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, and once it’s normalized it can be analyzed. Tackling these challenges are the key focus areas for Ariba’s Spend Analysis module.
Spend management is a crucial tool in today’s modern businesses because it helps you make informed decisions.
It’s also a big umbrella that includes spend analysis and spend visibility. Those two concepts are actions you want to take: I want to analyze my spend. I’m looking for visibility into my spend.
That’s the arena SAP Ariba’s Spend Analysis software lives in.
You know you need to manage spend, but first you need to know what your goals are. Are they to:
By themselves, those are abstract statements. To make them specific actions you need to analyze where your money is going now, to know how to achieve those goals.
Spend analysis is the information that the decision makers need to improve the spend processes.
For example, a category manager might find that associates are buying five different pairs of mechanics gloves for their global locations. They can improve the process by requiring the buyers to consolidate their purchases to a single supplier or part number.
Spend analysts may also discover that your buyers are walking down the street to their local big box store to buy office supplies in an ad-hoc fashion. It makes more sense to engage with an established office supply brand to set up an Ariba Network Enterprise Account and make managed purchases for everything at a significant discount.
That consolidation of part numbers, or the analysis of how you’re buying things, whether it’s how much you’re buying from purchase orders, from catalogs, or from service order sheets.
If you’re like most companies, you have data all over the place. Perhaps you have three or four different ERP systems, maybe you’ve got an SAP system, maybe you have an Oracle system that another division uses, and maybe you have a corporate card system like AMEX for your travelers.
In addition to that transactional data, vendor master data, internal commodity codes, and general ledger accounts may be managed completely separately.
The SAP Ariba Spend Analysis tool allows you to pull together this disparate system data into a common data schema, or structure. Once its’s implemented and configured, it can be used for complete and consistent visibility into your information.
You can then use it to generate meaningful reports that can tell the leadership in various areas where money is being spent and help them identify areas where they can generate greater savings and efficiency.
But collecting all that 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 may enter their information differently in your expense system. The enrichment tool will ingest spelling and description variations and output a specific, consistent vendor name and parent hierarchy, as well as a ‘lodging’ commodity code hierarchy.
Without SAP Ariba’s “smart” enrichment tool, 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, as a person, must hunt for each variation, which is inefficient and time-consuming.
The 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. This also 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, instead focusing on the improving the “window dressing” on the reporting interface.
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.
With other upstream modules your implementation partner and shared services work with you to configure the realm, turn your system on, and then transition you to customer service for post-deployment support. A Spend Analysis implementation assigns a dedicated project manager to help you not only during the implementation, but also for the life of your engagement since 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.
However, 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, needs to be dedicated to that role so they can give it their full attention, otherwise users might just go back to the old way of disjointed offline analysis, which turns the Spend Analysis tool into an expensive waste of time.
In part two of our blog series on Spend Analysis, we’ll 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.