Statistical Analysis For Harvest Kitchen Business

Problem statement

New enterprises in the market face a lot of challenges in the market especially if the industry is dominated by organizations which have been in the business for long (Kotler , 2012) and (Romano, 2009). Start up business face various problems from competition to low sales volume due to unpopular company name or products that they sell. Harvest kitchen, a company that deals in green groceries has also not been spared. It has been facing challenges from competition from other established businesses to low sales.

The main challenge to harvest kitchen organization has been inability to close in on leads hence leading to low sales across the year. Other factors that contributed to the staggering sales levels were assumed to be rainfall and location of the produced products in their shops. In order to increase sales, harvest kitchen has decided to conduct an research survey to understand the various factors that are perceived to be influencing sales and hence gross profit.

The figure above is a graphical description of average sales for various products being sold at Harvest Kitchen. It can be observed that the products varied in terms of sales. For example, the graph shows that chocolates and slices performed poorly compared to the rest of the products. It had an average annual sale of 135.92 thousand dollars. Another relatively worst performing product is Ayurdevic which had an average annual sale of 678.75 thousand dollars. The best performing product in terms of sales is seen to be drinks which had an annual sale of 20637.7 thousand dollars. Other better performing products were bakery products which had a sale of 10137.55 thousand dollars.

Since Harvest Kitchen is a bigger company serving customers from various regions, they have diversified their modes of payments such that they accept visa and credits. To establish whether there was any difference in the two payment methods, a paired sample t-test was run to determine whether there was any significant difference in the two methods of payment. The report arrived at the decision of using a paired sample t-test because there were only two main methods of payments hence only two variables (Robert, 2008) and (Rubin, 2002). This parametric test was also chosen on the assumption that there is normal distribution in these data otherwise a non-parametric approach would have been employed.  

The hypothesis to be tested is therefore as below;

At 95% confidence level,

H₀: There is no significant difference in the two payment methods

Versus

H₁: There is a significant difference in the two payment methods

The paired sample t-test results are as seen in the results table below;

Paired Samples Statistics

From table 3 above it can be observed that the two variables (visa and credit payment methods) have a correlation coefficient of .93. This is an indication of a very strong correlation in the two methods of payments. In the t-test table, the results show that the p-value, .00 is less than the level of significance which is .05. We are therefore directed to not to accept the null hypothesis and not to fail to accept the alternative. It is therefore clear that the conclusion is that there is a significant difference in the two methods of payment.

Graph of best and worst performing products

Products arrangement in a shopping space can influence the amount of sales of a given product through various ways. For example if a product is conspicuously displayed, then it will be able to attract more customers who will then be tempted to buy it thus increasing its sales. To add on, if a low moving product is placed next to a fast moving product, there is likelihood that this could influence the buyer to also consider buying the low moving good out of impulse (Mowlana & Smith, 2003). In this way, the sales levels of the product whose turnover is low will have been boosted. The other approach in terms of location that can influence sales of a given product is whether the product is located near the entrance or in the back end shelves. Products at the entrance can be spotted very first by the customers thus get stuck into their minds and this can also prompt impulse buying. Products that are shelved at the back end of a shop can be very difficult to be spotted by customers especially those in a hurry. In regard to location, Harvest Kitchen limited wanted to establish whether there has been a variance in sales of various products according to where they are located in the shop (Mowlana & Smith, 2003). There are five locations in the shop and therefore five variables. This means that the analysis of variance is employed in this case to establish whether a significant difference in sales exist due to difference in location.

The test hypothesis

H₀: There is no significant difference in sales levels for products in various locations in shop.

Versus

H₁: At least one product’s sales level from one location is different.

The analysis of variance results above indicate that the p-value calculated is .00. Comparing this value with the level of significance which is .05, it is found that the p-value is less. According to an ANOVA test, if the p-value is less than the level of significance then the test is advised to reject the null hypothesis and fail to reject the alternative hypothesis. This therefore means that according to the hypothesis above, the null hypothesis that there is no significant difference in sales levels for products in various locations in shop is rejected and the alternative upheld. It is then concluded that At least one product’s sales level from one location is different. If the research is interested in finding the different items then a further Duncan’s test is recommended.

Different seasons affect sales and hence the gross profits that are realized for the same periods. A certain month may experience an upsurge of sales levels while another month might experience a decline in sales levels. These variations are normally due to various factors due to weather. A favorable weather encourages an increase for example in agricultural products and other products that go with weather such as umbrellas and trench coats during rainy and cold seasons respectively. On the other hand during dry seasons there is low production of agricultural products. This may mean that the supply will go low and demand will rise hence raising sales volume and hence gross profit. The converse also holds true. Harvest kitchen being a company that deals with agricultural products is bound to experience lows and highs in terms of sales volume and hence gross profit across the months. It is for this reason that it carried out an analysis of variance to ascertain whether there were significant differences in gross profit and sales across the months.

Establishing The Existence Differences In Payment Methods

The test hypothesis is as below;

H₀: The mean sales level is generally the same across all the months of the year.

Versus

H₁: At least one month is different in terms of sales.  

The test’s confidence level is 95%

The analysis of variance results above indicate that the p-value calculated is .00. Comparing this value with the level of significance which is .05, it is found that the p-value is less. According to an ANOVA test, if the p-value is less than the level of significance then the test is advised to reject the null hypothesis and fail to reject the alternative hypothesis (Richler, 2012) and (Lucas, 2009). This therefore means that according to the hypothesis above, the null hypothesis that there is no significant difference in sales levels for products across the 12 months of the year is rejected and the alternative upheld. It is then concluded that At least one month’s product’s sales level is different. If the research is interested in finding the different items then a further Duncan’s test is recommended.

Test For Gross Profit Difference Between The Months

Hypothesis

H₀: The gross profit is almost the same across the 12 months of the year.

Versus

H₁: At least one month is different in terms of gross profit.  

In this hypothesis, 95% confidence level has been applied.

The ANOVA results are tabulated as below,

The analysis of variance results above indicate that the p-values calculated is .00. Comparing this value with the level of significance which is .05, it is found that the p-values are more. According to an ANOVA test, if the p-value is more than the level of significance then the test is advised to accept the null hypothesis and fail to accept the alternative hypothesis. This therefore means that according to the hypothesis above, the null hypothesis that there is no significant difference in gross profit levels for products across the 12 months of the year is accepted and the alternative rejected. It is then concluded that gross profit is almost the same across the 12 months of the year.

To establish whether there was a correlation between rainfall and sales, a Pearson correlation test was run to determine whether any kind of relationship existed between the two variables. The results were as tabulated below;

The results of the Pearson correlation are as shown in table 5 above. The coefficient is .057. Since a strong perfect correlation is 1 or -1, it can therefore be concluded that there is a weak correlation between rainfall and sales amount. It is positive though weak relationship

To establish whether there was a correlation between rainfall and sales, a Pearson correlation test was run to determine whether any kind of relationship existed between the two variables. The results were as tabulated below;

The results of the Pearson correlation are as shown in table 6 above. The coefficient is .017. Since a strong perfect correlation is 1 or -1, it can therefore be concluded that there is a weak correlation between net profit and sales amount. It is positive though weak relationship.

Graph of monthly gross profit.

It can be observed that the gross profit is normally distributed across the year. This is indicated by the graph which assumes a bell-shape but with a relatively flat top. It can also be observed that the mean gross profit for the months has little variance.

This report has come up with various recommendations and conclusions based on analysis results. The analysis revealed that there is no correlation between rainfall and sales.t therefore it is advisable for the company to not project their production and hence sales on rainfall since it deals with agricultural products. The management should therefore concentrate on other factors influencing sales and not rainfall. The report also found that there was no significant difference in the levels of gross profit across the months. This is an indication that there is no improvement of gross profit from month to the next month. The report therefore recommends that the management should come up with approaches such as sales and marketing so as to close leads on business opportunities

References

Kotler , P. (2012). Marketing Management: Analysis, Planning, Implementation and Control,. Englewood Cliffs, NJ.: Prentice-Hall.

March, R. (2009). Tourism marketing myopia”, Tourism Management. (Vol. 15).

Mowlana, H., & Smith, G. (2003). Marketing in a global context: the case of frequent traveler programs. Journal of Travel Research, 33, 20-27.

Romano, C. (2009). Research strategies for small business: a case study. International Small Business Journal, 7, 35-43.

Lucas, H. C. (2009). Methodological issuesin information systems survey research. .

Richler, J. (2012). Behavior research methods.

Robert , G. (2008). “Two-Sided Confidence Intervals for the Single Proportion: Comparison of Seven Methods,” Statistics in Medicine,

Rubin, D. B. (2002). Statistical Analysis with Missing Data (2nd Survey Methods & Sampling Techniques 4 Ed.). New York.

Order an Essay Now & Get These Features For Free:

Turnitin Report

Formatting

Title Page

Citation

Outline

Place an Order