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SAP Analytics
Thought Leadership

Rolling Forecasts – Effecting EPM Techniques

Posted by
Martin Lloyd
Martin Lloyd
on Wed, Jul 17, 2013 @ 11:07 AM

In a previous blog I examined the trend away from the traditional annual budget and periodic reforecasting regime and towards the rolling forecast. I’d now like to examine the particular factors which are driving companies towards rolling forecasts and the implications that this has on the required tools and methodologies.

Aberdeen Group recently identified that the three leading pressures which were driving organizations towards rolling forecast were:

•    Changes in their operating environment resulting from both market volatility and internal pressures;

•    Increasing operational costs; and

•    A desire to reduce the time taken and resources required to deliver budgets and forecasts.

They are experiencing the desire, if not the need, to be able to reflect changes in both external and internal circumstances in rapidly changing market conditions together with the realization that the current forecasting processes are taking too long. This is pointing organizations towards changes in their approach to forecasting but also is leading them to re-examine the tools they are using.

Forecasts are never 100% accurate and the level of accuracy is further reduced in volatile conditions. This can be alleviated by regularly reviewing both the forecasting methodologies and the drivers. But this must be done against a background of reducing the resources required to deliver the forecasts, one of the other pressures organizations are under. To achieve this it is necessary to review the methodologies and tools employed.

EPM Rolling BudgetOne of the key differences in approach between the rolling forecast and the traditional budget and periodic reforecast is that in the rolling forecast the last period as the rolling horizon moves forward, e.g. the 24th month in a 24-month rolling forecast is forecast from scratch. The current period will be updated with Actual data and other periods may be updated to reflect new information such as the acquisition or loss of a customer.

I frequently discuss with organizations the frequency of their forecasting activities. Often they are used to producing forecasts for the current year on a quarterly basis. They have bought into the concept of the rolling forecast but suggestions of monthly, or even more frequent, updates seems like a step too far.

When asked why, if you had won a new customer or suffered a large movement in raw material costs, you would wait three months to reflect this in a forecast, they appreciate the need to be more responsive; but the key to achieving this is the use of the correct tools and the appropriate methodology.

Before we look at some of the key areas to consider when examining the possible tools for developing rolling forecasts we will consider whether Excel should be included in the list. After all, it is the business tool that users will be most familiar with. It’s also the tool used by two economists Carmen Reinhart and Ken Rogoff of Harvard when they were developing a paper, published in 2010, justifying austerity in the aftermath of the global recession. Unfortunately, they forgot to drag their Excel formula down five more cells and omitted data for five of its 19 countries. In many cases, Excel is just too easy to use and isn’t subjected to the rigorous testing which is expected. That’s not to say that Excel doesn’t have a role but it needs to be used appropriately. We’ll see what I mean by that as we consider the factors that determine what tools are appropriate. 

Some of the factors to consider when considering the most appropriate tools and the best practices for the development of rolling forecasts are:

•    Whilst the use of Excel as a delivery mechanism may be appropriate it should be used as a window on the data which is stored in a structured controlled environment. For example, SAP EPM 10 includes the facility to report live from the Microsoft Office Suite. This allows the use of familiar tools alongside the controlled data of an application environment.

•    By structuring the data appropriately we can process it in a consistent way and report on it flexibly. The selected tools, together with the right implementation advice, should encourage and facilitate the appropriate structure.

•    Control includes the ability to secure access to the data as well as controlling submission and locking of data. You should expect to have this delivered as standard but easily configured application functionality.

•    Driver based forecasting, with the ability to report on driver values, gives greater transparency on how the forecast was developed.

•    Report against actual and prior forecast data. With the actual and forecast data held in a structured database flexible reporting across data type and version should be easy.

•    Measure forecast accuracy – reports such as waterfall reports, which show the trend of the forecast of each period and the actual result achieved, allow forecast accuracy to be analyzed.

•    Don’t restrict the frequency of forecasts. If a change occurs mid-month then why not immediately assess the impact on future periods?   

•    Allow use of a prior version of the forecast as a starting point; change drivers and recalculate. This allows ad-hoc forecasts, prepared to reflect some change in market conditions, to be prepared very quickly.

•    Scenario modeling – take a copy of the existing forecast and amend assumptions to assess the potential impact of possible changes in external or internal drivers. Produce best-case or worst-case scenarios.

•    Encourage wide participation in planning and review. This both ensures that grassroots information is available to feed the forecasting process but also promotes ownership.

•    Plan with appropriate analyses e.g. product, customer, business unit, and choose the appropriate level e.g. SKU, product line, product group, customer delivery location, customer group.  

•    Appropriate delivery mechanisms: reports, dashboards, scorecards. What is appropriate depends on both the data to be presented and the audience.

•    Balance speed of forecasting with accuracy.

By employing the correct tools, which were designed to provide the required functionality and which facilitate the application of best practice forecasting methodologies, you will be able to respond more quickly to changes in business conditions, reduce the cost of the forecasting process and deliver more accurate forecasts more quickly. Of course, in addition to the right tools, you need guidance from people who can help you navigate through the design and implementation process. The change management implications of introducing a new forecast methodology should also not be ignored.

Planning, Budgeting & Forecasting for Professional Services Webcast
Watch this session which features a case study by Orrick, Herrington & Sutcliffe LLP, who has implemented SAP Business Planning and Consolidation (BPC).  We will also provide an overview of BPC along with Best Practices and design options when configuring BPC in Professional Services organizations.


Topics: Best Practices, Process Improvement, Excel, Thought Leadership, Enterprise Performance Management (EPM), Financial Information Management, BPC (Business Planning & Consolidation), Forecasting


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