Answer the following question: Select two of the seben common problems with data collection identified on p. 353-354 of your textbook. For the two problems you select, explain in your own words why that problem exists, give a real world example of a situation in which that problem might be encountered (the example cannot be from the book), and describe how a cost analyst can overcome the problem in your example. In your description of how to overcome the problem, you should be specific about what needs to be done. DO not simply repeat what is in the text book.
6. The relationship between the cost driver and the cost is not stationary. That is, the
underlying process that generated the observations has not remained stable over
time. For example, the relationship between number of machine-hours and manufacturing
overhead costs is unlikely to be stationary when the data cover a period in
which new technology was introduced. One way to see if the relationship is stationary
is to split the sample into two parts and estimate separate cost relationships—
one for the period before the technology was introduced and one for the
period after the technology was introduced. Then, if the estimated coefficients for
the two periods are similar, the analyst can pool the data to estimate a single cost
relationship. When feasible, pooling data provides a larger data set for the estimation,
which increases confidence in the cost predictions being made.
7. Inflation has affected costs, the cost driver, or both. For example, inflation may
cause costs to change even when there is no change in the level of the cost driver.
To study the underlying cause-and-effect relationship between the level of the cost
driver and costs, the analyst should remove purely inflationary price effects from
the data by dividing each cost by the price index on the date the cost was incurred.
To answer your question, I will explain two common problems with data collection from your textbook and provide real-world examples for each. I will also describe how a cost analyst can overcome each problem, using specific steps.
Problem 1: The relationship between the cost driver and the cost is not stationary.
Explanation: This problem occurs when the underlying process that generated the observations has changed over time. For instance, if new technology is introduced during the data collection period, it is unlikely that the relationship between the number of machine-hours and manufacturing overhead costs will remain the same.
Real-world example: Let's consider a manufacturing company that upgrades its production line with advanced robotic machinery. This technological advancement is expected to significantly improve efficiency and affect the cost relationship between machine-hours and manufacturing overhead costs.
Overcoming the problem: To determine if the relationship is stationary, the cost analyst can split the data into two periods: before the technology introduction and after the technology introduction. Then, they can estimate separate cost relationships for each period. If the estimated coefficients for both periods are similar, it indicates a stable relationship. The next step is to pool the data from both periods to estimate a single cost relationship. Pooling data increases the size of the dataset, providing more confidence in cost predictions.
Problem 2: Inflation has affected costs, the cost driver, or both.
Explanation: Inflation can cause costs to change even when there is no change in the level of the cost driver. To study the cause-and-effect relationship between the cost driver and costs, it is essential to remove purely inflationary price effects from the data.
Real-world example: Let's say a cost analyst aims to determine the relationship between the number of miles driven and fuel costs. However, during the data collection period, the country experiences significant inflation that affects fuel prices. The analyst needs to account for this inflationary effect to accurately assess the relationship between the number of miles driven and fuel costs.
Overcoming the problem: The cost analyst should divide each cost by the price index on the date the cost was incurred. By doing this, they can remove the pure inflationary price effects from the dataset. This adjustment enables the analyst to isolate the relationship between the cost driver (number of miles driven) and costs, unaffected by inflation.
In summary, the cost analyst can overcome the problem of a non-stationary cost relationship by splitting the data into periods, estimating separate cost relationships, and then pooling the data if the coefficients are similar. In the case of inflation affecting costs, the analyst can remove the inflationary price effects by dividing each cost by the price index on the date it was incurred. These steps ensure accurate analysis of the relationship between the cost driver and costs.