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Why flexibility matters so much in enterprise performance management

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Why flexibility matters so much in enterprise performance management


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When forward-thinking finance organizations are asked, “What is the most important attribute of an enterprise performance management system?” very often the number one requirement is flexibility! Why is flexibility so important? For the following reasons:

  1. Your enterprise performance management (EPM) system should allow the model to be configured in a manner that truly reflects the way you run your business. The solution should not force you to change the way that you run your business to fit into a rigid, software-imposed structure.
  2. Who knows what the future may bring? There could be changes in the business climate, a downturn in the economy, or an acquisition. New competitive pressures could drive a need to change a business process to gain extra margin from the sales of existing products. The performance management system should be nimble and flexible enough to adapt to those changes quickly so that you can stay ahead of the game.
  3. While integration with general ledger systems is, of course, important, your performance management system should not be coupled with it too tightly. The reason is that the general ledger is a backwards-looking system which contains only financial and not operational views of the data. The performance management system should use both financial and operational views and reflect the future as well as the past.
  4. Total cost of ownership (TCO) and agility. A performance management system should not burden your organization with huge implementation and ongoing maintenance costs.

Flexibility matters — in spreadsheets or cubes

As an analogy, suppose that you were using a spreadsheet to develop an operating expense budget. You might have capital purchases in one worksheet, your headcount plan in another sheet, and office expenses in another. Then you link all of the sheets to an “Operating Expense Summary” sheet. What if your spreadsheet application forced you to put everything into a single sheet in a single workbook? That would be inflexible, defeating the purpose of the modular design of spreadsheets.

Of course, these days we have performance management systems and cube technology to get around the limitations of spreadsheets. But the issue of flexibility is still there. It just manifests itself in different ways in our spreadsheet example.

Putting EPM flexibility to the test

During a recent IBM proof of concept (POC) at a large manufacturing company, the IBM team developed a model in IBM Planning Analytics, which reflected the requirements set out by the company’s project team. During a 3.5 day period, the IBM team was able to install the software, connect to four different source systems, load a complete data set, and build the company’s entire model. A schematic of that model is shown below.

Small, manageable models follow business structure and logic using shared dimensions. Intelligent business object mapping consolidates relevant data.

There are several benefits of this approach:

  1. The model maps exactly to the way the company runs its business. It is easy to visualize.
  2. You can break down the model into simple, easy to understand chunks and focus on those.
  3. You don’t have to know everything that you may need to do right from the start. For example, if you need to add in some headcount planning in the future, you can add it later and just link it into the existing structure.
  4. The impacts of changes are minimized. If you need to make a change, you only change the impacted module. All the other modules remain intact.
  5. You only need to include the dimensions required for a particular planning purpose. For example, if you are doing employee planning, you don’t have to be concerned about supply chain issues.

Coping with curveballs in planning

The flexibility and agility of IBM Planning Analytics was demonstrated numerous times during this particular POC. We were presented with a number of “curveballs” and were able to rapidly find solutions and implement them in the model. As an example, we discovered that the forecast data was held at a higher level than sales grade. We were able to quickly allocate the forecast down to sales grade using historical actuals. Consider this type of change in the real world, where you may need to model some change in the business environment and quickly see its impact on your company.

Also, the IBM team was able to complete the entire POC in only 3.5 days. That bodes well for what a company might experience in the areas of total cost of ownership, time to value for implementation, and ongoing maintenance costs.

The downside of single-cube architecture

The approach taken by other performance management solutions on the market today is somewhat different. Those solutions often require the entire business model to be housed in a single cube. This is shown below.

The lines with arrows denote business rules that need to be applied within the cube.

 

At first glance, one might conclude that this architecture is simpler and that all of the data is in the same place. In fact, it is not as simple as you might think. There are some important considerations that really do have an impact on flexibility and agility, and on your ability to meet your business goals.

With this single-cube architecture:

  1. It is more difficult to visualize how the plan structure maps to the way that you run your business.
  2. You have to know all of the dimensions that you might ever need right from the start. What If you wanted to perform some capital or headcount planning once you had built this model? To add a new dimension at a later date, you would have to evaluate and maybe change all of the business rules and reports that are using the existing structure.
  3. Making changes is more difficult. You can’t just focus the changes on the specific area in question. You have to consider the impact on all of the business rules across the entire cube. In fact, depending upon the nature of the change, you may need to re-implement the entire system.
  4. Since all of the dimensions are enclosed in a single structure, when designing business rules or reports, you have to consider all of the dimensions. Why should you be concerned about, for example, supply chain issues when you’re putting together an employee plan?
  5. Due to some of the reasons outlined above, you may be forced to put some applications into other cubes. That may provide a usable workaround. In effect, however, you are creating silos of data. How do you integrate the data in those cubes?
  6. Total cost of ownership and time to value tend to be significantly higher because of the added complexity.
  7. You are constrained by the application, which impacts flexibility and agility.

Dig deeper before you commit to an EPM system

When you compare planning solutions, it’s important to keep these differences in mind. Although a solution may demonstrate well, look functional, and appear to meet your needs, you need to dig deeper. Look at how your business functions and consider the integration needed across business units, processes, and workflows so you can get a true sense of how the solution will fit your requirements.

Only then will you know if it offers the flexibility you need for your unique circumstances. If you don’t consider all the variables or contingencies, you could end up with a partial system that’s capable of performing only part of your planning and analysis, with spreadsheets still filling the gaps that the system didn’t cover.

More factors to consider for your EPM system

Want to try out IBM Planning Analytics for yourself? Click here to start your free trial. Meanwhile, if you want to learn more about choosing an enterprise performance management system for your organization, you can read my recent blog articles for more suggestions on important factors to consider:

Guy Jones is Worldwide Technical Sales Executive for Planning Analytics at IBM. With an early background in the design of financial solutions as a Systems Analyst, Guy brings more than 20 years of experience and domain expertise in Financial Performance Management to his role at IBM.

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