Problem 1: Tessmer Manufacturing Produces Inventory
Problem 1 Tessmer Manufacturing Company Produces Inventory In A Highl
Problem 1 - Tessmer Manufacturing Company produces inventory in a highly automated assembly plant in Olathe, KS. The automated system is in its first year of operation and management is still unsure of the best way to estimate the overhead costs of operations for budgetary purposes. For the first six months of operation, the following data was collected: Machine-hours, Kilowatt-hours, and Total Overhead Costs for January through June. The data includes: January (3,800 machine-hours, 4,520,000 kWh, $138,000 overhead), February (3,650, 4,340,800, $136,000), March (3,900, 4,500,200, $142,000), April (3,300, 4,290,800, $130,000), May (3,250, 4,200,000, $125,000), and June (3,100, 4,120,000, $120,000).
Paper For Above instruction
In manufacturing industries, accurate estimation of overhead costs is crucial for budgeting, pricing, and cost control. Tessmer Manufacturing Company's scenario exemplifies challenges faced in initial production phases, particularly when relying on data to develop cost estimation models. This paper explores the application of the high-low method to determine cost functions based on machine-hours and kilowatt-hours as cost drivers, and analyzes which driver more accurately predicts overhead costs for a specific month.
Introduction
Overhead costs constitute a significant part of manufacturing expenses, often comprising indirect costs such as utilities, maintenance, and factory supplies. Effective cost estimation methods are vital for decision-making processes, especially when a new automated system introduces uncertainties in cost behavior. The high-low method simplifies this process by utilizing only the extreme points in the activity data, providing a quick estimation of variable and fixed costs associated with specific cost drivers.
Application of the High-Low Method
The high-low method involves selecting the periods with the highest and lowest levels of activity and corresponding costs. The variable cost per unit of activity is then calculated by dividing the change in total cost by the change in activity level. Fixed costs are estimated by subtracting variable costs from total costs at either activity level.
Estimating Cost Function Based on Machine-Hours
Data for machine-hours indicates:
- Highest activity: March (3,900 hours, $142,000)
- Lowest activity: June (3,100 hours, $120,000)
Calculations:
Variable cost per machine-hour (b):
Change in cost: $142,000 - $120,000 = $22,000
Change in activity: 3,900 - 3,100 = 800 hours
b = $22,000 / 800 hours = $27.50 per machine-hour
Fixed cost (a):
Using the high activity point (March):
Overhead = a + b * activity
$142,000 = a + $27.50 * 3,900
a = $142,000 - ($27.50 * 3,900) = $142,000 - $107,250 = $34,750
Estimate of total overhead cost function based on machine-hours:
Overhead = $34,750 + $27.50 * Machine-hours
Estimating Cost Function Based on Kilowatt-Hours
Data indicates:
- Highest activity: March (4,500,200 kWh, $142,000)
- Lowest activity: June (4,120,000 kWh, $120,000)
Calculations:
Change in cost: $142,000 - $120,000 = $22,000
Change in activity: 4,500,200 - 4,120,000 = 380,200 kWh
b = $22,000 / 380,200 kWh ≈ $0.0578 per kWh
Fixed cost (a):
Using March (highest kilowatt-hours):
$142,000 = a + $0.0578 * 4,500,200
a = $142,000 - ($0.0578 * 4,500,200) ≈ $142,000 - $260,146.36 ≈ -$118,146.36
The negative fixed cost indicates that kilowatt-hours may not be the best predictor of overhead costs, as the model suggests inconsistency when extrapolated.
Comparison and Analysis
When analyzing the fit of the models, the machine-hours-based cost function appears more reasonable, with a positive fixed component and a logical variable rate. The kilowatt-hours model produces a negative fixed cost, indicating poor predictive power for overhead estimation in this context.
Application to July Data and Cost Driver Prediction
In July, the company reports 3,000 machine-hours and 4,000,000 kWh used, with total overhead costs of $114,000. Using the derived models:
Based on machine-hours:
Estimated overhead = $34,750 + $27.50 * 3,000 = $34,750 + $82,500 = $117,250
Based on kilowatt-hours:
Estimated overhead = -$118,146.36 + $0.0578 * 4,000,000 ≈ -$118,146.36 + $231,200 ≈ $113,053.64
Comparing these predictions with actual costs, the kilowatt-hours model provides a closer estimate ($113,054 vs. actual $114,000), suggesting it is the better predictor for July despite initial issues.
Conclusion
The high-low method enables rapid estimation of cost functions; however, the choice of cost driver significantly influences accuracy. In Tessmer Manufacturing Company’s case, kilowatt-hours, despite initial anomalies, appears to be a better predictor for overhead costs in July. Management should consider refining the model further with more data or adopting additional methods like regression analysis for better accuracy.
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