How do Machine Learning and Predictive Analytics work together?
As we have read above, data is the key to successful demand forecasting. To be more precise, we could say: the solution is hidden among a wealth of data. This is where Machine Learning can really play out its full potential. Not only does it process a vast amount of data in a short time, the algorithm recognizes patterns and cross-dependencies far better than any human ever could – especially when this involves unstructured external data which is becoming more and more important for forecasting. But how does machine learning work exactly? Let us explain this process step by step.
Once the optimal machine learning algorithm has been found, there is nothing standing in the way of demand forecasting with the aid of Predictive Analytics. By constantly monitoring all of the relevant influential factors, precise forecasts can be made and be responded to accordingly. To sum up: the more volatile the demand, the more identified and assumed cross-dependencies there are and the more short-term planning cycles you have in your market, then the sooner you should be thinking about machine learning and Predictive Analytics for demand forecasting.
Would Predictive Analytics models have been able to predict the toilet paper shortage?
In retrospect such a question is barely admissible but let us run through the scenario all the same: if we could have been able to train an algorithm beforehand by using relevant data on consumer behavior in crisis situations and if the relevant data were connected: then the answer might be yes; in particular if we had succeeded in including external sources such as a systematic evaluation of social media activities.
Maybe the increae in sales of certain food would have alarmed the algorithm. It would have known from experience that such an increase means that people are highly likely to cover their requirements to withstand a crisis – and that toilet paper also belongs to this category. The first real purchase data might have shown that the share of toilet paper per purchase had increased successively. A systematic evaluation of social media activities would have shown that at some point the population was worried about a supply bottleneck. If, due to constant monitoring, these factors had indicated that the tendency of all factors was increasing, the model would have predicted the increase in demand and, under due consideration of current capacities, would have been able to predict the shortage.
Would Machine Learning and Predictive Analytics also have been able to suggest which capacity adjustments would have had to be made to counter the shortage?
No. But with the help of their “big sister”, Prescriptive Analytics, this would have been possible. How Predictive and Prescriptive Analytics work hand in hand can be seen in our short presentation on our Predictive Blueprint. (see below).