Most organizations allocate some or all of their indirect expenses to lower order subsidiaries, business channels, product categories or customer segments to measure their profitability. Some do it just for the segmented reporting they include in their annual financial report; but others do it to provide guidance on where to grow the business to improve financial performance.
Profitability analysis is always a trade-off between the accuracy of the results and the amount of effort and expense invested in producing them, and where you come down on that continuum really depends on what the resulting information is being used for. If it is simply for high level segmented reporting, then having results that are ‘directionally’ accurate is more than adequate. But as soon as the information starts to be used for strategic or tactical decision making, then it is imperative to improve accuracy.
When Apportionment is not Enough
Frequently, profitability reporting is little more than crude apportionment—splitting aggregated indirect expenses by high-level drivers such as revenue. Clearly this produces inaccurate results, as expenses are simply being ‘peanut-buttered’ and do not necessarily reflect the amount of resource that the subsidiary, product category, or customer group actually consumed.
To produce more accurate profitability reporting, every line item of indirect expense needs to be allocated to its target (e.g the subsidiary, product group, or whatever), using a driver that reflects its consumption of that resource. So for instance, rather than apportion the total cost of a corporate headquarters office to subsidiaries based on their revenue, individual elements of corporate expenditure would be allocated in different ways to reflect the actual cause and effect relationship (e.g. allocating the cost of corporate HR by headcount and the cost of corporate IT help desk by the number of calls etc.)
However, adopting a more scientific approach to allocations involves building more complex models that contain larger volumes of granular data and a greater number of rules. Both requirements are beyond the capabilities of typical planning and budgeting solutions, and result in costs for services and implementation support, with a negative impact on performance.
Achieving Greater Accuracy Without Greater Effort
Reading through various discussion forums on the internet suggests that many finance folk are frustrated at the lack of progress that they have made in improving the accuracy of their allocations and profitability analysis. Sometimes their peers discourage them from even trying, and I must admit I can understand why. The more I think about it, the more I am convinced that profitability analytics will not really advance until we tackle the root causes that are holding them back. To my mind, that means:
- Using more appropriate drivers
- Migrating to self-managed finance solutions
- Implementing systems that cope with big data volumes
1. Accessing and using non-financial drivers from transaction systems and databases
The first thing that finance teams need in order to start improving profitability analysis is the ability to quickly access and use non-financial drivers from other transaction systems and databases. Achieving that is not so easy if all the information resides in different silo-ed databases, so it’s understandable why drivers such as revenue and headcount, which are already in enterprise planning and budgeting solutions, tend to be used most often. But the easy access to data that comes with cloud-based solutions makes it possible to use more appropriate drivers that better reflect the cause-and-effect relationship between a particular indirect expense and the subsidiary or product group it is being allocated to.
2. Migrating to a solution that enables your front line people to build and maintain business rules themselves without outside help
However, simply accessing better data to drive allocations is not enough. Unless finance folk can build and maintain models themselves, they are going to have to rely on the availability of an expert from their own IT team, or constantly call on external services support. Either option means additional expense, so improved accuracy comes with a high price attached. Thankfully, the newer generation of cloud-based planning, budgeting, and modeling solutions are much more intuitive to use and do not require ‘black-belt’ skills to write allocation rules or roll up results in different ways.
3. Implementing solutions that can manage the data volumes
A third factor that has stopped finance teams from improving their allocations is that the calculation engines that underpin their enterprise planning and budgeting solutions simply cannot cope. As soon as you move from apportioning aggregated expenses and start allocating line items individually, you increase the amount of data. If you also increase the granularity of the allocations to the level of individual products or pack sizes, you are further increasing the amount of data. Many solutions cannot process such volumes of data without a significant deterioration in performance. The advent of applications with in-memory calculation engines has removed this constraint, particularly if they are also built using a data architecture designed to cope with big data. In-memory calculation also makes it possible to immediately assess the impact of changing allocation rules, rather than having to wait on batch processing, making it far easier to fine tune models on the fly.
Business users are demanding profitability reporting that is both more accurate and more detailed. This has led some finance functions to implement specialist software needed to support activity-based costing methodologies. But experience suggests such implementations can quickly become so unwieldy, and expensive to sustain, that the costs tend to outweigh the benefits.
A more pragmatic approach is to give business users more control, and systematically improve the accuracy and detail of the profitability reporting already in place. However, if you accept my analysis of the factors that are constraining attempts to improve the accuracy of profitability analysis today, to my mind you get to an inevitable conclusion: we need to stop doing allocations in spreadsheets and traditional enterprise planning and budgeting solutions and move the task into tools with modelling capabilities that are up to the job. There, that’s contentious. So let us know your thoughts in the comments.