A Practical Guide to Reducing Spend Leakage and Improving EBITDA

Strategies to improve the bottom line by analyzing how the organization spends money.

Organizations are under constant pressure to uncover ways to save money. One of the ways to achieve this is by understanding organizational spending patterns. By analyzing this spend using modern AI-driven techniques, areas of waste, fraud or abuse become much more clear and thereby creates the opportunity to reduce this spend and improve EBITDA.

How big is the opportunity?

It depends on the size of the organization and the volume of purchase transactions. Larger organizations with a large supplier base can lower total spend by as much as 5-10% in the first year alone. This often equates to 10s of millions of dollars in first year savings!

Additionally, by analyzing the policies that enable this type of spend, the organization can take steps to correct this problem and put practices in place to prevent this level of waste from occurring in the future.

What is Spend Leakage?

Spend Leakage is the use of funds in a non-optimal way. Often this is spending that is out of compliance or outside of documented policy. For example, let's say you have in place a global contract for digital advertising which offers volume-based discounts. An example of spend leakage is when someone decides to use an alternate supplier and therefore does not take advantage of the benefits under the current contract. Money "leaks out" from the organization in this manner.

This leakage is often found in the portion of spending called "Tail Spend".

What is Tail Spend?

Tail spend is the portion of total spending that is of non-strategic nature and often spent with non-strategic suppliers. Often this spend represents about 20% of the total spend. However, it also represents a pool of suppliers which represents roughly 80% of the total supplier population.

This means that while Tail Spend is only a fraction of total spending, managing this spend takes up 80% of the administrative effort. This imbalance means that spending leakage is more likely to occur in this portion of total spend.

• Contract to Spend comparison - comparing categorized transaction data to master agreement information will allow the organization to easily spot non-standard spending and areas of less than optimal spend
• Denied transactions - the patterns behind multiple denied transactions can indicate the root cause of policy abuse
• Holiday or seasonal pattern changes - a number of spending categories tend to trend upwards during certain holiday periods. Detecting this will allow the organization to get ahead of the problem
• Chained transactions - are there repeated transactions of a similar amount on the same day and to the same vendor? A pattern like this likely makes no sense. However, it does if employees are attempting to game the system by issuing smaller receipts to avoid scrutiny.

Just a few ideas to consider. The question is: How often does your organization perform similar analysis? How often does it adjust its policies in the face of changing business conditions?

A 30-Day Plan to Fix Leakage

A 30-day pilot engagement is likely enough to understand the scope and scale of an organization's leakage problem. This would be the first step in a Crawl, Walk, Run approach and has the potential to offer a quick ROI. A quick pilot that has minimal impact on existing resources is the key to success. A pilot can offer insight into immediate ways in which the organization can save money and quite literally pay for itself. This is especially true for organizations with a large supplier base and high transaction volumes. It is also true in scenarios where this type of analysis is rarely performed.

The steps involved:

• Data Collection - A handful of data assets will be needed for this effort. Data such as purchasing card spending data, purchase order details, and master agreement details. This data is often readily available and easy to procure. This data will then be fed into a common datastore that will be housed within the organization's private server environment for security purposes.
• Data Quality and Classification - Once the data has been collected the data goes through a data quality and classification process. The goal is to cleanse, organize and classify the data so that proper analysis can be performed. The end result is data asset that is very useful for analysis which can be used in a variety of ways.
• The Application of Artificial Intelligence (AI) - AI can help sift through the mountains of data to help spot transactions that might represent waste, fraud or abuse. The data would pass through this algorithm to help the team identify areas of concern.
• Final Review - A final presentation to the executive steering committee will allow stakeholders to clearly understand the results of the pilot and offer the chance to address any specific questions.

Walking and Running - What's Next

The pilot accomplishes the goal of the Crawl stage in which two things happen 1) the scale of leakage is better understood and 2) the organization can take immediate steps to correct the problem.

Next is the "Walk" stage. At this point the organization is able to implement policy changes and other steps to address things like corporate culture and other factors that lead to leakage. Having the ability to manage and visualize the variables that lead to leakage means that future scenarios can be avoided. Organizations must still approach implementing these efforts with caution as immediate and drastic change can hurt employee moral. The Walk stage is about improving audits, testing changes and gently offering both carrots and sticks.

The Final "Run" stage is about automation. It allows the organization to work proactively to conditions and isolate specific transactions or events as they occur. The Run stage means that data is flowing in an automated fashion and with minimal human effort. The algorithm will also improve with the increased workflow activity and users will be able to mange the environment based on exception and not the rule saving both time and energy.

Why Our Approach?

The Process Tempo approach to Tail Spend analysis is both unique and comprehensive. It comprises a state-of-the-art data and analytics platform combined with proven expertise. Our Tail Spend algorithm has been co-developed with industry experts who have solved this problem for a number of large enterprises.

The technology that Process Tempo brings to the equation comprises of several key capabilities such as:

• Multiple deployment options (e.g. your private cloud)
• A flexible data model
• Rapid data ingest and analysis
• A large library of machine learning methods
• A templatized algorithm that can be tuned for each engagement
• Self-service dashboards and workflow for non-technical users

When combined, these features offer a comprehensive and unique approach to improving insight into spending patterns and thus positively impacting metrics such as EBITDA. To learn more please visit: www.processtempo.com/spend-analytics www.processtempo.com/spend-analytics or use this link to schedule a time to speak with one of our representatives.

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