Spend Analysis and Opportunity Assessment

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Spend Analysis and Opportunity Assessment
There's Gold in Them There Hills ... Of Data



Contents

Executive Summary

There's a common saying, "you don't know what you don't know." In the absence of a Spend Analysis project, companies won't know what they don't know. Not knowing what and where you're spending means not knowing where you can save, especially the more disparate and geographically spread-out you are. To find the real savings opportunities, organizations must paint a true picture of the spending landscape by utilizing Spend Analysis tools to look at more detail, look at spend of international sites, and break down spend into more refined categories.

The improved availability of data yielded by a Spend Visibility & Analysis tool allows businesses to employ evidence-based decision making at all levels of their organization. Data-driven Spend Analysis means more capabilities and savings opportunities at lesser cost. Sometimes, uncovering opportunities in just a few commodities can save millions of dollars. Statistics taken from 100 customer engagements show a minimum savings of 10x's the ROI on the Spend Analysis project.

What is Spend Analysis:

What is it?

Technically, spend analysis is the process of aggregating, classifying, and leveraging spend data for the purpose of gaining visibility into cost reduction, performance improvement, and contract compliance opportunities. It is part of an overall spend management and visibility process that includes the analysis, award, and monitoring of corporate spend. Additionally, it is the first and last step of the strategic sourcing process that drives total value.

Generic spend analysis enables one to answer the following questions:

  • Who is buying
  • What
  • From whom
  • When
  • (optionally) Where
  • At what price

Who needs it?

However, good spend analysis is much more than that. It is the process of organizing a company’s spend in such a way that one can understand it, slice it, dice it and uncover hidden savings opportunities. Additionally, it impacts more than just the sourcing team. Spend analysis / visibility serves three internal user community groups:

  • Leadership and CxOs: who need up-to-date reports to drive strategic direction
  • Managers, accountants, etc.: who need to drill down into a spend data set to explore specific areas of interest or track down payment specifics
  • Sourcing power users: who need to locate, drive, and monitor the next set of savings initiatives
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What is unique about spend analysis is that each user community needs to look at the same data in different ways at the same time – same data, different reports. Leadership needs to see the data in broader, aggregated groups, while sourcing power users need greater detail to drive specific commodity decisions.

The Evolution of Spend Analysis

Spend Analysis has recently emerged as a larger, more integral piece of corporate spend management. Formerly, spend analysis was primarily the purview of big companies with large amounts of spend under management and significant internal budgets for managing that spend. New technology, new methods of collecting and managing spend data, along with the growing acceptance that in order to manage your spend you have to understand it, have raised interest in spend analysis from companies of all sizes.

In a challenging economy with many companies experiencing sales that are either trending down or flat, managing costs may be the only way for companies to improve their bottom line. In recent years, spend analysis tools have made dramatic functional and technical improvements while the costs have gone down. The affordability and power of new tools and technology are proving less sophisticated spreadsheet and gut-feel methods of analysis to be inferior. Many companies are embracing the idea that they can't afford not to incorporate spend analysis into their spend management system.

How Spend Analysis Fits into the Overall Sourcing Process

Spend Analysis is actually the first step in Spend Management workflow. When organizations begin implementing Spend Management they typically start with some form of strategic sourcing, choosing an obvious commodity based on highly visible factors such as quantity purchased, number of suppliers, or widely varying price points. Eventually organizations run through low hanging fruit and realize that in order to manage their costs they must understand where they are spending their resources: what goods and services they are purchasing, how often, at what cost, and from which suppliers. The process of collecting and classifying this information is Spend Analysis.

Procurement Lifecycle


Functional Evolution:

Organizations start with Spend Classification, eventually achieving Spend Visibility, before progressing to Organizational Spend Management.

Spend Analysis Functional Progression

Technical Evolution: New Advanced Capabilities and Criteria

With greater customer success at each level, newer technology continues to push advancements in Spend Analysis.

Adnavced Spend Analysis

Riding the Wave of Business Intelligence

Data was always king but Business Intelligence has made it a rock star. Business Intelligence tools are making data easier to manage, store, maintain and classify. Better access to data is allowing businesses to employ evidence-based decision making at all levels of their organizations. The ease of collecting data is driving organizations to look at how they can best use the information. Spend Analysis is one of the ways that businesses are extending the power of Business Intelligence data by connecting the information about a product or service with the people responsible for choosing suppliers and negotiating prices. The additional availability of data is increasing demand for data-driven business solutions and Spend Analysis is no exception to this trend. Data-driven Spend Analysis means more capabilities and savings opportunities at less cost.

Spend Analysis Basics

Spend Analysis Approaches:

In-House Efforts

Because Spend Analysis appears to be a straightforward database application, in-house personnel (typically IT staff) are often eager to take on the challenge of building a Spend Analysis system using tools at hand. These tools may be On-Line Analytical Processing (OLAP) databases such as Hyperion or SQL Server; or ordinary relational databases like Microsoft Access; or even BI systems such as Business Objects, Cognos, or SAP BW.

Few of these efforts succeed, because data mapping, hierarchy organization, and refreshing of data become problematic and burdensome over time without technology specifically developed to enable them. However, at least one leading e-sourcing vendor in the 1990's produced a Spend Analysis system using a third-party OLAP database, a third-party OLAP viewer, and third-party services for cleansing and mapping spend data – without ever developing any of its own technology. So there is at least an existence proof that it is possible to build an in-house Spend Analysis system from existing components and services, albeit an expensive one.

Manual Approach

Spend Analysis can be performed with nothing more than ordinary tools like Excel and a sharp pencil. Many sourcing consultants, for whom Spend Analysis is a necessary prerequisite to any advanced sourcing effort, still use largely manual techniques. The problem with manual spend analyses is that they are not repeatable; they are one-off efforts that support one-off sourcing projects. Thus, most of the effort is thrown away and is unable to be re-used the next time spend information is required.

Packaged Solutions

Starting in the early 1990's, a few vendors began offering custom Spend Analysis systems. Then in the late 1990's, a much larger number of vendors began to produce Spend Analysis applications; now, in 2007, every e-sourcing suite vendor offers a Spend Analysis application, as do several independent vendors, despite significant consolidation in the space.

The choices available to customers now range from inexpensive desktop solutions to to seven-figure all-in-one services+software offerings.


What you don't know costs you money!

There's a common saying, "you don't know what you don't know." In the absence of a Spend Analysis project, companies won't know what they don't know. Not knowing what and where you're spending means not knowing where you can save. Organizations that collect more data and classify it to their sourcing categories discover more opportunities for leveraging suppliers. These may be opportunities that were completely hidden from view (this is especially true for disparate and geographically spread-out companies). Spend resides in multiple systems (AP, PO, P-Cards, Budget, Expenses, Finance, etc) and it might be different systems for different divisions or entities. By integrating all of this granular data, companies can achieve greater insight into expenditure areas that were not previously visible. Most companies identify 100-400 different categories of expenditure breakdown where the number of purchases made from multiple suppliers reveals opportunities for leveraging price and volume discounts.

Company spend can be compared to household expenditures. If a household collected detailed data on purchases for a few years, classifying all the expenditures for item categories such as food, rent, property, glassware, software, etc., then it's likely the household would discover better ways to make purchases in the future. Many households have the potential to save thousands of dollars with this simple, detailed spend analysis. If you apply this to a worldwide organization with many sites and many systems, the potential for finding better purchasing methods is exponential. The larger the company the larger the data sets that must be collected and managed (spend is much more broad and difficult to analyze within a larger company) but the scope of savings is also much larger. There is so much data to sort through that companies need spend analysis tools that are specifically designed to compile factors and identify opportunities for savings. Sometimes uncovering opportunities in a few commodities can save millions of dollars.

The Risks of Putting off Spend Analysis: The longer you wait the more money you waste

In 2000, a Fortune 200 Health Care Manufacturing Company decided to have an internal IT resource consolidate a few databases reflecting the primary US spend within the organization. Accounts payable data was added (few line item details) along with some supplier enrichment data that had been captured for a small subset of the suppliers. Because the organization was at the beginning of a large SAP implementation, they deemed this level of information to be adequate in the short term. Some small savings were achieved as the consolidated database was analyzed but, after 6 years of implementing SAP, spend details were still not available as needed for deeper sourcing needs. They still did not understand their spend at the level of detail required for sourcing purposes; data was collected at levels that were too high and was not useful to drive meaningful leverage and savings. Deciding to invest in a new round of professional and more comprehensive Spend Analysis and incorporating Global Spend enabled them to discover millions of dollars in hidden opportunities that they could have been saving every year.

Which Companies Benefit the Most from Spend Analysis

Companies who typically perform spend analysis have gone beyond tactical purchasing and are adopting strategic sourcing disciplines. These companies may have $50 million in spend or billions of dollars in spend. Companies of all sizes share the need for more data, and more intelligence, which will lead to greater savings opportunities. Modern, cost-effective tools have helped progressive purchasing organizations realize that there is too much savings at risk not to collect and analyze spending data. Looking at spend across the company and managing it better is an untapped capability for many companies.

The rewards of Spend Analysis are dramatic and easily justified for companies with:

  • Annual revenues of $500,000,000 or higher
  • More than 25,000 suppliers
  • 50 or more different category codes

Spend Analysis makes companies more competitive and more profitable. Identifying and achieving cost savings goes directly to the company’s bottom-line, immediately increasing profitability. The current economic recession has placed a larger, brighter spotlight on cost reduction activities than ever before. Savings stories vary depending on the size of the spend and the nature of the commodity. Spend Analysis can save an organization $200 on a first pass look at P-Card spend or save millions of dollars on initial freight analysis. Some large companies with aggressive and mature spend analysis practices performed throughout their organization credit it with saving over $40 million per year.

Why Some Companies Overlook the Benefits of Formal Spend Analysis

Every company tracks some information about their spend. They usually have some basic understanding of how much it costs to provide their product or service so that they can price their deliverables correctly. But many organizations underestimate the value of different data elements, therefore they don't collect additional bits of information from various areas of their organization. Frequently the individuals responsible for purchasing are linked to specific commodities or business units, which results in the creation of information silos. These silos lack relevant information across different commodities and limits analysis. Elevating an organization's spend intelligence requires an investment of time and effort. Without an organization-wide strategic initiative with strong executive support, many organizations fail to get momentum behind a spend management initiative.

Find the Benefits by Getting a True Picture of Your Company's Spending Landscape

To find the real savings opportunities, organizations must paint a true picture of the spending landscape by utilizing modern spend analysis tools to look at more detail, look at spend of international sites, and break down spend into more refined categories

Justifying Spend Analysis

How Much Are Companies Losing by Not Doing It?

Companies who approach Spend Analysis in a structured manner usually discover opportunities that enable them to save millions of dollars in savings. The following facts represent benchmark data on cost savings from real-world companies performing Spend Analysis. These statistics were generated from over 100 customer engagements across various industry segments.

  • On average companies save .25-1% on total spend dollars. For a company with $1 billion in spend, this equates to $2.5 - $10 million in new cost savings on an annual basis (that's every year, not just a one-time savings opportunity).
  • This savings generally represents 10x and, in some cases, up to 100x ROI on the Spend Analysis project.
  • Average savings for sourcing efforts on individual commodity items are 4%- 60%.
  • Companies NOT doing Spend Analysis forego millions of dollars that would go straight to the bottom-line of the company's profitability.
  • Formal Spend Analysis and ongoing Spend Management yield significant results for companies of any size and makeup. Generally, the more divisions, people, and systems in an organization the more complicated the spend and the greater the opportunity for savings from performing Spend Analysis.

Why ERP (Enterprise Resource Planning) Systems Are Not Enough

Spend Analysis had its beginnings in the 1980's through the early 1990's at progressive companies such as GE, as well as at consulting firms such as McKinsey. The idea was to mine existing spend data to identify areas where sourcing effort would be most profitably applied. They used many different approaches but they eventually focused on three main areas: vendors, commodities, and cost centers.

Spend Analysis originated when they realized that the ERP or accounting system data did not adequately support sourcing because it's generally incomplete, contains duplicate vendors, does not contain good commodity information, and is far more static than spend data which changes all the time.

Additionally ERP systems do not provide any data standardization and categorization tools. Getting spend data into a clean and detailed format that can be used to determine spend is critical. Why is cleansing and categorizing data so important? Understanding the volume of a category and the number of suppliers who provide your organization with goods and services in the category provide the primary tools for negotiating better pricing.

Furthermore, many organizations have more than one ERP system, especially if they have grown through mergers and acquisitions. The data in each of these systems does not talk to each other or share a "common language," thereby making it difficult for sourcing to run reports. When we say common language, we do not mean the spoken language, but how the data is described from one system to the next.

ERP Data is Generally Incomplete

ERP systems are very good at processing transactions, which is what they were designed to do. The ERP system normally contains only the transactions performed via the ERP system. Any payments done outside the system (e.g. credit card transactions or data from a merged entity) will not appear in the ERP system. Other useful data may include supplier performance metrics and supplier diversity or risk classifications. Spend Analysis needs detailed information about all transactions and it needs them to be tagged so that they can be sorted, grouped, and consolidated to achieve cost reduction. When this data is merged, it paints a more detailed picture of corporate spend.

ERP System Contains Duplicate Vendors

A single vendor can show up multiple times in a single ERP system. This may be due to different divisions, different billing addresses, typos, or result from a merger. There are cases where ERP systems have been known to have a single supplier represented in over 50 different ways. Each occurrence spelled or abbreviated differently. Knowing how much business you're doing with a supplier is critical to negotiating better prices. If the data shows a commodity representing $350,000 dollars of spend involving over 100 different suppliers when there are actually only 3 suppliers then you'll never know to negotiate volume discounts with those suppliers because you don't know the volume of the business you're doing with them.

The ERP System Does Not Contain Good Commodity Information

A good ERP system does allow those requisitioning products to assign category codes but in many cases the requisitioner is not familiar with the coding structure, especially from a Spend Analysis perspective. Very often people do not take time to search for the correct code because they do not understand the importance. As a result the data is incorrectly tagged with the infamous "9999 – Miscellaneous" category.

When a payment is recorded in the ERP system, it contains the vendor name, the organizational unit responsible and the general ledger (GL) code against which the spending is booked. The GL codes are designed to support the accounting function, not the procurement function. These codes are heavily regulated by the authorities. For example, when a bank buys a printer from HP, the procurement group wants to link it with all other printers bought by the bank. However, the accounting for this printer will vary depending on how it is used, and on specific accounting rules that may apply. Two hundred printers bought at once would generally be considered capital goods in one set of accounts, but the same printer, bought as a single item, might be expensed. This means that the spending for a single commodity can be scattered across the balance sheet.

ERP Data is Unchanging and Spend Analysis Data Changes All the Time

Sourcing teams face the dilemma that their ERP systems contain data with both inconsistent vendor information and incorrect category codes. In order to maintain accounting accuracy, the ERP system resists restatement of historical information. ERP systems are designed to limit changes to data after the data has been entered. When a month closes, it is a major auditable event to modify records. ERP systems do not manage data with the flexibility required for Spend Analysis because that's not their purpose. Rigid rules for updating data means that issues like duplicate vendors and missing or incorrect commodity codes cannot be easily changed. In a Spend Analysis system, these changes are easy to do. If the procurement group decides that the commodity structure needs changing, this can be done. Using an ERP system, making a change on historical data can take weeks or months. These reasons remain as valid today as they were in 1985.

Business Intelligence (BI) Systems and Spend Analysis

Spend Analysis is a form of BI that focuses on helping companies maximize the value of every dollar they spend. Spend analysis projects should be approached in a systematic manner – formally and with an expected return. There are four main components of a Spend Analysis / BI project:

  • 1. Collection: A means of collecting organizational and outside-the-organization data in a structured, rigorous manner.
    • In the past- this has been ETL (Extract, Transform and Load) oriented systems
    • Today- these are more sophisticated and holistic data management systems.
  • 2. Storage: Where the accumulated and future data will reside.
    • In the Past- usually "fixed" database structures that were needed to drive reporting.
    • Today- these are flexible and dynamic databases that match the company's data collection, storage, reporting, and management tracking needs.
  • 3. Reporting and retrieval: Analytical spend analysis reporting capabilities.
    • In the Past- typically spend cubes, spreadsheets
    • Today- data-driven reporting capabilities across multiple data sources.
  • 4. Ongoing management: Structured measurement and on-going management tracking programs. (require spend data refresh on a regular basis to further analyze spend)
    • In the Past- simple dashboards and rudimentary reporting on things like Diversity, Rebates, etc.
    • Today- more involved programs incorporate multiple measurements and sophisticated tracking improvements. The collection and integration of more data provide broader and more accurate pictures of what is going on in the organization. All these advancements are driven by more complete data.

Challenges for Organizations Implementing Spend Analysis

The current economic environment is forcing companies to prioritize and implement cost cutting programs faster than before. Useful data is increasingly "in the weeds" – as companies pick all the low hanging savings fruit. To achieve new results it becomes necessary to go below the data surface to look at the lowest level of the transaction, payment or related information. Other challenges include:

  • Lack of Spend Understanding
    • Spending data is often not up-to-date, incomplete or too high-level (e.g., not line item or part-level) to make the best decisions quickly and effectively
    • Many organizations are finding that they have overinvested in sourcing strategy development, purchasing automation, etc. but underinvested in the means to get at the data driving their decisions and actions
    • Companies have latched onto the "analytics" and "visibility" side of looking at spend and supplier data without first thinking through all of the implications about getting the right set of information in a usable format in the first place – they assume current providers are taking care of this initial step and gathering all of the appropriate information but in reality are sub-optimizing their programs and results
    • Some organizations are confusing data enrichment with cleansing and classification - enrichment can be a critical step following cleansing and classification, but enrichment alone will not lead to better data.
  • Lack of Resources
    • Traditional spend classification systems were designed to drive local opportunity identification and simple strategic sourcing programs – not programs on a global scale or those with any added degree of complexity (e.g., internal spend aggregation, buying groups, etc.)
    • Spend Analysis usually starts with Purchasing and Strategic Sourcing, who must request IT support or look within their own department for resources. Many times Purchasing is consumed with daily tasks such as buying basic materials for the organization. Although the need for IT support has lessened significantly, organizations still need to allocate resources to Spend Analysis and Strategic Sourcing to drive leverage across the organization.
  • Required Analytics Capabilities
    • The 80% solution classification (i.e., 80-90% data accuracy) often leads companies to make incorrect strategy decisions rather than providing sufficient information needed to get the job done.
    • The more advanced organizations now look at expanding the scope and definition of spend visibility to include other data types (e.g., diversity, performance, risk). This data can add data fields that are incomplete, inaccurate and potentially misleading.
    • Frequently, global companies miss huge savings opportunities by not leveraging spend across their many operating companies worldwide. The decentralized data should be accumulated and classified based on a common data language. This common data language can be standard industry codes (such as UNSPSC) or custom commodity groups.
  • Making Finance Your Ally
  • The Finance team can be a key ally to sourcing and spend analysis initiatives. The Finance team can document progress and track any savings achieved from sourcing efforts. They also make sure that identified and implemented savings to the corporation are rolled-up and attributed to departmental budgets. This role ensures that savings are realized and brought back to the organization rather than saving money in one area then spending it on another area that is not in the budget. To save in one area yet spend these savings in another unplanned area does not benefit the organization. You achieved more but you didn't actually save more.

    A Spend Analysis initiative helps measure the pattern of success. It makes it easier for Finance to measure and track ongoing success across the entire organization.
  • IT Opposition
  • Historically it was common for IT to wage turf wars over ownership of Spend Analysis projects. IT frequently opposed Spend Analysis initiatives because they had resources already dedicated to ERP, consolidating organizational data and providing management reporting. IT wants to centralize control of software applications and they considered Spend Analysis just one more application to manage, and a redundant one as well. They didn't understand its intrinsic value to the company, especially when it uses the same data found in other applications.

    However, with a growing level of respect for the value of data cleansing and classification, attitudes are shifting -- especially when IT is not required to invest a large amount of man-hours to support the initiative. A common problem in the past was that spend data was misclassified, lacked sufficient detail or was stale (out of date). Also, the entire process took a great deal of time starting with getting the data ready for the cleansing/classification process. Most of the heavy-lifting efforts fell on IT. When inaccuracies were discovered in the dataset, the entire dataset was considered flawed which made the whole Spend Analysis initiative seem less reliable.

    It has become much easier to support today's Spend Analysis applications, especially those that are SaaS (software as a service). IT would still be responsible for making the data available, but most of the heavy-lifting is now done by the Spend Analysis application, especially those with data-driven architecture. The Sourcing team bears the responsibility to give direction to cleansing/classification processes and highlight errors so they can be corrected. Refresh cycles are dramatically shorter (2-5 days vs. 2-4 weeks) so that spend data retains its "freshness."

    IT personnel frequently have some key skills that contribute to the success of the initiative:

    • Familiarity with corporate data sets and applications.
    • In-depth knowledge and access to the Corporate IT environment.
    • Hi-level knowledge and experience with corporate reporting tools.
    • Experience establishing repeatable processes to export and distribute data.

Choosing a Spend Analysis Approach

Spend Analysis Tools

When spend analysis tools were first introduced, around 1996, they were essentially spreadsheets on steroids. Spend data from multiple sources were manually normalized and merged together. Then the data was manually cleansed to address spelling errors and redundancy. At that point classification codes were applied. Once enriched, the spend data was loaded in a "viewer" that had a few basic reports and navigation capabilities. The entire process lasted several months since it was so labor intensive. Often these activities were outsourced to low-cost labor regions such as India. Additionally, it was prone to errors and inconsistencies, especially if there were different people working on the same dataset. Since there were no tools to automate the process, it was not scalable nor easy to repeat with new data.

Over time, there have been advances that have improved how data is collected, cleansed, classified, analyzed and managed. The adoption of industry classification structures, such as UNSPSC codes improved how spend data was classified and reported. Spend cubes allowed the data to be examined from multiple, simultaneous dimensions. With each advance, companies gained greater insight into their data and with shorter lead times. What started out as spend visibility has now transformed into spend analysis.

This timeline shows how approaches have been changing since 1996.

Evolution of Spend Analysis



Today there are two key approaches to how companies address spend analysis - database-driven and data-driven. These two approaches talk to how an organization's data integrates with the spend analysis software application. Older Generation - DataBASE-Driven Architecture: In database-driven applications, the spend analysis software has a fixed database structure. Corporate data must be mapped and exported to that file structure. There are extraction templates that help facilitate this process. Typically the IT team maps company data by forcing it into to a predetermined data-field definition. For example, one company might call a field "Op Location" in their native data, but we would now force them to declare that this field is called "Business Unit." Every data source needs to be mapped to the extraction templates to ensure that the data can be loaded into the spend analysis tool. This can take a great deal of IT time and resources, especially when there are multiple data sources across the organization.

Data-BASE Driven Architecture



Newest Generation - Data-Driven Architecture:

Applications with data-driven architecture utilize metadata to automate how spend data (that comes from multiple, diverse sources) is organized – essentially, data about data. Corporate data remains in its natural form and maintains the integrity of its structure and nomenclature. A spend analysis tool imports the data and then wraps it around the company's data – instead of forcing a company's spend data into a hard-coded format. A simple example is column headings that describe the data inside the column.

Data Driven Architecture



Spend Analysis tools with data-driven architecture require a simple data export and send via email or to an FTP site. Data-driven architecture does not require a lot of resources and time from IT since they only need to export the data. Data-driven architecture improves processes along the entire Spend Analysis spectrum -- including collecting, relating, cleansing, classifying, analyzing, and managing spend data. The following chart shows how methods of performing these activities are evolving to more modern approaches.

Data-driven architecture is highly scalable with multiple data systems. It works as easily with 1 data source as 150 data sources.

Comparing DataBASE-Driven with Data-Driven Spend Analysis Tools



In addition to improvements relating to the data architecture, other trends that are occurring in the broader Spend Analysis software market include:

  • Advanced automation processes embedded in data management applications are replacing manual/offshore and black-box data integration.
  • Companies realize that data preparation is the most important step towards spend visibility. Analyzing spend data that has pockets of inaccurately applied classification codes or is stale, compromises savings opportunities and business decisions.
  • With access to better tools, organizations invest in more complicated challenges surrounding data collection, integration and management.
  • Professionally managed Spend Analysis programs are now delivering new levels of real savings far beyond their costs.
  • Organizations can now obtain greater levels of self-sufficiency with data-driven Spend Analysis tools and not be reliant on their vendors for data management.



Additional Improvements to Spend Analysis




To UNSPSC or Not to UNSPSC? New Approaches to Spend Analysis Classification

Organizations need "spend category" intelligence specific to their constantly evolving business programs. Many organizations decided to standardize on generic industry categories. Examples of industry categories include UNSPSC, eCl@ss, SIC, NAICS and NIGP. The goal of using these categories was to enable companies with numerous data sources to standardize to a common language. With a common language in place, companies can generate reports that let them dig deeper into their spend data or monitor trends and patterns across time, a business unit, supplier or category. Armed with this information, organizations can make better business decisions about what they buy and from whom.

Additionally, by standardizing on an industry code, companies use artificial intelligence to classify massive amounts of data in shorter time frames. The use of artificial intelligence with generic codes expedites initial classification and refresh cycles, but can still result in many misclassifications, especially with indirect materials. Uncorrected misclassifications leads to distrust of all the spend data.

In a growing trend, companies are shifting away from solely relying on generic classification structures such as UNSPSC codes. More and more companies discover that they do not "source" based on UNSPSC codes. UNSPSC codes can be inflexible and do not naturally roll-up to sourcing categories preferred by the sourcing team. Additionally, UNSPSC codes may work adequately for classifying direct materials, but prove inadequate for indirect materials.

Many companies find that if UNSPSC codes are used, it is critical to layer the company's own sourcing categories over their spend data. It makes more sense to develop sourcing strategies based on common-sense categories that capture the complete picture, than solely UNSPSC codes. There are even some companies that bypass generic classification codes and classify directly to their sourcing categories. Organizations want to rollup UNSPSC codes to sourcing categories so they capture as much information about the category for larger sourcing leverage.

UNSPSC has become a "middle man" - a standardized way to classify data to get some insights, but they are not always helpful for sourcing. In more and more cases, companies are using spend classification capabilities found in today's tools and classifying raw data straight to sourcing categories. As direct category classification increases in popularity, the old standards like UNSPSC, or eCl@ss, etc. are becoming less and less important. They become a means to analyze data, but are not always necessary to create sourcing categories and support company sourcing programs. With new Spend Analysis tools you can have both -- UNSPSC classification and company sourcing categories – all tied together. You can add other taxonomies as needed as well. Multiple taxonomies can provide multiple dimensions for advanced Spend Analysis. The more levels of classification taxonomies companies have, the more they can break down and analyze data. Focusing classifying energy on sourcing categories is proving successful in terms of long-term management of those categories.


Why Home-Grown Tools Don't Compete Well against Spend Analysis Tools

Spend is a unique animal and requires cleansing and classification to create meaningful information. In-house efforts to create Business Intelligence do not leverage years of industry experience, accuracy, and reporting that can be provided by Spend Analysis vendors. In-house efforts tend to fail because they fail to develop solutions that manage data systemically. Also, once the internally developed tool is created and launched, there is no ongoing development effort to improve its usability. It becomes harder and harder to use as more data is dumped into it. Spend Analysis vendors continuously develop their tools and design them specifically for spend management.


Classification "Magic" – Outsourcing Classification to Low-cost Labor Pools

In the early days of Spend Analysis this was the only method available to classify vast amounts of data in a cost effective manner. Spend data was shipped to organizations, often located in countries like India, where a team of people would normalize, cleanse and classify the data. The process took a great deal of time – six to twelve weeks, sometimes more – especially when several data sources existed, each with a different file structure and nomenclature. And once the initial data classification is completed, it is time to refresh the data, and this may take as long as the initial launch. Many software organizations still rely on low-cost labor to manage their customers’ classification efforts. To expedite the process, they may use a software tool that leverages a type of artificial intelligence to classify a portion of the spend, but the rest must be cleansed and classified manually.

There are several drawbacks to the manual classification process.

  • Spend intelligence – With the manual process, an individual retains the classification knowledge, not the software tool. If that individual leaves the team, which occurs often, the knowledge is lost. A software tool with Spend Intelligence can assimilate new rules with each data set classified.
  • Classification rule audits – It is very difficult to audit classification rules in a manual or partially manual process. Classification that is fully managed by a software application with explicit rules can be quickly audited and updated. And when a flaw is identified, the rule can be quickly updated and applied across all the spend data.
  • Speed - The manual or partially manual classification takes time. It takes time to map the data to fixed structures, time to normalize the data and time to cleanse and classify the data. This time is dramatically compressed with a Spend Analysis tool that fully automates processes with embedded cleansing and classification rules. Today's tools can collect, cleanse and classify spend data in approximately 10 days and refresh it in 3 days.


The Spend Analysis "Manufacturing" Process

Transforming raw spend data into visible savings opportunities requires a rigorous and structured process, as spend data must be significantly manipulated and sophisticated metrics applied, to accurately identify savings opportunities for the organization. Processing spend data for in-depth savings insights requires not only engineered structure and automation, but also quality assurance processes. The Spend Analysis process can be compared to the manufacturing processes, to translate the information into real business intelligence. The entire Spend Analysis process has significantly advanced along these lines in 2009 and 2010.


The Spend Analysis Process

Regardless of what Spend Analysis tool an organization uses, the process remains fairly consistent. Each step flows into the next, and then repeats with each refresh cycle that adds new spend data.

Report and Analyze



1.Collect – The process of identifying data source systems, capturing their spend data and exporting to a file for distribution.

2.Relate – Associating data elements within a data set and across multiple data sets so that they correctly relate to one another.

3.Cleanse – Normalizing data elements such as supplier names, transactions and part information into one common name or part. This also includes standardizing spelling. Cleansing means that all references to one particular company, such as HJ Heinz, are consistent, even though it may be spelled numerous ways (H.J. Heinz, Heinz, HJHeinz, etc.).

4.Classify – Organize the spend data based on a common language, or classification structure. For example, custom sourcing categories or UNSPSC codes.

5.Report & Analyze – Make the spend data available so that stakeholders can analyze it to make business decisions.


Data Collection Improvements

  • Area of Improvement – Today's Spend Analysis tools can now accept spend data from any data file such as A/P data, GL data, PO data, P-card data, supplier performance information, expense data, invoice and contract data, etc. They no longer require IT to map the organization's data to a fixed database structure so it can be loaded into the tool. IT simply exports the relevant data and sends it either via email or a secured FTP site. Once received, the data can be imported directly into the Spend Analysis tool with no additional manipulation.
  • Benefits – The most dramatic benefit is time savings. The simplicity of today's tools removes a huge burden from the IT team since they do not need to use their scarce resources to map corporate data to a fixed template. This step now becomes a simple data export exercise. With this burden removed, it is easier for IT to be an ally of the project. This benefit is heightened when the Sourcing team wants data from multiple source systems that may be located all over the globe. Even though a company has standardized on a single ERP system, such as SAP or Oracle, each instance is different from the next and requires mapping from the old process. With data-driven architectures, data collection is more scalable across a large organization with many source systems.



Data Collection Time Frames



When the Data Collection process is shortened, the rest of the steps start earlier, thereby shortening the overall project.


Relate Improvements (Data Consolidation and Integration)

  • Improvements – Improved database structures enable newer Spend Analysis tools to dynamically assimilate the imported data within the tool. Additionally, they can create associations across a virtually unlimited number of data field associations. What this means is that the Spend Analysis tool can relate different spend data files from different source systems within the tool itself. In the past, different data files had to be consolidated and normalized outside the Spend Analysis tool before the file could be imported into it. This was a time consuming (and often costly) step in the process that was traditionally done by IT.
  • Benefits – Time savings is the most significant benefit resulting from improvements to the “Relate” stage. IT is relieved of the burden of the time consuming step of consolidating data into one massive file based on a fixed file structure. As each data set is exported from its source, it can be imported directly into the Spend Analysis tool. The different data sets will be related to one another within the Spend Analysis tool itself.



Example Comparison



When the Data Consolidation process is shortened, the rest of the steps start earlier, thereby shortening the overall project and its costs.

An additional benefit of relating data within the tool is that the tool can enable more associations between the data elements. The data associations are not limited by fixed database architecture; they are based entirely on the data imported. Where there are more data associations, the team has greater diagnostic, analysis and reporting options. Additionally, data that was previously overlooked in analysis can now be included.

This also increases translation capabilities that can be handled by both the Spend Analysis tool and database. It becomes easier to include global locations in a Spend Analysis initiative.


Cleansing and Categorization Improvements

  • Improvements to Cleansing – Today's tools have evolved to include thousands and thousands of structured rules and cleansing routines (some tools have over 40,000 rules and routines). These rules continue to grow with every Spend Analysis cleansing process. Although structured rules and routines will never cleanse data to a level of 100% on the first pass, today’s tools are coming closer to that threshold. Quick adjustments to the rules and routines provide more extensive data cleansing for the second pass.
  • Improvements to Categorization / Classification – Companies can choose to either categorize spend data to industry codes (such as UNSPSC), custom sourcing categories or both. More companies choose to categorize directly to strategic sourcing categories without the need for sub classifications like UNSPSC. They can also classify to multiple item and supplier breakouts for even deeper analysis capabilities. Other categorization choices include creating business-specific commodity grouping in concert with the original sourcing programs and incorporating foreign language within classifications with translation capability.
  • Benefits - Organizations have access to higher levels of accurately cleansed and classified data which results in improved analysis. Stakeholders have greater trust in the data and resulting analysis. Categories considered "non-source-able" can be easily removed so that the Sourcing Team can focus their resources efficiently. Companies can more easily create a consolidated Vendor Master List. Improvements to cleansing and classification make the entire process repeatable and therefore scalable. As a result, process time is compressed and costs to support a Spend Analysis initiative decrease. Also, data refresh cycles can occur more frequently and companies can include more locations.


Reporting and Analysis Improvements

  • Improvements – The reporting and analysis part of the process is "the easier part" of Spend Analysis. When the underlying spend data is properly prepared, it is simple to access insightful reporting and analysis. Today's Spend Analysis tools include the user's ability to access a library of standard reports, create custom ad hoc reports and drill-down into the data. Analysis is supported with in-memory capabilities.
  • Benefits – Users can navigate with minimal training and be completely self-sufficient regarding analysis and report generation. This results in better project prioritization and business decisions.


Impacts of the Spend Analysis Process Improvements

Regardless of what Spend Analysis tool an organization uses, the process steps remain consistent. What has improved is the way each step is managed. The current generation of Spend Analysis software has built-in automation components that help shrink the overall time to manage the entire process. Other organizational benefits include the following:

  • Transparency – Whereas spend data was previously sent into a "magic black box," organizations now have visibility into the entire data management process (data collection, relating multiple data sets to each other, cleansing, classification and reporting). With this visibility comes greater control that companies craved. There no longer is a need for offshore manual classification.
  • Business Driven- In the past most organizations classified their spend based on industry codes such as UNSPSC, or they paid a high price for custom classification structures. With today’s software tools, organizations can now classify directly to their business sourcing categories. The Spend Analysis data can directly support Sourcing categories and easily evolve with the ever changing business sourcing programs.
  • Flexibility – Organizations now have a myriad of options for classification – they can classify not only to sourcing categories, but also other integrated taxonomies and formats (SIC, NAICS, UNSPSC, SAP Material Groups, Indirect/Direct, CAS, Custom, etc., with no limits to Spend breakouts).
  • More Data- Organizations can expect to collect more data for analysis, leveraging multiple data sources (G/L, A/P, PO, P-Card, invoice, expenses, etc.). They can process not only all spend data, also but financial data, sales data, freight bills, and more for deeper savings analysis.
  • Speed – Organizations should expect to accomplish data cleansing and classification in days or weeks, versus what would take months or years with previous approaches.
  • Value – Organizations should expect new (and lower) cost standards for affordable classification solutions.
  • Success – Organizations should expect higher levels of classification coverage- > 95% of the spend, of the items, and in improved accuracy. And when an error is identified, it can be quickly adjusted and applied to the entire data set. Future refresh cycles will include the adjustment.
  • Outcomes – Organizations should expect to broaden Spend Analysis outcomes rather than narrowing opportunities. By presenting broader, more accurate and richer data sets, companies gain greater visibility to more specific savings opportunities, allowing them to pursue strategies that previous solutions did not identify or take action upon. They now have the ability to engage and leverage existing assets and systems without requiring expensive upgrades or standardization.
  • Global – International organizations should expect global answers to a global problem, letting companies apply and create rules for global source data (in European and Asian languages) even without regional/cultural understanding or linguistic fluency.


After Deployment

  • Spend Decision
    Data drives the decision as sourcing opportunities readily present themselves. Spend data can be systematically mined to uncover sourcing opportunities and priorities. Additional information can be processed in an interactive manner to increase leverage, reduce suppliers and drive cost savings to the bottom-line.
  • Refresh and Maintenance
    By consistently delivering structured spend data to the organization; “Spend Management” is now possible. Structured refresh cycles create ongoing tracking capabilities to update and drive measurement, performance, and category management programs on a regular basis. These capabilities are available today at much more affordable levels. As a result more and more organizations are refreshing their spend data more often, monthly rather than quarterly, as their tracking and management programs mature over time.
  • Category Management
    Category Management is the ability to define organizational categories and create “Knowledge Centers” and expertise for each category. Refreshed category data updates any measurement and tracking programs in place, such as Rebate Tracking, Supplier Risk, Diversity Measurement, and much more. Market Data can be incorporated. All Sourcing history can be captured. Savings can be measured and tracked.
  • Data Enrichment
    Many organizations want to extend their own supplier information with third party supplier enrichment data. This is a natural fit. External supplier information, such as DUNS/SIC/NAICS/Parent Child, Diversity, and Risk related Supplier information, can be easily integrated with corporate supplier data. Supplier enrichment data packets build additional analytical and spend management capabilities. If the process is not managed properly, it can become expensive.

    For today's tools, adding this supplier enrichment data is simply another source file. It is easiest to build this into the process in the early phases of an initiative, but it can also be added later. Either the company itself or the Spend Analysis vendor can obtain supplier enrichment information from a third party organization. It is typically considered a pass-through cost. There are techniques that help companies manage costs more effectively.

    Optimizing the supplier enrichment process is as follows:

    • 1. Create a Clean Vendor Master – through the spend data collection process, suppliers are collected from all sites, cleansed, grouped, classified, and rationalized to a consolidated vendor master. At this point the company can break down their suppliers relative to spend and internal priorities, then identify which ones should potentially be enriched.
    • 2. Mix and Match Supplier Enrichment Data Packets – enrichment data for SIC/NAICS or Parent Child relationships are usually in different data packets than data packets for diversity and risk. Each data packet has different costs, with risk usually being most expensive. You can analyze your supplier base to determine which suppliers should get what level of data packet, such as preferred suppliers only, or suppliers with large spend dollars, or suppliers that need information related to risk.
    • 3. Process and integrate suppliers to/from the provider – the handshake of providing the actual supplier file to the supplier enrichment provider, getting it back, and integrating it into your Spend Analysis tool, should be handled easily by your Spend Analysis provider.
    • 4. Create More Advanced Analytics, Reporting, and Spend Management programs – once the data is returned from the supplier enrichment provider, it must be integrated with all the related spend data for that supplier. Additionally, the new data can now be added to the overall analytical capability the organization needs to track diversity, compliance, conduct deeper analysis, and better manage overall sourcing programs.


    All the above should be done easily (and at low cost) by your Spend Analysis provider, thereby creating significant value to handle this important and more advanced management capability for your organization.


Spend Decision

The analyzed spend data will give insight into past and current spending patterns. It does not predict the future. It is the user’s job to decide what to do with that information. Leadership’s key questions include: is the organization on track; off track; on the wrong track? What strategic decisions need to be made so that the organization gets on and stays on the right track? Management and power users must make decisions about specific commodities to ensure the company meets its goals. To accomplish this, management and power users use data to prioritize spend projects. Two types of filters help prioritize and determine strategies for the spend projects.

  1. Contract Status– evaluate spend based on:
    • Contract status (available, unavailable)
  2. Commodity Characteristics– evaluate spend based on:
    • Commercial attractiveness (high, medium, low)
    • Definable requirements (high, medium, low)
    • Competitive supply base (high, medium, low)
    • Savings opportunities (high, medium, low)
    • Inherent risk (high, medium, low)
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Contract Status and Project Strategies

The availability status of a contract determines the appropriate sourcing strategy. A commodity under a long-term contract requires different sourcing strategies than a commodity with no (or expiring) contract terms. For example, sourcing teams would not run a reverse auction on a commodity under a 3 to 5 year contract. Instead, they may extend the contract terms to cover additional locations. Some appropriate sourcing strategies for commodities under contact include:

  • Spend compliance tracking – ensure that company representatives make purchases based on negotiated terms and conditions.
  • User adoption campaign – communicate to company representatives how to buy the goods and services they need. This ensures high savings’ implementation rates.
  • Supplier collaboration – work with the supplier to extend and strengthen existing contract terms, develop better inventory processes, faster product life cycles, etc.
  • Contract termination – terminate a relationship with a supplier who does not meet quality, term or cost expectations (thereby making the contract ‘available’).

Category Characteristics and Project Strategy

If a commodity is contractually available, then category characteristic filters should be applied to determine the project strategy. The application of category characteristic filters might determine a different strategy for each distinct set of characteristics identified. A commodity should be evaluated based on all Category Characteristics, not just a few. The sourcing team should rank the commodity – high, medium or low – for each filter. The outcome provides guidance for the best possible project strategy, with the final decision made by the Sourcing Team.

  • Commercially attractive – a commodity that ranks HIGH in this filter would be very attractive to potential suppliers. It could be attractive to suppliers based on high dollar value, attractive terms or acquisition of a premier reference account.
  • Definable requirements – a commodity that ranks HIGH in this filter has specifications that are easily defined, current and available. If the product drawings are ten years old, or the service requirements inconsistent, then the commodity would rank LOW.
  • Competitive supplier base – though sourcing teams can have a successful market with two suppliers, it is risky. A competitive market has multiple, high-quality suppliers that could deliver the commodity equally well. This scenario would denote a HIGH ranking.
  • Savings opportunities – savings opportunities should be evaluated based on the commodity, not across commodities. For example, identified savings rates for printed circuit boards are higher than savings rates for chemicals. Both may represent HIGH savings opportunities for the company depending on different company-specific factors. There may be minimal savings opportunities if a commodity has been bid frequently over the past two to five years. Savings opportunities may also be associated with cost avoidance. In rising markets, there may be HIGH savings opportunities by containing costs ahead of the market.
  • Inherent risk – risk is associated with different areas. Perhaps a strategic partnership is tied to a specific commodity. This scenario represents a HIGH inherent risk scenario. Another HIGH risk scenario may involve eliminating regional/geographic diversity for high priority direct materials. A LOW risk scenario may involve low priority goods and services, such as office furniture.

Some appropriate sourcing strategies for commodities not under contract (or with expiring contracts) include the following.

  • Reverse auction – It is a type of auction in which the role of the buyer and seller are reversed, with the primary objective to drive purchase prices downward. In an ordinary auction, buyers compete to obtain a good or service. In a reverse auction, sellers compete to obtain business (as noted in Wikipedia).
  • Sealed bid – In a sealed bid (reverse auction), each participant submits a secret, or sealed, bid on the item to be auctioned (as noted in Wikipedia).
  • Request for Information (RFI) – An RFI is a standard business process whose purpose is to collect written information about the capabilities of various suppliers. Normally it follows a format that can be used for comparative purposes (as noted in Wikipedia).
  • Request for Proposal (RFP) – An RFP is an invitation for suppliers, through a bidding process, to submit a proposal on a specific product or service. An RFP typically involves more than the price. An RFP is usually part of a complex sourcing process. Discussions may be held on the proposals (often to clarify technical capabilities or to note errors in a proposal) (as noted in Wikipedia).

Once a commodity is ranked based on its category characteristics, it is easier to determine the best project strategy. Not all sourcing projects should be reverse auctions. Not all sourcing projects should be RFPs. The commodity profile helps determine the best strategy. Additionally, a sourcing project can include one or a combination of the listed strategies depending on it complexity. Best practices for each sourcing strategy – reverse auction, sealed bid, RFI, RFP, RFQ – will be discussed in future Wikis. Based on different category characteristics, listed below are guidelines on appropriate strategies. It is the responsibility of the experienced Sourcing Professional to choose the best sourcing strategy that balances corporate goals and the commodity’s spend profile.

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Refresh and Maintenance

At regular intervals, typically monthly or quarterly, the Spend Analysis dataset must be updated. This generally requires the insertion of another group of transactions; the importing of new index files to pick up new GL codes, Vendors, and Cost Centers; and grouping and mapping activities associated with these new index files and transactions. In some cases, the vendor must perform the refresh; in others, the refresh can be performed by customer personnel.

In effect, Refresh and Maintenance is really the repeat of the earlier spend analysis steps, with new insights occurring in in Analysis and Assessment as a result of the new data.

The requirement for continual refresh of the Spend Analysis dataset means that a commitment to maintenance of the dataset must be made, and that services associated with the Spend Analysis dataset are required on a regular basis. Thus, Spend Analysis has been historically associated with long-term vendor service contracts, blurring the distinction between "product" and "service," although this is becoming less true as vendors make dataset creation tools more available to end users.

Although maintenance of the data is usually performed by most Spend Analysis systems at the same time as refresh, this is not true of all systems. New tools from some vendors enable Spend Analysis data to be altered and corrected in real time, as opposed to having to wait for a monthly or quarterly refresh period.

Spend Analysis Technology Requirements:

A spend analysis tool must have a minimal set of capabilities. This section overviews, at a high level, the core capabilities required by every spend analysis application as well as some more advanced capabilities that an organization might want to look for in a spend analysis solution. This is not meant to be a complete list, but a starting point for the technical evaluation of a spend analysis system.

Rules Engine

Each of the systems discussed in the previous section store their data in different formats, as they were designed for various uses. Most of these systems will use different identifiers, supplier codes, and, if present, commodity codes, and many systems will contain multiple identifiers for the same supplier, commodity, or category. Thus, in order to correctly classify, synchronize, and amalgamate an organization’s data into a single system in an efficient and repeatable manner (since the project will require that the data in the spend analysis system is augmented, or rebuilt, on a regular basis), the spend analysis system will need to contain a rules engine where sourcing professionals can define mapping rules for automatic application and re-application as required.

Reporting Engine

In addition to a standard set of pre-packaged ready-to-go out-of-the-box reports, sourcing professionals also require a powerful reporting engine that can be used to drill down into the relevant data and construct reports across any subset of commodities, organizations, time periods, and suppliers that need to be analyzed in ongoing spend analysis efforts.

Pattern Detection

The first enhanced capability that an organization might want in its spend analysis application is the ability to automatically detect spending patterns across commodities, categories, divisions, and suppliers and determine which patterns are inconsistent. Inconsistent patterns often identify potential sources for improvement. Also, if the system can construct an idealized spending pattern based on a contract and automatically compare it to the actual spending pattern, it can automatically detect on each system refresh whether or not spend is in compliance, and, furthermore, determine whether or not it is off-contract maverick spend or inaccurate billing by a supplier. And if it can compare organizational spending patterns to industry average (by importing data from external sources), it can often automatically detect savings opportunities and targets for the next rounds of negotiations.


Spend Analysis Technology Approaches:

Tools Use

Some vendors perform the bulk of dataset building services themselves; others allow end users to contribute to the process; still others enable end users to perform the entire job independently. The tools sets that vendors provide vary from online tools that operate in real time on the dataset, to offline tools that require a publishing step before changes can be integrated.

Both onshore and offshore services are typically available from Spend Analysis vendors and from third party service providers. If there are issues regarding data security, such as are common among financial services firms, concerns about shipping data to vendors or to third parties – or overseas – need to be considered. In some cases it may be inappropriate or even impossible to allow data to leave the company premises or to be transmitted outside company firewalls. In that case, dataset construction and maintenance must be performed by end user personnel, or by third party personnel working on site.

Reporting

Data warehouse systems generally provide reporting services, or facilities for building reports. However, if these facilities are usable only by data processing experts, their utility for business users is dubious. Many spend analysis vendors provide standard reports to try to overcome this limitation, and some vendors make broad claims as to the usefulness of these report suites.

Other vendors do provide avenues for business end users to create their own reports. These can vary from extraction of raw transactions to the desktop, to third-party reporting packages such as Crystal Reports, to proprietary reporting tools that are supplied as part of the Spend Analysis system.

Spend Analysis Applications:

Once a Spend Analysis system is in place, there are a number of studies that can be performed that improve visibility with regard to appropriate sourcing strategies. These include:

Commodity-Specific Datasets

Building a separate dataset focused on a specific commodity can add insight. Is the organization being charged appropriately for what it is buying? Are there variances from contract that can result in refunds?

Demand Reduction

What is the spending across organizational units on a particular Commodity? Why is one department spending more than another?

Contracts System Integration

Do contracts exist with our vendors? If not, why not? Are these contracts useful in terms of spend reduction, or not useful?

Fraud Detection

Why is there spending in this Commodity from this department? Why is spending out of line in this organization?

Opportunity Assessment

How many vendors are being used for a Commodity? Too many? Too few? Is the organization buying from the right vendor(s)? At the right price?

Challenges of a Spend Analysis Project:

As one can infer from the previous sections, implementing a successful spend analysis project is not necessarily easy. It takes a lot of work and a lot of challenges will need to be overcome, especially the first time the organization undertakes a considerable spend analysis project. This section discusses some of the challenges the organization might face and the methodologies that the organization can use to overcome them.

Lack of Spend Understanding

Chances are that organizational spend data currently exists scattered throughout disparate systems, each of which uses different classification schemes and that no one knows for sure how much is spent on the same supplier or same commodity across the organization.

Migrating all of the organizational spend data to a centralized repository with a common classification scheme is the only way to ever get the spend understanding sourcing professionals need to truly leverage spend and its associated opportunities. However, this will require the construction of a common classification scheme that all of the disparate data sources can be mapped to in the data centralization and amalgamation efforts. Furthermore, even though the sourcing team may be able to base organizational data classification on a common industry standard, such as UNSPSC, the sourcing team will probably have to devise appropriate extensions to consolidate all of the disparate spend data in a meaningful fashion.

Lack of Resources

The spend analysis effort should begin with the development a business plan that outlines the expected savings that will result from the implementation of an initial project and demonstrate that the return will be much greater then the investment required in additional temporary and full time resources required to make the project a success. Such a plan may be critical for the team to obtain C-level executive buy-in to help them find the budget and support that they need.

Required Analytics Capabilities

Significant analytics capabilities are needed both in the extraction and cleansing of the data and in performing the spend analytics. This is true from both a technological perspective and a human resource perspective. Not only is there a need for technology that automates a significant amount of the work, but there is the need to be able to understand what the technology does, how it is used, and how it can be best applied by the sourcing team to organizational needs, and how to verify and extend the results.

If the right technology is not in place, it will need to be obtained. There’s no way around it. If the team does not fully understand what the spend analysis tool needs to do or how to use it, the team will need training to insure that the organization receives the full benefit of the technology.

Best Practices:

When undertaking a spend analysis project, always keep the following best practices in mind. They will contribute to the overall success of a spend analysis project in more ways then might initially be visualized.

Identify Business Needs and Organizational Goals

What is the major reason the organization is embarking on a spend analysis project? What is the upcoming crisis that can only be averted by the application of a spend analysis solution? This will usually be sky-rocketing costs, forthcoming regulatory compliance, unduly long cycle times, a recent or upcoming merger or acquisition, or some combination thereof. The specific need forms the basis of a business plan and defines the spend visibility that the organization requires.

Without this need, it could be a struggle for a forward-thinking sourcing professional to sell senior management on a spend analysis project to get all of the support she needs, even when she knows it will generate almost instant ROI when effectively deployed. However, tying a project to a coming crisis or major initiative (such as supply base rationalizing or 10% cost cutting across the board) will help her get the support and resources she needs to do the project right.

Define Corresponding Spend Visibility Requirements

Cost Reduction, regulatory compliance, cycle time reduction, and M&A activity all require different types of spend visibility. The following table provides some guidelines:

Need Visibility Required
Cost Reduction Spend Visibility: Clean, Normalized, Granular Data
Regulatory Compliance Enriched Supplier Visibility: SIC, Credit Ratings,
Diversity Status, SOX Compliance, C-PTAT, etc.
Cycle Time Reduction Process Visibility: Contract Cycle Times,
Expiration Tracking, Contracts per FTE, etc.
Merger / Acquisition Spend Visibility: Clean, Normalized, Granular Data

Understand and Baseline Organizational Spend

Once a sourcing team has achieved the required level of spend visibility, it needs to baseline its current spend against market data, contracts, and invoices to determine the best opportunities for improvement. From a cost reduction perspective, these will come in three main varieties: categories where spend is (significantly) more then market data averages, commodities that compose the organization’s largest volume buys, and commodities from suppliers to whom the organization is spending the most.

Identify and Segment Key Commodities

Once spend has been understood and baselined, it should be straightforward for the sourcing team to identify a set of commodities that represent the best opportunities for cost reductions. These will need to be grouped into commodity categories and merged with similar commodities that the organization is also buying a reasonably significant volume of. These categories will form the basis for the first group of strategic sourcing projects.

Leverage Category Expertise

Start with the categories on which the sourcing team is the most knowledgeable. Leverage that expertise in conjunction with spend analysis to negotiate the best deals.

Have a Holistic Approach

Remember to address direct, indirect, and MRO spend. It can not be predicted in advance where the greatest savings opportunities may lie. Remember to not only integrate data from all of the internal systems, but to augment it with external data whenever the opportunity arises. After all, the best way to quantify savings opportunities is to have a good baseline.

Analyze Continuously

Spend analysis is not a one time undertaking … it’s a continuous process. Contracts come up for renewal. New products are launched. Old products are retired. Business is dynamic. New opportunities for spend reduction and value improvement arise regularly and old opportunities go away. The only way to assure continual success is through vigilance and continuous analysis.

Utilize Decision Support Tools

Spend analysis is not a task that can be performed manually. And it's more than just computing totals by supplier, commodity, or financial period. It’s in depth pattern-driven and exception-driven cross-spectrum analysis that requires sophisticated decision support tools. Preferably, one should use a spend 2.0 solution that will allow one to reclassify data and build new cubes for analysis on the fly, as this forms the basis for sophisticated analyses that will allow the organization to see a continual return on its investment.

Ask the Right Questions

The right opportunities are discovered by the right analysis. The right analysis is usually the result of asking the right questions and searching for the right answers. Here’s a list of questions to start from:

  • Are all commodities accounted for and consolidated?
  • Are all commodities aggregated across the enterprise?
  • How does each supplier fit into the spend?
  • Are there formal agreements for the majority of the commodities?
  • Does each commodity (category) have a sourcing strategy?
  • Is every (key) stakeholder accessing and using the system?
  • Are contracts being continuously monitored for compliance?
  • Is there continued support from C-level management?
  • Does the platform incorporate best-of-breed capabilities? And are they being used?
  • Is a shared-service approach to resource development being followed?

Supply Base Optimization

Optimize the supply base. If there are more then a handful of suppliers for the same commodity, consolidate. If a commodity is being single sourced, expand to mitigate risk. Find the right balance.

Cover the Majority of Global Spend

Spend analysis should not be localized to the small percentage of spend identified by a sourcing team as the most critical or most likely to contain the most opportunities because one never knows where an organization’s true and best opportunities are until the majority of spend data has been analyzed.

Institutionalize Knowledge

An organization should learn from each and every spend analysis project it undertakes. The key to continued organizational success in the long term is to institutionalize the knowledge gained. Continually document what is learned, add it to a centralized knowledge repository, and improve organizational processes on a regular basis.

Invite Everyone to the Party

The most successful projects are those which involve, and have the support of, all of the key stakeholders. Invite everyone to participate, gather feedback, and act on all of the good ideas received.

Build More Than One Dataset

Analysts should build many different datasets to explore commodity-specific and other data sources that contain more detail than "classic" accounts payable data.

Conclusion

The current economic recession has placed a larger, brighter spotlight on cost reduction activities than ever before. That's why today's generation of advanced Spend Analysis tools improve how data is collected, cleansed, classified, analyzed and managed- delivering new levels of real savings far beyond its cost. Greater insight and shorter lead times enable companies to make better business decisions across all levels of their organization. By identifying and achieving cost savings, companies immediately increase their profitability.

Part 2 of Iasta's Spend Analysis Wiki Paper will take a deeper look into Spend Analysis technology, the challenges of Spend Analysis projects and how to overcome them and Spend Analysis Best Practices.


A Selected Bibliography

(The) 12 Days of X-emplification: Day 2 - Spend Analysis by Michael Lamoureux, December 14, 2007

(The) 6 Days of X-asperation: Day 3 - Questions to Ask Your Spend Analysis Vendor by Michael Lamoureux, February 5, 2008

Analytics vs. Optimization by Michael Lamoureux, March 27, 2007

ASSESS: Uncovering Significant Savings Opportunities Through Comprehensive Spend Analysis by David Clary, ICG Commerce, 2001

Free Spend Analysis Benchmark by David Bush, September 6, 2007

Global Spend Analysis: The Next Frontier by Zycus, June 2004

How Much Do You Know About Your Spending by Bernard Gunther, January 13, 2008

How to Get the Most from Your Spend Analysis System by Michael Lamoureux, September 28, 2007

Integrating Contract Management and Spend Analysis by Eric Strovink, October 17, 2007

Is it the case that Spend Matters Most? by Michael Lamoureux, November 20, 2006

On-Demand Spend Analysis Solution for the Mid-to-Enterprise Market by Ketera, 2006

Real Analysis Solutions Uncover Actionable Data by Michael Lamoureux, December 2, 2007

Screwing up the Screw-Ups in BI by Eric Strovink, January 6, 2008

Spend Analysis 101-1: An Introduction by David Bush & Eric Strovink, September 25, 2006

Spend Analysis 101-2: "Web 2.0" Spend Analysis - Introduction by David Bush & Eric Strovink, September 26, 2006

Spend Analysis 101-3: Data, data everywhere by David Bush & Eric Strovink, September 27, 2007

Spend Analysis 101-4: How clean is clean? by David Bush & Eric Strovink, September 28, 2007

Spend Analysis 101-5: "Change" does not equal "Refresh" by David Bush & Eric Strovink, September 29, 2007

Spend Analysis I: The Value Curve by Eric Strovink, January 23, 2007

Spend Analysis II: The Psychology of Analysis by Eric Strovink, January 26, 2007

Spend Analysis III: Common Sense Cleansing by Eric Strovink, January 29, 2007

Spend Analysis IV: Defining "Analysis" by Eric Strovink, February 1, 2007

Spend Analysis V: New Horizons (Part I) by Eric Strovink, February 5, 2007

Spend Analysis VI: New Horizons (Part II) by Eric Strovink, February 6, 2007

Spend Analysis VII: What Purchasing.com Got Wrong by Eric Strovink, May 10, 2007

Spend Analysis VIII: Aberdeen on Spend Analysis: Lost in the Trees by Eric Strovink, September 13, 2007

Spend Analysis Expectations by David Bush, April 11, 2007

Spend Analysis Minipaper 1: Introduction by Eric Strovink

Spend Analysis Minipaper 2: Where's the Analysis? by Eric Strovink

Spend Analysis Minipaper 3: Supplier Familying: Behind the Hype by Eric Strovink

Spend Analysis Minipaper 4: Perfect Cubes and Golden Chalices by Eric Strovink

Spend Analysis Minipaper 5: Suite Silliness by Eric Strovink

Spend Analysis Minipaper 6: Data Warehouses Disappoint by Eric Strovink

Spend Analysis Minipaper 7: ERP Is Not Spend Analysis by Eric Strovink

Spend Analysis Minipaper 8: Practical Solutions for Multiple Accounting Systems by Eric Strovink

Spend Analysis Minipaper 9: On Beyond A/P by Eric Strovink

Spend Analysis Minipaper 10: It's All About Visibility by Eric Strovink

Spend Analysis Minipaper 11: One Spend Cube Is Never Enough by Eric Strovink

Spend Analysis Minipaper 12: What Spend Analysis Can't Do by Eric Strovink

Spend Analysis Minipaper 13: Mapping Spend: Three Easy Steps by Eric Strovink

Spend Analysis Minipaper 14: Building Datasets: Experts Need Not Apply by Eric Strovink

Spend Analysis Minipaper 15: Why Spend Analysis Frustrates by Eric Strovink

Spend Analysis Minipaper 16: I Have No Resources To Do This by Eric Strovink

Spend Analysis: Ante Up for High Stakes Savings by Andrew Bartolini and William Browning of Aberdeen, August 6, 2007

Spend Analysis: (The) First Step in Strategic Sourcing by Rip GreenField, May 2005

Spend Analysis: MacroMap and MicroMap by Eric Strovink, November 9, 2006

Spend Analysis Playbook by David Bush, September 25, 2007

Spend Analysis Saves by David Bush, October 11, 2007

Spend Analysis Tools Help Companies Find Opportunities for Supply Chain Savings by Jean V. Murphy, October 2005

Spend Analysis: Working Too Hard For The Money by Andrew Bartolini and William Browning of Aberdeen, August 2007

Spend Matters Not by Michael Lamoureux, December 3, 2006

Strategic Sourcing and Spend Analysis by Michael Lamoureux, June 12, 2006

The Future of Spend Analysis by Eric Strovink, October 1, 2007

There's No Spend Analysis without the Slice 'N' Dice by Michael Lamoureux, January 19, 2007

There's No Such Thing as Spend Intelligence by Michael Lamoureux, July 30, 2006

Using Spend Analysis to Help Agencies Take a More Strategic Approach to Procurement by US GAO, September 2004

Using Spend Analytics to Impact the Bottom Line by David Bush, October 4, 2007

Why Spend Analysis Frustrates Those Who Need It Most by Eric Strovink, January 16, 2007

Spend Analysis & Data Scrubbing by Jeremy Thompson, June 14, 2011

Authors

David Bush - Iasta

Melissa Beuc - Iasta

Jami Parent - Iasta

Eric Strovink - BIQ

Rod True - Spend Radar

Kelly True - Spend Radar

Michael Lamoureux, PhD of Sourcing Innovation

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