The views expressed in this article is that of mine. It has nothing to do with SAP or SAP BPC product road map.
Since its inception from outlook soft, SAP BPC has evolved into matured EPM software. From its humble beginning of Outlooksoft to its current state of SAP BPC 10.1 on HANA, it has travelled a long distance. In its current form SAP BPC includes a redesigned, highly-intuitive HTML 5 web user interface and best-in-class integration into SAP NetWeaver Business Warehouse with the ability to build planning models directly on existing objects. In addition, SAP EPM Unwired 3.0 gives it a mobile edge. With features from like this, many renowned market research institutes like Gartner has positioned SAP EPM suites into “Leaders” quadrant.
However, as they say, change is only the constant thing; SAP has to build a robust strategy to keep the “Leaders” tag intact. This brings to the context of this blog – What will be the next logical step for SAP BPC?
According to Christopher Iervolino, a research director at Stamford, CT based Gartner and co-author of the 2014 CPM Suites Magic Quadrant “Cloud and analytics as forces driving innovation in corporate performance management (CPM) suites”. I believe adding an analytics component to existing features of SAP BPC will give SAP BPC suits an edge over its competitors. So what can be done to add analytics to SAP BPC?
Last Year SAP bought KXEN and launched SAP predictive analytics as next business driven big data solution. SAP Predictive analytics has all the basic features of a big data solution like clustering, built-in algorithm, visualization, sharing etc. Both are two separate products but it makes more sense to merge features of both into one. To substantiate this let’s look at one real life scenario:
In our current project, we have to develop a BPC sales forecasting solution based on external economic factors. To illustrate this further, client’s business is spread across multiple regions and sales in these regions are affected by external factors like manufacturing output, GDP etc. Business wanted to know which of these external factors are influencing their sales number in that particular region ? What is the correlation between these factors? They wanted to build the forecast model taking into account these external factors. Forecast as % of actuals are thing of past. With global economy, external factors play a major role. Considering external factors will not only help predict sales forecast number more accurately but also help take better business decision . In addition, business can address some of these factors to increase their sales number.
Working on this we considered multiple external factors that can influence sales in a particular region. Building a relation and testing the correlation between the factor and sales was cumbersome and time consuming. Once factors were determined, we used it to forecast the sales number. The whole exercise that we did for months could have been easily done using SAP PA. It is not only built to handle large sets of data but also has built in algorithm which helps to predict numbers more accurately. Visualization is another must have feature for a forecasting solution and SAP PA has a strong visualization feature that it can boast of. In addition, having open source R as part of SAP PA makes it much more powerful. R has been the backbone of many statistical forecasting methodology . Existing BPC lacks these features. They may have some built in trending and forecasting function or you can write your own, but think about a script which can utilize features of R? Having all these features in a forecasting tool will make it much more robust and powerful.
Thus at the end, i would like to reiterate the next logical step for BPC is to have Predictive analytics features built into it so as to maintain the “leaders” tag in global EPM world.