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BUDGETING

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Running Head: BUDGETING 1

BUDGETING 13

Budgeting

Organizations commence their operations with hope for profits but without a well laid down budget, they find it impossible to make a successful plan. Budgeting identifies currently available capital, provides an estimate of expenses and anticipate incoming revenue. Businesses refer to budgets for performance measurements against expenses and ensure that resources are available for initiatives that support business growth and development. The preparation of budgets also allows business owners to concentrate on cash flow. This will enable them to reduce costs thereby improving profits and increasing returns on investment (Zor , Linder & Endenich 2019). The information provided below will show the importance of budgeting for Shiraz, Inc.

According to the data set provided by Sara, the company’s accountant the organization will best use advertising expenses to form their speculative budgets as explained by the regression analysis. Regression analysis is used to estimate the relationship between two or more variables (Lawrence 2019). In our case, the variables are sales in units, advertising expenses and the number of dealers. The independent variables are the advertising expenses and the number of dealers. The dependent variable is the number of sales. Through the regression analysis, the user will understand what happens when one independent variable varies. This analysis will help the management to best choose a variable to make decisions (Abadie et.al 2020).

SUMMARY OUTPUT		SUMMARY OUTPUT
Number Of Dealers	Advertising Expense
Regression Statistics		Regression Statistics
Multiple R	0.591677	Multiple R	0.897801
R Square	0.350082	R Square	0.806047
Adjusted R Square	0.28509	Adjusted R Square	0.786651
Standard Error	25816.07	Standard Error	14102.93
Observations	12	Observations	12

The following table shows a summary output of the analysis. Multiple R is the correlation coefficient that measures the strength of a linear relationship between two variables. The value ranges between -1 and 1. The absolute value indicates the relationship strength. The larger the absolute value, the stronger the relationship. Form the data set, it is evident that the advertising expense has a higher correlation value that will enable the organization to choose the variable in proposing budgets. R Square is the coefficient determination. It shows the goodness of fit as it illustrates the number of points that fall on the regression line (Lely et.al 2019). From the table above, the ‘advertising expense’ has a higher coefficient determination than ‘the number of dealers.

	Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%	Lower 95.0%	Upper 95.0%
Intercept	128319	59931.13	2.141109	0.057927	-5215.82	261853.9	-5215.82	261853.9
Number of Dealers	457.8854	197.2882	2.320896	0.042708	18.2998	897.471	18.2998	897.471

Regression Equation	y=457.89(x)+128319

	Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%	Lower 95.0%	Upper 95.0%
Intercept	114473.3	23905.78	4.788518	0.000736	61207.87	167738.7	61207.87	167738.7
Advertising Expense	6.65615	1.032504	6.446613	7.38E-05	4.355589	8.956712	4.355589	8.956712

Regression Equation	*y=6.66(x)+114473*

The tables above will illustrate the coefficients useful in making the equations. From the first data set, the coefficient intercept is 128,319 and the number of dealers is approximately 458. Using the equation , where c is the intercept, while x is the independent coefficient and y is the dependent coefficient. The equation for the number of dealers is . The same case applies to the advertising expense.

From the dataset provided above, the organization will use the equation for “advertising expense’ which is The table will enable the organization to develop the capital budget estimates for the month of January to May 20x2. Thereby the sales in units are as follows:

Regression Equation	*y=6.66(x)+114473*

	January	February	March	April	May
Advertising Expenditures	$ 28,000	$ 25,000	$ 27,000	$ 26,500	$ 25,800
Sales (Units)	300953	280973	294293	290963	286301

SECTION ONE: DATA FOR BUDGETS
Sales Data:	January	February	March	April	May
Sales Units	300,953	280,973	294,293	290,963	286,301
Selling price per unit	$ 12.00	$ 12.00	$ 12.60	$ 12.60	$ 12.60
Percentage of sales collected in the month of sales	225,715	210,730	220,720	218,222	214,726
Percentage of sales collected in the following month	75,238	70,243	73,573	72,741	71,575
Variable Expenses:
Pounds of material needed per unit of Alpha	1,504,765	1,404,865	1,471,465	1,454,815	1,431,505
Price of material per pound	$ 0.80	$ 0.90	$ 1.00	$ 1.10	$ 1.20
Direct labor hours needed per unit of Alpha	150,477	140,487	147,147	145,482	143,151
Direct labor rate per hour	$ 10	$ 10	$ 10	$ 10	$ 10
Variable manufacturing overhead per unit of Alpha	300,953	280,973	294,293	290,963	286,301
Variable operating expenses per unit of Alpha	$ 150,477	$ 140,487	$ 147,147	$ 145,482	$ 143,151
Fixed Expenses:
Fixed manufacturing overhead	$ 25,000	$ 25,000	$ 25,000	$ 25,000	$ 25,000
Depreciation portion of fixed overhead	$ 3,000	$ 3,000	$ 3,000	$ 3,000	$ 3,000
Fixed operating expenses	$ 12,000	$ 12,000	$ 12,000	$ 12,000	$ 12,000
Depreciation portion of fixed operating expenses	1,500	1,500	1,500	1,500	1,500
Inventory Policy:
Desired ending inventory of Alpha (% of next month sales)	56,195	58,859	58,193	57,260	-
Desired ending inventory of DM (% of next month's production needs)	70,243.25	73,573.25	72,740.75	71,575.25	-

Other Information:
Capital Expenditures	$ 400,000	$ 200,000
Loan Repayments & Interest Expense	$ 17,411	$ 27,141	$ 27,141	$ 27,141	$ 27,141
Income tax rate	30%	30%	30%	30%	30%
Minimum cash retained at the end of month	$ 20,000	$ 20,000	$ 20,000	$ 20,000	$ 20,000
December 20X1 Sales dollars	$ 4,350,000
Cash balance as of January 1, 20X2	$ 21,000

SECTION TWO: BUDGETS
Sales Budget	January	February	March	April	May
Sales in Units	300,953	280,973	294,293	290,963	286,301
Unit Selling Price	$ 12.00	$ 12.00	$ 12.60	$ 12.60	$ 12.60
Sales in Dollars	$ 3,611,436	$ 3,371,676	$ 3,708,092	$ 3,666,134	$ 3,607,393

Production Budget	January	February	March	April	May
Sales Units	300,953	280,973	294,293	290,963	286,301
Add: Desired Ending Inventory	56,195	58,859	58,193	57,260	-
Total Required Units	357,148	339,832	352,486	348,223	286,301
Less: Beginning Inventory	75,238	70,243	73,573	72,741	71,575
Required Production Units	281,909	269,588	278,912	275,482	214,726

Direct Materials Budget (Purchases Budget)	January	February	March	April	May
Units to be Produced	281,909	269,588	278,912	275,482	214,726
Direct Material Qty Required Per Unit of Alpha (pounds)	1,504,765	1,404,865	1,471,465	1,454,815	1,431,505
Total Direct Materials Needed for Production (pounds)	1,409,546.75	1,347,941.75	1,394,561.75	1,377,412.25	1,073,628.75
Add: Desired Ending Inventory Direct Materials	280,973	294,293	290,963	286,301	-
Total Direct Materials Needed	1,690,520	1,642,235	1,685,525	1,663,713	1,073,629
Less: Beginning Inventory of Direct Materials	376,191	351,216	367,866	363,704	357,876
Direct Material Purchases (pounds)	1,314,329	1,291,019	1,317,659	1,300,010	715,753
Cost Per Pound	$ 0.80	$ 0.90	$ 1.00	$ 1.10	$ 1.20
Total Cost of DM Purchases	$ 1,051,463	$ 1,161,917	$ 1,317,659	$ 1,430,010	$ 858,903

Cost of Production Budget (Usage Budget)	January	February	March	April	May
Units to be Produced	281,909	269,588	278,912	275,482	214,726
Direct Material Costs	$ 1,051,463	$ 1,161,917	$ 1,317,659	$ 1,430,010	$ 858,903
Direct Labor Cost	$ 1,504,765	$ 1,404,865	$ 1,471,465	$ 1,454,815	$ 1,431,505
Variable Manufacturing Cost	$ 150,477	$ 140,487	$ 147,147	$ 145,482	$ 143,151
Fixed Manufacturing Cost	$ 25,000	$ 25,000	$ 25,000	$ 25,000	$ 25,000
Total Production Costs	$ 2,731,704	$ 2,732,268	$ 2,961,270	$ 3,055,307	$ 2,458,559
Cost of Production Per Unit	$ 9.69	$ 10.13	$ 10.62	$ 11.09	$ 11.45

Operating Expense Budget	January	February	March	April	May
Variable	$ 421,334	$ 393,362	$ 412,010	$ 407,348	$ 400,821
Fixed	$ 12,000	$ 12,000	$ 12,000	$ 12,000	$ 12,000
Total Operating Expenses	$ 433,334	$ 405,362	$ 424,010	$ 419,348	$ 412,821

Budgeted Income Statement	January January	February	March March	April	May
Sales ($)	$ 3,611,436	$ 3,371,676	$ 3,708,092	$ 3,666,134	$ 3,607,393
Cost of Goods Sold	$ 2,731,704	$ 2,732,268	$ 2,961,270	$ 3,055,307	$ 2,458,559
Gross Profit	$ 879,732	$ 639,408	$ 746,822	$ 610,827	$ 1,148,834
Operating Expenses	$ 433,334	$ 405,362	$ 424,010	$ 419,348	$ 412,821
Income from Operations	$ 446,398	$ 234,046	$ 322,812	$ 191,479	$ 736,013
Interest Expense	$ 17,411	$ 27,141	$ 27,141	$ 27,141	$ 27,141
Gross Income	$ 428,987	$ 206,904	$ 295,670	$ 164,337	$ 708,872
Income Taxes	$ 128,696	$ 62,071	$ 88,701	$ 49,301	$ 212,661
Net Income	$ 300,291	$ 144,833	$ 206,969	$ 115,036	$ 496,210


Cash Budget	January	February	March	April	May
Beginning Cash Balance	$ 21,000	$ 20,000	$ 20,000	$ 20,000	$ 20,000
Add: Receipts
Current Month Sales	$ 3,611,436	$ 3,371,676	$ 3,708,092	$ 3,666,134	$ 3,607,393
Total Receipts	$ 3,611,436	$ 3,371,676	$ 3,708,092	$ 3,666,134	$ 3,607,393
Total Cash Available	3,632,436	3,391,676	3,728,092	3,686,134	3,627,393
Less: Disbursements
Direct Materials Purchases	$ 1,051,463	$ 1,161,917	$ 1,317,659	$ 1,430,010	$ 858,903
Direct Labor	$ 1,504,765	$ 1,404,865	$ 1,471,465	$ 1,454,815	$ 1,431,505
Fixed Manufacturing Overhead	$ 25,000	$ 25,000	$ 25,000	$ 25,000	$ 25,000
Variable Manufacturing Overhead	$ 150,477	$ 140,487	$ 147,147	$ 145,482	$ 143,151
Fixed Operating Expenses	$ 12,000	$ 12,000	$ 12,000	$ 12,000	$ 12,000
Variable Operating Expenses	$ 421,334	$ 393,362	$ 412,010	$ 407,348	$ 400,821
Income Taxes	$ 128,696	$ 62,071	$ 88,701	$ 49,301	$ 212,661
Capital Expenditures	$ 400,000	$ 200,000	$ -	$ -	$ -
Loan Repayment & Interest Expense	$ 17,411	$ 27,141	$ 27,141	$ 27,141	$ 27,141
Total Disbursements	$ 3,711,145	$ 3,426,843	$ 3,501,123	$ 3,551,098	$ 3,111,183
Excess (deficiency) of available cash over disbursements	$ -78,709	$ -35,167	$ 226,969	$ 135,036	$ 516,210
Financing/Borrowing	98,709	55,167	$ -206,969	$ -115,036	$ -496,210
Ending Cash Balance	$ 20,000	$ 20,000	$ 20,000	$ 20,000	$ 20,000
Notes Payable	0	0	0	0	0

If the organization makes increases the direct labor by 15% and the direct labor by 5 %, the organization will experience a decrease in net income as shown below. It would be in the interest of the organization to maintain the cost and look for other cost-cutting methods that enable them to have more income (Zhao et.al 2019).

	January	February	March	April	May
Net Income Before	$ 300,291	$ 144,833	$ 206,969	$ 115,036	$ 496,210
Net Income after	$ 247,624	$ 95,663	$ 155,468	$ 64,118	$ 446,107
% change	82%	83%	75%	56%	89%

References

Lawrence, K. D. (2019). Robust regression: analysis and applications. Routledge.

Abadie, A., Athey, S., Imbens, G. W., & Wooldridge, J. M. (2020). Sampling‐Based versus Design‐Based Uncertainty in Regression Analysis. Econometrica, 88(1), 265-296.

Lely, J. C., Smid, G. E., Jongedijk, R. A., W. Knipscheer, J., & Kleber, R. J. (2019). The effectiveness of narrative exposure therapy: a review, meta-analysis and meta-regression analysis. European journal of psychotraumatology, 10(1), 1550344.

Zor, U., Linder, S., & Endenich, C. (2019). CEO characteristics and budgeting practices in emerging market SMEs. Journal of Small Business Management, 57(2), 658-678.

Zhao, K., Hua, J., Yan, L., Zhang, Q., Xu, H., & Yang, C. (2019, July). A Unified Framework for Marketing Budget Allocation. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 1820-1830).