By Jeffrey D. Camm.
Industry’s recent increased focus on data driven-decision making and the use of analytics in all sectors from sports to financial services to technology and healthcare has led to a resurgence in the interest in traditional operations research tools such as optimization, simulation, and decision analysis. As organizations mature analytically, it seems likely that we will see a further increase in interest in prescriptive analytics, including optimization modeling, which is the focus of this tutorial. With massive amounts of data being routinely collected in real time and an increased awareness on the part of management of the value of data, the availability of data is typically no longer the bottleneck in the optimization modeling process. Increased computing speed, improved algorithms, parallel processing, and cloud computing have increased the size of optimization problems that can be solved to optimality. Considering better data availability and the dramatic increase in our ability to solve problems, what are the impediments to keeping us from having significant influence and impact on decision making? Going forward, it is possible that our ability to (1) structure a messy decision problem into a useful optimization framework, (2) properly use the model to deliver valuable insights for management, and (3) communicate to management the value proposition of our insights, will become the new reasons we might fail to have the impact we know is possible. In this tutorial, we review types of optimization models and the art of modeling, that is, the process of going from mess to model. We discuss how to use an optimization model to provide not simply “the answer” but insights that will be useful to managers and influence their decision making. We discuss the importance of communication in influencing, and provide examples and best practices relevant for optimization. We conclude with thoughts on how optimization modeling is important to the bustling fields of data science and artificial intelligence.