Predictive analytics is a complex process that involves many steps and procedures—from collecting and preparing data to communicating findings through eye-catching dashboards. But there’s one stage that data scientists enjoy doing more than others: predictive modeling and algorithms. As an integral part of data science, modeling involves building a solution, mining the data for patterns, and refining algorithms.

In a world filled with volatility and unpredictability, organizations must be prepared to deal with disruption. This is especially true for the manufacturing sector, where the slightest error or variation can have a ripple effect across the production line and the organization. For example, as the global pandemic closed restaurants and crowded grocery stores, supply chain management had to be reimagined.