Spend management is a critical component in today’s streamlined supply chain activities. However, to start, you need to organize the mountains of information generated by your company. Once it is organized, it needs to be normalized, once it’s normalized it can be analyzed.
Tackling these challenges are the key focus areas for SAP Ariba’s Spend Analysis module.
Before you can reach your spend management goals, you need to know what you want to achieve. It may be as simple as just saving money, or as complex as mitigating supply chain supplier risk in crucial categories or geographies for your business.
To act on those goals requires analysis around where your spending activity is now so you can achieve your desired result.
In other words: know where you are today so you know how to reach your destination.
What is Spend Analysis?
Successful spend analysis provides both the information and the context necessary for decision makers to plan the way forward to improved spend processes.
For example, a category manager may find that associates are buying five different mechanics gloves for their global locations. Further analysis identifies that consolidating their per piece volume to a single brand would save significant monies.
Spend analysts may also discover that your buyers are walking down the street to the local mega-mart to purchase office supplies as needed. They may find that it makes more sense to engage with an established office supply brand to set up an Ariba Network Enterprise Account and make managed purchases to acquire everything at a significant discount.
Why SAP Ariba Spend Analysis?
Most companies have data all over the place. Perhaps you have three or four different ERP systems, say SAP and Oracle in different divisions, and a corporate card system like AMEX for travel. In addition to that, transactional data, vendor master data, internal commodity codes, and general ledger accounts may all be managed separately.
The SAP Ariba Spend Analysis tool allows you to pull together this disparate system data into a common data schema, or structure. Once it’s implemented and configured, it can be used for complete and consistent visibility into your information.
Collecting and organizing this information is only part of what the SAP Ariba Spend Analysis tool does. The second step of your engagement, Data Enrichment, uses meaningful values in that information to normalize vendor data and assign a commodity code to transactional data.
SAP Ariba uses proprietary algorithms, machine learning, and standardized industry information to comb through your transactions and add this normalization and enrichment alongside your source information.
For example: multiple travelers in your company stay at the same hotel, but each one enters their information differently in the expense system. The enrichment tool ingests spelling and description variations to output a specific, consistent vendor name and parent hierarchy, as well as a logical commodity code hierarchy.
Why is Data Enrichment so Crucial?
Today, if you want to find out how much money was spent with that hotel last year and there are multiple variations in the name entries, then you must personally hunt for each variation.
Spend Analysis enrichment makes the data usable for aggregating spend not only with an easily found specific location, but also amongst the entire brand or group. That arms you with significant leverage when negotiating future rates with corporate accounts.
This data organization, normalization, and enrichment are truly the primary value proposition of the SAP Ariba Spend Analysis module – not the accompanying reporting tool that is common to the rest of the Ariba environment.
Interestingly, this enrichment element of the tool, as useful as it is, is the part that customers may spend the least time on. At CCP Global we find that the customers happiest with the Spend Analysis module are those that also focus on and stay engaged with the enrichment and refinement efforts so that data relevancy continues to improve over time.
How Is Ariba Spend Analysis Implemented?
In other upstream modules your implementation partner and shared services work with you to configure the module. Then they turn your system on and transitions you to customer service for post deployment support.
But a Spend Analysis implementation assigns a dedicated project manager to help not only during the implementation, but also for the life of the engagement because the data in your spend analysis tool is refreshed on a regular cycle.
The project manager will assist in explaining configuration options, but the data analysis approach and interpretation responsibilities are ultimately incumbent upon your organization.
The long-term success of your Spend Analysis solution requires the same types of best practices planning that we discuss in our blog series on Post-Deployment Success. You must have the right administrative and support staff in place to enable and encourage user adoption. This person, or persons, must be dedicated to that role so they can give it the appropriate attention, otherwise your user base may return to the old ways of disjointed offline analysis, which threatens your return on investment in the Spend Analysis tool.
In part two of our blog series on Spend Analysis, we explore some of the challenges we often see in using the Spend Analysis module and how they can be overcome to make this a truly useful tool in your spend management effort.