Decision Technologies Agribusiness Problems †MyAssignmenthelp

Question: Discuss about the Decision Technologies for Agribusiness Problems. Answer: Introduction Good harvest is a small health food shop in Sunshine coast which sales organic food and has been in operation for a year now and it moving to the second year in business (Harvest, n.d.). They deliver weekly organic produce directly from their farms and other local farms directly the customers doorstep using their home delivery service model. Good harvest farm produce ranges from Ayurvedic, bakery, dairy, drinks, fruit, grocery, harvest kitchen etc. up to water. Their main mission is to connect local community or local people with local farmers, supplying chemical free and safe produce at an affordable price, support farmers who invest in ecologically responsible farming and finally provide education on seasonal, nutrition consumption and sustainability. However, there are problems affecting this agribusiness industry as stated by (Lowe, 2004) that the supply of food in agribusiness is characterized by a number of uncertainties in both supply and demand chain and it required better te chnological tools and management in decision making in the sector. Good harvest is facing challenges of high cost of goods to be sold to the customers, revenue which might be lower depending on the sales and finally average sales. Good harvest might also face problems of people in the community wanting to buy produce which are not local and also the problem of supply and demand where the local farmers arent able to meet demand with their supply. Problem definition and business intelligence Two datasets were provided which had data for sales from Good Harvest Company on all their sales for their first year in business. Our data variables for the first dataset were product class, product name, product category, quantity, weight, total sales, COGS, net profit, location in the shop and total profit. For the second data set our variables were day, month, season, GST inclusive, GST exclusive, gross sales, net sales, total cash, credit total, MasterCard total, visa total, house account, total orders, average sales, staff cost, weekday, rainfall and profit total. We had four research questions as listed below What are the top/worst selling products in terms of sales? Is there a difference in payment methods? Are the differences in sales performance based on where the product is located in the shop? How does this effect both profits and revenue? Is there a difference in sales and gross profit between different months of the year? Are their differences in sales performance between different seasons? How does this relate to rainfall and profit? A number of statistical methodologies were applied to answer the above research question which ranged from test of association (chi-square test of association), test of difference of means (t test methods) (Rouder, 2009) descriptive statistics using custom tables. For the first research question (1) SPSS custom tables was used to produce the results where products were placed in the row field and sum of sales on the column field the reason for using this method is to produce results which are tabulated for easy comparison among the produce. The second part of the question (1.a) which was used to test if there was a difference is payment method, a one way ANOVA was applied to test the significance since one way ANOVA tests whether the mean value of all payment methods was equal. On the second research question, we tested the association between sales and location of the product in the shop using chi-square test of association the reason is that chi-square help us to find out whether t here is an association between the variables (Goodman, 1971). On the second part of the question custom tables were used to show the distribution of sales and profit against location in the shop the reason is for provision of good visualization to help in comparison. On the third question one way ANOVA was used for comparison of means of profit and sales within months. Finally, on the last question, one way ANOVA was used to test the whether the mean sales within seasons were different and on the second part of the question custom tables was used to show the distribution. Visualization and descriptive statistics Descriptive statistics are simple statistics which describe variables, this include mean, range, variance etc. (Daniel, 1995). Table 1: Distribution of produce by good harvest (top 5 by count). Product Class Frequency Percent Snacks Chocolates 110 10.6% Personal Products 96 9.3% Dry Goods 84 8.1% Vegetable 76 7.4% Dairy 66 6.4% Table 2: Distribution of produce by good harvest (bottom 5 by count). Product Class Frequency Percent Market 2 0.2% Snacks 2 0.2% Juicing 1 0.1% Pastas 1 0.1% Salad Greens 1 0.1% The figure below is a pie chart sowing distribution of good harvest products. Table 3 below shows descriptive statistics for the payment method used by Good harvest with both mean, minimum and maximum. Descriptive Statistics N Minimum Maximum Mean Std. Deviation Cash_Total 366 0 1195 404.29 153.643 Credit_Total 366 0 1407 584.80 228.860 Visa_Total 366 0 1407 555.85 244.870 Mastercard_Total 366 0 399 22.09 67.823 House_Account 366 -264 1113 37.39 113.204 Valid N (listwise) 366 Table 4 below shows descriptive statistics of total profit with mean total profit, minimum and maximum. N Minimum Maximum Mean Std. Deviation Total Profit 1034 .00 8702.93 164.7338 482.10651 Valid N (listwise) 1034 Results and Analysis In this section, we present the results of our analysis where from our first research question we identified vegetables as the most selling product with sales of $66,233 while juicing was the worst selling product with sales of $5 only. The other products which includes bakery, grocery etc, their sales falls in between the sales of vegetables and juicing. From this results we can establish that vegetables are the mainly bought product from good harvest compared to the rest of the products with juicing being the worst selling product among them. Table 5 below shows a snippet of our results from the analysis. Table 5: Distribution of sales among products Total Sales ($) Sum Product Class Ayurvedic 679 Bakery 19038 Chocolates Slices 185 Coconut Water 5656 After performing ANOVA test for comparing the cash total, visa total, MasterCard total, credit total and house account total, our p-value was found to be 0.00 (p0.05) hence we reject the null hypothesis which states that there is no difference between the payment method and conclude that there was a difference between the mean of payment method. On the second question where we were testing whether there was a significant difference between sales performance and location of the product in the shop we obtained a p value of 0.00 (p0.05). Table 5 below shows the results after performing a chi-square test of association. Table 6: Results of chi-square test of association between sales and location Chi-Square Tests Value df Asymptotic Significance (2-sided) Pearson Chi-Square 3627.238a 3340 .000 Likelihood Ratio 2464.848 3340 1.000 Linear-by-Linear Association 1.302 1 .254 N of Valid Cases 1034 From this results we reject our null hypothesis which stated that sales and location of the product in the shop are independent. Hence, we conclude that sales performance are largely affected by where the product is located in the shop. A comparison between profit and location of the products in the shop showed that items in the front part of the shop are likely to generate more profit compared to the items placed on the other parts of the shop. In our results products on the front would generate 39,073.98 as total profit while items placed in the outside front generating the least profit of 34,192.37. Table 6 below shows the distribution of profit against location of products in the shop. Table 7: Distribution of location of products in the shop against total profit Total Profit Sum Location of product in shop Front 39073.98 Left 37430.42 Outside Front 21715.52 Rear 37922.48 Right 34192.37 A comparison between sales and location of the products in the shop showed that items in the rear part of the shop are likely to generate more sales compared to the items placed on the other parts of the shop. In our results products on the rear would generate 96,493 as total sales while items placed in the outside front generating the least sales of 40,612. Table 6 below shows the distribution of sales against location of products in the shop. Table 8: Distribution of location of products in the shop against total sales Total Sales ($) Sum Location of product in shop Front 88777 Left 82052 Outside Front 40612 Rear 96493 Right 74607 On testing whether there was a difference in sales and profit between different months of the year, we obtained p value of 0.222 (p0.05) on gross sales. Hence, we fail to reject the null hypothesis and conclude that there is no difference in sales between months. On profit our p value is 0.000 (p0.05) meaning that we reject our null hypothesis and conclude that profit there is a significant difference in profit between months of the year. Table 8 below show the results of our analysis. Table 9: Results of ANOVA analysis of sales and profit between months ANOVA Sum of Squares df Mean Square F Sig. Gross Sales Between Groups 1508892.5 11 137172.043 1.300 .222 Within Groups 37349615.5 354 105507.388 Total 38858507.9 365 Profit Total Between Groups 35370.9 11 3215.541 3.867 .000 Within Groups 294370.0 354 831.554 Total 329741.0 365 Finally, testing sales performance between seasons gave a p value of 0.153 (p0.05) meaning that we fail to reject the null hypothesis and conclude that there is no significant difference between performance of sales and seasons. Table 10: ANOVA on performance of sales and between seasons ANOVA Gross Sales Sum of Squares df Mean Square F Sig. Between Groups 560240.410 3 186746.803 1.765 .153 Within Groups 38298267.52 362 105796.319 Total 38858507.9 365 On season and profit, Spring was found to be the most profitable season and Autumn the least profitable season as shown below. Table 11: Distribution of profits between seasons Profit Total Sum Season of the year Summer 2859.02 Autumn 1815.35 Winter 2542.19 Spring 4023.21 On season and rainfall, Winter received the highest rainfall in comparison to the other seasons. Table 12: Distribution of rainfall between seasons Rainfall Sum Season of the year Summer 414 Autumn 379 Winter 441 Spring 218 Discussion and recommendations From our analysis, we have established that vegetables are the most selling product and juicing is the least sold product and in regards to this Good harvest should concentrate in stocking more of vegetables and least of juicing in their shops. On payment methods, we established that there is no significance difference among the payment method and therefore the company can use any payment method with its customers. The location of products in the shop is very important and therefore Good harvest should position its products such that products with low demand should be placed in a strategic place to boost sales and revenue. We established from our analysis that there is no significant difference in sales between months and season and this means that the company can always make sales regardless of the month or the season of the year. References Anova, S., 2002. Statistical computing: an introduction to data analysis using S-Plus. Daniel, W., 1995. Biostatistics: a foundation for analysis in the health sciences.. Goodman, L., 1971. Partitioning of chi-square, analysis of marginal contingency tables, and estimation of expected frequencies in multidimensional contingency tables. Journal of the American statistical Association, pp. 66(334), pp.339-344. Harvest, G., n.d. good harvest organic. [Online] Available at: goodharvest.com.au Lowe, T., 2004. Decision technologies for agribusiness problems: A brief review of selected literature and a call for research. Manufacturing Service Operations Management, 6(3), pp.201-208.6(3). s.l.:s.n. Rouder, J., 2009. Bayesian t tests for accepting and rejecting the null hypothesis.