Please remember what Aaron Levenstein, Business Professor at Baruch College, said about statistics:

Statistics are like bikinis. What they reveal is suggestive, but what they conceal is vital.

Why? Because a large number of predictive / forecasting / trending algorithms are statistics-based. While good statistics, with good sufficiently-sizeable data sets, can reach a very high, calculable, probability of accuracy a statistically high percentage of the time, if a result is only 95% likely 95% of the time, then the right answer is only obtained 95% of the time (or 19 / twenty times), and the answer is only “right” to within 95%. This means that one time out of twenty, the answer is completely wrong, and may not even be within 1%. It’s not the case that one time out of twenty the prediction is off more than 5%, it’s the case that the prediction is completely wrong.

And if these algorithms are being used to automatically conduct sourcing events and make large scale purchases on behalf of the organization, do you really want something going wrong one in twenty times, especially if an error that one time could end up costing the organization more than it saved the other nineteen times because it was primarily sourcing categories that were increasing with inflation or decreasing according to standard burn rates as demand dropped on outdated product offerings, but one such category was misidentified. If instead of identifying the category as about to be in high-demand, and about to sky-rocket in cost due to the reliance on scarce rare earth metals (that are about to get scarcer as the result of a mine closure), it identified it as low-demand, cost-continually-dropping, over the next year and chose a monthly-spot-buy auction, then costs could increase 10% month over month and a 12M category could, over the cost of a year, could actually cost 21.4M (1M + 1.1M + 1.21M …), almost double! If the savings on the other 19, similarly valued, categories was only 3%, the 5.7M the permissive analytics system saved would be dwarfed by the 9.4M loss! Dwarfed!

That’s why it’s very important to select a system that not only keeps a record of every recommendation and action, but a record of its reasoning that can be reviewed, evaluated, and overruled by a wise and experienced Sourcing professional. And, hopefully, capable of allowing the wise and experienced Sourcing professional to indicate why it was overruled and expand the knowledge model so that one in twenty eventually becomes one in fifty on the road to one in one hundred so that, over time, more and more non-critical buying and automation tasks can be put on the system, leaving the buyer to focus on high-value categories, which will always require true brain power, and not whatever vendors try to pass off as non-existent “artificial intelligence” (as there is no such thing, just very advanced machine-learning based automated reasoning).