Spend analysis datasets are typically MacroMap datasets; that is, datasets consisting primarily of AP transactions which are grouped, mapped, dimensionalized, and displayed. Data are typically enriched with MWBE information and so on, but the end result is an enterprise-wide view of spend across commodity
MicroMap datasets, on the other hand, are commodity-specific investigations of spending and usage patterns. Examples we’ve seen at BIQ customers include:
- analyzing which makes/models/years of totaled vehicles are best auctioned off for residual value, and which should be returned to insurance company customers for disposal;
- analyzing cell phone usage to drop low-minute and high-minute users from the program;
- analyzing fleet vehicle usage for guideline discrepancies;
- analyzing cable box retrieval and repair patterns;
- analyzing call center data to identify high- and low-performing agents
However, an increasingly important use for MicroMap datasets is in the compliance arena. Michael Lamoureux at Sourcing Innovation says some estimates show that up to 70% of negotiated savings are never realized due to lack of compliance. However, a traditional payables audit, which typically uses automated techniques to look for discrepancies against contracted rates, is unlikely to find patterns in the data that indicate complex non-compliance. For example, there may be nothing auditably wrong with a small package freight charge for a five-pound package. But what about a pattern of hundreds of such charges, when a five-pound package should be a rather unusual event? Might it be that the mail room is not checking off the package weight, and the carrier is filling it in for them? A MicroMap analysis, focused on invoice line-item detail, finds such patterns easily.
At The Buying Triangle, MicroMap analysis is being applied to commodities such as PC purchases with stunning effect. One recent TBT effort is likely to return, in the form of rebates from vendor over-charges, sufficient monies to fund an entire strategic sourcing effort. As above, the savings do not necessarily come from auditable errors on contract terms; rather, wins may come indirectly, for example from applying benchmark pricing to invoice analysis, and concluding that promised discount levels were unmet.
Using a spend analysis platform for MicroMap datasets is critical, because mechanisms for loading, cleansing, familying, and mapping data are as useful (and necessary) for MicroMap as they are for MacroMap. And, once the data are loaded and organized, the spend analysis system becomes a versatile platform for more complex data analysis and visualization.