Getting Started with Spend Analysis
What is Spend Analysis?
Spend analysis is the process of reviewing current and historic corporate spending with the goal of identifying cost reduction opportunities, improving strategic sourcing, and reducing procurement costs.
Spend analysis has three main parts:
- Spend Visibility– Having clean spend data as well as KPIs and other metrics as a way to see spending from many points of view.
- Spend Analysis– Asking questions about corporate spending and procurement, finding the answers in the metrics, and creating ways to reduce costs and improve results.
- Procurement Process Improvement– Taking the results of the analysis and implementing changes to improve future performance meeting corporate goals.
1. Spend Visibility
Spend Analysis depends on accurate information from all purchasing-related data sources to effectively measure sourcing variables that took place over a period of time. Key Performance Indicators (KPIs) give direct measurements regarding the past and current state of the process. Be it a simple spreadsheet chart covering the last quarter, or Spend Analysis software with several years of data, KPIs and metrics provide the visibility needed to understand the procurement data.
After all, if you can’t see it, you won’t save it.
Data Gathering and Processing for Spend Analytics KPIs and Metrics
We all know the phrase “Garbage In, Garbage Out”. This is certainly true when it comes to analyzing procurement costs. Most procurement teams follow the following process to get the granular data needed for the most accurate results.
Identify all procurement and sourcing-related data sources.
Data sources can include general ledgers, ERP systems, e-procurement software, expense systems, P-cards, etc. Collect spend data from everywhere-all departments, business units, and manufacturing plants. Remember, it’s valuable to analyze both direct and indirect spending.
Gather the data into one, main location.
Compiling data can prove difficult since the data commonly is in different formats, different currencies, or different languages. Specifically designed extract, transform, load (ETL) procedures exist to overcome these issues. A Spend Analytics solution handles the variances and ETL automatically. When using spreadsheets, procurement analysts need to process the differences themselves or use a data management tool.
Cleanse data for more accurate processing.
Besides language and currency, product and supplier fields, such as names and descriptions, are compared and normalized to be the same. For example, three different business units may buy laptops from Dell each using a different supplier name—DELL, Dell Technologies, Dell, Inc. Standardizing sourcing data makes it easier for companies (and machines and algorithms) to interpret the data.
Enrich data for complete entries and additional metrics.
Data coming from varied sources will have different fields potentially causing issues when they are brought together. Common problems include missing specific fields, abbreviations, and misspellings. Smartly combining the data generates more complete entries for each item. Include outside data sources to enrich data for more ways to analyze, for example, industry codes, supplier diversity status, and ISO certifications.
Categorize items and materials into logical hierarchies.
Having all spending data in a unifying taxonomy allows procurement professionals to understand and track where the money is being spent. There are existing categorization standards, such as UNSPSC (United Nations Standard Products and Services Code), NAICS (North American Industry Classification System), or eClass. Regardless if a company uses its own classification system or variants of existing ones, all spend must be accurately categorized including marketing, travel, office supplies, and legal services. The deeper the categorization, the more granular and informative the spend analytics can be. Classification can be a tedious, detailed, months-long task. Using Spend Analytics software with an AI-powered classification engine can speed that process to days and categorize 60-70% of the first pass data automatically. After human review and correction, the system learns and improves, classifying a higher percentage each time. The ROI on analysis solutions like these can be nearly immediate – the savings in time and resources alone are tremendous.
2. Spend Analysis
From purchase price variance to supplier diversity to items purchased across- business units, there are actionable insights in almost every area from the spend analysis process. The information leads to smarter, data-driven, business decisions regarding the purchases made in every part of an organization. The outcome of the analysis is a set of initiatives that will help improve purchasing efficiency, save cost, and reduce supply risk.
3. Procurement Process Improvements
The implementation process begins here as each initiative is assigned an owner and the activity begins. There are several common ways to make improvements, particularly in vendor management including:
- Improved item pricing and contract terms when business units buy through the same contract.
- Increase efficiency by reducing the number of suppliers for particular items or categories.
- Stronger supplier negotiation position from internal and external pricing benchmarks.
- Reduce processing time for purchase orders.
Some newer metrics are driven from internal and external compliance, like
- Level of spending on diverse suppliers—minority-, women, and veteran-owned businesses—both Tier 1 and Tier 2.
- Quality, Security, and Safety Compliance – ISO, SOX, SOC2, UL, N95
- ESG (Environmental, Social, and Governance) Initiatives
In order to measure the improvement, another analysis needs to be executed. This is not a problem when using a good spend analysis tool. Processing additional data should be fast and highly accurate based on the previous analysis. Processing the latest data provides real-time results that can show the progress made for each initiative.