The quality of your model depends on the quality of your drivers
When building a cost model, the main concern is ensuring that the model "balances," meaning that all inputs match the outputs, ensuring nothing is...
Throughout my career, I have seen that after months of tremendous effort in creating a cost model and after investing many thousands of dollars and hours, the cost model ends up being abandoned, becoming an ironically very costly failure. You may read many articles out there that tell you how to make a cost model, but very few will tell you what not to do. Or, in other words, what you need to do to condemn a cost model to failure. Let's begin.
From the design of the model, there is a huge temptation to cost every last activity and task. We go from costing nothing to wanting to cost absolutely everything. This extreme approach leads us to forget a fundamental principle: models are, by nature, iterative. That is, they are designed to be refined and improved over time, not to achieve perfection from the start.
The model serves a strategic purpose, and to achieve this, it is neither necessary nor desirable to cost at an excessively detailed level. Why? Because in most cases, the effort required to maintain and update the model at that level of detail does not justify the added value obtained. An excessively detailed model can become an operational burden, diverting resources and attention from more critical and strategic activities.
Furthermore, a model that is too complex can hinder its understanding and use by its users. The key is to find an appropriate balance: costing at a level that provides sufficient information for strategic decisions without overloading the system with unnecessary details. This balance allows the model to be more agile, easier to maintain, and most importantly, useful for the organization.
An iterative approach also allows the model to evolve organically, adjusting to the changing needs of the organization. Instead of trying to capture all the details from the beginning, adjustments and improvements can be made based on feedback and the results obtained. In this way, the model becomes a dynamic and adaptable tool, capable of providing valuable insights without imposing an excessive maintenance burden.
In a cost model, drivers are extremely important, as I explain in my article called "The Quality of Your Model Depends on the Quality of Your Drivers". This leads to the temptation to use drivers that distribute costs optimally, but where the effort to obtain their values is too laborious. Being manual, this process is very time-consuming and prone to errors.
The alternative is to seek a balance between precision and efficiency. Opt for drivers that, while perhaps not perfect, allow for more agile data collection and are less prone to errors. An example could be the use of averages or estimates based on historical data, which can provide a reasonably accurate distribution of costs without incurring the administrative burden of a fully manual approach.
Therefore, the choice of drivers must be a strategic decision that considers both the quality of the data obtained and the practicality of its collection and management. In my article, I discuss various strategies and techniques for finding this balance.
I have always maintained that the model must be a true reflection of the organization. If the organization changes, the model must adapt and change with it. What does this imply? When the model is very detailed, keeping it updated to reflect the constant changes in the organization becomes a monumental task. Over time, if the model is not adequately adjusted, it ceases to represent the reality of the organization and therefore loses its usefulness, which inevitably leads to its abandonment.
This challenge of keeping the model updated is especially critical in dynamic environments where processes, structures, and strategies change frequently. The lack of updates not only affects the accuracy of the data but also undermines users' confidence in the validity of the model. If the model stops being a mirror of the organization, its analyses and reports become irrelevant, and the efforts invested in its development and maintenance are wasted.
Therefore, it is essential to establish a continuous update process that allows the model to evolve along with the organization. This may involve implementing automated systems, creating clear processes for data updating, and allocating dedicated resources to model management. Only in this way can it be ensured that the model remains a valuable and accurate tool for decision-making, always reflecting the current reality of the organization.
This point is closely related to the drivers. Here, however, it also includes the manual loading of other types of information such as expenses by account, income, numerical and text attributes, etc. All this information, when loaded manually, requires considerable effort and time. This effort is normal during the construction of the model and in the initial runs. During these stages, it is justified because many adjustments are needed until the model is stabilized. But beyond this point, if the data loading is not automated, the effort to load the model with new period information becomes prohibitive.
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Cost models generate an enormous amount of information, and this is precisely where the richness of such models lies. From this vast data source, KPIs can be obtained, detailed reports on process and activity costs, profitability analyses of products and services, top ten cost lists, resource utilization, data cubes generation, and more. Each of these reports can be filtered by the different dimensions contained in the model, allowing for deeper and more specific analysis.
The key here is that to fully leverage the potential of the cost model, it is essential to have these reports in a timely manner. If the generation of these reports takes too long, the risk of missing the opportunity to make strategic decisions at the right moment is high. Efficiency in creating and distributing these reports is crucial to ensuring that data-based decisions are accurate and timely, which can significantly impact the competitiveness and profitability of the company.
Any model that does not have the support of top management is practically dead on arrival. The reason is that many resources and collaboration from different areas are needed to build the initial model. This involves numerous interviews with process owners to understand how the cost flow should be, existing dependencies, and all the nuances that need to be incorporated into the model. Furthermore, once built, the data needs to be refreshed periodically with expenses, drivers, attributes, income, etc., for each subsequent period. Without management support, the individuals responsible for the cost model will not have the authority to request collaboration from the various areas of the organization, resulting in a partially updated model that is unreliable for decision-making.
Doing It in Excel
This point might surprise you since many would think that Excel is the tool where a cost model should be created. However, this is not always the case. If we are talking about a small, not very complex model with few data updates, perhaps focused on a department, then Excel might be sufficient. But when we talk about a company-wide model, a corporate model that includes branches, various channels, product families, products defined by different dimensions, Excel falls short. Due to formula issues, data loads, referential integrity, and other aspects, which I explain in my article titled " Is Excel Cost Modeling a Path to Failure?" it is not recommended to do it in Excel.
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What About the ERP?
Current ERPs like SAP, Oracle, or MS Dynamics already incorporate costing functionalities where parallel accounting can be carried out with a more strategic focus aimed at decision-making. These offer more freedom in assignments and cost distribution without affecting financial accounting. So why not use them for costing? The investment in the ERP has already been made; why not take advantage of all these costing functionalities that the ERP provides? I am not saying that they should not be used for costing. Costing in the ERP can be successful and provide the information a company needs. However, there is a wall we face when having a model in the ERP, which is the lack of flexibility. Flexibility to change drivers, flexibility to change assignments, to create new activities, to create hypothetical lines of new products and services. Flexibility to create scenarios and flexibility to undo everything and return to the initial point. Can all this be done in the ERP? Yes, but at what cost and effort? A specialized costing tool is always much more flexible than any ERP to play with the data creating "What if..." scenarios. This ease of manipulating the data is precisely what allows for information analysis.
The success of a cost model does not solely depend on its initial design but on a series of practices and strategic decisions made throughout its implementation and maintenance.
To avoid the failure of a cost model, it is essential to avoid excessive detail that can make the model unmanageable and costly to maintain. It is crucial to find a balance between precision and practicality, opting for drivers that, although not perfect, are efficient enough to provide useful data without overburdening the staff.
Additionally, the model must be dynamic and adaptable to organizational changes. Keeping it updated is vital to ensure it accurately reflects the reality of the company and remains a useful tool for decision-making. Automating data loading and efficiently generating reports are key factors to ensure the model remains relevant and useful.
The support of top management is indispensable for the model's success. Without this backing, obtaining the necessary collaboration from different areas of the organization is difficult, which can lead to an outdated and unreliable model.
While Excel may be a suitable tool for small models, it is not ideal for complex corporate models. ERPs, although useful for certain costing aspects, may lack the necessary flexibility to handle complex scenarios and rapid changes.
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