Right now most of the leading analytics vendors are rolling out or considering the roll out of prescriptive analytics, which goes one step beyond predictive analytics and assigns meaning to those analytics in the form of actionable insights the organization could take in order to take advantage of the likely situation suggested by the predictive analytics.
But this won’t be the end. Once a few vendors have decent predictive analytics solutions, one vendor is going to try and get an edge and start rolling out the next generation analytics, and, in particular, permissive analytics. What are permissive analytics, you ask? Before we define them, let’s take a step back.
In the beginning, there were descriptive analytics. Solutions analyzed your spend and / or metrics and gave you clear insight into your performance.
Then there are predictive analytics. Solutions analyzed your spend and / or metrics and used time-period, statistical, or other algorithms to predict likely future spend and / or metrics based on current and historical spend / metrics and present the likely outcomes to you in order to help you make better decisions.
Predictive analytics was great as long as you knew how to interpret the data, what the available actions were, and which actions were most likely to achieve the best business outcomes given the likely future trend on the spend and / or metrics. But if you didn’t know how to interpret the data, what your options were, or how to choose the best one that was most in line with the business objectives.
The answer was, of course, prescriptive analytics, which combined the predictive analytics with expert knowledge that not only prescribed a course of action but indicated why the course of action was prescribed. For example, if the system detected rising demand within the organization and predicted rising cost due to increasing market demand, the recommendation would be to negotiate for, and lock-in supply as soon as possible using either an (optimization-backed) RFX, auction, or negotiation with incumbents, depending upon which option was best suited to the current situation.
But what if the system detected that organizational demand was falling, but market demand was falling faster, there would be a surplus of supply, and the best course of action was an immediate auction with pre-approved suppliers (which were more than sufficient to create competition and satisfy demand)? And what if the auction could be automatically configured, suppliers automatically invited, ceilings automatically set, and the auction automatically launched? What if nothing needed to be done except approve, sit back, watch, and auto-award to the lowest bidder? Why would the buyer need to do anything at all? Why shouldn’t the system just go?
If the system was set up with rules that defined behaviours that the buyer allowed the system to take automatically, then the system could auto-source on behalf of the buyer and the buying organization. The permissive analytics would not only allow the system to automate non strategic sourcing and procurement activities, but do so using leading prescriptive analytics combined with rules defined by the buying organization and the buyer. And if prescriptive analytics included a machine learning engine at the core, the system could learn buyer preferences for automated vs. manual vs. semi-automated and even suggest permissive rules (that could, for example, allow the category to be resourced annually as long as the right conditions held).
In other words, the next generation of analytics vendors are going to add machine learning, flexible and dynamic rule definition, and automation to their prescriptive analytics and the integrated sourcing platforms and take automated buying and supply chain management to the next level.
But will it be the right level? Hard to say. The odds are they’ll make significantly fewer bad choices than the average sourcing professional (as the odds will increase to 98% over time), but, unlike experienced and wise sourcing professionals, won’t detect when an event happens in left-field that totally changes the dynamics and makes a former best-practice sourcing strategy mute. They’ll detect and navigate individual black swan attacks but will have no hope of detecting a coordinated black swan volley. However, if the organization also employs risk management solutions with real time event monitoring and alerts, ties the risk management system to the automation, and forces user review of higher spend / higher risk categories put through automation, it might just work.
Time will tell.