Spend Analysis: The Nexus of Spend Management. November 2011 Constantine G. Limberakis

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1 Spend Analysis: The Nexus of Spend Management November 2011 Constantine G. Limberakis

2 Page 2 Executive Summary Due in part to the number of solutions that contain spend information in organizations today, a continuing struggle for procurement professionals is obtaining a 360-degree view of spend data. Those that can distinguish themselves as Best-in-Class not only extract, cleanse, classify and analyze their spend data from multiple sources, but also leverage spend analysis as a predictive measure to improve spend compliance and reduce supplier risk through market and supplier data. This research report examines the crucial set of processes that encompass spend analysis by exploring the pressures, strategic actions and capabilities of 132 organizations surveyed globally in September Research Benchmark Aberdeen s Research Benchmarks provide an indepth and comprehensive look into process, procedure, methodologies, and technologies with best practice identification and actionable recommendations Best-in-Class Performance Best-in-Class organizations are noted for their ability to capture spend for analysis and identify opportunities for improvement. Three key performance criteria used to distinguish these top performers include: 96% of enterprise spend captured in spend analysis efforts (compared to 50% for Laggards) 9% cost avoidance savings identified as a part of a spend analysis initiative (compared to 5% for Laggards).1% of payments duplicated (compared to.5% for Laggards) Competitive Maturity Assessment Survey results show that the firms enjoying Best-in-Class performance also shared several common characteristics, including: 33% more likely to have automated data collection from multiple sources within the enterprise than all other organizations 25% more likely to have standardized procedures for data extraction, cleansing and classification, and analysis and reporting 21% more likely to use have a common enterprise-wide classification schema for commodities and services spend Required Actions In addition to the specific recommendations in Chapter Three of this report, to achieve Best-in-Class performance, companies must: Standardize formal spend intelligence / spend analysis processes and methods across the organization Automate the integration of spend data from ERP systems and procurement related systems into a spend analysis platform Improve user access and the ease of use for spend analysis tools This document 2011 Aberdeen is the result Group. of primary research performed by Aberdeen Group. Aberdeen Group's methodologies provide for Telephone: objective fact-based research 5200 and represent the best analysis available at the time of publication. Unless otherwise noted, the entire contents of this publication are copyrighted Fax: 617 by Aberdeen Group, Inc. and may not be reproduced, distributed, archived, or transmitted in any form or by any means without prior written consent by Aberdeen Group, Inc.

3 Page 3 Table of Contents Executive Summary... 2 Best-in-Class Performance... 2 Competitive Maturity Assessment... 2 Required Actions... 2 Chapter One: Benchmarking the Best-in-Class... 4 Business Context... 4 The Maturity Class Framework... 7 The Best-in-Class PACE Model... 8 Best-in-Class Strategies for Spend Analysis... 8 Chapter Two: Benchmarking Requirements for Success Competitive Assessment Capabilities and Enablers Chapter Three: Required Actions Laggard Steps to Success Industry Average Steps to Success Best-in-Class Steps to Success Appendix A: Research Methodology Appendix B: Related Aberdeen Research Figures Figure 1: Priority Assigned to Spend Analysis program... 4 Figure 2: Top Pressures for Spend Analysis Initiatives... 5 Figure 3: Spend Analysis Automation in Spend Analysis... 6 Figure 4: Strategic Actions for Improving Spend Analysis... 8 Figure 5: Importance of Strategic Vendor Involvement... 9 Figure 6: Usage Classification Codes for Products and Services Figure 6: Common Process Steps for Spend Analytics Figure 7: Deployment of Spend Analysis Figure 8: Adoption of Spend Analysis Enablers Figure 9: Enrichment of Third-Party Supplier Data Tables Table 1: Top Performers Earn Best-in-Class Status... 7 Table 2: The Best-in-Class PACE Framework... 8 Table 3: The Competitive Framework Table 4: The PACE Framework Key Table 5: The Competitive Framework Key Table 6: PACE and the Competitive Framework... 21

4 Page 4 Chapter One: Benchmarking the Best-in-Class Spend analysis, when performed correctly, provides the capability to increase visibility into spending, promote insightful sourcing decisions and identify cost saving opportunities. However, the success of any supply management program is largely dependent upon the ability to properly access, organize, and analyze spend data. This requires a unique combination of process and technology to get spend analysis done right. Business Context One of the most important strategies for any procurement organization is spend analytics. In combination with sourcing, contract management and purchasing, it provides a window to improve spend behaviors and a direct means for increasing spend under management. As a proof point to its importance, recent Aberdeen research, Dynamic Procurement: CPO as Collaborator, Innovator and Strategist, indicated 89% of Best-in-Class organizations had enabled spend analysis compared to 63% for Industry Average, and 46% for Laggards. Moreover, organizations using spend analysis on average captured 24% more spend under management than those not using it, a metric determined by two out of three respondents as a top criterion for measuring the effectiveness of spend analysis. Figure 1: Priority Assigned to Spend Analysis program 14% 14% 5% 9% 58% Top priority High priority Neutral Low priority Not important at all n = 132 Fast Facts Category managers made the highest percentage of primary users for spend analysis at 38% A little more than one out of three CPOs or VPs of procurement considered themselves primary users of spend analysis technology Only 4% of CFOs considered themselves a primary user of spend analysis Best-in-Class Metrics Spend Under Management: Often debated and discussed as a qualifying metric in the world of spend management, the definition of spend under management is the percentage of all non-payroll, tax, tariff and fee-related spend that falls under management or the influence of the procurement organization. Source: Aberdeen Group, September 2011 As a result of such findings, it is clear that spend analysis demonstrates the foundational reality for driving performance and establishing cost savings. Given such benefits, it is therefore not surprising that spend analysis catches the attention of C-level executives, where a quarter of primary users of spend analysis based on this study are considered to be CPO or VPs of purchasing. Furthermore, due to the savings opportunities that can be captured through its activities, spend analysis programs remain a high priority for most procurement organizations. For example, 67% of 132 of

5 Page 5 survey respondents indicated that spend analysis is a high or top priority for their enterprise procurement program. Interestingly enough, this statistic jumps to 88% when looking at C-level executives exclusively. Challenges In Spend Analysis Considering spend analysis is high on the priority list for procurement, it proves to be dynamic exercise for most organizations. Effectively leveraging spend analysis requires integration of disparate data sources, clearly-defined data processes and the use advanced technology. But getting spend analysis right is not just about technology; it also requires coordination from several areas of business entities like IT, supply chain and finance, but starts with leadership from procurement. As procurement organizations face increased global market challenges in this current economic environment, procurement leaders cannot run the risk of missing savings opportunities and therefore must be able to address the key pressures surrounding spend analysis initiatives within their organization. Figure 2: Top Pressures for Spend Analysis Initiatives Poor quality of spend data from Procurement & ERP systems Inability to identify and forecast savings opportunities Pressure to place more spend under management Labor intensive process for managing and collecting data Inability to identify and prioritize top spend categories Lack of measurable insight into success of cost reduction initiatives 24% 33% 36% 40% 51% 49% 0% 10% 20% 30% 40% 50% 60% Source: Aberdeen Group, September 2011 Illustrated in Figure 2 and first demonstrated even as far back in Aberdeen Research in 2007 with Spend Analysis: Working Too Hard for the Money, our research continues to show that a core issue surrounding spend analysis is the poor quality of spend data (i.e. coming from ERP, procurement, and sourcing systems) that results directly in an inability to identify and forecast savings opportunities. With 50% of organizations using between three-and-four systems generating spend data, the quality of data becomes an exacerbated challenge particularly given the ongoing use of manual spreadsheets or traditional data warehouses that are limited in the ability to accurately classify, normalize Fast Facts Poor quality of spend data coming from external spend and ERP systems was indicated by 65% of the Bestin-Class compared to 47% for all other organizations Fast Facts Lack of standardized spend intelligence/spend analysis processes and methods is the top barrier to spend analysis, indicated by 54% of respondents Enablement of automation for spend cleansing, classification and data enrichments lags behind that of data extraction by 26% on average Use of technology to automate data intensive processes (spend data extraction, cleansing, classification) was noted as a critical success factor by 46% of respondents

6 Page 6 and enrich spend data. In this regard, 32% of respondents indicated they are still using basic spreadsheets as the primary approach to spend analysis. Looking for Leadership in Spend Analysis The answer directly tied to these challenges is the need for investment in innovation as demonstrated by Best-in-Class organizations. For instance, Aberdeen research, Spend Analysis: Transforming Data into Value, found that Best-in-Class organizations are 50% more likely than Laggards to automate spend analysis processes and 124% more likely to have automated spend data collection from multiple sources. As a result of converting automation into opportunity, Aberdeen s report, Spend Analysis: Pulling Back the Cover on Savings identified that organizations leveraging a fully-automated system for spend analysis experience an average savings of 11% from sourcing efforts. Furthermore, advancements in gathering supplier data into spend analysis is demonstrating to be a tool necessary for managing suppliers. By loading supplier information into spend data systems, organizations can establish an improved basis in monitoring supplier behavior, assessing supplier risk and measuring supplier performance. Spend analysis is therefore becoming critical for the creation of linkages between spend management, industry risk, and financial visibility through integration of supplier and spend data. Based on The Year of the Supplier: Perspectives on Supplier Management, Bestin-Class companies report using spend analytics for managing suppliers 25% more than their peers. Figure 3: Spend Analysis Automation in Spend Analysis 60% 50% 40% 30% 20% 10% 0% Automated Manual Outsourced 54% 51% 49% 51% 38% 30% 25% 21% 10% 9% 11% 2% Data Extraction Cleansing Classification Enrichment Source: Aberdeen Group, September 2011 "Having the ability to extract top supplier spend by general ledger account or commodity classification over the past three years has had a great impact on identifying category opportunities through our spend analysis initiative. ~ SVP, Chief Sourcing Officer Midwest Regional Bank As demonstrated in Figure 3, even for those organizations already leveraging spend analysis as a strategic enabler, it is clear that more investment in automating areas such as data cleansing, classification and enrichment are needed for increasing accuracy of spend opportunity identification. In fact, 46% of respondents claim the use of technology to automate data intensive

7 Page 7 processes as the most critical to the success of their spend analysis program. Yet today, findings point to the fact that many organizations lack the ability to manage spend data initiatives in house. In these areas it is becoming evident that outsourcing spend analysis to turn-key spend data management (i.e. consulting / software based solution providers) has become more common due to a lack of data analysis skills internally for managing certain processes inherent in spend analysis such as data cleansing and data enrichment. Based on this study, it was found that 17% of organizations outsource data cleansing and 18% outsource data enrichment. The Maturity Class Framework Understanding spend analysis within organizations requires a multidisciplinary approach that considers understanding many data facets and processes that can measure and benchmark performance. Organizations that effectively use spend analysis are marked in their capability of capturing spend from multiple sources and in their ability to demonstrate savings as a result of its use. The findings of this benchmark demonstrate that a variety of capabilities are necessary for improving or implementing a spend analysis program. Aberdeen identified the following three key performance metrics to distinguish the Best-in-Class from Industry Average and Laggard organizations: Spend captured in spend analysis efforts (factor focusing on automation / ability to manage from multiple sources) Cost avoidance savings based on spend analysis efforts (factor focusing on efficacy of spend analysis on working capital) Duplicate payments (factor demonstrating efficacy of spend analysis on P2P processes) Table 1: Top Performers Earn Best-in-Class Status Definition of Maturity Class Best-in-Class: Top 20% of aggregate performance scorers Industry Average: Middle 50% of aggregate performance scorers Laggard: Bottom 30% of aggregate performance scorers Mean Class Performance of Spend Analysis 96% spend captured in spend analysis efforts 9% cost avoidance savings based on spend analysis.1% duplicates of annual payments 70% spend captured in spend analysis efforts 8% cost avoidance savings based on spend analysis.3% duplicates of annual payments 55% spend captured in spend analysis efforts 5% cost avoidance savings based on spend analysis.5% duplicates of annual payments Best-in-Class Metrics Spend captured in spend analysis Amount of spend captured in spend analysis efforts from various sources that includes direct and indirect spend Cost avoidance savings Cost avoidance is the capturing of cost reduction opportunities resulting from intentional action, negotiation, or intervention Duplicate payments Examples of duplicate payments include payments being made twice using two different payment types or individuals in multiple departments making payments and not using rigid standards to avoid duplicate payments Source: Aberdeen Group, September 2011

8 Page 8 The Best-in-Class PACE Model Leveraging a modern spend analysis initiative that incorporates these performance metrics describes aspects of analytics as shown in the PACE Framework in Table 2. Table 2: The Best-in-Class PACE Framework Pressures Actions Capabilities Enablers Poor quality of spend data Standardize formal spend analysis process and methods Automate integration of spend data with spend analytics Improve user access / ease of use for spend analysis tools Ability to collect spend data from multiple sources Standardized procedures for data extraction, cleansing and classification for spend analysis Established executive support for spend analysis initiatives Common visibility of goods and services spend across the enterprise Best-in-Class Strategies for Spend Analysis Figure 4 demonstrates that Best-in-Class organizations are not as focused on standardization (11% less) and automation (11% less) than their peers. So while all other organizations (the Industry Average and Laggards combined) continue to focus on fundamental areas critical for spend analysis relative to their maturity such as standardization and automation, Best-in-Class organizations by a margin of 19% are focusing more on improving user access and the ease of use for spend analysis tools. Moreover, Best-in-Class organizations are pursuing strategies that further promote the use of spend analysis within the organization through the pursuit of higher user adoption and usage of spend analysis, this strategy ultimately provides more opportunities for identifying savings through strategic sourcing, contract compliance and invoice reconciliation focused tactics. Data extraction Data enrichment Data cleansing Data categorization Advanced spend analysis reporting Performance monitoring and management Integration with e-sourcing Integration with contract management Integration with e-procurement Source Aberdeen Group, September 2011 Figure 4: Strategic Actions for Improving Spend Analysis Best-in-Class All Others Standardize formal spend intelligence/spend analysis processes and 50% methods Automate integration of 53% spend data with spend analytics 42% 61% Fast Facts 15% of organizations have 20 or more data systems feeding spend analysis Most organizations, 46% average between two and five data systems feeding spend analysis Improve user access and ease of use for spend analytics tools 35% 54% 0% 10% 20% 30% 40% 50% 60% 70% Source: Aberdeen Group, September 2011

9 Page 9 Impact of the Technology Provider Keeping in mind that spend analysis is a technology-intensive enabler within spend management, strategic approaches involving the underlying vendor or solution provider is an important consideration for organizations involved in spend analysis initiatives. Based on this concept, key factors contributing to the success of spend analysis programs suggest that technology vendors or service providers can also play an important role in the success of an existing or new spend analysis initiative. When asked to rank on scale of 1 5, (1- limited impact, 5- high impact) best-practice development averaged the highest with a score of 3.01 followed closely by enablement services (2.89) and commodity experience (2.84) (i.e. demonstrated as part of the spend analysis deployment or services). Organizations looking for improvement need to consider vendors as a part of their strategic plan. Figure 5: Importance of Strategic Vendor Involvement Best Practices Development Enablement Services Commodity Expertise Training 2.60 Social / Community Networking Source: Aberdeen Group, September 2011 Aberdeen Insights Data Classification Challenge The UNSPSC Example Pressures to standardize are exemplified in the goal of using a universal schema for products and services. This idea is a logical one for increasing the ability to share product and services information between buyers and suppliers and one critical for building knowledge around spend. For example, standardized around a widely used classification scheme for catalog items, the United Nations Standard Products and Services Code (UNSPSC ) provides an open, global multi-sector standard for classification of products and services. UNSPSC is a five-level hierarchy coded as a 8-digit number. The five levels define Segment, Family, Class, Commodity, and Business Function. Example: 42 Segment Medical equipment and accessories and supplies 31 Family Wound Care products 22 Class Suture and related products 01 Commodity Suture continued

10 Page 10 Aberdeen Insights Data Classification Challenge The UNSPSC Example The benefits to the approach include leveraging a common format that can help with the standardization of data to a universal structure and can be used to clean up items masters and increase visibility into line items with greater detail. Figure 6: Usage Classification Codes for Products and Services 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% 88% 78% Internally developed 20% 33% Best-in-Class All Others 8% 8% 11% 8% 2% UNSPSC eclass SIC NAICS Source: Aberdeen Group, September 2011 However, one of the challenges in any standardization approach for data classification is that one size does not always fit all, adding to the complexity of trying to automate classification. As demonstrated in Figure 6, despite the goal of creating a universal standard around common libraries such as UNSPSC, many organizations continue to use internally developed / custom classification schemes. 7%

11 Page 11 Chapter Two: Benchmarking Requirements for Success Best-in-Class organizations today demonstrate an ability to encompass traditional areas of spend analysis such as visibility and reporting, but are also demonstrating leadership in their ability to integrate the seven systems (on average) containing spend data and twice as likely to integrate external data on markets and suppliers that are crucial for generating value in their spend analysis system. Case Study: Leading Medical Device Company As one of the best-known and most respected healthcare brands in the world, this medical device company demonstrates how globally-respected organizations are leveraging spend analysis. Currently in their third year of using spend analytics and about 3.5 years of data in the system, the centralized global sourcing and procurement team leverages their spend analysis to help manage over a billion in spend. To facilitate spend analysis the company loads their data into the SaaS based spend analysis tool, the same platform used for strategic sourcing and contract management. Procurement uses spend analysis for data visibility, aggregation and enrichment. They start the process of spend analysis by loading global data from over 20 source systems that include various financial and production based ERP systems. To get their data to the cloud, the company performs manual extracts from all the various systems and then does a pre-cleanse / pre-consolidation effort before giving it to a solution provider. Although establishing automatic extracts is possible and is a potential consideration for the future, the company has found that consolidating files manually is more economical and ensures the quality of the data enrichment process. Moreover according to a one of the managers of Procurement Process and Technology, Our spend analytics application has proven to be a tremendous resource in helping our Category Teams move past the tactical execution of low complexity opportunities into a more strategic view of category and supplier management. We are no longer just doing reports, but rather conducting complex analytics on enriched data to leverage corporate relationships, commodity indicators, global consolidation, and market intelligence. Based on the experience of using spend analysis thus far, the company has been able to generate average savings over 15% hard cost savings through the use of spend analysis. Overall the company feels that as result of the spend analysis effort they are much better prepared to react to changes in the industry and regulatory environment. Furthermore, through their spend efforts, spend analysis has helped them to identify sourcing opportunities across the different businesses through cross functional collaboration and has helped develop global category strategies that tie with the corporate goals and objectives. Fast Facts 73% of the Best-in-Class have visibility at the supplier, category and transaction level 67% of the Best-in-Class have the ability to collect spend data from multiple sources 54% of the Best-in-Class can identify strategic sourcing opportunities based on spend analysis

12 Page 12 Competitive Assessment Aberdeen Group analyzed the aggregated metrics of 132 organizations to determine whether their performance ranked as Best-in-Class, Industry Average, or Laggard. In addition to having common performance levels, each class also shared characteristics in five key categories: (1) process (the approaches they take to execute spend analysis operations); (2) organization (corporate focus and collaboration among stakeholders); (3) knowledge management (contextualizing spend data and exposing it to key stakeholders); (4) technology (the selection of the appropriate spend analysis tools and the effective deployment of those tools). These characteristics (identified in Table 2) serve as a guideline for best practices, and correlate directly with Best-in-Class performance across the key metrics. Table 3: The Competitive Framework Process Organization Knowledge Technology Performance Best-in-Class Average Laggards Reporting visibility into enterprise spend across all categories 73% 50% 20% Reporting visibility at the supplier, category and transaction level 73% 60% 40% Ability to collect spend data from multiple sources 67% 50% 40% Established executive support for spend analysis initiatives 72% 58% 47% Commodity management expertise within procurement and/or sourcing departments 63% 49% 41% Common enterprise-wide classification schema for commodities and services spend 63% 52% 24% Common visibility of goods and services spend across the enterprise 56% 36% 29% Spend Analysis Technologies / Technology Services: 91% Data 58% Data 38% Data Extraction Extraction Extraction 61% Data 46% Data 24% Data Cleansing / Cleansing / Cleansing / Normalization Normalization Normalization 50% Data 38% Data 21% Data Enrichment Enrichment Enrichment 48% Data 38% Data 32% Data Classification Classification Classification Ability to prioritize opportunities for savings within a spend category 50% 38% 29% Ability to identify opportunities for supplier rationalization 46% 33% 32% Source: Aberdeen Group, September 2011

13 Page 13 Capabilities and Enablers Best-in-Class organizations have demonstrated a strong reliance on specific process, organization, knowledge, technology enablers, and performance factors to effectively improve spend analysis initiatives that lead to results. Aberdeen research for Spend Analysis: Transforming Data into Value demonstrates spend analysis can lead to a direct increase in spend under management from 20% to 35%. While the competitive framework provides an overview of capabilities in general for spend analysis, the following includes a deeper analysis of the comparative capabilities demonstrated by the leadership of Best-in-Class organizations. Process: Consistent Data Collection and Reporting Based on the survey, 53% of respondents noted a lack of standardized spend analysis processes and methods as a main barrier to initiating or improving a spend analysis program. With 70% of respondents using spend analysis, organizations are continuing to focus on identifying ways to make improvements to the spend analysis process cycle, as shown in Figure 7. "Implementing spend analysis has allowed us to automate processes with standardized classification of the categories and supplier offerings, and use dashboard metrics for reporting granularity. ~ Senior Purchasing Specialist Specialty Chemical Manufacturer Figure 7: Common Process Steps for Spend Analytics Define Spend Scope Extract Spend Data Cleanse, Categorize & Enrich Create Spend Profile Analyze Spend Data Identify Opportunity Source: Aberdeen Group, September 2011 Extracting Spend Data One of the initial stages of the spend analysis cycle, extracting spend data from multiple sources is a critical process step for achieving true visibility into all the areas of spend. As described earlier, organizations are using a number of systems that describe spend data. And with 41% of respondents indicating that too many data sources, and data compatibility, are key barriers to improving spend analysis, it is clear that improving the process of data integration for extracting data into spend analysis is a must. It is also apparent that ERP solutions represent 76% of all data sources extracted for spend analysis systems and 38% of all integration functionalities used in spend analysis. As a result, there is a clear need for adopting processes and technologies that are certified on enterprise platforms for providing more seamless integration into spend analysis systems. If consistency is not managed, not only will savings opportunities be missed even after data cleansing, classification and enrichment downstream in the cycle, but analysis on spend may not reflect the true picture of spend within an enterprise, particularly if processes for extracting data from multiple sources are not resolved. Considering data extraction is a key process goal, Best-in-Class organizations demonstrate that they are 21% more likely than all other organizations to collect spend data from multiple sources. Increasing Spend Visibility Another key process differentiator is the ability to achieve spend visibility, which expands beyond just reporting and across the organization through Data Level Definition Level 1: Supplier-level visibility (i.e., you know total spend against a supplier, and supplier master names, and corporate hierarchical structure are cleansed and consolidated) Level 2: Category-level visibility (i.e., you know what you are buying in broad categories such as IT, hardware, software, etc.) Level 3: Commodity-level visibility (i.e., you have itemlevel visibility into purchases within a given category and have the ability to aggregate your category spend into smaller sub-categories according to a standardized system of classification)

14 Page 14 advanced reporting capabilities. An example of this is getting a quick snapshot view for reporting on the supplier, category or item level, where all maturity classes averaged 50%. However, a clear difference was established in enterprise visibility of spend. For example, Best-in-Class organizations are 33% more likely to achieve visibility into enterprise-wide spend across all categories (goods and services). As a result of this, the Best-in-Class were 1.25 times more likely to achieve Level 3 visibility into their spend data and suppliers, which translates into higher item-level visibility into purchases within a given category and the ability to aggregate category spend into smaller sub-categories according to a standardized system of classification. This advantage was also demonstrated in the ability to achieve drill-down visibility into suppliers, where the Best-in-Class were 21% more capable. However, the importance of spend visibility, of course, is in the ability to identify strategic sourcing opportunities and prioritize opportunities for savings within a spend category; Best-in-Class companies are 11% more likely to have this capability than all other organizations. Organization: Executive Support and Building Expertise Perhaps more than any other aspect of establishing effective spend analysis initiatives, it is organizational factors that dictate the perception and the mindset of its value for other business units. Much of the success of any program is getting buy-in from all the players connected to spend data such as finance, treasury, and supply chain as well as from C-levels that would sponsor initiatives as part of a wider program. Based on the capability of creating a consensus for understanding the value of spend analysis, the Best-in-Class were 19% more likely to have established executive support for spend analysis initiatives than all other organizations. Another critical aspect is the organizational focus on building the proper resources for creating expertise in the field. For example, 32% of all respondents indicated they had insufficient skills necessary to execute a spend intelligence program (i.e. lack of commodity knowledge, insufficient data analysis skills). Best-in-Class organizations also demonstrated an advantage in their capability of establishing organizational-wide knowledge, where 63% indicated having commodity expertise within sourcing or procurement departments compared to 46% for all other organizations. Knowledge Management: Establishing Common Standards Establishing visibility is a critical aspect to spend analysis. From a knowledge management standpoint, creating common spend visibility starts with creating common definitions of spend data and how they will be used for identifying opportunities. This starts with the consideration of creating enterprise-wide classification schemas for products and services spend through the use of commodity codes being leveraged within the organization. Commodity codes are used in finding use in workflow as they control how requisitions move through the system. Therefore the choice of custom classification codes (or standardized ones such as UNSPSC, SIC or NAICS) have an important impact on how e-procurement systems in place link

15 Page 15 information related catalogs, contracts and ultimately purchase orders and invoices. As discussed earlier in this study, it is evident most organizations are leveraging internally developed/home grown classification schemes and 33% of respondents indicated a key strategic action was establishing a single classification schema across the enterprise. From a knowledge management perspective, Best-in-Class organizations also demonstrate a higher usage of internally developed standards than other organizations, 88% versus 78% for all other organizations. What this indicates is the need for coordination within the organizations and all the stakeholders of spend data for using a common language and common definitions particularly with higher custom use that relate to special business or organizational requirements at hand. This also assists in the ability to establish common visibility of goods and services spend across the enterprise, an advantage of the Best-in-Class compared to all other organizations, 56% versus 33%. Technology: Deployment Approaches and Enablers Perhaps more than other solution areas in spend management, spend analysis is almost exclusively dependent on technology for its use. How organizations are using it based on existing technology investments provides insights for those looking into spend analysis initiatives on the optimal way to deploy or implement a spend analysis platform. In this regard, Figure 8 shows those using a spend analysis solution most are using spend analysis on a stand-alone basis (32%) followed by usage of spend analysis from an ERP platform (23%) and usage as part of a sourcing suite (18%). Based on these structures of deployment, those using spend analysis typically already have e-procurement (53%), contract management (45%), e-sourcing (44%). Looking overall at deployments, 68% of organizations indicated they are using on-premise or installed software versus 32% using SaaS. Moreover, in looking at the top two means of deployment, for those leveraging their ERP systems the usage of on-premise spend analysis was much more predominant at 94%, where as those using a stand-alone system the break down was much more even, at 52% for on-premise and 48% for SaaS. This is an indicator that existing investments in ERP for spend management have a strong influence in determining the approach or even platform of choice for spend analysis at this time. "Having the ability to extract top supplier spend by general ledger account or commodity classification over the past three years has had a great impact on identifying category opportunities through our spend analysis initiative. ~ SVP, Chief Sourcing Officer Midwest Regional Bank Figure 8: Deployment of Spend Analysis 13% 18% 9% 5% Stand-Alone Spend Analysis Platform Part of an ERP system 32% 23% Part of a Strategic Sourcing suite Custom-developed application Part of an E- Procurement/SRM suite Part of a Supplier Management Solution Source: Aberdeen Group, September 2011

16 Page 16 In examining spend analysis as an application alone, there are five key areas of functionality that can be identified where organizations have adopted technology. Based on Figure 9, it is apparent the Best-in-Class have a high deployment (91%) of data extraction components of spend analysis, an area critical for the ability to eliminate discrepancies and duplicates. Best-in-Class organizations are 27% more likely than Laggards to be automatically extracting data and 30% less likely to have manual processes. However, the level of spend analysis deployment tapers off for all maturity classes for other spend analysis enabler areas such as spend visibility (53%) essential for customizable reporting/dashboards, data enrichment (50%) for additional or complimentary information on suppliers, and classification (48%) critical for mapping data elements and mapping spend data to industry standard classification systems. With the exception of data extraction being highly automated, leveraging technology providers also does not necessitate that all processes handled by a technology provider are 100% automated. For example, some providers send data intensive aspects of spend analysis off-shore or to third party providers such as cleansing and enrichment (e.g. formatting errors, duplicate suppliers, parent child, attribute formatting, diversity/risk enrichment). Yet with improvements in technology, automated methods are rapidly becoming more available on recognized spend analysis platforms for spend data transformation that leverage artificial intelligence driven approaches. Fast Facts How organizations are deploying spend analysis: 71% On-Premise / Installed Software 29% SaaS / Subscription Figure 9: Adoption of Spend Analysis Enablers Data Extraction 50% 91% Data Cleansing 36% 61% Spend Visibility Data Enrichment 31% 40% 53% 50% Best-In-Class All Others Data Classification 35% 48% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Source: Aberdeen Group, September 2011 Performance Management: Improving Visibility for Action Perhaps more than any other area, the goal of spend analysis is the increased visibility that provides the means for improving spend and supplier performance within a procurement organization. One key area that distinguished Best-in-Class organizations in the performance management capability is the ability to prioritize opportunities for savings within a spend category (Best-in-Class 50% versus 34% for all others). Prioritization within spend analysis is based on the ability to get insights through enhanced automation of spend visibility and reporting (i.e. drill down and multi-dimensional reporting) currently used by 60% of Bestin-Class respondents.

17 Page 17 The second is the ability to identify opportunities for supplier rationalization (Best-in-Class 46% versus 32% for all others) which is a direct tie to capabilities in analyzing spend combined with understanding supplier performance and risk. In this regard, the Best-in-Class have a 44% higher capability in the ability to measure supplier business critically (i.e. how important a supplier is to a business), and 42% higher capability in analyzing the overall strength and stability of a supplier s business. Aberdeen Insights Supplier Enrichment Sources of Data Understanding the supplier base is critical in minimizing risk as well as decreasing costs. As a part of a holistic approach to spend management, enriching supplier data provides a more holistic view of suppliers. Areas of supplier data that are currently enriched focus on corporate information / relationships, supplier information, supplier financials, regulatory compliance and corporate social responsibility information. Figure 10: Enrichment of Third-Party Supplier Data Corporate Information (Address, Tax ID, SIC Codes ) 44% Corporate Parentage (Supplier parentage /corporate hierarchy) Corporate Compliance (Diversity Data, EPA, OSHA) 33% 33% Corporate Ownership (Principal owner, Parent-Child, M&A) Financial Information (Revenue, Financial Ratios) Supplier Credit Information (credit scoring, bankruptcy, litigation) 28% 25% 22% Corporate Social Responsibility (cultural, economic, environmental, social) 15% 0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50% Source: AberdeenGroup, September 2011 Yet while many organizations are currently enriching this information within suppliers in supplier managed systems, ERP or procurement systems, only 15% of organizations are actually integrating core supplier risk and performance information into their spend analytics solution. This clearly represents an opportunity for organizations to shift from a tactical view of spend data to a strategic one. Furthermore, 27% of respondents are strategically focused on enriching spend data with externally provided supplier information such as financial, risk and or other legal information within the next 12 months.

18 Page 18 Chapter Three: Required Actions Whether a company is trying to increase its performance in the capture of spend analysis from Laggard levels at 55% to that of Best-in-Class at over 95%, organizations must evaluate their current spend analysis initiatives and confirm the standardization of process and the use of technology are consistent, yet advancing by continually looking for areas of spend analysis optimization and improvement. Laggard Steps to Success Increase automated integration of spend data with spend analytics. Improving this process will increase Laggards ability to push spend data for spend analysis efforts which currently stands at 50% compared to the Industry Average at 59%. Improve the ability to enrich spend data. Laggards are 1.2 times less likely than Industry Average organizations to enrich their spend data. Spend analysis is increasingly becoming a transforming source of information for not only building insight into spend, but also intelligence around suppliers and the impact of that spend. This includes the ability to track supplier parentage via corporate hierarchies reports, which is in place at only 15% of Laggard companies. Establish enterprise-wide classification schemas. As discussed earlier, establishing a common language is an integral part of creating linkages through commodity codes that influence everything from catalogs to purchase orders. Where only 24% of Laggards are capable of this currently, 56% plan to improve in this area in the next 12 months. Industry Average Steps to Success Automate data collection from multiple sources. Given the number of spend data sources for spend analysis, establishing automation for extracting spend data becomes essential for the sake of efficiency and accuracy. Industry Average companies are 30% less likely than the Best-in-Class to have implemented technologies for integrating / extracting data into spend analysis. Develop the ability to cleanse and classify data. Another key facility identified in spend analysis efforts is leveraging cleansed and classified data. This promotes the opportunity to identify opportunities beyond manual processes for increased category efficiency and the ability to identify duplicates such as payments or suppliers. In this regard, the Industry Average are 19% less likely than the Best-in-Class to have technology enablers in this area. Increase visibility at the supplier, category and transaction levels. Thirty-seven percent (37%) of Industry Average organizations indicated a major barrier in the ability to identify and prioritize top spend categories. Organizations that understand the Fast Facts 36% of the Best-in-Class integrate spend analysis with ERP solutions 30% of the Best-in-Class integrate spend analysis with p-cards 28% of the Best-in-Class integrate spend analysis with contract management "Our first criteria for success of spend analysis would be spend classified to an acceptable level for each category manager. We have our own custom classification based on UNSPSC codes and have been working to align the automated UNSPSC classifications to our custom classification to get the view we desire. " ~ Sourcing Analyst Large Midwest bank-based Financial Services Company

19 Page 19 dynamic of spend at multiple levels promote the ability to identify spending trends and the ability to distinguish preferred suppliers through capabilities such as power-user capabilities, drill-down, multi-dimensional reporting.37% of the Industry Average plan to implement or add spend visibility tools in the next 12 months. Best-in-Class Steps to Success Improve understanding of supplier risk through the use of spend analysis tools and dashboards. With only 17% of Best-in- Class organizations having this capability in place, it is imperative that organizations increase the investment in linking spend analysis and supplier management capabilities. This suggests improvements are needed for integrating and enhancing supplier data such as financial, corporate and corporate social responsibility related information. Develop a cross-functional review of spend data for strategic planning. Only being executed in a third of Best-in-Class organizations, improving the voice of the procurement organization through cross-functional review of spend data is a key action, particularly for CPO. By increasing involvement of spend analysis to groups outside of procurement, organizations are better prepared to make strategic decision on their spend and enhance their ability to influence key stakeholders (such as the CFO). Increase the usage of third-party economic data. As a means to improving insights for how economic and world events impact existing data, the Best-in-Class need to improve their usage of third party economic data that includes commodity prices, inflationary data, exchange rates and market indices. On average less than a quarter of Best-in-Class organizations are integrating this type of data into their spend analysis systems. Aberdeen Insights Summary Fast Facts 36% of the Best-in-Class integrate spend analysis with ERP solutions 30% of the Best-in-Class integrate spend analysis with p-cards 28% of the Best-in-Class integrate spend analysis with contract management Spend analysis has come a long way over past decade. Initially focused on transaction oriented insights, best-practice spend analysis today is providing a more holistic view of spend through increased aggregation of spend data and automation. Every organization has a different start and end point with regard to technology; spend analysis is no different. In this regard, the goal of spend analysis is to understand how spend is being managed within an organization. Therefore increasing visibility into spend starts with standardizing formal spend intelligence (cleansing, categorization, enrichment) processes and methods across the organization, increasing automated integration of spend data from ERP systems and procurement related systems, and improving user access and the ease of use for spend analysis tools. For those more advanced in their spend analysis initiative, taking the next step involves increasing the integration of supplier data with spend data and considering how predictive analytics, an emerging area, can provide greater insights into how economic and natural events impact the performance and approaches to spend management.

20 Page 20 Appendix A: Research Methodology In September 2011, Aberdeen examined the use, the experiences, and the intentions of 132 enterprises using supplier management in a diverse set of industries. Aberdeen supplemented this online survey effort with interviews with select survey respondents, gathering additional information on strategies, experiences, and results. Responding enterprises included the following: Job title: The research sample included respondents with the following job titles: Executive (CEO, President, Chairman, Owner, Other) (5%), EVP/SVP (2%), CPO (7%), CIO (2%), Vice President (7%), Partner/Principal (2%), Director (12%), Manager (40%), General Manager (2%), Staff (6%), Other (15%). Department / function: The research sample included respondents from the following departments or functions: Procurement / Purchasing (70%), Logistics/Supply Chain (6%), Operations (2%), Finance/Administration (7%), Logistics Supply Chain (6%), Corporate Management (7%), Product Development (2%) Industry: The research sample included respondents 24 various industries which included Aerospace, Automotive, Chemicals, Computer Equipment, Construction, Consumer Packaged Goods, Education, Financial Services, Food/Beverage, Government, Health (Medical & Dental), Industrial Equipment, IT Consulting, Metals, Oil/Gas. Geography: The majority of respondents (53%) were from North America. Remaining respondents were from Europe (25%) and Asia- Pacific region (16%) and Middle East & Africa (3%), and other (3%). Company size: 52% of respondents were from large enterprises (annual revenues US $1 billion or above); 32% were from midsize enterprises (annual revenues between $50 million and $1 billion); and 16% of respondents were from small businesses (annual revenues of $50 million or less). Known Headcount: 60% of respondents were from large enterprises (headcount greater than 2,500 employees); 25% were from midsize enterprises (headcount between 251 and 2500 employees); and 14% of respondents were from small businesses (headcount between 1 and 250 employees). Study Focus Responding executives completed an online survey that included questions designed to determine the following: The evolution of spend analysis from its beginnings of tactical approaches to strategic Usage of automation as part of the spend analysis solution Ability manage and standardize processes Ability to have visibility into spend Understand the amount and type of spend going through spend analysis systems Understand the features and functionality being used in spend analysis

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