Cause And Effect: Please Respond To The Following From The E

Cause And Effectplease Respond To The Followingfrom The E Activity

Cause and Effect" Please respond to the following: From the e-Activity, compare and contrast the cause and effect of at least one cost driver in the company you researched with a cost driver at a local company in your area. Determine the repercussions of selecting an incorrect cost driver on the local company you identified and provide an example to support your discussion. High-Low Method and Regression Analysis" Please respond to the following: Create a scenario where using the high-low method results in flawed decisions for pricing or performance evaluations. Determine the benefits of using the regression analysis in the scenario created above.

Paper For Above instruction

Understanding the intricacies of cost drivers, their causes, and their effects is fundamental in managerial accounting, especially when making informed decisions regarding cost behavior. Additionally, choosing the appropriate method for analyzing costs—such as the high-low method or regression analysis—is crucial for accurate assessment and strategic planning. In this paper, I will compare and contrast a specific cost driver from a researched company with a similar driver from a local company, discuss the repercussions of selecting an incorrect cost driver, and demonstrate how regression analysis can provide more accurate insights compared to the high-low method.

Comparison and Contrast of Cost Drivers

Cost drivers are factors that cause changes in the cost of an activity or product. For instance, in a manufacturing company, machine hours often serve as a primary cost driver influencing production costs. In the researched company, which operates in the automobile manufacturing sector, the main cost driver identified was the number of units produced. This driver directly affects material costs, labor, and overheads. The causal relationship is straightforward: as the number of units increases, total costs increase proportionately.

In contrast, a local bakery’s primary cost driver might be the number of customer transactions or bakery items sold. While both companies have operational costs tied to activity levels, the nature of their cost drivers differs. The automobile manufacturer’s costs are expected to fluctuate strictly with production volume, whereas the bakery’s costs might fluctuate based on sales volume, which can be affected by seasonal factors, marketing efforts, or location traffic.

The effects of these drivers are significant; inaccurate identification or measurement can lead to misallocated costs, affecting pricing strategies and profitability analysis. If, for example, the automobile manufacturer incorrectly identifies labor hours as the sole driver when machine hours actually influence costs more significantly, it could result in underestimating production costs at high output levels, leading to underpricing and loss.

Repercussions of Incorrect Cost Driver Selection

Selecting the wrong cost driver can have serious repercussions for a company. For the local bakery, if sales volume or number of transactions is used as a cost driver for costs that are actually more directly linked to ingredient usage or labor hours, the bakery might misallocate overheads, leading to distorted product costing and pricing. This can cause the bakery to set prices too low, cannibalizing profit margins, or too high, reducing competitiveness.

An example of this would be assuming the bakery’s overheads are tied to sales transactions. If overheads are predominantly driven by ingredients used per batch or staff hours worked, basing costs solely on transaction volume would underestimate costs during busy periods with high staff utilization or ingredient consumption, and overestimate during slow periods. Consequently, the bakery could either lose profits or misprice its products, influencing its market position.

High-Low Method versus Regression Analysis

The high-low method simplifies cost analysis by taking the highest and lowest activity levels and their corresponding costs to estimate variable and fixed costs. However, this approach can lead to flawed pricing or performance evaluations when the data points do not accurately represent the overall trend. For example, if the high activity period coincides with an abnormal event, such as a promotional sale or a seasonal spike, the cost behavior observed may not reflect typical operations, leading to distorted cost estimates.

In contrast, regression analysis uses all available data points to statistically estimate the relationship between costs and activity levels. This method reduces the influence of outliers and provides a more precise cost function. For instance, in a scenario where a company’s costs are analyzed over multiple months with varying sales figures, regression analysis can produce a cost equation that accounts for fluctuations and offers a more reliable basis for decision-making.

Scenario Demonstrating Flawed Decisions with High-Low Method

Imagine a manufacturing firm evaluating its production costs to set product prices. Using the high-low method, the company takes the highest month with 10,000 units produced at $50,000 total costs, and the lowest month with 2,000 units at $12,000 total costs. The high-low method might conclude that variable costs are $4 per unit and fixed costs are $2,000. Relying on this simplified analysis, the company might set a price based on these estimates.

However, suppose the highest activity month includes one-time expenses, such as equipment repairs, inflating costs. As a result, the high-low method overestimates the variable costs. When the company sets prices based on this flawed calculation, it could overprice its products, reducing competitiveness and market share. Conversely, during normal months, the actual costs may be significantly lower, eroding profit margins.

Benefits of Regression Analysis in the Scenario

Applying regression analysis in this scenario would analyze multiple data points across different months, capturing the true cost behavior without being skewed by anomalies. The regression would generate a cost function that accurately reflects the relationship between units produced and costs, including normal fluctuations. With this precise estimation, the company can set more realistic prices, accurately estimate profitability, and make informed decisions regarding production levels and cost control measures.

Conclusion

In conclusion, identifying the correct cost drivers and choosing appropriate analytical methods are critical for effective managerial decision-making. Comparing a manufacturing company's cost drivers with those of a local business highlights the importance of context. The pitfalls of relying solely on the high-low method emphasize the need for regression analysis to obtain more accurate cost estimates. Implementing these refined analytical tools can lead to better pricing strategies, improved profitability, and more effective operational evaluations.

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