The SAP Spend Performance Management application comes with a strong repository of 120+ out-of-the-box spend analysis reports in a variety of dimensions on spend, trend, and variance reports, including exception reports, what-if reports, and waterfall analysis reports. I discuss what the SAP Spend Performance Management application is needed for, what business benefits it brings, and the key business drivers for investing in such an application. I also show the various kinds of analytics supported by this application. Finally, I discuss an end-to-end case study of an SAP Spend Performance Management implementation for a consumer goods company, including the business case and project scope, the different phases of the project, and a high-level overview of the activities performed and the key learnings from each stage of the implementation. 

Business Case for a Spend Performance Management Application

Many organizations face challenges in terms of visibility of how they spend across their different direct spend categories (like production of raw material and labor costs) and indirect spends (such as office supplies and travel expenses). To illustrate, here are some common questions that many buyers regularly fight to get answers to:

• What is my total organizational spending across all categories?
• Who are my largest suppliers overall, category-wise?
• What are my largest spend segments?
• Over the last three years, which segments are growing in my overall spend? Which segments are shrinking?
• What segments or parts had the largest price inflation in the last three years?
• Am I paying more to one supplier than another for the same component? If yes, by how much?
• Which buyer is managing the most items or spends? Which buyer is managing most of the contracts?
• Where can I quickly cut costs by taking action?
• Am I paying the contracted price for all items covered in the contract? If not, why?
• How much am I buying off contract? Why?
• What percentage of my spending is on procurement from low-cost countries?

To gather all this information, companies are spending a lot of time accessing a variety of systems, consolidating many Excel spreadsheets, and uploading and downloading many files from these many different systems on a regular basis. Solutions such as SAP Spend Performance Management are designed to simplify this process.

There are many reasons why purchasing organizations faced challenges in the past to get this information in one place. Some of the common challenges around gathering spend data are:

• Issues in extraction due to a lack of standardization and aggregation of supplier payment data. (Data can be recorded in a variety of places across the organization, such as in the company’s books of accounts, general ledger, travel reports of employees, and corporate procurement cards. This makes it very difficult to come up with a standard method to collect necessary data.)
• Incomplete or missing data.
• The same suppliers may be listed by different names in different systems. (This is quite common in companies with multiple ERP instances in different countries.)
• Spend categories are not defined (or named) uniformly across the organization, leading to confusion and duplication of efforts (as well as records).

SAP Spend Performance Management can help organizations deal with some of these challenges, and save organizations money while doing so. Some common business key performance indicator (KPI) improvements can be found in the following areas:

• Reduced process cycle time – A substantial reduction of spend analysis process cycle time can be realized. In my experience, the process cycle time can be reduced by as much as 50 percent when taking into account time saved in extracting data from different applications, cleaning, and consolidating it.
• Better contract compliance – This involves identifying areas where there are new contract opportunities or the existing contracts are not monitored effectively. In my experience, with better contract compliance, companies can reduce overall spend by as much as five to 10 percent.
• Cost savings in material and service costs – Identifying opportunities for unit price reduction by better consolidation of material or service procurement. While cost savings opportunities here are directly related to the project scope (i.e., the amount of spend under the scope of application), this is also an area where benefits can be realized early.
• Improved supplier base – Analysis of supplier spend can result in variety of steps such as supply base rationalization for certain items, having alternate suppliers in place for  back up (to mitigate any risks), and checking for and eliminating any duplicate suppliers.
• Improvements in meeting compliance standards – SAP Spend Performance Management application helps companies to quickly identify suppliers who are not meeting delivery compliance standards in terms of delivering on time, and quality compliance in terms of not meeting quality standards and sustainability standards.

Now let’s take a look at some of the key capabilities of the solution. 

SAP Spend Performance Management Reports

The SAP Spend Performance Management solution comes with close to 120 out-of-the-box reports as part of the delivered content. These reports offer benefits to sourcing professionals with a variety of regular and KPI analysis options. Following is a discussion of some of the types of reports offered by this solution. (For a representative list of some of the available reports and their details, refer to the sidebar “A Partial List of Standard SAP Spend Performance Management Spend Analysis Reports” later in this article.)

Spend Analysis Reports

The SAP Spend Performance Management application provides a variety of different categories for creating spend analysis reports. For example, total spend can be analyzed by item category, supplier, purchasing organization, or cost center. Spend also can be analyzed in a variety of ways, like absolute spend (for a category or supplier) or a percentage of total spend (e.g., the spend percentage per category or supplier). These reports help procurement professionals identify what are their top spend categories or top suppliers—and which of those provide the largest cost savings opportunity. Beyond this, the SAP Spend Performance Management solution also offers reports on a few target solution areas. A good example of this is in the contract management area where there are a number of reports available, including reports on the ages of contracts, which contracts are close to expiring, contract status, and how contracts are used.

Spend Exception Reports

Spend Performance Management provides a variety of spend exception reports; for example, purchases made without a contract or without a purchase order (PO). These exception reports can be created with different views (e.g., by item, buyer, supplier, or geographic location) to identify which buyers or geographic areas are doing non-contract spending and for which items it is done. These non-contract spending and non-PO spending reports help in identifying potential maverick spending, and provide opportunities for bringing these purchases into line with the contract or PO process. This can immediately result in cost savings because of consolidation of purchases and because it offers a better negotiating opportunity for suppliers to bring down unit prices for items, and enables companies to realize volume discounts when purchasing products.  

Spend Trend and Variance Reports

The Spend Performance Management application helps in doing sets of trend analyses. For example, how spend varies month-to-month or year-to-year by category, supplier, item, or cost center. This helps sourcing analysts determine if and how the largest spend categories or the top ten suppliers have changed over time. This, in turn, helps in developing future sourcing strategies to meet these changing needs.

The Spend Performance Management solution also provides many variance reports, such as price variance reports (e.g., the item price in POs vs. the actual price in invoices) and spend forecast variance reports (budgeted spend vs. actual spend).

The following sidebar is a list of some examples of the different types of reports provided by SAP Spend Performance Management.

What-if Analysis

Procurement managers may need to build and compare different sourcing scenarios to make the best sourcing decisions. Two good examples of different sourcing decisions companies have to make are buy now vs. buy later, or buy from a local source vs. buy from another country.

SAP Spend Performance Management enables business users to do a variety of what-if analyses by providing a framework based on global variables, local variables, and formulas. Figure 1 shows an example of a what-if simulation where the total landed cost is simulated against a set of global and local variables.

Figure 1

What-if analysis using local and global variables

Waterfall Analysis

Waterfall analysis and a waterfall chart view of reports can be useful ways of looking at reports. They help identify trends by performing a period-over-period comparison of a particular dimension and comparing dimensions on the same scale (percentage) over two time periods. Spend Performance Management 3.0 supports such analyses.

Figure 2 shows a waterfall analysis diagram for the total landed cost of an item where cost is broken down into several sub-cost categories (like material cost, freight charges, and inventory carrying costs). It’s possible to compare different cost categories through such analysis—for example, how freight charges compare with material costs, or what the percentage of inventory carrying cost is compared to material cost.

Figure 2

Waterfall analysis of total landed cost

Spend Performance Management Implementation – A Case Study

Now, using a case study, I show how you to implement SAP Spend Performance Management to meet your company’s requirements.

Background

A large global consumer goods company selling products in over 150 countries implemented the SAP Supplier Relationship Management – Enterprise Buyer Professional (SRM – EBP) solution for indirect procurement. The company was using the SAP NetWeaver Business Intelligence solution and most of its reporting needs were addressed through this. The company developed complex rule-based logic for spend classification in BI. However, the company was facing challenges such as maintaining data for such a complex rule engine in BI. This is resource-intensive and users were not always classifying spend data properly in the SRM – EBP application. As a result the company was not in a position to put spend data in the proper place, leading to improper calculations of the cost of goods sold and, ultimately, to making inappropriate sourcing decisions.

Objective

The company decided to go with the SAP Spend Performance Management application in the hopes that this would help it better classify spend, provide self-service capabilities for users, provide better contract-monitoring capabilities, and, finally, provide better analytic capabilities. One of their critical requirements was the ability to drill down to the most detailed document-level information reporting from a corporate summer report. Another objective for this Spend Performance Management solution implementation was to gain context about a variety of information, such as commodity prices, budgets, contract, and PO data.

Scope

The project scope included all direct, indirect, and logistics spends. This was a global implementation using source data from multiple ECC applications and one central SRM application. Scope of analysis for spend data covered spend information from the accounts payable system, POs, travel data, budgets for direct spend, and data from external sources (mainly commodity prices). As part of this, the company wanted to migrate two years of past data to the Spend Performance Management application for analysis.

Phases of the Project

The project had three main phases. They were:

1. Extract spend data using extractors
2. Data standardization, classification, validation, and enrichment
3. Building the Spend Performance Management data model

While the phases were parallel, in this article I discuss them sequentially.

Phase 1: Spend Data Extraction Using Extractors

The company had to depend on a variety of applications for their spend data, including ECC Financial Accounting application, the ECC materials management application, SRM application, SAP NetWeaver BW application, and two non-SAP applications that managed travel and project expenses. This included both master data (e.g., item master, vendor master, or contract master) and transaction data (e.g., purchase requisitions, orders, or payments).

The company used SAP-provided extractor starter kits for data extraction from their two ECC applications (ECC 4.7 and ECC 6.0) and one SRM application. These starter kits were customized based on the company’s specific data requirements, design, and performance expectations.

In this phase of the project, one of the critical decisions was which data to exclude as only some data—not all—needs to be extracted for analysis. For example, the company did not extract all of the contract, PO, and invoice data. Instead, it was determined that, to meet the company’s data analysis requirements, only data for active contracts and POs for the last two years needed to be extracted. In this same way, all intercompany spends were excluded from extraction as this was not paid to any outside vendor and hence this was not in the scope of the needed analysis. Data for selected material types, vendor groups, and General Ledger accounts were also excluded from the scope of extraction as there was no analysis required for these. These data extractors help to schedule extraction jobs and define programming logics for extractions.

The following are some of my lessons-learned about data extractors:

• Examples of key SAP Spend Performance Management extractor terminology: Object, Project, Table, Table Relationships, and Subroutines. An Object is a logical unit for each data type. A Project is a logical collection of objects. In the extraction it has to be defined which tables are to be used to define an Object. Subroutines help to define any custom logic that is to be applied for extraction.
• The SAP extractor starter kit can be downloaded from SAP Note 1239883 (login required).
• The most current Spend Performance Management patches to obtain extractor data load performance. Often, outdated patches create incompatibility problems.
• Transaction code Z_SA_DEPD helps in creating a new data extraction project or access an existing project. From this screen, a new Object (for extraction) can be created or an existing Object can be accessed (click F7 to show all the available existing Objects). Tables used and the relationships between them also can be defined from the same screen.
• Flat files are the mechanisms used to load data in Spend Performance Management. As a result, Spend Performance Management extractors are designed to generate flat files for each Object.
• In transaction code SM30, if ZSA_FFCUSTTABL is entered, you are able to configure the output directory and filename.
• In transaction code SM30, if ZSA_ D_FLDVAL is entered, you are able to maintain the exclusion table.

Phase 2: Data Standardization, Classification, Validation, and Enrichment

The company had many challenges with its supplier and spend data. Some of these were as follows:

• No proper taxonomy for spend classification.
• Correct parent/child relationships among their suppliers were not known. This means different child companies of the same parent supplier are supplying the customer organization under different names, and the company was not able to analyze their spend by suppler and was in a not in a position to leverage total spend with a supplier parent
• Supplier data needed normalization to maintain consistency of supplier data across multiple systems as the same supplier had different names in different systems.
• Supplier data needed enrichment as supplier identities were often vague due to poor data quality and lack of content. The company required additional supplier data with content from external sources about the credit or financial risk rating of suppliers (by third-party agencies), the diversity status of supplier (e.g. minority-owned or women-owned), and if the supplies came from a low-cost country.

The company had carried out a three-step process for data standardization, classification, validation and enrichment.

• Normalize and validate data to remove duplication – This step was used to remove duplicate supplier data.
• Enrich supplier data – After normalization and validation are completed, this step was used to enrich supplier data with four additional pieces of information:

1. Identifying parent/child relationship between suppliers
2. The credit ratings of suppliers
3. The diversity statuses of suppliers
4. The low cost country status of suppliers

• Classify spend data – The final step of data supplier validation and enrichment (DSE) was to classify and assign spend data and transactions to categories according to a goods and services structure classification. This classification structure was based on a global business directory (in this project, the North American Industry Classification System –commonly referred to as NAICS—was used). This single spend classification structure enabled them to get a standard view of all goods and services purchased across the enterprise.

For the DSE process in this case, the company sent their unclassified or unstructured data to a third-party service provider on a regular, agreed-upon basis. Then the third-party service provider did the classification and enrichment of the data according to the company’s requirements. Once done, the data was sent back to the company and then loaded into the Spend Performance Management application.

Phase 3: Building the Spend Performance Management Data Model

In this phase, the company worked on building a data model for the Spend Performance Management implementation. The Spend Performance Management data model is made up of three layers: inbound, detail, and reporting layers. The inbound layer helps in data upload for the transaction objects. The company used four transaction objects (e.g., contract, PO, invoice, and payment data) for uploading. In the inbound layer, the company had to do two transformations for conversion of amounts. One transaction was to convert the different transaction currencies of different countries (like the Euro, AUD, and SGD) to one single reporting currency (USD). The second was to convert different transaction units of measure (such as kilogram, pound, and case) used in different countries to one reporting unit of measure (MT). Since all the data targets in this layer are direct update Data Source Objects (DSOs), there were no transformation and Data Transfer Processes (DTPs). All data is loaded through SAP-provided APIs.

All the data enrichments that were discussed earlier in this article (in phase 2) were done at the detailed level. Supplier master data was updated with proper hierarchy (like parent/child relationship between suppliers), credit rating, diversity status, and low-cost country status.

Finally, the reporting layer was designed to meet the specific reporting needs of the company. This means designing the right InfoCubes to support reporting on all dimensions of the data, including selecting (from what is available) and creating key figures as required. The Spend Performance Management application provides a standard multi-provider on top of the InfoCubes. In this case, this was modified to meet the reporting needs.

Here are some lessons-learned about Spend Performance Management data modeling:

• Most of the transformations in the detail layer are performed using Expert Routines.
• For PO and invoice integration, data from contracts, PO, and master data attributes are looked up in the transformation from the inbound DSO to PO integration, and the DSO and invoice integration DSO. 
• In the reporting layer, data is aggregated and key figure calculations take place. A few key figures are calculated based on the data enrichment of invoice and PO integration done earlier. These key figures are invoice price variance amounts, PO price variance amounts, and counts for invoices for buyers and suppliers.
• Designing of process chains is an important part of Spend Performance Management data modeling. Process chains are used to load data from the inbound layer to the detailed layer and from there to InfoCubes. Each data type (such as contract, PO, and invoice) has individual process chains. These process chains are triggered from the data management screens in the SAP Spend Analytics user interface. For master data, process chains are used to load data and create hierarchies.

Lessons Learned (Overall)

From an overall SAP Spend Performance Management solution implementation perspective, a few key learnings from this project were:

• Data validation is key for any Spend Performance Management project. This is an exercise that is often not given enough attention at the beginning of a project, and actually takes a lot of time and effort to do well. Not doing this early on leads to wasted time and resources later on in the project timeline.
• Spend hierarchies need to be defined in a way that supports the different sourcing initiatives of the organization. While it supports the objectives of analytics and reporting for Spend Performance Management implementation, it can also be the basis on which materials are grouped or the way buyers are organized.
• The project team needs to understand the complexity of the data that needs to be analyzed (for example, the master data and transaction data in the procure-to-pay process).
• It is imperative that the different spend elements are classified properly from the outset.

Rajesh Ray

Rajesh Ray currently leads the SAP SCM product area at IBM Global Business Services. He has worked with SAP SE and SAP India prior to joining IBM. He is the author of two books on ERP and retail supply chain published by McGraw-Hill, and has contributed more than 52 articles in 16 international journals. Rajesh is a frequent speaker at different SCM forums and is an honorary member of the CII Logistics Council, APICS India chapter and the SCOR Society. 

You may contact the author at rajesray@in.ibm.com.

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