. The business is however still at the start phase and the main sources of the problem are revenue, cost of goods and average sales.

A statistical research is conducted to determine the past performance of the business to influence is future (business analytics). This will involve determining why the business is still at the starting face even after one year of operations, main factors that may influence this and how they will be fixed to come up with a solution for a healthy business.

1. What are the top/worst selling products in terms of sales?
   1. Is there a difference in payments methods?
2. Are the differences in sales performance based on where the product is located in the shop? How does this effect both profits and revenue?
3. Is there a difference in sales and gross profits between different months of the year?
4. Are their differences in sales performance between different seasons?
   1. How does this relate to rainfall and profits?

The questions will help in determining the exact area where an improvement is needed.

To achieve this, business data will be acquired and analysed so as to arrive at a conclusion of the past business performance and how this can be improved. The kitchen business data given in the link is therefore applied and analysed through the SPSS to help arrive at conclusions.

This analysis was done through descriptive statistics to visualize how the different products compare in terms of the sales they make. The table below gives a filter of the product class with sales over $500 and the product class with the sales below $50. As can be seen, only 6 product classes had total sales amounting to more than $500 while 11 product classes had total sales below $50. The products with sales above $500 can be regarded as top selling products while those with total sales amounting to less than $50 can be regarded as worst selling products.

Table 1: Top/Worst selling products

Row Labels	Average of Total Sales ($)
Water	$1,867.08
Fruit	$1,048.67
Vegetable	$871.51
Dairy	$619.12
Drinks	$574.31
Coconut Water	$514.27
Bakery	$432.73
Fridge	$354.31
Dry Goods	$341.24
Health products	$332.88
Oils & Vinegars	$310.84
Snacks & Chocolates	$246.22
Ayurvedic	$226.33
Milks non dairy	$224.56
Freezer	$202.52
Household	$196.32
Meats Small goods	$176.65
Pasta	$114.33
Spreads, Sauces, Sweeteners	$113.64
Grocery	$108.88
Market	$89.00
Tea Coffee	$88.58
Personal Products	$84.45
Packaging	$62.25
Tinned Goods	$48.13
Harvest Kitchen	$45.25
Chocolates & Slices	$37.00
Pastas	$36.00
Other	$33.56
Stocks Sauces	$32.50
Salad Greens	$25.00
Snacks	$20.50
Spices	$19.07
Herbal Teas	$18.00
Juicing	$5.00
Grand Total	$370.02

The data given also showed that bananas sell the highest and the calico s sell the worst. This is shown by the high mean of the bananas (Bananas) and the (2.27) of the calico sell.

In this analysis our main aim was to compare the different payment methods and identify whether the total cash received from these payment methods are different or not. The company received cash from four different payment methods i.e. master card payments, visa card payments credit payments and cash payments. For analysis purposes, the payment methods were grouped into two different payment groups. That is, the card payments (comprising of visa card and master card payment methods) and the cash and credit payment methods. Two hypothesis were then tested out. The first one involving the cash and credit payment methods and the second one involving the master card and visa card payment methods. Independent t-test was used to test for the two hypothesis. The hypothesis tested are stated below;

H₀: There is no significant difference in the total cash received between the cash and the credit payment methods

H₀: There is significant difference in the total cash received between the cash and the credit payment methods

Research Questions

Table 2: t-Test: Two-Sample Assuming Equal Variances

	Cash	Credit
Mean	412.1755	604.6356
Variance	20811.55	42140.48
Observations	359	354
Pooled Variance	31401.02
Hypothesized Mean Difference	0
df	711
t Stat	-14.5002
P(T<=t) one-tail	3.14E-42
t Critical one-tail	1.647
P(T<=t) two-tail	6.27E-42
t Critical two-tail	1.963306

We conducted an independent samples t-test to compare the mean total cash that was received from credit payment method and that that was received from the cash payment method. The credit payment method (M = 604.64, SD = 204.28, N = 354) received more total cash when compared to the cash payment method (M = 412.18, SD = 144.26, N = 359), t(711) = -14.50, p < .001, two-tailed.

H₀: There is no significant difference in the total cash received between the Visa and the MasterCard payment methods

H₀: There is significant difference in the total cash received between the Visa and the MasterCard payment methods.

Table 3: t-Test: Two-Sample Assuming Equal Variances

	Visa	MasterCard
Mean	576.3144	152.5472
Variance	50355.11	12000.98
Observations	353	53
Pooled Variance	45418.44
Hypothesized Mean Difference	0
df	404
t Stat	13.49813
P(T<=t) one-tail	7.81E-35
t Critical one-tail	1.648634
P(T<=t) two-tail	1.56E-34
t Critical two-tail	1.965853

We conducted an independent samples t-test to compare the mean total cash that was received from master card payment method and that that was received from the visa card payment method. The master card payment method (M = 152.55, SD = 109.55, N = 53) received less total cash when compared to the visa card payment method (M = 576.31, SD = 224.40, N = 353), t = 13.50, df = 404, p < .05, 95% CI for mean difference 365.02 to 485.48).

With the products placed at the outside front, a large number of sales is realised. This is followed by the front, left and right and then finally the rear placement. This is shown in the table below;

Table 4: Descriptive Statistics

Location of product in shop	Total Sales ($)	Net Profit ($)
Front	Mean	572.75	252.09
N	155	155
Std. Deviation	1430.657	693.594
Left	Mean	218.22	99.55
N	376	376
Std. Deviation	427.614	195.844
Outside Front	Mean	3384.37	1809.63
N	12	12
Std. Deviation	4719.347	2343.359
Rear	Mean	536.07	210.68
N	180	180
Std. Deviation	1072.153	387.742
Right	Mean	239.89	109.95
N	311	311
Std. Deviation	553.004	299.191
Total	Mean	369.96	164.74
N	1034	1034
Std. Deviation	1014.719	482.106

The table shows a high mean and standard deviation for the outside front (M = 3384.37 SD = 4719.345 respectively) and the lowest are realised in the rear placement (536.07 for mean, 1072.153 for std. Deviation). The profit realised at the outside front is even larger compared to all other net profits.

With this analysed, the revenue will thereby increase with goods sold outside front. This is based on the fact that an increase in income results to the increase in revenue.

Table 5: ANOVA Table

	Sum of Squares	df	Mean Square	F	Sig.
Total Sales ($) * Location of product in shop	Between Groups	(Combined)	134299725.024	4	33574931.26	37.176	.000
Within Groups	929333380.817	1029	903142.26
Total	1063633105.841	1033
Net Profit ($) * Location of product in shop	Between Groups	(Combined)	36561758.739	4	9140439.69	46.211	.000
Within Groups	203534122.514	1029	197797.98
Total	240095881.253	1033

From the ANOVA table above, we can see that the p-value is 0.000 (this value is greater than α = 0.05), we therefore reject the null hypothesis and conclude that the total sales of the products is significantly different for the different locations where the products are placed within the shop.

From the data given, a hypothesis is written to help answer the question

H₀: sales and gross profits are different in different months

H₁: sales and gross profits are not different in different months

From the data given, the mean and the standard deviation proves that there is a difference.

The ANOVA table also shows that there’s a difference since we fail to reject the null hypothesis

Table 6: ANOVA Table

	Sum of Squares	df	Mean Square	F	Sig.
Average_Sale * Month of the year	Between Groups	(Combined)	335.651	11	30.514	1.979	.030
Within Groups	5333.831	346	15.416
Total	5669.483	357
Profit Total * Month of the year	Between Groups	(Combined)	35370.948	11	3215.541	3.867	.000
Within Groups	294370.006	354	831.554
Total	329740.954	365

Table 7: Measures of Association

	Eta	Eta Squared
Average_Sale * Month of the year	.243	.059
Profit Total * Month of the year	.328	.107

The above data clearly shows that there’s difference in sales and gross profit throughout the different months of the year (p-value < 0.000)

The hypothesis set in place for the above question is

Analysis 1: Best and Worst Case Returns

H_o: there’s difference in sales with changes in seasons

H₁: there’s no difference in sales with changes in seasons

The below tables shows that there’s no significant difference between the means of various sales in various season.

Table 8: Descriptive statistics

Average Sale
Season of the year	Mean	N	Std. Deviation
Summer	18.18	87	3.480
Autumn	18.74	89	3.033
Winter	18.58	92	5.215
Spring	18.58	90	3.864
Total	18.52	358	3.985

Table 9: ANOVA Table

	Sum of Squares	df	Mean Square	F	Sig.
Average Sale * Season of the year	Between Groups	(Combined)	15.148	3	5.049	.316	.814
Within Groups	5654.334	354	15.973
Total	5669.483	357

Table 10: Measures of Association

	Eta	Eta Squared
Average Sale * Season of the year	.052	.003

The p-value is given as 0.814 (this value is greater than the 5% significance level), From the table of mean, the lack of significant difference shows that there’s not effect in the changing seasons as far as sales is concerned.

In this analysis we sought to find out whether there exists a significant difference in the amount of rainfall based on the seasons.

Table 11: ANOVA Table

Rainfall
	Sum of Squares	df	Mean Square	F	Sig.
Between Groups	323.983	3	107.994	1.123	.340
Within Groups	34709.983	361	96.150
Total	35033.967	364

Analysis of variance (ANOVA) was conducted to compare the mean amount of rainfall received for the different seasons. The p-value was found to be 0.340 (a value greater than α = 0.05), we therefore fail to reject the null hypothesis and conclude that the mean amount of rainfall does not significantly differ across the four seasons.

In this analysis we sought to find out whether there exists a significant difference in the profits based on the seasons.

Table 12: ANOVA Table

Profit Total
	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

	N	Mean	Std. Deviation	Std. Error	95% Confidence Interval for Mean
Lower Bound	Upper Bound
Summer	91	31.4178	31.66537	3.31943	24.8232	38.0124
Autumn	92	19.7321	16.62147	1.73291	16.2899	23.1743
Winter	92	27.6325	18.71748	1.95143	23.7562	31.5088
Spring	91	44.2111	41.35005	4.33466	35.5995	52.8227
Total	366	30.7098	30.05661	1.57108	27.6202	33.7993

Analysis of variance (ANOVA) was conducted to compare the mean profit totals for the different seasons. The p-value was found to be 0.000 (a value less than α = 0.05), we therefore reject the null hypothesis and conclude that the mean profit totals do significantly differ across the four seasons. The descriptive table shows that Spring had the highest average profit (M = 44.21, SD = 41.35) while Autumn had the lowest average profit (M = 19.73, SD = 16.62).

A number of hypothesis tests were conducted. The first being that comparing the total cash received between cash payment method and credit payment method. Results showed that there was significant difference in the total cash received from the cash and credit payment methods. We also conducted a hypothesis test to check whether the total cash received from the visa card payment method and master card payment method were significantly different. Just like the case of cash and credit payment methods, we found out that total cash received from visa card payment method and master card payment method were significantly different.

Results also showed a statistically significant difference in the sales performance of products based the location where the products are placed in a shop. There was also significant difference in the profits based on the months of the year. However, there was no significant difference in the sales performance based on the months and season of the year.

Conclusion

Results from this study showed that total sales do not significantly differ across the months however the gross profit different significantly based on the month of the year. From the above analysis, various factors indicated in the data influence the performance of the business, the business operators should therefore take into consideration these factors; the kind of products (they should produce in large scale the products that sells much).

They should put most of their products at the outside front as it is very influential in terms of sales. The products that are not much selling should be relocated as this may be a factor that prevents their sales.

The management should also find out why some months had lower profits than others, it could be that the cost of production are higher in those months resulting to lower profits.

References

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Freedman, D. A., 2005. Statistical Models: Theory and Practice.  

Moore, D. S. & McCabe, G. P., 2003. Introduction to the Practice of Statistics. p. 764.

Nick, T. G., 2007. Descriptive Statistics.

Trochim, W. M., 2006. Descriptive statistics. Research Methods Knowledge Base.