Hands-On Prescriptive Analytics

Chapter 4. Probability Essentials

I provide a high-level background on probability theory and its use in Prescriptive Analytics in this chapter. The reason for this chapter is twofold. First, decision-makers deal with uncertainties every day: they make decisions under a cloud of uncertainty as I noted in Chapter 1. There are no definitive statements, conclusions, or answers to the complex issues they must decide. Each decision involves a simple “Go/No Go” order such as:

• Reduce price or not

• Perform preemptive maintenance on a manufacturing robot today or wait until it fails

• Introduce a new product or not

• Remain at the current head count or reduce (increase) it

• Sell part of the business or not

• Make the decision today or wait until next year

This is a small listing, not meant to be exhaustive. It is, nonetheless, sufficient to show what decision-makers confront. And for each decision, anything could result. So they try their best to get the most Rich Information to help them make the right choice to maximize their chance for success. In some instances, however, they may be so overwhelmed by the complexity of the decision that they may even feel they would do as well tossing a (fair) coin and letting the “chips fall as they may.” This, of course, immediately introduces probabilities into decision making. So the second reason for this chapter is that uncertainties are expressed in probabilistic terms.

Unfortunately, many decisions are not as simple as my examples. Those ...