How To Write An Executive Summary Of Krog's Case
Executive Summary The settlement from the insurance company was more than reasonable. The mean estimate excess cost of operations was roughly $254,000 but that is just the mean estimate. Unfortunately for Krog’s MetalFab Inc. we have zero confidence in this model’s exact mean estimate of $254,000. We are 95% confident that the estimated excess costs of operation was between roughly $9,600 using the lower 95% and almost $500,000 using the upper 95%. Because of this, the insurance company should have not immediately settled for $254,000 and should have only offered $9,600 as their starting settlement offer. Krog’s got a very good deal here. The way that I figured out these numbers is by creating a regression to estimate the materials, labor,
overhead, and I also created a model for overall cost estimation. The materials regression is a mixed cost model meaning that there are some fixed costs that we have but also have some variables costs that we are able to control based upon output. The R-square for this model is over 99% which is the goodness of fit which explains how well the model is able to explain the sum of the squared errors. The labor estimate is also a mixed cost because there is always a cost even at zero output. We still have some control over this variable as well depending on our output so it is mixed. This also has an R-square of over 99%. Finally, I used a mixed cost model as well for the overhead cost and the R-square was only 85% but I could have tightened this model up by dropping the Feb. outlier but I chose not to so that we could keep the dataset complete. The F-stat for all of the models were high which means that the overall regression model is statistically significant. Krog’s does have an additional claim against the insurance company. Once again, we do not have any confidence in the mean estimate but we do have a 95% confidence interval. The mean estimate of the lost sales assuming a 7% increase in sales is $1,589,132. To calculate the lost profits we need to take the difference between the lost sales and the estimated material, labor, overhead, selling costs. The mean estimate gives us a lost profit figure of $886,586. Once again, we do not have any confidence that this is the exact lost profit but we do have 95% confidence that the figure is between $814,646 and $958,526 for the lost profits. These figures are based upon the lower and upper 95% estimates for material, labor, overhead, and selling costs. It does not seem likely that Krog’s Metalfab padded the additional claim for lost profits. The cost data has a very strong relationship with the sales data and has very high R-squared. Unless Krog’s did something to manipulate the 2003 relationships, the estimates for 2004 are very strong. It would be very difficult for Krog’s to have foreseen the fire and manipulate their cost-volume relationships so that they could recover more on their loss. The only other evidence that Krog’s padded their data for the additional claim is that they only provided the 2003 data which may have been a very unique year for the cost-volume relationship. This could be done by increasing the costs in 2003 when the relationship is one that is less costly in earlier years. By doing this, the costs would be higher in 2003 when the costs are actually lower if you examine it in earlier years.
overhead, and I also created a model for overall cost estimation. The materials regression is a mixed cost model meaning that there are some fixed costs that we have but also have some variables costs that we are able to control based upon output. The R-square for this model is over 99% which is the goodness of fit which explains how well the model is able to explain the sum of the squared errors. The labor estimate is also a mixed cost because there is always a cost even at zero output. We still have some control over this variable as well depending on our output so it is mixed. This also has an R-square of over 99%. Finally, I used a mixed cost model as well for the overhead cost and the R-square was only 85% but I could have tightened this model up by dropping the Feb. outlier but I chose not to so that we could keep the dataset complete. The F-stat for all of the models were high which means that the overall regression model is statistically significant. Krog’s does have an additional claim against the insurance company. Once again, we do not have any confidence in the mean estimate but we do have a 95% confidence interval. The mean estimate of the lost sales assuming a 7% increase in sales is $1,589,132. To calculate the lost profits we need to take the difference between the lost sales and the estimated material, labor, overhead, selling costs. The mean estimate gives us a lost profit figure of $886,586. Once again, we do not have any confidence that this is the exact lost profit but we do have 95% confidence that the figure is between $814,646 and $958,526 for the lost profits. These figures are based upon the lower and upper 95% estimates for material, labor, overhead, and selling costs. It does not seem likely that Krog’s Metalfab padded the additional claim for lost profits. The cost data has a very strong relationship with the sales data and has very high R-squared. Unless Krog’s did something to manipulate the 2003 relationships, the estimates for 2004 are very strong. It would be very difficult for Krog’s to have foreseen the fire and manipulate their cost-volume relationships so that they could recover more on their loss. The only other evidence that Krog’s padded their data for the additional claim is that they only provided the 2003 data which may have been a very unique year for the cost-volume relationship. This could be done by increasing the costs in 2003 when the relationship is one that is less costly in earlier years. By doing this, the costs would be higher in 2003 when the costs are actually lower if you examine it in earlier years.