What supports accurate spend analysis?

From the May-Jun 2024 edition of Supply Chain Management Review.

May-Jun 2024

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Supply chain organizations are always looking for greater visibility into spending. Leaders are dedicating additional resources to spend management, which seeks to answer the following three questions for an organization.

1.    How much are we spending?
2.    With which suppliers?
3.    Are we getting what has been promised?

Spend analysis enables procurement professionals to identify the value of spending with key suppliers. It also allows staff to consider whether the organization receives the best value for its purchases.

To conduct spend analysis, procurement staff need reliable data from sources across the enterprise. Usually, the process involves time-intensive data activities such as data cleansing. Increased automation and improved data management practices can streamline these efforts, leading to faster and more accurate spend analysis.

APQC recently used its 2024 Supply Chain Priorities and Challenges research and data from its Open Standards Benchmarking in Procurement to identify how organizations are conducting and improving their spend analysis. The data shows many organizations are embracing the idea of automation for spend analysis tasks. However, they must ensure that they have set a firm foundation for the use of automation through standardization, governance, and data management.

Better data management is vital

Supply chain professionals recognize that data management is having (and will continue to have) a major impact on supply chains. For its annual survey on supply chain priorities and challenges, APQC asked supply chain professionals to indicate the trends that will make the most impact on supply chain organizations over the next three years. Data management was among the top three trends identified by survey respondents.

Supply chain professionals understand the importance of reliable data for the many activities touching supply chain: from demand forecasting to spend analysis to building better supplier relationships. In a related survey result, nearly half of supply chain professionals name lack of governance and poor data management as a major obstacle to the improvement of their supply chain processes.

These results emphasize the need for formal accountability for data governance to ensure the availability and quality of supply chain data. Poor governance and data management practices can quickly lead to problems. Aside from inefficiency, inaccurate data can lead to inaccurate forecasts and can undermine any benefits offered by new procurement technology and damage efforts to build stronger supplier relationships.

A first step: Standardize commodity coding

Standardized commodity codes can improve data governance. Although most organizations use commodity codes to some degree to help categorize purchases, only one-third of supply chain teams are using these codes to their full extent (Figure 1). Organizations must ensure they have protocols in place so that codes are used consistently across the enterprise. This ensures that procurement staff can conduct accurate spend analysis to identify spending patterns, areas for supplier consolidation, and areas for potential savings.

Organizations have the option of adopting an external, widely used commodity code system or developing one in-house. External systems require less work to adopt, and their broad use enables companies to benchmark against other organizations. On the other hand, using a system developed in-house gives the organization the ability to customize the coding taxonomy so that it aligns with the unique needs of the business.

Automate spend analysis and data cleansing

Process automation can help organizations optimize standardized activities and data aggregation. Many organizations have already started using robotic process automation (RPA) within procurement and are in the stages of implementing, operating, or optimizing its use. APQC considers spend analysis and data cleansing to be ideal processes for using RPA. What makes these processes good starting points is:

• the number of rules-based transactional tasks involved,
• the few exceptions involved,
• the potential cost savings,
• the frequency of occurrence, and
• the critical need for accuracy.

Specifically, organizations can automate a large proportion of the spend analysis tasks related to collecting, cleansing, and classifying procurement data. In fact, many organizations are taking advantage of the benefits resulting from automating spend analysis. As shown in Figure 2, 91% of organizations have at least some degree of automation for their spend analysis, and nearly half have automated this process entirely.

Data cleansing is part of spend analysis, and it consists of several tasks:

• gathering and harvesting structured and unstructured data from disparate sources,
• cleansing and pre-processing data,
• removing duplicates, and
• converting to a uniform format to make records comparable.

All of these have the potential to be automated, with the  results immediately  loaded into spend profile reporting as well as
other key procurement analysis, dashboards, and reporting feeds.

As shown in Figure 3, nearly all organizations have some degree of automation for data cleansing, and 47% have it fully automated. Compared to the number of organizations that have fully automated their spend analysis, slightly fewer use full automation for data cleansing. APQC strongly recommends organizations consider fully automating data cleansing tasks. Given the significant amount of procurement data inputs, automation enables organizations to quickly collect and clean data while minimizing errors. 

The impact of better data quality

Most supply chain professionals recognize the positive impact that quality data makes on spend analysis. As shown in Figure 4, over a quarter report that having access to quality data makes a very significant impact on the speed and accuracy of spend analysis.

Interestingly, 13% of supply chain professionals believe that data quality has minimal or no impact, and 27% indicate it has only a moderate impact. When supply chain staff do not see a positive impact resulting from data quality, it calls into question their organizations’ use of standardized data management and data governance practices. As noted, the use of technology is only as good as the processes that it supports. Establishing solid data management and governance is a foundational step that organizations must not overlook.

Prioritize strategy and consistency

There is more pressure than ever before to maximize the impact of procurement spend and identify areas for improved efficiency. Well-managed spend analysis offers organizations the opportunity to achieve these goals, and technology provides a way to complete many of the tasks in this process automatically. However, there are multiple levels of tasks that organizations must do effectively in order to best inform the spend analysis process.

Accurate analysis requires accurate data. At the heart of effective spend analysis are robust, strategic data governance and management guidelines that are implemented consistently. These can then inform the activities of collecting and storing procurement data from various sources across the enterprise.

Robotic process automation can reduce the amount of time spent cleaning and analyzing data for the spend analysis process. As with any technology, there is a need for human judgment in creating guidelines for the use of automation within an organization. Careful consideration will yield accurate data while reducing the time and human effort needed for the spend analysis process.

Even with taking these steps, organizations must remember to conduct maintenance on their data management practices. Regular reviews of data availability, quality, and security will ensure that companies get the most benefit from their data, yielding the best analysis for making forecasts and decisions. 

About APQC

APQC helps organizations work smarter, faster, and with greater confidence. It is the world’s foremost authority in benchmarking, best practices, process and performance improvement, and knowledge management. APQC’s unique structure as a member-based nonprofit makes it a differentiator in the marketplace. APQC partners with more than 500 member organizations worldwide in all industries. With more than 40 years of experience, APQC remains the world’s leader in transforming organizations. Visit us at apqc.org and learn how you can make best practices your practices.

About the author

Marisa Brown is senior principal research lead, supply chain management, APQC. She can be reached at [email protected].

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