Expense Management Solutions

Expense management solutions are not "one size fits all" across the enterprise. Managing expenses (processing, paying and auditing invoices) for some types of payables involves comparison of the invoices to purchase orders. For other invoices, the process is much more complex, involving a comparison of the invoice with currently deployed telecom assets and services (for telecom expense management) and comparison to contract rates (in freight expense management). 

Effective expense management solutions are based on these key principles:

1. Not all invoice types or expense categories are of equal strategic importance.

2. Process automation should be used to manage the majority of invoices “hands free” so that knowledge workers can devote time to exception management.

3. Expense data extracted from invoices can be developed into powerful business intelligence.

4. Electronic invoices are rich data sources and can be processed at lower costs than paper and are, therefore, always preferred.

Neither Expenses nor Invoices are Alike in Strategic Value

In any organization, certain expenses (and therefore certain invoice types) account for a larger percentage of total spend. Some expense categories are more controllable than others. Strategically speaking, where should you spend your time? Remember the 80/20 rule and think about where it applies in your business. Do 20% of your invoices generate 80% of your total spend? Do 20% of your invoices take the most time to process or contain the most errors? If you acknowledge that any of these situations exist, then you acknowledge that all invoices are not alike.

The 80/20 rule (also known as the Pareto Principle) emphasizes that somewhere within the set (whether it’s a set of invoices, issues, errors, accounts, customers, transactions etc.) is a subset that warrants more attention than the whole. In other words, rather than divide attention equally across the entire set, it will be more productive to focus on that “20%” that will really yield results.

Once you understand that all invoices are not equal and apply the Pareto Principle, it’s easy to see why it’s strategically valid to implement special processes for certain invoice types. It’s possible that 70% or 80% or more of your invoices can be treated uniformly through a generic accounts payable process. However, the other 20% of invoices should be examined more carefully for their own uniqueness and information value.

Most organizations already have special processes for travel and expense, but not all organizations have thoroughly explored best practices for other complex payables such as those for telecom, energy and freight. Cass offers specialized expense management services for these complex payables.

Complex payables are any type of payable that requires specialized tools and processes to manage effectively due to their high complexity, error rate and content value.

Expense Management Solutions Support Spend Analysis

A significant benefit from introducing higher levels of automation in invoice management is the data that becomes available for spend analysis. Complex payables require their own business intelligence platforms and analytic tools because of the uniqueness of the data extracted from these invoices. In each area, considerable domain expertise (in logistics, IT or energy management) is required to fully leverage these information assets.

In telecom expense management (including mobility management), huge amounts of data are extracted from invoices. Telecom invoice data plays an essential role in telecom inventory management and even in monitoring compliance with corporate policies for wireless usage. In the utility area, invoice data is exceedingly valuable in building data marts that help organizations monitor and track their carbon footprint – as well as manage overall energy spend. In transportation, freight invoices must be associated with a number of documents and records such as bills of lading, re-weigh certificates, etc. A best-practice solution for freight payment will capture an enormous amount of data used for specialized supply chain analytics.