CFIN+ blog series: How are we doing? A quick guide to measuring and managing business performance

Introducing our CFIN+ blog series where TruQua experts cover topics relating to the future of finance transformation and best practices for driving value by using the next generation of SAP finance solutions. 

The value of deploying SAP Central Finance cannot be understated—it allows organizations to reap the benefits of innovation provided in the SAP S/4HANA platform. This includes unifying diverse financial systems into a single source of truth for accounting through the Universal Journal in accelerated time frames due to real-time replication and automatically enforced data quality checks. However, the implementation alone is only part of the mission; although Central Finance projects are generally less resource intensive than a full S/4HANA implementation, it is still an investment. As with any investment, we still need to be able to measure the added value to justify it to our stakeholders. Doing so means ensuring we have all the information readily available to support any legal or regulatory requirements in a timely fashion. This approach efficiently captures the insights about a business’s performance that add value and drive more effective decisions on how to meet management’s strategic objectives.

To capture value from the unification of transactional processes that Central Finance provides, the business needs to have a seamless analytic strategy that makes it easy to monitor and act on changes in key performance indicators, or KPIs. These indicators aren’t necessarily all financial in nature; in fact, there are different “flavors” of KPIs that are critical to account for in today’s business climate:

• Financial: These types of KPIs are the most familiar to your typical analyst (revenue, gross margin, EBITDA, EBIT, net margin and more) because they hold a high degree of importance to both external and internal stakeholders. Externally, stockholders are entitled access to these metrics so they can adequately monitor the performance of their investments. At the same time, regulatory agencies can ensure that they have been prepared according to the required legal standards and accounting practices of the regions the business operates in. Internally, management will monitor key financial metrics to understand how well the business is being run and whether improvement is needed. Because Central Finance brings together financial and management accounting into one unified environment, all transactional information needed to provide both perspectives are readily available. The goal of the analytic strategy should be to make it as easy as possible to extract insights about which segments are performing well and which need to improve.
• Operational: These KPIs are typically more internally focused with a goal of understanding which functions of the day-to-day operations are driving performance positively or negatively. Where financial KPIs generally focus on telling the story of how the business is performing, operational KPIs focus on telling us why the performance sits where it is:
  • Is revenue down because the sales team is failing to convert leads into deals effectively?
  • Are cost of goods sold (COGS) high because manufacturing processes are incurring losses from idle capacity?
  • Are selling, general and administrative expenses (SG&A) spiking because high attrition rates are driving increased hiring and training costs?

A good performance management and analysis strategy must seamlessly integrate financial and operational KPIs to effectively model these driving forces. While our Central Finance landscape should have all the financial information we need, there might be other sources of information that need to be tapped to access operational KPIs.

• Environmental, social and governance (ESG): In recent years, an increased weight has been placed on not only providing stakeholders metrics for a business’s financial performance, but its ESG performance as well. Issues including climate change, advances in social justice and equity and higher demands for transparency of how consumer data is being used are key ESG performance points. These points are another area that company can target to differentiate itself from its competitors when being evaluated by investors. The European Green Deal introduced the EU taxonomy system for certifying activities as environmentally sustainable. In the US, the US Securities and Exchange Commission is moving toward requiring climate disclosures to be included in the 10-K reports of publicly traded companies. Performance management reporting and analytics must be able to coalesce the financial information with these ESG metrics. This reporting not only meet the needs of governing bodies, but also helps them understand the short-term costs and long-term benefits of shifting toward a more sustainable end-to-end value.
• Macroeconomic: The previous categories of KPIs focus on understanding how a business works and performs within its own “microeconomy” (comparing against itself across time or against its own predictions). This last category turns the focus outward and brings the performance into the perspective of the ecosystem the company operates within. Bringing in industry or market benchmarks to compare performance without divulging confidential data about any specific competitors establishes what is typical of the competition. These benchmarks then frame performance against the context of the global or regional economy by accounting for factors such as GDP growth rates, commodity prices, inflation and labor market trends and more.

Altogether, the analytics that we design around these different KPIs tell us a story about how the business is performing. A great performance reporting and analysis strategy makes this story easy for stakeholders to read and understand. A great performance management strategy allows us to constantly improve the story so that we tell a better one next month, quarter and beyond.

One critical element of a solid performance management strategy is the ability to build out robust allocation methodologies to write a richer story about the business. This strategy includes modeling the relationships between the characters and settings (that is, master data) and the events (transactions or transaction data) that occur to drive our KPIs. For example, we can easily associate products and customers with revenue KPIs through the information in sales orders or material costs to products and manufacturing plants based on production orders. Many of these relationships are less direct though. If we want to know the role of each product or customer or project in arriving at that net profit KPI, we need to understand how they drive manufacturing and administrative overhead costs. To fully grasp the environmental sustainability of our products or R&D projects, we need to read the hidden layers that model how each drives the emissions for a manufacturing or research facility. Or go even deeper into the value chain and understand the sustainability of procuring, shipping and storing the materials and labor.

To make these relationships clear to the readers of our business performance story, we cannot just dump our data into one system and hope for the best. We must develop allocation rules that map these indirect elements of our KPIs to the different characteristics of the business. While rules for external reporting requirements need to conform to predefined regulatory standards and accounting principles, supplying these rules for internal managerial processes is more of an art than a science. We need to make sure that the relationships we model truly reflect what drives the performance of the business in one direction or the other based on the business’s own goals. Sometimes getting them right can take some experimenting with different allocation techniques. Allocations such as cost center owner, sales manager, plant controller and more, have an impact on the final performance of a responsibility center. These groups will naturally want to understand how the rules are being applied and how the results affect the measurement of their performance as individual employees. For these reasons, a truly robust allocation process should have access to various allocation drivers, the ability to rapidly iterate through different rules to test different approaches and transparency to users about how the logic works. This approach helps ensure that we end up with a well-written story that is easily interpretable to our internal stakeholders.

Allocation design allows us to model the rich relationships between the characteristics of our business and the events their actions result in. On the other hand, simulations allow us to play with different actions based on those relationships to see how the story would change. This piece is crucial to managing performance rather than simply understanding it. The ability to simulate different actions lets us take the story that we’ve already been told and try to write a better one for the future (one that’s as closely aligned to our strategic objectives as possible). This piece is what truly brings our performance analysis together with the ability to manage it. We want to ensure that we can tightly integrate our understanding of what has already happened and why it happened. Simulation capabilities enable us to reflect on what could have happened if certain actions were taken, and what actions should be taken to get us to our desired results.

With the basic principles of performance management and analysis in mind, let’s explore some of the options that are available for a Central Finance architecture to deploy a clear vision in this area.

The SAP Fiori design experience lets users execute financial processes in Central Finance by running transactional applications that are more intuitive, upgraded versions of the legacy transactional codes in SAP GUI. The Fiori launchpad can also give business users access to a wide library of analytical applications which leverage the width and depth of data in the Universal Journal to dissect different components of the business. For those interested in the list of applications available, please refer to the SAP Fiori Apps Library.

One set of applications we will focus on in the context of performance management are the ones that drive the S/4HANA “Universal Allocations” functions. Traditionally SAP ERP solutions have required users to navigate various different transaction codes in SAP GUI to run different allocation contexts. Examples of these codes include cost and profit center assessments of overhead or shared service costs, top-down distributions to profitability segments for margin analysis, intercompany allocations and more. However, the Universal Allocations functionally delivers a streamlined set of applications that delivers greater standardization and an improved configuration experience to the allocation flows in an S/4HANA architecture. The functions are accessed through six key Fiori applications:

• Manage allocations: View, edit, create, copy or delete allocation cycles for different contexts (cost center, profit center or margin analysis) and types (distribution, OH allocation, intercompany allocation or top-down distribution)
• Run allocations: Perform test runs or actual runs of configured allocation cycles, as well as reverse any existing runs if preferred
• Allocation results: Create variants to analyze the outputs and errors from executed allocation runs
• Allocation flow: Display a visual flow of senders and receivers for an allocation and the respective debit and credit amounts
• Manage cycle run groups: Organize allocation cycles into groups to enable parallel processing
• Manage allocation tags: Create and assign semantic tags to allocation cycles and segments that allow you to group and identify those cycles and segments

While Central Finance deployments and S/4HANA landscapes deliver many powerful analytics around the Universal Journal, there are several limitations that hinder providing a truly robust performance management solution:

• The most obvious is that the tools have access only to the data in the S/4HANA system. For a Central Finance deployment, this means that we have access to our unified system for financial transactions. However, if the deployment is being used to rapidly integrate financial systems without requiring a full cutover to S/4HANA as the ERP, there will be significant gaps to the operational and ESG KPIs that we have access to analyze and manage.
• Additionally, these data gaps limit what information is available to leverage as drivers for cost and profit allocations. We can use financial document details, simple percentages or historical reference data in our allocation cycles, which might be sufficient for management reporting focused on margin analysis and standard costing. What we lack is access to assess financial outputs based on different and often more relevant drivers, for example, assessing IT costs to receiver cost centers based on information from a ticketing software. This issue can make Central Finance alone difficult to use for full end-to-end decision support.
• While the Universal Allocations solution delivers a significantly improved user experience for allocations over SAP’s legacy architecture, the process still requires navigation across multiple Fiori applications to manage and run the process. There are limits on the flexibility to rapidly iterate through different rules and drivers in a productive environment.
• The stand-alone Central Finance environment requires a higher degree of technical know-how than the average business user, as ad hoc reports and simulations must be built against the baseline unified financial model. This limits the flexibility to rapidly evolve analytics beyond the delivered Fiori applications.
• S/4HANA does include a Universal Planning Journal (ACDOCP) that generally follows a transaction-based structure akin to ACDOCA. The ACDOCA is far more granular and less capable of driver based and predictive planning than businesses usually want for strategic planning processes. Also, there are limitations on which types of allocation rules can be performed on plan data which can create gaps in variance reporting across certain groups. For example, top-down-distribution for plan data is not available in Universal Allocations.

Organizations will inevitably come across reporting and analysis needs in the future that they couldn’t anticipate or design for up front. Being able to adapt the solutions once they are live to keep them from becoming outdated is as critical as getting the implementation right in the first place. The goal is to leverage the Central Finance system of record in a performance management strategy that can be owned, managed and updated by business users without requiring heavy intervention from IT resources. To accomplish this, it’s useful to explore complimentary solutions that can help fill some of these gaps.

SAP Analytics Cloud, or SAC, has evolved from primarily an analytics tool into SAP’s next-generation software-as-a-service (SaaS) solution. This tool powers the “intelligent enterprise” and allow business users to plan, discover, predict and collaborate all in one place. While many longtime SAP users are likely familiar with SAP Business Planning and Consolidation (BPC) as a planning option, SAP is moving toward a cloud-focused planning strategy. Part of this includes seamless integration with S/4HANA architecture, not only on-premises, but also with S/4HANA Cloud. This is done by leveraging the virtual Core Data Service (CDS) views that sit as the underlying layer of the Fiori applications we have been discussing. There is a combined shift towards SAP Data Warehouse Cloud (DWC) as the next generation cloud-based data warehousing approach versus BW or BW4HANA. SAP is aiming towards a cloud-focused platform architecture with SAC as a key focal point for planning and analysis.

SAC as a compliment to Central Finance provides a variety of advantages over simply using the latter in a stand alone closed-loop performance analysis architecture. It provides more streamlined, strategic-oriented planning capabilities that allow business users to take greater advantage of predictive analytics and simulation without IT intervention, at least at a high level. It also allows integration with various data sources through both physical and virtual data access, expanding the scope of drivers and analysis axes outside of the data in S/4HANA alone. And compared to the creation of new reports and applications in Fiori, SAC provides a much easier experience for business users to create self-service analytics through “Stories.”

SAP Analytics Cloud includes features for running driver-based planning calculations off these drivers, predictive analytic modeling for identifying key drivers and modeling simulations, and allocations for running allocations on actual and plan data. While the table-based configuration provided for allocations is easier for a business user to work with than the configuration in S/4HANA, it has severe limitations regarding the volumes of data it can efficiently process. You will see longer than ideal runtimes running an SAC allocation on a high volume of transactions or down to granular dimensions of detail. In addition, the table follows a very simple “sender->driver->receiver” format, with limited ability to customize offset postings, iterative cycles or handling of unassigned items. Lastly, attempting to execute performance management allocations in SAC reveals a few integration challenges:

• First, SAC allocations can only be performed against data that is physically replicated and imported into the SAP-managed backend.  This feature introduces data replication and latencies into the architecture for our financial journal of record (that is, we must reimport ACDOCA to capture any changes since the last time we did so). It also limits the options a customer has to maintain tight control over their enterprise data.
• Second, while SAC has an API that seamlessly allows writeback of planning data into ACDOCP in the S/4HANA system, there are no analogous out-of-the-box export feature for pushing allocated data back into ACDOCA. This limits our ability to push the results of an allocation we would run on actuals back to the general ledger as debit/credit postings.
• Lastly, SAC has a rich library of sample accelerators for financial planning and even certain elements of planning operational metrics. But its content in sustainability and ESG management is less well rounded and covers a limited range of scenarios out-of-the-box.
• SAP Analytics Cloud can enhance our strategic planning processes and even augment our ability to build self-service analytics to review performance. Regardless, there are some drawbacks to relying on it as the calculation engine for executing the allocations needed to understand and manage that performance on deeper levels. Luckily there is an additional tool in SAP’s business analytics product portfolio that can help plug some of these gaps in our architecture—Profitability and Performance Management.

A Central Finance stand alone architecture, or one complemented with SAP Analytics Cloud for planning and analytics, can struggle with more granular allocations of financial and operational KPIs or with providing a strong foundation for sustainability management. SAP Profitability and Performance Management (PaPM) provides a new generation of integrated performance management applications that can use and reuse existing information models from other SAP and non-SAP applications in the cloud or on-premises. This ability provides an additional option for augmenting the architecture to close these gaps.

• PaPM was originally conceived as “FS-PER” (Performance Management for Financial Service), a product designed to handle the complex allocation and modeling scenarios requested by banks or actuaries to assess performance of individual financial instruments, investments and contracts. However, the power of its calculation engine led developers and customers alike to recognize use cases across a wider range of industry scenarios and it was rebranded accordingly.
• PaPM is built on the advanced native in-memory capabilities of SAP HANA and is designed to run simply and comfortably for business users through the principles of SAP Fiori’s user experience.
• PaPM enables the integration of operational systems and data warehouses at high speeds to aggregate business data with little or no data replication. This means that S/4HANA data and other operational and ESG measures and metrics can be used in the PaPM performance management calculations without requiring physical replication of the source data.
• The calculation engine of PaPM allows business users to design and execute financial and business models by combining functions across a range of areas. These areas include profitability and allocation, transfer pricing, and modeling risk, solvency and cash flow scenarios.  These functions can be configured by the average business user with little to no code and can process high transaction volumes across large numbers of granular dimensions very efficiently.
• Allocation configuration is much more streamlined from creation to analysis than the multiapp configuration of S/4HANA Universal Allocations, and handles scenarios like multistep allocations or unassigned items much more smoothly
• Remote function adapters can be used to write back to the results of financial allocations directly into the G/L to close the loop with Central Finance.
• The ease of iteration of the above-mentioned business rules coupled with additional simulation application capabilities enable execution of what-if scenarios for business users based on managing assumptions and drivers.
• PaPM delivers a wide library of sample content to cover a plethora of performance management topics both cross-industry and specifically focused. This library includes best practice content to build a sustainability management strategy around, including scenarios such as:
  • Corporate sustainability management: Focuses on the corporate environmental performance of their business operations as well as their contributions to the wellbeing of employees and societies.
  • Financing and investment sustainability management: Provides an example of an ESG and credit investing method that calculates ESG and credit scores for companies and instruments.
  • Value chain sustainability management: Offers best practices for modeling an end-to-end value chain sustainability calculation model that is used to calculate and simulate the ecological footprint for a plan production accounting use case.
  • Sustainability impact measurement and valuation: Provides a modeling framework to perform a complete analysis of upstream and corporate operations with direct, indirect and induced impacts and monetary equivalents for selected indicators across economic, social and environmental areas.
  • Energy transition management toward Net Zero: Provides best practice content for analyzing and managing a business’s path toward carbon neutrality through three core pillars: assessing emissions through the GHG Protocol, setting emission reduction targets in accordance with SBTi Net Zero Standards and evaluating emission reduction projects through both a financial and environmental lens
  • EU taxonomy management: Provides an end-to-end process and calculation model for companies to evaluate the alignment of their operations in line with the Taxonomy Regulation by the European Union.

PaPM adds value when designing a performance management architecture that uses Central Finance deployments as the system of record. However, customers should understand that this is an additional license and should assess if their requirements are complex enough to justify that. The most pervasive drawback now is that the integration with SAP Analytics Cloud isn’t as seamless today as it is with S/4HANA. Options for consuming planning data from SAC in PaPM today require either consuming it as external OData through an API (an option only available in PaPM Cloud but not on-premises) or pushing the SAC data into another system. For example, pushing the plan data from SAC into ACDOCP in S/4HANA and consuming it from there into PaPM calculations. Comparatively, accessing outputs of PaPM calculations into SAC for analytics is significantly easier. This step can be done by connecting SAC to the BW (for PaPM on premise) or HANA (for PaPM Cloud) backend artifacts that are automatically generated when the PaPM functions are activated. Additionally, integration between SAC, Data Warehouse Cloud and PaPM Cloud promise to become more tightly connected as SAP continues investing into the vision of the Business Technology Platform.

The complementary solutions that SAP offer provide insight into how customers can determine what they need to provide end-to-end performance management and analysis for their business using Central Finance deployment as their financial system of record. While there is no “one size fits all” answer, the following decision tree can help guide your assessment of these options.

Additional factors will include the appetite for beginning to manage ESG KPIs in addition to financial and operational ones. Custom performance management content and reporting can be developed for any combination of these solutions. But the best practice accelerators available in PaPM mean that including it within the architecture will drastically speed your time to value in this area.

For more information about the complementary SAC and PaPM solutions, customers can visit the SAP Help pages. Customers can also access a range of own thought leadership publications and insight blogs from TruQua experience across CFIN, SAC and PaPM implementations through their website.