Spend Analysis 101:
Comprehensive Guide to Procurement Spend Analytics
Welcome to Spend Analysis 101.
A complete guide to spend analysis. You’ll find everything you need to know about procurement spend analytics from definitions to examples and best practices.
You’ll find all the key topics organized into easy-to-follow chapters. You can skip ahead to the most relevant areas or download the whole guide for future reference as a pdf.
Who is this guide for?
This guide is for anyone who wants a refresher on the most important aspects and best practices for analysing procurement spend. We cover the basics as well as some hot topics everyone should know.
Read and enjoy!
What is Spend Analysis?
Spend analysis is the practice of reviewing procurement spend data to decrease costs, increase efficiency or improve supplier relationships. Procurement spend analytics is the process to collect, cleanse, classify and analyze spend data through either dedicated software or one-off spend cubes.
Spend analytics is one of the key tools that procurement organizations use to proactively identify savings opportunities, manage risks and optimize their organization’s buying power. It is often regarded as the fundamental foundation of sourcing. It is a tool that sourcing executives can utilize to engineer superior performance. Data from spend analysis can improve visibility into corporate spend, as well as drive performance improvement, contract compliance, and most importantly, cost savings.
Analysing procurement spend provides data that can be used as a baseline to measure improvements, and to also provide reliable data for deciding strategies to realize short and long term savings. As procurement moves to a more strategic function in the company, spend analysis is its fundamental strategic technique which establishes a parallel process that guides senior leaders and budget holders in maximizing value for the organization’s dollar.
The process of spend analysis involves pulling together purchase history data to answer and assess the who, what, when, where, why, and how of an organization’s expenditures.
- What are we buying?
- Who are we buying it from?
- Who is buying it?
- How often do we buy?
- When did we buy it?
- How much did we pay?
- Are we getting what we had been promised?
- Where were the items delivered to? (geographical location)
- How does the data compare from previous years?
Sources of Spend Analysis Data
Any spend visibility project starts with the identification of relevant spend data sources. So where do you start?
Here are some of the most common sources of procurement spend analysis data.
- enterprise resource planning (ERP) tools
- general ledger information i.e. an organisation’s financial data
- purchase orders
- data shared by suppliers
- other internal systems
Direct vs. Indirect Procurement Spend
The difference between direct and indirect spend often causes confusion. Let’s review definitions and examples for both key areas of procurement.
Direct spend in procurement refers to goods and services that are directly related to making products. Examples may include raw materials, components, hardware and services related to manufacturing processes.
Indirect spend in procurement is the sourcing of goods and services not directly related to manufacturing of products. Indirect procurement enables businesses to maintain and develop its operations. Examples of indirect spend categories include:
- marketing services (media buying, agency fees)
- professional services (consultancies, advisors)
- travel and lodging
- MRO (maintenance, repair and operations)
- information technology (hardware, software)
- HR related services (recruitment, training)
- transportation and fleet management
- utilities (gas, electricity, water)
Spend Categories in Procurement
Both direct and indirect procurement spend can be grouped into categories, enabling analysis and management of similar goods or services.
A spend category is the logical grouping of similar expenditure items or services that have been clearly defined on an organizational level. For example, “information technology” may be considered a spend category covering both IT software and hardware.
The spend taxonomy is the way a procurement organisation classifies spend into hierarchies. One way to view spend categories is like a tree with many branches for different levels or sub-categories of spend. The number of levels in a spend taxonomy depends on the procurement organization’s needs, ranging from three to six levels of categories and sub-categories.
Standard taxonomies such as the UNSPSC (United Nations Standard Products and Services Code) may be used to categorize procurement spend, or as a starting point to create an organization specific spend taxonomy. A list of procurement spend categories for the UN includes:
- Audio and Visual Devices
- Building and Maintenance Services
- Business Professional and Administrative Services
- Computer Hardware and Software
- Emergency Preparedness Kits
- Engineering, Research and Technology Based Services
- Fuels, Additives and Lubricants
- Food and Beverage Products
- Healthcare Services
- Insurance Services
- Medical Waste Disposal
- Specialized Equipment for Agriculture
- Personal Hygiene/Sanitation Products
- Medical Equipment and Supplies
- Pharmaceutical Items
- Public Order, Security and Safety Services
- Vehicles and Fleet Management
- Transportation Storage and Mail Services
- IT Services
- Graphic Design
- Postal/Courier Services
Spend Analysis KPIs and metrics
Procurement data can be sliced and diced based on a number of key performance indicators (KPIs) relevant to the procurement organization. Some of the most common spend analysis metrics and KPIs include:
- spend by commodity or category
- number of suppliers by commodity/ category
- number of transactions by commodity/ category
- key figures and reports regarding compliance to pre-established buying policy (e.g. Maverick Buying Quote)
- average purchase order value
- spending distribution of the key customers
- material prices or material price changes
- total expenditure by supplier
- payment terms and conditions
- number of transactions and transaction distribution by currency
- spend by procurement function and the number of people involved per commodity
Spend Analysis vs Spend Visibility
Spend analysis is often viewed as part of a larger domain known as spend management. There are three core areas of spend analysis: visibility, analysis and process. Visibility in the spend management area refers to the ability of an organization to have a comprehensive view of the metrics that drive improved cost savings, process efficiency and supply-chain performance. Having spend visibility gives way to the possibility of analyzing past spend that can be utilized for planning future direction.
Spend visibility goes beyond tracking spending as it gives both a detailed and holistic picture of how money is moving through your company. Within the process of collating, cleansing, categorizing and analyzing expenditure information, spend analysis provides consistent spend visibility information on suppliers, spend and compliance.
To understand more, say you’re a CEO of a mid-size company, with about 300 employees. Let’s say you’ve run out of paper and pens. Will you still place more orders when your budget for office supplies is on the verge of maxing out? You need to determine what portion of your budget for office supplies has already been spent, and if more can be spent without exceeding the budget.
Without spend visibility — in this case, a real-time count of how much of their budget has already been spent — most companies would have ordered the office supplies anyway. They would find out later on that they exceeded the budget for office supplies after finance publishes a quarterly report.
Spend visibility is the cornerstone of superior procurement performance. It brings about knowledge into the core components of spend categories. Organizations with clearer spend visibility into their sourcing activities can utilize their reports and insights to drive better performance, and to make more informed business decisions.
Spend Analysis Benefits
Spend analysis offers procurement organizations a number of key benefits, including ways to:
- Achieve full visibility on procurement spend
- Identify savings opportunities and realize incremental savings
- Align and streamline procurement processes across business units
- Manage risk and maverick spending to ensure compliance
- Evaluate supplier performance for better relationship management
- Benchmark performance internally or with peers
- Do data-driven strategic sourcing
- Work collaboratively with other organizations
Achieve full visibility into all corporate spend.
The key benefit that spend analysis can provide to an organization is better visibility and actionable spend intelligence. Spend analysis offers an organization greater transparency into the amount of money it spends purchasing materials and services. It allows the procurement organization to have a look into the core of their expenses and purchases. As the data needed for spend analysis is often extracted from multiple systems across an organization, a lot of de-duplication, cleansing and classification may be needed before analysis can be performed. Data accuracy and consistency can only be achieved if organizations take full advantage of spend analysis. Spend analysis not only gives them a more effective way to collect, store, and manage the enormous amount of data they have but also provides a deeper understanding that can be used to develop initiatives and make confident spending decisions.
Identify savings opportunities and realize incremental savings.
As a sourcing manager, one reason why you want to conduct a spend analysis is to meet your cost reduction goals. When all the numbers have been crunched, the resulting metrics will show the spending patterns and the potential savings in several categories. Depending on the reports conducted, purchasing managers may then be able to cut costs through the use of alternative products, supplier consolidation, and merging products that were purchased separately into groups that can be negotiated on and contracted together. Price reductions can be achieved through contract buying, improved contract compliance, and reductions in maverick spending. Organizations can also achieve additional savings on indirect items ranging from office supplies to temporary staffing, contractors and consulting services.
Streamline and centralize procurement process and other administrative efficiencies.
Spend analysis has been proven to contribute to driving cost effectiveness and process efficiency in a lot of organizations . The whole process will vastly improve, from financial reporting to budget preparation, if there is detailed information organized around multiple dimensions. A more productive and efficient procurement function conducting spend analysis will build deeper relationships with fewer key suppliers and need fewer employees for unnecessary delegated tasks. There will be a significant reduction in cycle time for creating reports and ad-hoc analyses, therefore reducing labour costs or freeing up time for more productive work.
Manage risk and maverick spending to ensure compliance.
When your spend data is enriched with suppliers’ credit scores and other revenue information, you can better assess the overall supply chain failure risk of your organization. Good spend analysis data will also allow you to track and identify suppliers who have non-contracted spend, as well as spend with non-contracted vendors. You can identify the categories of spend where there may be too many suppliers with no contract in place. The risk in the contract is reflected in the pricing, and that can be from a lack of orders being made or alternatively not being able to scale up fast enough to deliver the volume of goods and services required. The reduced contract risk to the vendor often translates into lower costs. Contract compliance information can drive savings, while enriching spend data with supplier risk information helps the organization in utilizing spend data to avoid supply chain disruptions.
Evaluate supplier performance for better relationship management.
The starting point for superior procurement performance and supplier relationships is information. Spend analysis provides data and insights into the potential value of improved supplier relationships. Once the organization determines which suppliers offer the best value, it can work with them to establish more evolved procurement processes and inventory programs. Procurement professionals can peer into the performance of their suppliers to encourage proactive supplier development. At the same time, they can root out non-performing suppliers and help boost contract compliance by monitoring pricing on a continuous basis. Scorecards help evaluate suppliers and vendors by capturing metrics that evaluate performance. Having a comprehensive spend analysis gives more information on the amount of money an organization spends on purchasing materials and services, and with which suppliers it spends the most. This information is useful in contract negotiations and can be used to maximize the money the organization spends on procurement. When successfully implemented, this would leave an organization with fewer suppliers to work with to attain greater value and establish a more efficient and leaner procurement process.
Spend analysis gives you the opportunity to benchmark your performance internally across business units in different locations. This paves the way for meaningful comparisons that can be used for strategic decision-making. Collecting and organizing spend data together in one place enables you to answer a wider range of questions such as the average number of vendors or spend by category, and which vendors are generating the highest cumulative revenues. Understanding this is crucial to set targets for improvement that are realistic and achievable.
Leverage spend data across business units.
Data extracted and analysed in spend analysis systems plays a major role in the strategic planning of the procurement function. However, other internal business units are also currently leveraging spend analysis to achieve their business objectives. The finance department can leverage spend analysis in the vein of the procurement’s main goal: gain a better understanding of corporate spend. Finance professionals can leverage spend analysis systems to analyze data from purchasing card, invoice, requisition or invoice sources as a means of generating more accurate accounting reports.
Work collaboratively with other organizations.
Each individual organization should develop their own blueprint to deliver savings and efficiencies, but working with a group can help generate a more powerful strategic plan. A group of organisations can decide to make purchases of commonly procured goods and services together to achieve savings and/or better contract terms. A collaborative spend analysis project provides the group with the visibility to plan the most effective time to carry out a joint competitive solicitation for those commonly procured goods and services. Having a firm understanding of which members of the group are buying those goods or services can already go a long way towards delivering savings and efficiencies for all involved. Having all spend data in the same, consolidated format makes it easier to get everything in one place. This generally has the effect of making your collaborative efforts more strategic. When you can easily identify common suppliers, you can more quickly see savings opportunities. A collaborative spend analysis provides the basis for more proactive and strategic discussions with other members of the buying group.
How to do Spend Analysis
While spend analysis projects vary in shape and size, they typically include six key steps from spend identification to analysis.
Spend Analysis in Six Steps
Step 1: Identify Data Sources
To start a spend analysis, the first step is to check the extent of spend. Doing this allows you to restrict those required to purchase to just a few, instead of scouting through thousands. You can segment your spend into different groups and from there, you can determine all the spend data sources available from all of your departments, plants and business units. Start by identifying the areas of your business that make purchases such as procurement, finance and marketing.
Step 2: Data Extraction
Once you have narrowed the scope down, you can now capture your spend data and consolidate all of it into one central database. Data is usually in different formats, different languages and different currencies, so collecting it into one single source might be challenging. There are, however, software programs available to make this step easier.
Step 3: Data Cleansing
Cleansing is about detecting inaccuracies and removing corrupt records and redundancies from a set of data. This includes finding and eliminating errors and discrepancies in descriptions and transactions to ensure its accuracy. Through data cleansing, you can identify which contacts in your database are incomplete or irrelevant. Typos are removed and missing codes are validated and corrected for up-to-date information.
Step 4: Data Enrichment
Data enrichment refers to the process of enhancing, refining, and improving raw spend data. It also includes standardizing the spend data for easy viewing. Enriching the spend data makes sure that all the header and line-level names and details are accurate and to a specific naming standard. Data is often missing specific fields, and misspellings and abbreviations are common — as are incorrectly coded fields.
Step 5: Classification
Group your suppliers for better supplier management. For e.g. purchases made from Microsoft like Microsoft Office, Skype and Surface should all be grouped together. At the same time, categorize the data into meaningful groups (for example: marketing, office supplies, software) to identify how and where the business is spending its money. Unifying heterogeneous spend data into clearly defined categories makes spend easier to address and manage across the whole organization. Classification is about harmonizing all purchasing transactions to a single taxonomy, enabling procurement to gain visibility to the global spending in order to make better sourcing decisions.
Step 6: Analysis of Data
Now that data is extracted, cleansed, enriched and classified, you can analyse it to identify opportunities for savings and other procurement improvements. For e.g. to ensure that you have negotiated the best contract deals per supplier, analyze your data and check if all of your buyers are purchasing from preferred suppliers. With this, you can identify opportunities of reducing the number of suppliers per category and negotiating better rates. The best probable method for cost savings can only be realized after the confirmed estimates have been calculated properly.
Types of Spend Analysis
Tail Spend Analysis
Tail spend is the spend in any organization that is not actively and strategically managed in all the spend categories. It is the place where procurement organizations may be leaving money and utilizing their resources inefficiently because it is usually gets the least focus. Though tail spend is generally considered low-value purchasing, as it contains only a small portion of spend (usually 10-20% under each spend category), it is a significantly important area of any organization’s spend management. Because a large number of suppliers are accounting for it, it has an impact on the company’s financial performance.
With companies making millions of purchases every year, there are those purchases that are too small or too infrequent that they often get neglected. Procurement teams invest heavily in their core spend areas, but the tail-end remains a largely untapped opportunity for most companies. There is little understanding of how much money is involved in tail spend, and even less knowledge on how to manage it to realize the potential savings. This can lead to potentially losing millions of dollars annually.
Doing an in-depth spend analysis on tail spend helps encourage compliance and identify maverick spend, which refers to non-compliant transactions. The most common way of doing this is carrying out a traditional spend analysis, and then ranking the suppliers based on annual spend. The smaller suppliers that add up to around 20% of total spend are defined as the tail.
The figure above illustrates the simplest approach to analyzing a company’s tail spend, which is calculating the ratios of spend to suppliers at various points along the purchasing range. Here, the Y-Axis represents spend per supplier while the X-Axis represents the total supplier base, with suppliers ranked in descending order of size from left to right.
Tail-end spend management has been growing recognition and increasing importance within procurement. Putting a significant effort on it may not only yield potential savings, but can also reduce costs and get more spend under management. Data compiled by The Hackett Group states that when tail spend is managed effectively, it can lead to 7.1% savings on average. When there is enough visibility into the tail spend, it is easier to identify areas that need to be sourced strategically.
Organization that are successfully managing tail spend often start by segmenting the tail spend away from strategic sourcing managers, and allocating dedicated resources with the right incentives, tools and capabilities to manage the tail.
Vendor Spend Analysis
Vendor spend analysis is identifying how much of the spend comes from the critical vendors. It involves creating a detailed spend profile for each vendor using historical consumption data. Knowing this can help focus efforts on getting the best value from these preferred vendors and consolidating the relationships.
A vendor type report collects spend based on the vendor, and gives users the ability to select a comparable year and a review year. Spend data is optimized by identifying opportunities for consolidation and enhanced compliance. It helps visualize spend insights in several ways: by vendor, category, geography, etc. and enables multi-faceted analysis for data-driven decisions.
There are usually many low-value transactions with multiple vendors across many business units. The total number of one-off and small value vendors is usually big. Knowing this can help in streamlining and leveraging spend by identifying contract leakages and maverick spending. The aim is to reduce the number of vendors in each category. In the chart below, you can see what portion of the spend is with your core vendors.
Every dollar spent is important and savings opportunities can be missed through off-contract purchases. Vendor spend analysis will facilitate the identification of purchasing trends, buying patterns, as well as monitoring utilization and spend consolidation of key strategic suppliers.
Category Spend Analysis
The first step in doing a category spend analysis is understanding the scope and breadth of the category. Are you buying similar goods and services from too many different vendors? This analysis is built on hierarchies, and the spend transactions are categorized into the most appropriate category. The reporting allows you to explore the spend in the defined spend category hierarchy, which in turn allows you to identify spend leakage issues.
Allocating spend consistently into categories makes the data easier to navigate, interpret, and understand. When organizations can focus on prioritizing their top spend categories, it helps them identify and forecast savings opportunities. Prioritization will allow better negotiations for key spend categories to ensure more favourable contracts and pricing. By drilling into their spend data, procurement professionals are also gaining a deeper understanding of their spend categories.
When you have a high-level overview of spend by category, it is easier to identify categories that help in delivering savings and to realize which projects bring strategic importance to the organization. With this, you can easily figure out which action to take based on what gives the most impact on staff or operations, and what the risks associated are. Access to detailed information on spend by category gives you the data to determine priorities and allocate resources in order to deliver the highest return on investment for the level of effort required.
Item Spend Analysis
Item spend analysis refers to analyzing expenditure at an item/ SKU level. It takes into account every individual purchase, classifying each one of them to identify what department it was for and what supplier was used. This analysis gives the ability to know whether a specific item is being purchased from various suppliers, or in several locations and at different item prices. Doing this analysis can highlight the different ways of purchasing in the business and potentially identify spend leakage issues, such as purchasing from non-preferred vendors and maverick spend.
Payment Term Spend Analysis
Payment term spend analysis provides excellent insights for companies to analyze payment practices and terms within their purchase to pay (P2P) processes. It explores the opportunities of leveraging all possible discounts or interest from the invoice payment process.
Suppliers may reward early payment of invoices with discounts, but early payment of invoices may also mean lost interest on working capital. Payment term spend analysis utilizes data and gives a comprehensive view that enables you to identify unrealized discounts through late payments of invoices or opportunities to renegotiate better payment terms to capture unrealized interest. It also covers the review of payment patterns so a company could identify practices and activities that are not done properly.
Contract Spend Analysis
This spend analysis tells companies if they are complying with their existing negotiated contract terms. It analyzes spend with vendors by contract to identify spend leakage through non-compliant contracts. It ensures that the best contract deals per supplier have been negotiated and that all the buyers are purchasing from preferred suppliers.
Best Practices in Spend Analysis
Here are some strategies common among organizations with the most successful spend data management programs:
- Classify spend data at a detailed level and adopt a common classification schema in the company.
- Pursue a permanent solution versus one-time efforts.
- Have an automated approach to spend data cleansing and classification.
- Access all spend-data sources within and outside the organization.
- Continuously improve and expand the scope of their spend data management program.
- Collaborate with IT and other key stakeholders, like finance, in the whole process.
- Define category strategies and measure impact.
- Take actions based on data insights to deliver savings opportunities/ savings program management.
Classify spend data at a detailed level and adopt a common classification schema in the company.
Categorizing at the item level proves to be the most effective way to do spend analysis. This not only provides visibility but also enables more details of all the attributes, enough to do estimates and comparisons. Aim for at least 95% accuracy. Higher-level classification has its own benefits, but item-level proves to be more effective as it gives a precise view of spending with each supplier and for each commodity. Organizations should adopt a common internal taxonomy or industry-standard classification schema. For example, UNSPSC provides a universally accepted metadata layer for organizing and controlling spend data. This standardization is key to driving accurate organization and correlation of spend data and to enabling actionable analyses. Often broader than internally developed classification codes, these standards allow organizations the ability to map all spend data to a single schema.
Pursue a permanent solution versus one-time efforts.
Using traditional, labour-intensive procedures and systems are not recommended due to the volume and complexity of spend data within an organization. External services usually provide a temporary solution, which requires the organizations to engage with the consultants on a continuous basis to keep data up-to-date. Outsourcing also limits the transfer of the process knowledge and expertise to the organization, leading to dependence upon consultants in the future. Adopting a more sustainable and standard procedure can help organizations get monthly refreshes of their spend data, and a more efficient operation of examining the spend categories.
Have an automated approach to cleansing and classification.
Automated spend analytics solutions capture data classification rules and attributes for a wide range of spend categories. Because of their self-learning abilities, these solutions can present what the sourcing experts know into the system. But there will be a need for commodity managers to classify exceptions from time to time. Establishing automated extraction routines to aggregate and refresh data on a regular basis allows accurate and repeatable spend analyses. Automation also increases the frequency of analysis, which is critical as the business environment is dynamic, with prices changing and contracts expiring all the time.
Access all spend-data sources within and outside the organization.
There are times when your vendors, suppliers or other affiliates have better data than you. Organizations that access spend data from all relevant sources can gather a more comprehensive and accurate idea of their total spending.
Continuously improve and expand the scope of their spend data management program.
Spend data is a work in progress. Continuously improve your spend analysis. Organizations should constantly look for ways to expand the uses and scope of spend, and its data cleansing and classification capabilities. Conducting reviews will help identify immediate areas for improvement and illustrate the positive impact that a particular initiative has on the performance of the organization.
Collaborate with IT and other key stakeholders, like finance, in the whole process.
To achieve full potential savings, a collaborative partnership between procurement and other business units must be created, and everyone should be held accountable for results. Leveraging spend data requires cooperation within the entire procurement organisation. When procurement and ﬁnance work together, they can create systems that reliably capture and deliver real cash savings to management. This in turn creates a loop for improving performance.
Define category strategies and measure impact.
Develop category plans aligned to the business objectives and key stakeholders with a strategic approach to maximize value, reduce risk and effectively manage the supply of goods and services. These plans should influence sourcing strategies and initiatives. A careful review of these strategies will assess and confirm their business impact and determine if a revision or repetition is required.
Take actions based on data insights to deliver savings opportunities/ savings program management.
The data provided by the procurement teams can prove valuable as it generates insights about organization buys and how it makes the purchase. These insights and ideas must be implemented into actual strategies that will drive savings to the bottom line. Act on these strategies and make sure that they will be translated into savings opportunities.
Why Spend Analysis Projects Fail
Many organizations tackle the problem of spend visibility, but these projects often fail in delivering the value that was expected initially, despite the significant need that the organization had for effective spend analytics. Here are some reasons why spend analysis projects fail:
- Poor quality data/ dirty data
- Complex and labour-intensive cleansing and classification process
- Leaders without a data-driven mindset
- Lack of planning results in unrealistic expectations, unclear goals and misplaced priorities
- Wrong tools or having too many tools to choose from
- Lack of skills and user competence
- Fear of losing relevance or control of data
- Limited analytics solutions
- Spend analysis as a one-time effort
Poor quality data/ dirty data
The most common reason why most spend analysis projects fail is because of the poor quality of data. There are times that suppliers have better data than the systems the organizations can provide. Many organizations spend 80% of their time cleaning up data. Common examples of dirty and inconsistent data include basic hygiene issues like empty data fields and wrong spellings which can interfere with analysis. An effective spend data classification and analysis requires detailed information but often has unstructured data within different business systems. The information is often rife with errors and discrepancy in different departments or missing critical data fields, such as supplier name, product attributes, or account codes. Part/item descriptions for the same category might vary significantly, words can be abbreviated, and supplier names may be misspelled.
Complex and labour-intensive cleansing and classification process
For most large organizations, classifying their billions of dollars of spend is not easy. The problem is not just in volume of spend, but the immense sets of spend related data, which could take years to properly classify. But this granularity is necessary to take business actions that generate value. Some methods to overcome this problem have proven to be ineffective, like classifying spend data at the highest level commodity class. Such methods provide insufficient insights and often give inaccurate analysis. Another spend classification method uses human collaboration, but it is also not sustainable as the tremendous efforts exerted would require frequent repetition of the process and this would render the newest data out-of-date.
No solution will give you 100% classified data all the time. The key thing is to build in appropriate checks and balances so most errors can be caught and corrected immediately. Making sure that this is done consistently will maintain trust in the data and in the classification process, and enable the data to be used consistently for ongoing decisions.
Leaders without data-driven mindset
The leadership team also has a role to play in why some spend analytics projects fail. Failure may be due to a lack of agility and continuous involvement or sponsorship by executives in the analytics process. Many business leaders trust the more familiar way of doing things and may resist adopting a more data-driven approach. The top management may not be the right people to link the insights gained from spend analysis with the strategy and objectives of the organization. The solution is to adopt a more lightweight approach. The management team doesn’t necessarily need to be directly involved in the spend analysis project, but there have to be periodic, short feedback loops. Letting them know of project progress will ensure they see immediate, accumulative results. Getting them in the loop drives better engagement and management buy-in.
Lack of planning results in unrealistic expectations, unclear goals and misplaced priorities
Part of the reason that many spend analytics projects fail is that organizations rush to accumulate and analyze as much data as possible all at once and without much of a plan. This usually leads to huge costs and an overwhelmed team. Starting big isn’t always the way to go. With more data and more heavyweight tools and capabilities to rationalize data across silos, it can feel like there are more opportunities than there are resources to exploit them. These organizations would fail to see the insights for the data.
Start small. It is not only much more fruitful and lower cost, but also minimizes risk. Some of the most valuable business insights have been derived from surprisingly small data sets. Starting small also leads to a clearer path to smarter business decisions and priorities ensuring data analytics success. Spend analytics is not a one-time project for you to set up and reap the benefits right away. It needs a plan, and has to be transparent with immediate, disciplined, and regular feedback loops.
Wrong tools or having too many tools to choose from
Spend analysis is a project that should start with the right tools. It is always about understanding what your organization needs and getting the proper solutions that will address the current situation. Attempts to do spend analysis incorrectly and without the proper solution have put the validity of analysis in jeopardy. Organizations should compare their options and weigh the pros and cons of each solution before deciding. Most spend analysis initiatives fail to deliver additional results after 12 to 18 months because they have insufficient or ineffective systems in place. Challenges are daunting and many solutions fail to address all of them. An organization should ensure that the solution selected addresses all issues and challenges that are relevant to its own situation.
Lack of skills and user competence
A deep product and domain knowledge is needed for correcting spend data classification errors. This expertise varies across the company, resulting in different and unpredictable results. Many organizations put data cleansing and classification duties in the hands of IT professionals who may not have a complete understanding of the parts and services that require review. There may be a lack of ability among existing staff to access, organize and analyze spend data for sustainable use. For example, when you do a classification deep dive in some categories, where the knowledge is limited to a few people, the right expert needs to be associated with the right spend items. If the initial data is mapped out poorly, repeated efforts may be required before the reports will be useful. Very often category teams are the ones with the right information, but they are usually not involved. When this happens, the sourcing analysts might have already missed out on valuable opportunities.
Fear of losing relevance or control of data
Data silos inhibit productivity and waste resources and can be due to many different reasons including cultural, structural and technological factors. Fears over losing relevance or control are cultural barriers to effective spend data analysis. When you’ve had control of something for a long period of time, it is often hard to let go. Data owners like the IT team may feel like they have no choice but to share data sets with other departments. More so when an external expertise is brought in to support the data analytics project. Silos are conquered when technology is contained in a place that lets the owners have access to the relevant data. When errors have been identified, it is easier to recognize actionable insights and fear is eventually overcome.
Too many data sources/ disparate systems
Multiple disparate systems drive complexity and confusion. Spend data is often sprinkled throughout different systems across the organization, including accounts payable, general ledger, ERPs and many others. These employ different classification schemes, making it difficult to extract and analyze. When there are too many or incompatible data sources, organizations cannot efficiently leverage their spend analytics efforts in sourcing activities. To reap the full benefits of spend analysis, spend data must be migrated to one centralized repository in a standardized fashion.
Limited analytics solutions
Spend analysis is not a black box effort. Therefore, using basic spreadsheet applications as primary analysis tools limits the possibilities that analyses can offer. Home grown BI systems are not spend analysis solutions. Some organizations have powerful data warehouse solutions and a well-equipped IT team who can build their own spend analysis tools by acquiring report builders, but even the most powerful of these types of solutions have limited flexibility. This often results in preformatted reports that don’t meet the analysis needs of the procurement organization. So if an organization wants more return on investment and real value from spend analysis, they need to consider a solution that is built on proper technical foundations and capabilities for the needs of procurement.
Spend analysis as a one-time effort
Spend analysis should be a continuous process. It is time-consuming but an evolving part of your long-term procurement transformation. It is a project that will continue to yield incomplete and inaccurate results and therefore has to be done several times to achieve the desired results. Doing it just once yields only a one-time benefit. Repeated analysis is often required to identify changes in an organization’s spend and monitor progressive spend against contracts to ensure that real value is delivered.
Spend Analysis Tools
Spend Analysis in Excel
Microsoft Excel, though it’s been on the market for over 30 years, is still an excellent tool for making powerful dashboards that can provide analysis and deliver insights in a timely manner. A lot of people use Excel to analyze spend data, but fail to do so in the most effective and efficient way. The spend data that is categorized by supplier or by name is usually found as raw data. When this data is in an Excel spreadsheet, it provides a company with an overview of their spend structure and helps it understand which part of supply chain needs to be prioritized.
While doing spend analysis on Excel is doable, most organisations will still encounter issues. For example, Excel is not scalable for spend data in the hundreds and thousands of rows. Challenges like over-generalized classification, data inconsistencies and data formatting issues, same supplier different names, and regional settings causing inconsistency will cause a normal analyst to do more data cleaning work than actual data analysis. Even if you’ve been able to do all this properly, it can take hours or even days of work, and you will then need to update and classify new data each month, which will not be scalable. There are solutions in the market that have developed technology for just this and provide their service as a cloud solution.
Spend analysis processes run on Excel and Access-based tools don’t benefit from the repeatable process that spend analysis automation enables. Manual processes lack timeliness and speed of data updates and refreshes, as well as present the risk of limited reporting and analysis capabilities. Without the ‘slice-and-dice’ ability of many spend analysis systems (the ability to cut spend data in a myriad of ways for efficient analysis), the reporting process of the spend analysis function is limited.
Pros and Cons of using Excel in Spend Analysis
|Spreadsheets are within procurement’s comfort zone. They’re inexpensive and work with templates and formulas to aggregate data||Spreadsheets are time consuming and users spend a significant amount of time collecting spend data|
|Spreadsheets are good for documenting and reporting very simple stand-alone requirements||They become exponentially difficult to manage when multiple compliance sets and multiple locations are involved|
|It is easy to create data collection tools and simple to create charts||Spreadsheets are not designed to record an audit trail of accountability and struggle to assign owners to processes|
|No need to extract data from external systems, all data is right at your fingertips||They do not deliver automated workflow driven processes and require manual intervention to deliver reports that are more prone to error|
|Using Excel, reporting is usually easier and more hassle-free||This is not a secure process due to people using email to send updates and creating different versions of the spreadsheet|
Spend Analysis with BI Tools
Using business intelligence enables companies to have a better understanding of their costs, which makes it easier to align expenditures with revenue. One focus is spend analysis because it provides the visibility and insights needed to pave the way to many cost reduction and improved procurement performance measures.
Microsoft Power BI
Power BI (Business Intelligence) is a suite of business analytics tools used to analyze data and share insights. It is a cloud based data analysis platform which can be used for reporting and data analysis from a wide range of data source. Power BI dashboards provide a 360-degree view for business users providing them the ability to see all of the most important metrics in real time, and usually on different kinds of devices. Users can examine the data behind the dashboards with just one click. The intuitive tools help make finding answers easier. The pre-built dashboards and hundreds of connections to known business applications make doing analysis simple and quick.
Power BI, with all its portals and applications, can unify all of your organization’s data. With better data management and access, companies can get the visibility and insight they need to improve procurement performance. These are some things that can be addressed:
- Materials (volumes and prices included) that the procurement organization purchased this period and if there are any changes within a specific period of time
- The number of vendors whom the company has purchased from during a particular year and the amount of money spent per vendor in a given time
- The number of transactions done in several stages of the procurement cycle
- The number of requisitions, contracts, and purchase orders processed across the organization by buyer and the average value of each transaction
Image source: Microsoft Dynamics
Tableau is an industry leading business intelligence tool that focuses on data visualization, dashboards and data discovery. As a leader in the Gartner Magic Quadrant for the past couple years, it is an interactive tool that provides side by side analysis of spend data with tons of visualization possibilities. It is very simple for non-technical people to easily create customized dashboards that provide insights that can be used for company strategies. With its easy user-friendly interface, drill-down capabilities and intuitive way of working with data, it transforms the way people use data to solve problems. It also comes with real-time data analytics capabilities and cloud support.
Tableau provides information easily on relevant questions like who the most profitable customers are, what they buy and how much is spent in different categories. You can look at sales by region, segment, category, and year with just a few clicks and hover over data to see the details instantly. Having easily understandable dashboards paves the way to more data-driven decisions.
Image source: Tableau Business Intelligence
QlikView is a Business Intelligence (BI) tool that enables a user to create reports and dashboards for any use case. It is commonly used by business users who consider the power of modelling the data as well as data preparation before doing the analysis and visualizations/dashboards as a key differentiator. On top of that, Qlik and it’s patented associative technology allow a user to unearth relationships within the various data sources. It also encapsulates the data into compressed memory for faster analytics vs. other providers who mainly rely on direct connections to data sources.
Because it offers guided and collaborative analytics, even non-professional users without IT skills can build and deploy analytics apps easily and quickly. This results in a faster response to changing business requirements and driving more insights across the organization.
A flexible platform, the QlikView consolidates data from multiple sources to provide centralized data for high level reporting. The intuitive click through dashboards makes it easy for users to understand hidden trends and gather insights from them. With QlikView, possibilities are endless for making adhoc queries because it does not require tedious defined structures and hierarchies. Effective and accurate decisions are made faster with the right and easily accessible information available.
Image source: Executive Dashboard Best Practices
Pros and Cons of BI Tools in Spend Analysis
|Data visualization is easier, quicker and nicer||Data security is questionable|
|Manage big data in real time||People can make different conclusions from the same data|
|People can go ‘hands-on’ with the data||There might be a need for multiple BI applications|
|More possibilities for customization||More expensive than light weight tools like Excel|
|Many solutions available today that can let you operate at a scale that is right for your organization|
|The platforms usually give content developers and line-of-business analysts a more rapid approach to defining, executing and saving queries|
Spend Analysis Software
Spend analysis software provides a consolidated view on procurement spend including data from invoices, purchase orders and other business financial records. Spend data may be collected from a number of different sources such as enterprise resource planning systems (ERPs), purchase-to-pay suites or even shared excel reports.
Spend Analysis Software Types
Spend analysis software is either bought from a specialized software vendor or created specifically for the needs of a procurement organization.
- In-house solution – a bespoke software solution is created for the procurement organization, either on top of an existing business intelligence solution or as a dedicated piece of software. Maintenance and upgrades are dependent on the organization’s information technology resources.
- Licensed software – Software is sold as a commodity, where a single-use license allows for an installation of the software for a set amount of machines. Depending on the update agreement, larger updates might require a new purchase of a license. Most of the time licenses are sold as a lump-sum purchase.
- Software as a service (SaaS) – Software is sold as a subscription and is delivered flexibly. Often the software is hosted in a separate location, allowing for centralized management by the provider. Updates are carried out as part of the software subscription agreement.
Enterprise vs. Small Business Solutions
Spend analysis software comes in many forms, shapes, and sizes ranging from self-service solutions for small businesses to configurable dashboards for large enterprise organizations.
- Small Business software – is designed for smaller operations and a smaller amount of data. These can be provided as self-service software, add-ons to ERP packages or on-premise solutions with limited need for configurations or custom data processing steps.
- Enterprise systems – are designed to handle a large amount of data and provide deep and bespoke insights from the organizations different source systems. Enterprise level software deployed with larger software deployment projects are increasingly sold and maintained on the cloud under a Software-as-a-service (SaaS) model.
Where is spend analysis software data hosted?
There are a number of options for hosting spend analysis data ranging in complexity and resources of the team conducting spend analysis.
- On-premise – Software installed inside a private user network and operated on a server location managed by the procurement organization. Updates are performed on a case-by-case basis, depending on the software license agreement scope. On-premise installations also include local installations of software.
- Private cloud – Software is accessed via a thin client or a web browser, while all the essential elements are hosted in a private cloud server maintained by the spend analysis software vendor. A cloud server is a centralized system that can be scaled according to load and demand to provide centralized software deployments.
- Public cloud – Similar to private cloud hosting, but data is hosted on public cloud services, such as Amazon Web Services (AWS) or Microsoft Azure.
How to Compare Spend Analysis Software
With dozens of different types of spend analysis software to choose from, it may be challenging to compare different alternatives. Popular ways to evaluate alternatives include:
- Seeking expert advice from procurement consultancies or management consultancies.
- Reviewing customer reference cases or interviewing existing customers of different software vendors.
- Investigating independent analyst benchmarks or reports, such as the Spend Matters SolutionMap for Spend and Procurement Analytics.
- Conducing a spend analysis request for proposal (RFP) with a detailed list of questions for a shortlist of possible software solution providers.
- Developing a proof-of-concept (POC) where one or more vendor is given a set of spend data to analyse with a limited scope and time-period. In cases where spend classification speed or quality is an issue, POC can help identify suitable alternatives.
Spend Analysis Reports
Here are some data visualization techniques:
Online analytical processing, or OLAP reports are multi-dimensional analysis of business data.
Pivot tables are a convenient way to build intelligent, flexible summary tables. You can look at the same information in different ways when analysing large amounts of data.
When you start nesting more dimensions into pivot tables, they tend to become very large and cumbersome. In this case, you can use a cross-tabular report, which shows the same information but with dimensions both as rows and columns.
Information in pivot tables and cross-tabular reports can also be presented in a variety of graphs, including several types of 3D-bar, 2D-bar, line, area, pie, box, plot and error bar charts.
Waterfall charts are usually not part of the standard charts, but are extremely useful in depicting information in a very simplified manner.
In spend analysis, Pareto charts are very useful in opportunity identification because they visually show the 80/20 rule; which refers to the top 20% commodities (or suppliers) that account for 80% of the spend.
Treemapping is a method of visualizing hierarchical data with proportionally sized squares. For spend analysis, this plot shows relative spending by the size of a block. Treemaps can be very sophisticated and interactive. They are also very powerful for visualizing the relative spend across a single dimension such as commodity, suppliers, etc.
A multidimensional report is probably the most powerful type of interactive report. These reports show spend across all dimensions at the same time. The interactive nature of this report means that when the user drills into the cube, all of the dimensions refresh simultaneously to show the spend corresponding to the drill point.
A map report is a chart type that shows spend on a geographical map. Map reports are usually interactive, so clicking on a particular country or geography will open a specific chart into which the user can further drill.
OLAP (Online Analytical Processing), is the traditional approach used when resources are scarce, as it provides the ability to do a multidimensional analysis of data and to take the complicated calculations into consideration. As the foundation for many kinds of business applications, OLAP enables end-users to perform ad-hoc analysis of data in multiple dimensions, thereby providing the insight and understanding they need for better decision making.
The OLAP engine is the core feature of Spend Analysis modules. It is the enabling technology that provides answers to the most analytical questions in spend analysis and enables users to easily extract and view data from different points of view. The OLAP capabilities of your spend analysis vendor can be categorized based on whether the product is endowed with its own OLAP engine or it relies on third-party analytical services- and therefore it only acts as a presentation layer on top of the third-party OLAP engine.
Providing a multidimensional conceptual view on data is one powerful key feature of OLAP. Covering full support for multiple hierarchies, it allows users to analyze database information from multiple database systems at one time. Every data attribute is considered as a separate dimension in this multidimensional database. This includes product, time range or even the sales location. The information can be compared in many different ways. Moreover, attributes such as time periods can be broken down into sub-attributes.
The spend cube is a unique way of taking a look at spend data, where the data is projected as a multidimensional cube. The three dimensions of the spend cube are Suppliers, Corporate business units, and Category of item. The dimensions could include subcategories of the different units across the organization, from suppliers, categories, and cost centers.
The spend cube is typically the ﬁnal output of a spend analysis process. It allows you to look at all of the analyzed data from a variety of angles. A spend cube is usually needed if a company is not managing the full percentage of expenditures across all business units.
The 3 axes represent Category (What you are buying), Cost Center (Who you are buying it for), and Supplier (Who are you buying it from). These are the three legs of the stool – if any one leg is not there, the entire model falls apart.
Each axis of this cube contributes critical information. Category analysis tells what specific types of goods and services you buy. Cost center analysis reveals which functions (or end users) within your organization drive the demand. Supplier analysis tells you which suppliers you’re buying from. One benefit is knowing if expenditures are scattered or cumulative, or if suppliers have simultaneous contracts with different units in the organization.
Once you’ve got this data together, you can set your strategies. You can slice and dice data to analyze it from many different directions. This ensures that you have just one sourcing strategy and not hundreds. Getting this data on hand lets you decide which high spending end users to align with, and which suppliers you want to target for renegotiation.
Deep Dive: Machine Learning Spend Classification
Some of the greatest recent advancements in procurement spend analysis involve machine learning in spend classification.
Machine learning is the field of artificial intelligence where computer systems are given ability to learn from large amounts of data without explicitly being programmed.
In the context of Procurement, machine learning techniques can be utilized to classify spend more accurately or efficiently than data classified by human practitioners alone.
Examples of spend classification techniques include:
• Supervised Learning in Spend Classification – when humans train algorithms to detect patterns in spend, removing dull work of repetitive new spend classification.
• Unsupervised Learning in Vendor Matching – when algorithms are programmed to detect new and interesting patterns in vendor relationships without intervention or support from humans.
• Classification Reinforcement Learning – where spend classification actions taken by algorithms are reviewed by humans and rewarded or punished depending on the consequences.
While machine learning techniques can prove highly effective within procurement spend classification, human input is still required to capture category and customer specific knowledge.
Example of unsupervised learning – vendor matching:
Deep Dive: Procurement Big Data
What is Big Data?
Big Data describes extremely large sets of structured and unstructured data that can be mined for information and analysed through complex data-processing techniques. The data can be collected from a number of internal and external sources and stored in Big Data repositories.
Internal Data Assets
Internal data assets typically refer to data gathered from an organizations’ own IT-infrastructure, such as the enterprise resource planning (ERP) systems, but can include data provided by suppliers, or collected through ad hoc processes using Excel or Sharepoint.
External Data Assets
Any data that originates from outside of an organizations existing IT framework can be considered external data assets. This includes data publicly available on the Internet, 3rd party proprietary assets and data enriched and anonymized by external organizations.
Procurement Big Data
Procurement Big Data refers to the adoption of Big Data techniques and technologies within the framework of procurement performance optimization. In the context of procurement analytics, Big Data tools and techniques can be used to collect, organize and analyse internal and external data to identify savings opportunities and other value-adding activities.
This guide is regularly updated by a team of procurement analytics enthusiasts at Sievo. You can see why we’re passionate about spend analytics in the video below.
Sievo in brief
Sievo helps businesses turn procurement data into dollars. By consolidating all procurement related data under one platform, Sievo uncovers hidden value and provides insights for data-driven decisions. With AI-driven classification and data-driven external benchmarking, Sievo provides the leading procurement analytics solution powering procurement organizations worldwide.
If you would like to learn more about Sievo you can request a free 30-minute demonstration with one of our product specialists.
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