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Variance Analysis

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Spacely Sprockets
2001 Budget Executive Summary
File: SGMU M:MSMFin642Spacely-Budget.xls

Last Revised: 12/17/01
Prepared by: Tom Gruber

General Budget calls for reasonable increases in sales, prices and expenses except for labor. We have to keep overtime under control to return to profitability.

Sales
Set reasonable sales increase of 3-4% with a normal price increase of 3%

Materials
Steel prices look like they will hold as the economy softens.

Direct Labor
Reducing over time is the key to this year's budget. Planning raise at parity with labor market of 3%.

Overhead
Hold flat.

Fixed Expenses
Raises at market (3-4%).

Risks
Sales volume is always a risk, but biggest issue is controlling overtime.
Variance analysis, detailed visible form,

Instructions on Spacely variance, comparative budgets under two other files.

See attached file for full problem description.

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ONE PAGE MEMO TO BOSS

HIGHLIGHTS OF THE PERFORMANCE
1. ACTUAL SALES HAS EXCEEDED OUR EXPECTATIONS AND IT IS $4566388
2. SALES IN UNITS IS MORE THAN THE STANDARD AND IT IS 565000 UNITS
3. FIXED MANUFACTURING OVERHEAD IS LESS THAN THE BUDGETED COST
4. OTHER EXPENSES ARE IN CONTROL

MAIN CONCERNS:
1. ACTUAL PROFIT IS LESS THAN OUR EXPECTATIONS AND IT IS only $6561
2. Variable expenses are more THAN THE BUDGETED COST and it has exceeded by $614052. This is due to higher labour, material and variable expenses.
3. OUR CONTRIBUTION MARGIN IS LESS THAN OUR EXPECTATIONS BY 4%

CONCLUSION

THOUGH OUR SALES HAVE GONE UP BUT OUR PROFITS HAVE GONE DOWN ACCORDING TO STANDARDS.

Questions to be asked to Plant manger
When we look at variances, we are trying to establish the difference between what has actually happened (what we did achieve) and what we thought should have happened, namely the original budget (what we should have achieved).

Sales Variances
The sales revenue figure that will be included in the profit and loss account would have been calculated by multiplying the total units sold by the price per unit. When looking at the sales variances we once again consider two aspects; was the selling price as per the original budget (the price variance) and were the number of units sold as budgeted (the volume variance).

The first of these variances is in monetary terms and the second being in units.

The sales price variance, as you would expect, looks at the price that we did get, the actual revenue received from the sale of the units, and the price we should have got if the standard price per unit had been obtained. The variance is clearly UNFAVOURABLE since we did not receive the amount of ...

Solution Summary

This explains in detail the steps to calculate variances like material variance, labour variance, overhead variance, sales variance.

$2.19
See Also This Related BrainMass Solution

ANOVA Problem

When we want to test two samples to determine if it is likely that the population means (estimated by the sample means) are different, we typically use a t-test. If the samples are large, we can also use a z-test. (Note that the formulas for computing s, t and/or z in the case of a two-sample test are different than the formulas for computing the same values in a one-sample test. Use Excel data analysis to conduct tests comparing two sample means.)

Using ANOVA (short for Analysis of Variance), however, we can test 3 or more sample means to determine if at least one of the sample means comes from a population with a mean that is significantly different from all of the others in the test. We actually do this by estimating a combined population variance two different ways and comparing the two estimates (the ratio of these two variance estimates follows the so-called "F distribution").

Question:

Why do we need a new test method to compare the means of 3 or more populations? Why can't we just use a series of z-tests or t-tests to compare all of the possible pairs of population means to see if one (or more) is different?

Most of the testing is to determine one or two things:

1. Is there a statistically significant difference between two or more population means? (based on comparison of 2 or more sample means)

2. Is there a statistically significant relationship between two or more variables? We can use regression analysis or chi-square tests to answer this second question.)

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