How many times has this happened? You hire some experts to help with a sourcing effort, they produce a one-off spend analysis, you run some initiatives and realize some savings, and … a year later, you’ve got an obsolete spend cube with IP you’ve paid a lot of money for, but can neither use nor extend, because either the experts didn’t share the process they used to create the cube or, even worse, they used “AI” with “intelligent transaction pattern matching” and there simply aren’t any rules to share.
Or, as often happens (due to the competitive landscape), maybe your original vendor has lost interest in spend analysis, or has left the business, or was acquired and sidelined — and your spend analysis system is either end-of-life, largely unsupported, or obsolete. What then?
Well, you have two options:
- Write it off, throw it away, and start all over again
- Recover the cube
And yes, you read that right, recover the cube!
You’re probably saying, how can that be done, especially if the original cube was mapped with AI or one-time overlay rules that were created by an expert and lost in the sands of time?
With intelligence, observation, and an application of proper, inverse, AI that sifts through the evidence left behind and generates real rules to start you off — rules that can then be extended in a system that supports layering in a logical fashion to not only allow for a re-creation of the original cube, but an improvement that fixes original errors and takes into account changes in the business since the cube was created.
And yes, this is possible, because mappings leave evidence, the same way a suspect at a scene leaves evidence, and that evidence can be unearthed by applying the digital equivalent of classic archaeological techniques that have been used for over a century to interpret the past. (the doctor has given presentations on this and if you are intrigued, contact him)
And it’s even easier in the case of spend analysis when you remember that you can completely map even a Fortune 100’s spend by hand in less than a week to high accuracy by using the classic secret sauce of:
- map the GL codes
- map the suppliers
- map the suppliers and GL codes
- map the exceptions
- map the (significant) exceptions to the exceptions
… and then run the rules in the same order.
This works because the vast majority of spend cubes are on indirect spend, and indirect spend cubes can almost always be mapped effectively this way. Even if there is no specific GL code in the data set, there should be similar patterns around the key fields that determine GL code (product description, SKU, etc.) And what doesn’t match defines the exceptions.
In other words, it’s theoretically possible to do a reverse engineering when you understand the foundations of most spend cubes and learn how to interpret the mapping evidence left behind.
But, is anyone doing this?