Using simulation-based system dynamics and genetic algorithms to reduce the cash flow bullwhip in the supply chain

TL;DR: In this article, the authors explore what researchers have done so far with respect to Artificial Intelligence (AI) and what needs further exploration, and provide a decisional framework for adequate use of AI techniques in different supply chain processes.

Abstract: This article described three heuristics that are employed in making judgements under uncertainty: (i) representativeness, which is usually employed when people are asked to judge the probability that an object or event A belongs to class or process B; (ii) availability of instances or scenarios, which is often employed when people are asked to assess the frequency of a class or the plausibility of a particular development; and (iii) adjustment from an anchor, which is usually employed in numerical prediction when a relevant value is available. These heuristics are highly economical and usually effective, but they lead to systematic and predictable errors. A better understanding of these heuristics and of the biases to which they lead could improve judgements and decisions in situations of uncertainty.