We all know that overhead variances will occur when the amount of overhead applied to items produced differs from the amount that was budgeted to be applied to produced goods. The overhead that is applied is generally based on a predetermined rate that is set at the beginning of the year. In the most simplistic costing calculations, this predetermined rate is the expected overhead costs for the year divided by the total expected activity for the year (machine hours, labor hours, etc.). Because this predetermined rate is based on average annual figures, it will almost ALWAYS vary from the actual monthly rate due to actual production incurred. This can be labeled as a “built-in” variance.
This is commonly referred to as “peanut butter”, and also touted as “inaccurate”. You evenly allocate your overhead throughout the year as production occurs. If you have a crystal ball and you have accurately predicted your costs and let’s say, your machine hours for the year, you have no variances. However, we know that is NOT reality. Budgeting much beyond three months generally becomes more inaccurate the farther you move out, thus, will be variances. There are also factors at play such as seasonal production. Depending on how weighted each season is, these overhead variances can become quite large, but should even out over the course of the year.
I am working with a client right now who is facing this exact predicament. They have budgeted machine hours for the year, and then used a somewhat arbitrary method for allocating the machine hours to each month of production. The key word here is “arbitrary”. At this stage in the game, like MANY Companies, they do not have a sophisticated ERP package that tracks actual machine hours for every single machine on a job by job or day to day basis. While they are beginning to work on gathering and assembling that data, they don’t have it now!
The result? After several months into the fiscal year, the cumulative volume variances continue to increase. My comment to the cost accountant was that when you see the same work center causing the same problems each month (large contributor to negative volume variance) it is likely a result of bad standards. If the swings were up and down (positive one month and negative the next) then it is more likely a seasonality problem. We immediately started digging into how the machine hours were arrived at on a monthly basis in the model, and discovered that there was really no causality associated with the monthly production or sales level. Until that problem is fixed, it is really very difficult to delve into many other aspects of what might be causing the volume variance.
If you dig deep and find that your original base was calculated correctly, those accumulating variances could be caused by many other issues such as new efficient machinery or processes, outsourcing what was previously produced in house, fewer shutdowns than expected, or extremely consistent scheduling utilizing maximum capacity in fewer machine hours. However, if the variances are not accumulating but simply “bouncing” above and below the line, they are likely built-in variances resulting from the differential between the annual average rate and the actually monthly rate. These variances are the easiest to explain!
