SAP has quietly released Profitability and Performance Management, an analytical tool to help businesses with large data volumes make better data-driven decisions. Iain Snow swapped Bristol’s grey skies for some Catalonian sunshine to learn more.
Earlier this year, I attended a three-day training course at SAP in Barcelona. Whilst much of my time was dedicated to seeking out Barcelona’s best paella (Xiringuito Escribà wins that title), I also spent time learning to use one of SAP’s newest tools – SAP Profitability and Performance Management, or PaPM. In this blog, I’ll aim to give an overview of SAP PaPM - what it can and can’t do, its position in the market, and why a business may use it.
SAP PaPM is an analytical tool developed for SAP by MSG Global Solutions. In true SAP fashion, PaPM is not a wholly new solution, but rather a rebranding of a previous tool, SAP Performance Management for Financial Services (SAP FS-PER). This is intended to replace the outgoing PCM tool, which will no longer be supported after 2020. There’s a good article about moving from PCM to FS-PER here.
SAP FS-PER was launched in 2017, providing calculation capabilities with real-time access to source data without the need for data replication. SAP PaPM builds on this foundation, but with a broader reach than just the financial services sector. It now includes quick-start scenarios for many industries, including IT services and local government.
PaPM is a complete end-to-end analytical tool that takes data from many different places, performs homogenisation, calculations, and enrichment of the data, and analyses data outputs. It also has the ability to run simulations and predictions, intelligently or manually. PaPM’s only requirements are a SAP HANA database (Cloud or on-premise), and SAP NetWeaver 7.5. PaPM is run directly in a web browser.
PaPM has three main functionalities. I will briefly touch on these:
PaPM as a Business Data Aggregator: PaPM can utilise data from many sources (SAP or non-SAP) without the need for data replication. This allows real-time connections with very fast loading times. When setting up an environment within PaPM, you can access existing SAP BW InfoObjects – a great time-saver if you have them. Data replication is possible if desired.
PaPM as a Calculation Engine: PaPM can process large data volumes, calculations, and data enrichments in parallel. Calculations can include things such as joins, currency conversions, custom formula or SQL functions, and direct/indirect allocations. Calculations performed leave a data trail, allowing traceability and auditability. Calculations can be written back to a BW database, allowing PaPM to function as a basic planning tool.
PaPM for Analysis of Data: Data can be analysed for real-time data insights (assuming a live data connection), and the traceability of data allows easy identification of outliers or potential errors in data. PaPM outputs are built like queries and can be edited in the same way – ad-hoc and by the end-user if desired. Currently, PaPM will output as a table or one of three basic types of charts – line, row/column, or pie.
The user can perform what-if simulations on an output – for example, what would the output look like if raw materials increased in price by 5%? All these outputs are saved natively for later referral and can be shared.
So, that is a brief overview of SAP PaPM and what it can do. Now, let’s talk about what PaPM can’t do – at least for now.
As with many things, PaPM’s biggest strengths directly cause some of its biggest weaknesses. PaPM is purely browser-based, which makes it easy to access and use for anybody. It also means it is limited to functions available in a web browser, whilst other tools can take advantage of specialised applications.
PaPM’s primary focuses are clearly defined, and the first two (Business Data Aggregator and Calculation Engine) are well developed. The third tenet of the product though is lacking currently. Outputs are basic and limited to only a few types, and while there is basic ad-hoc reporting, the level of customisation of outputs is low.
At this relatively early stage in PaPM’s development, there are the expected stability issues which can cause some frustration. At the moment, the help menus are also empty.
The primary competitors to PaPM come from numerous sources – SAP’s own BPC product is well-established, while SAP Analytics Cloud is a newer product behind which SAP is putting its full force. Oracle, Apptio, and Anaplan also have competing tools.
BPC 11.0 Embedded shares similarities with PaPM as EPM tools, but while PaPM is more user-friendly, BPC 11.0 Embedded can be a more powerful tool.
|PaPM similarities with BPC 11.0 Embedded||PaPM-only features||BPC 11.0 Embedded-only features|
|Uses the power of the HANA database for calculations||Web-only front end||Uses Microsoft Excel Analysis for Office front end|
|Can use BW objects when building the model||Basic planning||Designed for complex planning; can use custom FOX formulae and planning sequences|
|Can perform calculations and transformations||User-friendly and easily configured by business users||More complicated to configure|
|Can link with other source systems||Native ability to run what-if simulations||What-if simulations require custom development|
SAC is the new kid in town but has been developed by a larger team and marketed more aggressively than PaPM, and the roadmaps from SAP show that it is only going to continue to get better.
|PaPM similarities with SAC||PaPM-only features||SAC-only features|
|Web-based||Simple and limited data outputs||More varied and detailed data outputs with better ad-hoc reporting tools|
|Must be built on HANA database||On-premise or cloud solution||Only cloud-based solution|
|No need for data replication||Can write back to a BW database||Currently no write-back features|
|Can link with other source systems||Complete end-to-end tool||Detailed analysis tool|
So, why should a business decide to use PaPM? Right now, I would recommend SAP PaPM for a business with an existing SAP BW system that requires an on-premise way to manage large volumes of data from a central tool, that needs to allocate costs across a business model; a business that requires real-time access to their data but not much planning capability or fancy pixel-perfect outputs. It is a tool that requires some IT knowledge to set up, but once it’s up and running, it is a tool very much developed for the business user. Its limited analytical outputs make it useful for simple analysis but won’t be enough for many larger businesses requiring more detailed insights into their data.
To conclude, PaPM is an interesting tool that has a relatively narrow use case but does its job very well. With future iterations bringing improved stability, better integration with SAC, AI-powered forecasting, planning and allocations, and improved graphical outputs, I can see PaPM becoming a powerful, though optional, analytical tool used to complement SAC in a year or two.