(c) 2019 Thomas Wiecki & Ravin Kumar

As advocates of Bayesian statistics in data science we often have to convince business-minded colleagues or customers of the added value of such an approach. While there are many good reasons for applying Bayesian modeling to solve business problems (Sean J Taylor recently had a great Twitter thread on some of the technical benefits), the question still stands as to what these super cool models allow me to do I couldn't do before.

One often underappreciated technique is not to just build a model and show fancy posterior plots, but to actually incorporate the model estimate directly into a decision making process. In brief, by defining a loss function we can use an optimizer to find the best decision(s) not only under the most likely scenario, but under all possible scenarios. This not only moves Bayesian modeling from something that informs a decision to something that makes a decision, it also allows you - the modeler - to communicate your results in the only language business cares about:

In this blog post we want to demonstrate this powerful method with a the general problem of supply chain optimization, an area where Bayesian statistics can have a big impact.