Determine how many cars, on average, turned to the Aalst location of Olympic Car wash during bad weather? What does this tell you about the managerial performance of the Aalst location?
From the two tables given in the case we could extract the following data: Actual revenue earned by Aalst in that quarter = 124,080 Vehicles washed on average per hour during good weather = 24 Average revenue earned per vehicle = € 11 Hours of good weather in that quarter = 470 Hours of good bad in that quarter = 450 Vehicles washed on average per hour during bad weather = unknown variable x Those variables can be put into a mathematical equation where: (470 hours of good weather * 24 vehicles washed per hour * € 11 revenue per vehicle) + (450 hours of bad weather * x vehicles washed per hour * € 11 revenue per vehicle) = 124,080 actual revenue earned by Aalst in that quarter. Solving the equation for x leads to: x = 0. Thus, during bad weather no vehicles were washed at the Aalst location. The fact that no customers visited the Aalst location to get their cars cleaned during bad weather is supposedly reasoned in them not wanting their vehicles to be dirty again right after they leave the car wash. This customer preference is out of control of the Aalst management and therefore is no indicator of their performance at all. What does the variance report tell you about the managerial performance of the Aalst location?
Price variance The first entry of price variance measures how much more gross profit was generated due to the fact that Aalst increased the sales revenues to € 11 and while assuming that the variable expenses stayed the same, namely € 5 per vehicle serviced. To calculate that adjusted budget, the hours where there was actually sun, were adjusted to the actual amount of 470 hours. Further, the average number of vehicles washed was adjusted to the actual level of 24. In essence, this calculation really focused exclusively on the net effect of increasing the sales revenue in the actual situation in spring 2002. Explanation of the calculation: 470 actual sunny hours * 24 actually serviced vehicles per hour * (€ 10 original sales revenue – € 5 variable expenses) = € 56,400. 470 actual sunny hours * 24 actually serviced vehicles per hour * (€ 11 actual sales revenue – € 5 variable expenses) =€ 67,680.
This measure does not state much, since it assumes that the variable expenses behaved differently than they usually did at Olympic Car Wash, namely that they make up 50% of the revenue charged per vehicle wash. Volume variance The second entry of the variance report assesses the difference in revenue as well as variable expenses incurred. The variable needed to derive at the (€ 35,600) are: 1. (€ 59920) = Difference actual to budgeted revenue 2. € 29960 = Difference in variable expenses assuming variable expenses are € 5 per vehicle (50% of revenue) 3. (5640) = Difference in adjusted budgeted variable expense use Explanation: The variable expense per vehicle is budgeted to be € 5, thus 50% of the budgeted sales revenue per vehicle, € 10.
Adjusting for the actual differences in sunny hours, 470 instead of 800, as well as average vehicles cleaned per hour, 24 instead of 23, and, but without adjusting the variable expenses to 50% of € 11, but instead keeping it stable at € 5, variable expenses are budgeted to be 470 * 24 * € 5, thus € 56,400. But instead, Aalst had incurred variable expenses of 50% of the revenue earned on average per vehicle, thus € 5. 50, and therefore had expenses of 470 * 24 * € 5. 50 = 62,040. The difference between the actual expenses and the adjusted budgeted are (5640). The indicator is thus not very clear, since it does not reflect that the difference in revenue earned was out of control for the Aalst management. Instead it mixes this insufficient measure with a measure of variable expenses, diminishing the informative value even more.
Nonetheless, after breaking down the individual measures included in this indicator, one can see that Aalst did perform as targeted in regard to revenue, considering an adjustment to bad weather. When solving for the original budget of € 184,000 of revenues, assuming 800 sunny hours and 120 hours of rain, solving for the unknown x derives at x = 0 (800 hours * 23 cars per hour * € 10 revenue + 120 rain * x cars per hour * € 10 revenue = € 184,000 ? 184,000 + 120x * € 10 = € 184,000 ? x = 0). Thus, the management of Olympic Car Wash already budgeted that zero vehicles would be brought to the car wash during rain. But the only weaknesses are the higher variable expenses. In this regard it should be further analysed what came first: the increase in variable expenses or the increase in sales revenue per car.
If the higher Euro amount in variable expenses what merely tried to be diffused by raising the price per service, then this is a negative trend that should be stopped. Although it is possible that the increase in variable expenses was due to uncontrollable cost increases for some resources/raw materials used in the service generation process. this lies outside the management? s control. Still, holding management accountable for changes in variable cost expenses can motivate them to improve upon resource/raw material usage in times of raising prices. Since labor costs are part of Aalst? s variable costs, this may mean that the Aalst management would lay off people, or improve their work handlings so that considerably less human work time is invested per vehicle wash.
But this decreased usage will only be possible to a certain extent and will in either case be dependent on the kind of resource/raw people in question, there is no percentage attached it to it, such as “Process improvements could be reached by lowering the usage of all resources and raw materials to a rate of 10% of its current usage level”. Operating Efficiency Variance This measure repeats the message that was already delivered by the previous entry: variable expenses are higher than budgeted (higher by € 5,640), after adapting the original budget to changes that occurred in that period, such as less sunny hours, a higher efficiency in handling vehicles per hour, and a higher revenue per vehicle. Fixed Expenses Variance An increase in fixed expenses will possibly have been due to external changes in the environment, which the management of Aalst could not hold control over.
A factor such as raise in rent for the car wash cannot be influenced, neither can a lawful increase of minimum wage. Conclusion The variance report is not a good tool to really assess the performance of the Aalst management. The only performance indicator that gives a clear answer to the changes in revenues, sales, and profits is the measure of operating efficiency, which uncovered that the variable expenses were exceeded by € 5,640. Yet, variable costs might not always be controlled by the management, since they will usually only be able to lower their exposure to the increased cost factor to a certain extent. Accordingly, this result should be further analyzed before actual performance payments are attached to it.