Allocation processes often deal with estimates because at times we are dealing with the future, and the future is not yet known.
For example, in the case of allocating the costs of building a factory over the life of the units produced, waiting until the factory has produced all the possible units will make for a very accurate allocated cost, but it will be a bit late at the end of the life of the factory for the first unit sales price to include appropriate factory costs.
The estimated cost per unit is often called a standard cost. When out estimate of the units the factory will produce is too high, we end up with under allocated costs; if some units are not produced then we are left with costs that did not get charged to those un-produced units. If the estimate of units is too low we end up with over allocated costs, which means our price per unit might have been too high. Perhaps we might have sold more units if we had a better price.
It can be possible to try to allocate costs based upon things that are estimated. For example, one might say that the cost of the truck fleet for shipping the goods is in some measure dependent upon the number of units that will be produced. If our estimate of units to be produced is wrong for the factory, it might also now affect the shipping costs for the fleet as well.
These layers of allocations can add to complexity and errors in the resulting allocation processes, and the information produced from them. Small errors in one thing multiplied by large estimated numbers can be come very large errors at the end. Considering how to eliminate estimating errors can improve an allocation processes.
For example, one company decided to do a year-to-date allocation process, rather than simply allocating the new costs accumulated for the new month using update estimates. That way error estimates in the prior months were eliminated as better information became available in the current month, and was incorporated into the allocation process.
The more straightforward the allocation process, the more understandable the result analysis will be to those who attempt to use it, with less likelihood of errors.
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