Procurement Analytics Demystified
Comprehensive guide showing how procurement data can be extracted, refined and analyzed for insights and more business value.
Updated: May 18, 2022
Welcome to Procurement Analytics Demystified.
A comprehensive guide for procurement analytics - including definitions, examples and best practices. If you're new to the topic start by watching the video below.
This guide is for anyone who wants to explore the key topics and opportunities of utilizing analytics in procurement.
Some of the key questions we will cover:
- What is procurement analytics?
- Why is analytics valuable for procurement?
- What are examples of procurement analytics?
- What are the most important procurement metrics and KPIs?
- What are the key sources of procurement data?
- How to create an RFP for procurement analytics?
Read, take notes and enjoy!
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What is Procurement Analytics?
Procurement analytics is the process of collecting and analyzing procurement data to form meaningful insights and aid effective business decision making. Examples range from historic procurement spend analysis reports to advanced analytics to predict and budget future decisions.
Procurement analysis typically involves collecting data from a number of different source systems such as ERPs, classifying data to standard or use-case specific taxonomies, and displaying data in a visualization dashboard or within business intelligence tools.
The need for procurement analytics has developed from many organizations' desire to get a consolidated view on procurement spend. Initially offered through one-off projects such as spend cubes, procurement analytics has evolved to encompass a number of specialised solutions, dashboards and types of automation software.
For many businesses, analytics is about much more than data visualization. One way to think about procurement analytics is that it's like refining oil. It's about collecting, cleansing and enriching large amounts of data from disparate systems to create business value. In procurement analytics, value comes from more timely, accurate and actionable insights, and the ability to measure procurement's contribution to the bottom line.
Procurement organizations can utilize analytics to describe, predict or improve business performance. When utilized effectively, procurement analytics can enable data-driven decision making, where purchasing decisions and supplier relationships are managed more effectively.
Example of Procurement Analytics – Sievo Spend Overview Dashboard
Types of Procurement AnalysisThe field of procurement analytics has emerged from the need to understand past procurement performance and guide future decision making. Common types of analysis in procurement include:
- Descriptive Analytics – where procurement data is analyzed to describe what has happened in the past.
- Diagnostic Analytics – where procurement data is interpreted to understand why something has happened in the past.
- Predictive Analytics – where trends and patterns in data are used to forecast future procurement performance.
- Prescriptive Analytics – where predictive models based on procurement data aid decision making.
4 generations of procurement analysis solutions
Historically, procurement analytics has focused on understanding past procurement spend and supplier performance, but increasingly focus is shifting towards automated and prescriptive decision making. Over time the functionality and service models of spend analysis solutions have evolved to meet the growing needs of procurement organizations facing digital transformation.
Because every procurement organization has the interest to and manage spend, a broad field of solutions has emerged to cater to different needs and maturity levels. You can imagine the change as four different generations of solutions.
Generation 1 (1990 – 2000): analysis done in Microsoft Excel by consultants or business analysts largely focused on past spend analysis.
Generation 2 (2000 – 2010): desktop spend analysis software bought under license with data hosted on-premise or within the company firewall.
Generation 3 (2010 – 2015): browser-based spend analytics dashboards providing business intelligence level visualizations and usability. Licensed or bought as Software-as-a-Service.
Generation 4 (2015 – today): AI-powered, automated procurement analytics solutions combining many data sources. Encrypted and hosted on the cloud. Bought as Software-as-a-Service.
Even today, different generations of procurement analysis solutions co-exist to meet the needs of different types of businesses. Some procurement organizations retain their Excel-based reporting, while other organizations may continue to use their self-built or configured business intelligence solutions long into the new decade. Some procurement organizations can skip entire generations through the process of digitization, if the procurement leadership has a clear mandate to drive data-driven transformation.
For more information on advanced topics like predictive analytics in procurement, see the popular guide "Beyond Spend Analysis."
Value of Analytics in Procurement
Analytics is widely regarded as one of the most important resources and disruptive forces in procurement. According to a recent survey by Deloitte, most Chief Procurement Officers (CPOs) view analytics as the technology area with the most impact on business. What’s more, Ernest and Young identified analytics as the most disruptive force in procurement over the next decade.
Why is analytics so important for procurement?
A common misconception is that analytics in procurement only relates to spend analysis. In reality, analytics touches all activities from strategic sourcing to category management and procure-to-pay processes. Here are just some key reasons why analytics in important across different procurement functions.
Analytics in category management
When effectively used, analytics give category managers superpowers. Procurement analytics allows category managers to identify savings opportunities, segment and prioritize suppliers, address supply risk opportunities and facilitate innovation.
Analytics in strategic sourcing
The best business strategies are informed by data. In strategic sourcing, analytics helps identify the best times and areas to run sourcing events and requests for proposal. It can identify which suppliers to include in sourcing projects and provide rich information into supplier’s quality and risk positions.
Analytics in contract management
Analytics provide value across contract lifecycle management. It can alert when contracts need to be renegotiated, or provide data for supplier negotiations. What’s more, analytics can identify maverick spend to help compliance and improve contract coverage.
Analytics in procure-to-pay
Procurement analytics can also provide much value in the transactional side of procurement. With analytics, you can measure purchase order cycles and improve payment terms. You can evaluate payment accuracy, discover rebate opportunities, identify mistaken payment and reduce fraud.
Analytics in sustainability and CSR
Increasingly, companies are realizing the value of analytics in assessing sustainability and corporate social responsibility and related risk within the supply chain and procurement. Analytics can uncover the environmental or social impact of procurement decisions and identify opportunities for more sustainable alternatives.
CATS: a quick method to diagnose Procurement Analytics needs
The common goal of most procurement analysis solutions is to be a ‘single source of truth’ where procurement teams can make informed decisions with data they can trust. If the data is not reliable, you may run into a “bad data in, bad data out” situation, where costly mistakes are made based on unreliable analysis.
While most procurement organizations have similar aims for reliable data visibility, they all have unique resources and challenges to achieve the goal.
You can assess the complexity of your needs on four key dimensions:
- Connectivity - do you need to bring together multiple sources of data from within and outside of your procurement organization?
- Adaptability - can the solution fit into your team’s sourcing processes, culture, organization structure and workflows?
- Transparency - do you need to see and understand how spend is organized? Can you trust the data?
- Speed - how often do you require updated analysis and how actively is it used in your organization?
As a rule of thumb, the higher your needs for connectivity, adaptability, transparency and speed (or CATS) the more likely you are to need procurement analytics.
If you don’t require many connections between different source systems and a high level of transparency to the analysis process, you may be able to start with a one-time analysis done in Excel or with help by a consulting partner.
If you have a complex organization or global supplier networks, or you require regularly updated data, you may benefit from dedicated procurement analytics software.
Sources of Procurement Data
Procurement organizations often face the challenge of heterogenous data landscapes. As many businesses are formed from more than one business unit, financial processes may vary on a regional or international basis or across businesses. Increasingly, procurement analysts are starting to leverage data from outside of their own procurement organizations, combining the most valuable aspects of internal and external data.
Internal data assets are hosted or originated within corporate applications. It is common for businesses to utilize more than one transactional database, such as enterprise planning systems (ERPs) or accounting software. Procurement analysts may also utilize data provided directly by suppliers or different business units through Excel, or tap into data from the general ledger or other financial records.
External data assets are any data sources that come from outside of the companies own financial databases. These may be public systems, such as information on suppliers, commodity prices or currencies readily available on the Internet. External data sources also include 3rd party proprietary sources, such as supplier industry codes, credit ratings or supplier risk profiles.
In the past, independent analysts working with Excel have been limited with the ability to utilize external data sources. Cloud-based procurement analytics software and application programming interfaces (APIs) have enabled more automated, faster and more flexible uses of procurement data.
Examples of Procurement Analytics
Across different procurement organisations there may be different applications of procurement analytics. Some of the most common examples include:
- Spend analytics – the analysis of procurement spend data from internal or external data sources.
- Contract analytics – the analysis of supplier contracts and their meta-data, such as payment terms and expiration dates.
- Supplier analytics – the analysis of individual supplier’s performance, comparison of supplier performance, analysis of supplier risk, sustainability or diversity, or analysis of supplier base.
- Savings lifecycle analytics – the analysis of savings projects and their impact on the financial bottom-line.
- Spend forecasting – the forward-looking analysis of procurement spend data and its impact on profitability.
- Procurement benchmarking – the comparison of a procurement organizations’ performance to peer or market benchmarks.
Build Spend Intelligence or Buy Procurement Analytics?
With the increased availability of business intelligence (BI) and cloud computing technology, there has been an increase in procurement analysis solutions.
Most procurement organizations are faced with the common “build or buy” question related to procurement analytics. On one hand, you can choose to build your own reporting system in either Excel or a BI platform like PowerBI or Tableau. Alternatively, you can choose to buy dedicated procurement analytics software. Let’s go through the benefits and limitations of each option.
Business Spend Intelligence
Business Spend Intelligence (BSI) is the process of statistical procurement spend analysis to support executives in making informed business decisions. Often lead by data scientists or analysts, the process involves data mining, data visualization and reporting. Spend Cubes are a common example of business spend intelligence, where data is presented across three axis to answer three key questions for procurement: who is buying, what is being bought and who is bought from. BSI can be delivered either by specialized consultants, a centralized BI function or by analysts within the procurement organization, and is typically seen as a one-of exercise conducted in Excel or as a continuous reporting within a BSI tool.
Example: Procurement analysis dashboard in Microsoft PowerBI
Key benefits: relatively cost effective way to give executives a view on procurement spend. Can be aligned with the reporting needs of wider procurement organization or other functions.
Key challenges: data is often out-of date when analyzed. May require advanced analytics skills or dedicated data scientists. Can be challenging to maintain or refresh, eg. data re-classification or data validation may require regular manual work.
Enterprise Procurement Analytics
Compared to self-made business spend intelligence reporting solutions, procurement analytics is often seen as a continuous activity where data is used to influence key procurement goals or strategies. Procurement analytics is not limited to past spend data. Instead of reactive reporting, procurement analytics involves the discovery, interpretation and communication of meaningful patterns in procurement data. Analytics can be embraced by different roles within procurement organizations from category managers and analysts to C-level executives.
Example of procurement analytics software: payment term dashboard in Sievo
Key benefits: continuous process where data is refreshed and used to identify new opportunities across the procurement organization. Can be configured to include many sources of relevant data (not just past spend reporting
Key challenges: can be more costly to implement than one-off reporting, especially as procurement may not have an earmarked analytics software budget. May require training to build culture of analytics in procurement organization.
Best practices for evaluating Procurement Analytics software
Each procurement organization has their own way to evaluate and choose new software partners. There is no silver bullet to finding the right partner, but these common best practices can help in the process:
- Go beyond data categorization – in the past, analytics vendor evaluations focused greatly on spend classification and master data management. These are important but today, data quality should be an enabler to more strategic insights.
- Look for transparency – effective procurement leaders need to see the big pictures but also drill down to details when needed. Analytics solutions should allow for configurable dashboards, but also provide data transparency all the way down to the transaction level.
- Exploit relevant expertise – while each procurement analytics case is unique, specialist analytics vendors will have years of experience to share from similar customer or industry cases. Check for relevant customer references and look for a vendor that can guide you to the right solution.
- Involve key stakeholders early – increasingly, procurement analytics has benefits for different stakeholders outside of procurement from corporate risk management to financial and strategic planning. If alignment with other departments like Finance, IT and legal is important, include them early in the evaluation process.
Category analysis is the process of grouping goods or services into categories for analysis and opportunity identification. Category analysis can be conducted on a high-level view, or through drill-downs into specific categories.
Analytics in category management
In procurement, category analysis is often closely related to category management where an organization’s spend and supplier relationships are managed by a category manager or team of experts.
Category managers can utilize procurement analytics reports to review spend or supplier trends over time, or to identify new opportunities to improve category performance.
Category level metrics can include:
- managed spend per category
- number of suppliers in category
- average payment time per category
- how many suppliers manage 80% of category spend
Example of Category Analysis
Procurement teams can also map out a high-level category analysis to identify which categories of spend to prioritize based on quantitative and qualitative metrics.
Below is an example of a category analysis based on a subjective analysis of spend per category. The size of the ball relates to the total managed procurement spend per category.
In this example, the organization is most likely more focused on direct sourcing activities. For this reason, raw materials, finished goods and components are analysed as strategic categories with more detailed granularity. For this manufacturer, indirect categories such as travel, marketing and professional services are likely to be less strategic categories requiring less data granularity.
Whether they are manufacturers or service companies, each organization will have their own priorities for category analysis.
Business Cases for Procurement Analytics
Procurement analytics has the potential to improve operational efficiency across the entire sourcing and supplier management lifecycle. Common business objectives for procurement analytics include:
- Cost reduction – identification of savings opportunities and measuring savings projects impact on the financial bottom-line.
- Risk management – identification, measurement and mitigation of supplier or market risks within procurement operations.
- New opportunity identification – explorations of new or more strategic ways to manage suppliers or categories based on historic procurement data.
- Cash flow improvement – highlight ways to improve operational cash flow, for example, through payment term optimization.
Many organizations recognize that procurement is a critical business contributor. Typically 40 – 70% of all costs are procurement related, and these costs are often a volatile source of competitive advantage. Effective organizations leverage data to more effectively manage supplier relationships, growth and even bring new innovations to life. More data has been created in the last two years than in the previous history of mankind, bringing new challenges for procurement analytics. Advancing analytical technologies accelerate the process from data to insights and unlock new opportunities.
Procurement Analytics Case Examples
Procurement organizations of all shapes and sizes can have a business case for adopting procurement analytics. Each organization may have a different need based on organizational maturity and supplier landscape complexity. See procurement leaders explain the value of procurement analytics in their own words from three sectors: telecoms, FMCG and manufacturing.
Procurement KPIs and Metrics
Procurement metrics or key performance indicators (KPIs) provide organizations with quantifiable values to measure performance and guide procurement strategies. Metrics can also be used to benchmark procurement’s performance to peers or to prove contribution to company-wide goals and targets.
One challenge with procurement metrics is that they may be used inconsistently within a procurement organization, or have different meanings for key stakeholders such as finance. Here is a quick reference to some of the key metrics you can be measuring.
The 10 most important procurement metrics and KPIs you should be measuring
Which procurement metrics should you be tracking? Here are the 10 most important ones for best-in-class procurement organizations.
Examples of procurement metrics and KPIs
1. Spend under management
Spend under management is the total amount of spend that is actively managed by the procurement organization. This figure can include every region and category that procurement is working with, or can be divided into separate metrics that represents a specific region or category. Spend under management is an important metric for a procurement organization because it reflects maturity and control over spend. Example: "as a procurement team, we’ve increased our spend under management from 60% to 67% over the past 12 months."
Common tool used: Spend analytics
2. Spend vs. budget
Spend vs. budget tracks the realization of spend in procurement and compares it to the budgeted spend overall or per business unit. Tracking realized spend against budgets is the foundation of spend management and ensures alignment with key stakeholders such as finance. When evaluating spend vs. budget, success is not necessarily measured by a decrease in cost but by budgeting accuracy.
Common tool used: Savings lifecycle
3. Total cost of ownership
Total cost of ownership (TCO) is the cumulative cost of all spend purchases. TCO takes into account every cost that is incurred during the procurement phase, and includes all direct and indirect costs of a product or system. It’s not limited to just the purchase price, but includes transaction fees, warehousing and other incidental costs. TCO is a valuable metric for procurement because it provides a cost basis for the total economic value of an investment.
4. Cost Savings
Cost savings is measured by the cumulative amount of savings gained. These are then broken down by category for focused measurement. They are followed over a time period to see how cost saving targets are met. Cost savings could be achieved through aggregating spend across business units, taking out longer term contracts or introducing more competition, ordering in larger quantities, implementing vendor managed inventory systems, standardizing and rationalizing the spend, or using combinations of these different levers.
Common tool used: Savings lifecycle
5. Cost avoidance
Cost avoidance is used to describe any actions that help a company avoid absorbing inevitable additional costs. These costs could be due to inflation, shorter payment terms, exchange rate fluctuations, requirements for additional features or services, etc. Negotiating with cost avoidance measures helps in keeping future costs down. Cost avoidance will not be reflected in budget or financial statements, but can be used as a measure for procurement performance.
Common tool used: Savings lifecycle
6. Average payment terms
Average payment terms measure the average time (in days) invoices are paid by, calculated using every single instance of payment term information. Improving or harmonizing payment terms among and within suppliers is a key way of improving working capital. While the improvement in working capital will not be reflected in financial statements, you can calculate savings based on proxies such as the cost of borrowing. Example: by increasing average payment terms from 30 to 35 days across our managed contracts we were able to save $50,000 in interest charges.
Common tool used: Spend analytics
7. Number of suppliers
The number of suppliers informs how many distinct suppliers are being utilized in the procurement organization, or in a specific category. Reducing the number of overlapping suppliers in a category can result in efficiencies or cost savings. Increasing the number of suppliers in key categories may be advantageous to reduce supply risk.
Common tool used: Spend analytics
8. Contract coverage
Contract coverage measures the amount of spend that is covered by a contract. In contrast, maverick buying highlights the possible loss of value that occurs when buying off-contract. Increasing the amount of spend that is covered by contracts (or procurement approved purchase orders) can result in savings, while also reducing compliance risk.
Common tool used: Contract Management
9. Exchange rate exposure
Exchange rate exposure measures the changes currency fluctuations and conversions have on the overall spend. The long-term impact of exchange rates can be measured and isolated from realized savings measurement. Example: Over the last financial term, the procurement team’s contribution of $1,5m spend reduction was offset by an increase of $700K costs from the increasing value of the US dollar.
10. Vendor accountability
Vendor accountability measures suppliers’ performance and how they are responsible for handling errors and claims. Examples of vendor performance measurement include defect rate, lead time and the cumulative amount of incidents per supplier. The goal of vendor accountability is to ensure that the overall best product or service is delivered, and the development of more strategic supplier relationships.
Common tool used: Spend analytics
Once you’ve identified your key procurement metrics it’s time to develop a procurement performance dashboard. One method to utilize procurement metrics is through a balanced scorecard.
Balanced Scorecard for Procurement
A balanced scorecard is a strategic performance management framework to identify and improve various functions including financial and non-financial metrics developed by Robert S. Kaplan and Dave P. Norton. For procurement, a balanced scorecard helps managers and teams keep track of execution of activities and the consequences arising from these actions.
While there are some industry-level best practices, each company is likely to have it’s own scorecard of metrics related to their own business goals. In the balanced scorecard model the key focus areas to include are:
Financial Perspective – a small number of high-level key financial measures for the procurement organization that can be tracked and reported in a way recognized by finance.
Customer Perspective – while procurement doesn’t typically have external customers, this could include measures of customer satisfaction or responsiveness to internal stakeholders..
Learning and Growth – assessment and measures of the skills within the procurement organization and how new talents are brought in and retained in the organization.
Internal Processes – measures of the efficiency of the procurement organization. How quickly and accurately tasks are done and how they can be streamlined or automated.
Setting up metrics can be difficult, as the amount of information provided has grown over the years. Some procurement organizations highlight metrics with no actionable value. These so called “vanity metrics” do not provide real information to assess how the organization is performing or a connection to a business’ measured financial success.
Another mistake in performance management is having too many metrics in place. Too many trackable items can be detrimental as it leads to overanalysis. Overanalysis can lead to loss of meaningful performance, as there is a lack of an overall vision, and too much focus on tracking individual metrics rather than working on what will drive improvement in the big picture.
The most important element to any procurement scorecard is clear and realistic targets. In other words, not just measuring specific metrics but developing clear goals for where each metric should be headed. It is a good practice to have ambitious goals, but unrealistic goals may demotivate the procurement organization and hurt the business.
Procurement Analytics solutions often provide ready-made templates to track procurement metrics and develop procurement scorecards.
Three Steps of Procurement Analytics
Procurement analytics brings visibility into spend and supplier performance, and helps untap potential savings and opportunities. But even though spend data already exists in systems, making sense of it is often far from straightforward. Three data processing steps are required before insights can be uncovered. Think about it like oil. It starts as crude oil – raw and unfiltered. The only way to leverage its potential is to filter and refine it until it becomes gasoline, which is a usable product that powers the whole world.
Step 1 – Data Extraction
It starts with extracting the data from all possible sources, and consolidating it into one central database. Once it gets extracted, data is ready to be enriched and cleansed. Data extraction is the process that takes outdated and messy sources of information into a clean and consolidated format that can be easily understood and is ready for analysis.Step 2 – Data cleansing, categorization and enrichment
Next, data has to be classified into clear and defined categories. To make spend analysis effective, a precise data classification is needed, as it makes the heterogeneous spend data easier to address and manage across the organization. This process harmonizes all purchasing transactions to a single taxonomy in order to enable customers to gain visibility on their global spending. In this step, data can also be enriched through automated translations or supplier consolidation.Step 3 – Reporting and analysis
After the data has been classified, it is now ready to be analyzed. Spend Analysis gives you the needed spend visibility to deliver insightful analysis for accelerated opportunity identification, smarter sourcing decisions, and full control on your spending. Access to accurate spend analysis is the key for massive savings and potentially realizing opportunities.
To get a better understanding, we’ve invited product specialists to cover each step in more detail.
Deep Dive: Data Extraction
This deep dive was contributed by a subject matter expert with deep experience in procurement software development.
We all know that data is everything nowadays. However, many data extraction efforts focus too much on harvesting data, rather than understanding which data has the most significance. Unless you can extract the right data for your analysis purposes, you will get garbage in and garbage out. Pulling large amounts of data out of a myriad of sources without a standard interface can be quite a tedious task.
Let me give you some insights into how data extraction works here at Sievo – one of the leading procurement analytics software providers.
In many large companies data extraction is a challenging task which requires significant human and capital investments. On top of that, most software systems weren’t built for the intensive load an extraction will put on them, especially internal databases that need to continue serving users during the data pull. Because of the unexpected amount of stress put on the servers, considerable performance problems can occur during the process. The amount of data to extract and the amount of maintenance required may result in longer than expected lead times for data extraction.
The good news is, there’s always a better way to do things. At Sievo, we’ve built our own automated data extractor capable of collecting data from over 100 different source systems. The Sievo Data Extractor is designed to connect and extract the most complicated and extensive procurement data from all kinds of data sources and deliver it for further analytical processing.
Here are Sievo Data Extractor’s five key strengths:1. SPEED THROUGH AUTOMATION
Even if extraction can be done in-house, using the Data Extractor shortens the extraction lead time, as it takes away manual work that is slow and subject to human error. The challenge of spend data extraction lies in the fact that you need to collect data from multiple modules of an ERP (or even multiple different ERPs), in a way that gives you a coherent view on spend. Sievo’s Data Extractor has a pre-configured template for extraction to take the correct data, as well as the possibility for additional data fields. Building this logic is trickier than it seems at the outset, but luckily Data Extractor has pre-built capabilities of doing this for most types of ERPs. The tool significantly cuts down the amount of time and effort needed, including the number of people that need to be involved in the process. If, for example, functions like internal IT needed to be heavily involved each month or week to support the extraction, it would generally mean longer lead times.2. DATA SECURITY
Sievo Data Extractor represents an easy way to extract data from multiple systems with complete data security. Companies understandably want to have full control over sensitive data, and this tool guarantees a secured way of extracting data, delivering it for further processing with the possibility for customers to still keep control.3. QUICK INSTALLATION
Installed to a client’s computer within minutes and compatible with practically any ERP, procurement or finance system, Sievo Data Extractor seamlessly captures all the data needed and pushes it to the Sievo cloud. Correct extraction paves the way for conducting quick data cleansing and analysis.4. RELIABLE MONITORING
The tool allows us to monitor all processes, in case of exceptions. We have all the tools to proactively fix the issues, and we’ve learned through experience how to react to errors such as connections not working or only partial data sets getting extracted. This results in more reliable processes and up-to-date reports with less support needed from the customer.5. DATA CLEANSING
Once the data gets extracted, it is now ready to get enriched and cleansed. After the transformation logic, what you’re left with is a clean, consolidated and filtered format which then makes the rest of the process easier and faster.
In a nutshell, with its trusted and ready-made plug and play templates, the Sievo Data Extractor Tool does the heavy lifting. With massive amounts of data on hand, the best chance of success is to work with a company who not only has the technical expertise, but also understands the procurement data and has perfected this process. Sievo systems have the ability to deal with all kinds of idiosyncrasies and challenges that make dirty data, taking even outdated and messy sources of information into an easy-to-comprehend format ready for analysis.
Sievo customers also have the option of doing their own data extraction, but why opt for that when we’d be happy to take on the task for you. With Sievo Data Extractor’s power in our hands, all this can be done without compromising security.
This chapter was contributed by a subject matter expert with deep experience in procurement software development.
Head of Big Data
Deep Dive: Classification
This deep dive was contributed by a subject matter expert with deep experience in procurement software development.
Effective procurement analytics is not possible without precise data classification. Without classification, everything is cluttered and unorganized. Many of the large enterprises Sievo works with have several ERP’s, each having its own structure, material numbers, accounts and a lot more. Let’s take the example of a laptop. In a larger organization, this one item can be called differently depending on how it has been sourced (in tens of different ways), it can be Dell X260, Power Laptop, or perhaps Ordenador in Spanish.
Unifying heterogeneous spend data into clearly defined categories makes them easier to address and manage across the whole organization. Even with the example of a pen, knowing exactly how many pens are bought by different departments and subsidiaries of a company, and at what price, gives procurement the upper hand in negotiations with new vendors. So, in essence, classification is about harmonizing all purchasing transactions to a single taxonomy, enabling customers to gain visibility to their global spending in order to make better sourcing decisions.
Once you’ve properly classified your data, results are presented in intuitive reports and dashboards right away. The harmonized data can then show potential savings and opportunities.
The answer to accurate classification is not a black box
Complex data classification solutions are not always simple to implement, but they can be improved over time. What stands out with Sievo’s data classification tool is that it’s not a black box—it is transparent. Customers can see how information has been classified and make exceptions to the classifications where they feel necessary. For example, if one category is classified incorrectly, it can be changed effectively as needed. Sievo empowers users to add data categorization based on their own business logic and they can easily classify data through intuitive add-ons and different levels of categorization. They can slice and dice data to smaller manageable chunks on any attribute. Hierarchical classification decisions enable them to manage and pinpoint exceptions both at scale and in detail.
The visibility for customers makes the whole process collaborative, as the customers can freely participate and make exceptions if necessary. Once classified, the information goes standard across the organization and users can see what data characteristics the classification has been based on.
The key to success is collaborative classification
According to industry analyst Spend Matters, Sievo’s collaborative classification engine is one of the most distinct spend classification platforms in the market today. Not only does it break down spend into categories and sub-categories, the classification can also be assigned to customer category experts. This means the knowledge of the expert is capitalized in ensuring deeper level classification within a category or sub-category, resulting in a higher level of accuracy.
For organizations with billions of dollars of spend, this level of collaboration is revolutionary. No one person can be expected to know the full organization’s spend inside-out, but individual category managers can contribute to a more accurate level of classification, and the end result can be ground-breaking. The best practice in spend classification suggests at least 90% accuracy to identify spend opportunities, however reaching a 95% or better accuracy level is crucial for savings tracking and management. As the further breakdown occurs for deeper level reporting and tracking management, Sievo’s collaborative classification approach allows spend to be mapped down to the last dimension 100% error free.
The future is human + machine collaboration
Even when category experts can improve classification to close to the 100% error free level, there is still the question of prioritization. How many hours of an expert’s time is worth spending improving the classification of less strategic, long tail spend?
The answer to this comes from a relatively new field of computer science called machine learning.
At Sievo, we’re developing an approach of human + machine collaboration, where the advanced knowledge of procurement experts is complemented by our own proprietary machine learning solutions. In practice, we continuously train computers to learn and adapt the classification techniques performed by humans. The more data computers see classified, the more they learn. This approach is currently being tested with a limited number of clients, but we believe in the future we can reach even greater levels of precision on data classification by using the best skills of both humans and computers – the next stage in a truly collaborative classification experience for procurement organizations.This chapter was contributed by a subject matter expert with deep experience in procurement software development.
Senior Product Owner
Deep Dive: Data AnalysisThis deep dive was contributed by a subject matter expert with deep experience in procurement software development.
You’ve probably heard that data is the new oil. So has your CPO. Congratulations! Your company has realized the great value that your data stockpiles can bring. Be it supplier rationalization, identifying key vendors, purchase order coverage or supplier performance, your organization has endless possibilities in unleashing the potential value contained in your data. When we talk about analytics, we think about examining detailed data and applying proper statistical methods to uncover new insights that support decision-making.
During your data analysis project, the extracted data is refined like oil, as it is standardized, cleaned and properly categorized with your provider’s classification process. When the data has been refined, you are ready to fuel up the analysis engine to navigate towards valuable insights and truly data-driven decision making. After all, refined oil is used to fuel combustion engines and, in this case, our combustion engine is the analytics.
To steer the engine in the right direction, here are 8 analytics insights that are key for competitive procurement success.
The spend overview gives you a summary of how your spend is performing compared to other points in time, such as last year, last quarter and so on. What are the trends and are mavericks causing our spend to be higher than usual?
To know how you are spending and for what reasons is to know how much cost you are incurring, in both indirect and direct categories. The basis of all spend analytics is seeing how your spend compares to particular time frames. This basic function has its roots in the stock markets, but it has also found use in procurement analytics. KPI visualizations indicate the trends in your spend and are dynamic, responding to your drill-downs and data slice-and-dice operations.
Spend distribution by supplier
The Pareto principle, created by Vilfredo Pareto, is also known as the 80/20 law and can be usefully applied in many business contexts. In procurement, supplier Pareto analysis looks at what percentage of spend goes to what percentage of your supplier base. This analysis can help you identify categories where there is opportunities for supplier consolidation and categories where you may have too few suppliers. Relying on just one or too few supplier in a category has to be carefully thought out, as it can remove your leverage during negotiations and can expose you to risks.
Having a solution with the analytics capabilities to give you a view to categories, geography and time-periods is crucial. Creating this type of analysis from scratch is time consuming, but fortunately out-of-the-box solutions exist in the market.
Visualizing the spend-flow
Procurement analytics comes in many shapes, sizes and forms. The number of dimensions in the spend data open up different possibilities to get insights on how your organization is performing. However, this is not an easy task to execute correctly, as the visualization options make the possibility of presenting data too easy.
This usually ends up in a table and chart heavy dashboard, that can be hard to decipher unless you are a seasoned data analyst. The best way to get this information out is to create a visualized spend process. A spend flow-process allows you to present your data story in a way that is easily understandable outside your data analyst team.
Procurement analytics is an art form that helps your company to unlock the hidden potential lying in your data. One of these areas of potential lies in the prices of your direct spend materials. Diversifying purchasing to multiple suppliers produces pricing data that enables you to benchmark, giving you information on where the best supplier price is. You can include even more detail with peer benchmarking, where your spend data is compared to similar companies, expanding your possible saving opportunities.
Using price alone is not the best metric, so any opportunity related reports should also include other factors like on-time delivery, quality and opportunity fill rate.
Using the information provided by the system helps you in negotiating with sub-optimal suppliers, so you can match the best supplier price without sacrificing other supplier KPIs.
Everybody loves a waterfall
Communicating the cost reductions that stem from your savings program implementation is a key aspect of analyzing your spend. If your cost reductions can be tied to your executed savings plans, it provides great benefits for your business. The cost reductions do not always bring benefits, however, as they can be tied into changes in the seasonality, price fluctuation and currency shifts. Knowing where to attribute the spend change gives you a chance to do a gap analysis, enabling a powerful way of explaining the results to your stakeholders. The best way to do this is using the waterfall chart.
The above graph shows a waterfall model for realized savings measurement called the SavingsBridge(tm). In this high-level example, the procurement managed spend for this year (5.10B€) is compared to the previous financial year (4.84B€) with individual components impacting spend outlined separately:
- Volume difference shows the (+450M€) difference in spend resulting from buying more raw materials and components than the previous year.
- Currency impact (-40M€) shows savings made from positive changes to purchases made in foreign currencies.
- Market impact (+200M€) shows the overall increase in market prices (eg. for commodities) that is outside of procurement’s control.
- Finally, (-350M€) is the savings generated by Procurement activities once all the external elements have been isolated and removed from year-on-year difference in spend.
The variety of suppliers creates a need for optimization, so segmenting the suppliers by different variables is the key on going forward. One example is looking at your biggest suppliers and their growth in spending and comparing it to the previous year, showing you a matrix of high-low growth and high-low spend. This data can then be cross referenced with your category data.
Although a lot of analytics providers offer a geo-spatial analysis capability, very few of them offer actual value to the user. A quick improvement for creating value would be using different colors and data point sizes when mapping the data. For example, taking supplier location spend value (size) and then spend growth for supplier compared to the previous year (color). This gives you a way to instantly show where problematic spend is occurring. You could also do an analysis by a particular material and see where the material is sourced from and consider different supply chain optimizations.
These are just a few ways on how to create insight driven analytics, and practice has proven that there are a ton of more ways to analyze the data. Remember, the more you simplify your approach, the more effective you will be in communicating the insights found in your procurement data. The moment you deliver simplified and palatable analytics insights for the purpose of aiding stakeholders in making decisions, is the key moment when your procurement function gains competitive advantage. Data is being produced in vasts amount daily and it can be easy to get buried in all of that. The variety of spend analytics vendors offer you many different ways of getting insights, but only a few can deliver the insights that precisely work for the user groups in your organization.
With Sievo, you can be assured that the data is in good hands. Sievo helps you view and understand your procurement data.This chapter was contributed by a subject matter expert with deep experience in procurement software development.
Vice President, Product Management
RFP Template for Procurement Analytics
Are you looking to evaluate your options for procurement analytics? It may be time to create a request for proposal (RFP.)
Writing a comprehensive RFP is challenging and requires effort. At Sievo, we’ve received and answered hundreds of RFPs from leading procurement organizations around the world. Recently, we conducted a research into the 30 best RFPs we’ve received containing hundreds of different types of questions. Based on our experience, we wanted to share four key points to consider to write a best-in-class RFP. At the end of this post we have also provided a template with example RFP questions.
1. Preparing for an RFP
Define your scope
There are many procurement analytics software providers that all focus on different areas. As a rule of thumb, procurement application vendors can be divided into best-of-breed and full-suite providers. Best-of-breed vendors, such as Sievo, focus on a specific niche, for instance analytics, whereas full-suite providers aim at having all procurement applications under one platform. To narrow down the scope and to include only relevant vendors in your RFP process, it is a good idea to research to market and consider what services and solutions you would need. Shortlisting vendors and booking demo sessions with a few is a good way to get a basic understanding of their solutions.Align internally
Include all stakeholders in your organization that have needs from the software. For instance, Procurement, IT, and Finance should internally align their requirements for the software. Including relevant people in your organization helps in formulating useful RFP questions. If you don’t know what you need from your software, it is difficult for the vendor to guess what type of solution your organization is looking for. They might provide you with too much or too little and irrelevant information, which is time consuming for you to review.
2. Structuring an RFP
Give relevant background information
It’s important to provide the vendors with background information for them to understand your current situation, your organization’s needs, and the challenge you are trying to solve in addition to the purpose of the RFP, and other things that are relevant for your project, such as the timeline you have outlined. Stating necessary technical details at an early stage will prevent roadblocks in later stages of the project. Giving the vendors as much relevant information as possible at this point saves you some time, since you won’t have to answer the same questions they would ask you individually.
Depending on the software provider, some information vendors would often need from your organization to provide a pricing estimate include some or all of the following:
- Amount or ERPs/source systems
- Number of users
- Annual spend
- Internal and external spend
- Addressable/non-addressable spend
- Direct/indirect spend
- Amount of taxonomy levels required
- Description of categorization
- Number of invoices and purchase orders
Provide clear instructions
The deadline for a response to the RFP and the preferred format in which the vendors should submit their response should be determined, such as an Excel spreadsheet or a PDF file sent via email. It is also a good idea to define the length of the response you wish to get, for example by word count. Standard format RFP responses enable you to make comparisons across vendors and also the vendors know how detailed their answers should be.
Share the next steps in the RFP process
It’s a good idea to share with the vendors how their responses are assessed and which criteria are used. Let the vendors know how the selection process will continue, for instance, if the best two-three vendors based on their RFP responses are selected to a next phase in the process.
3. RFP questions do’s and don’ts
Section your question areas
If you send your RFP questions in an Excel file, it is worth considering to make separate sheets for each question area. In procurement analytics software RFPs the question areas could include some or all of the following:
- General questions
- Data management
- Analytics and usability
- Continuous use and service
Pose clear questions, get clear answers
Asking clear and specific questions allow for concise and informative answers. Do your best to formulate the questions in a way that all vendors answer the same question and understand it in the same way. Underline the importance of question areas where you’d like detailed descriptions and where vendors can elaborate on their answers in contrast to questions with simple yes or no -answers. Additionally, tell if sending appendices as a part of the response is allowed.
Instead of asking your potential vendors to “describe your solution” you might want to ask something like “can you extract data from multiple different ERP systems within our organization”. It is difficult for the vendors to know how detailed their answer should be if you ask them to describe their solution. Fitting a detailed description might also be challenging to fit it into an Excel cell and you might end up receiving an appendix of 50 pages explaining all the technical details of their offering.Question Template
Based on these key points we have created a question template that you can use when making your own RFP. You can download the free template here.
This guide is regularly updated by a team of procurement analytics enthusiasts at Sievo. You can see why we’re passionate about the topic in the video below.
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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.
Our solution is used by thousands of users in best-in-class procurement organizations, such as Deutsche Telekom, ISS and Kellogg’s. With our clients, we don’t stop at backward-looking reporting but deliver more by creating forward-looking forecasts and comprehensive analytics. We combine internal information with external data sources. With Sievo, human input and machine learning technologies are integrated together. In short, we translate procurement data into dollars.
Since our founding in 2003, we have experienced rapid, profitable and self-financed growth. Currently we employ more than 180 professionals and have offices in Europe and US.
SIEVO GLOBAL HEADQUARTERS
Mikonkatu 15 A FU-00100 Helsinki Finland
+358 207 890 340
SIEVO US HEADQUARTERS
20 W Kinzie St Chicago, IL 60654 US
+1 (312) 489 8271
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