Operations leaders routinely make critical decisions across the entire value chain. What combination of raw materials will minimize total cost? How can we plan production to maximize throughput? How can we schedule maintenance tasks to minimize disruptions?
Although such decisions typically involve complex tradeoffs, managers have often made them using rules of thumb or basic data analysis. Today, though, leaders can apply advanced analytics techniques—supported by cheaper computing power and improved data capture mechanisms—to make better-informed decisions that optimize value.
However, many operations leaders must climb a steep learning curve to understand the best ways to apply advanced analytics. For those without quantitative backgrounds, sorting through the hype and distinguishing among popular terms in the analytics field—such as big data, operations research, decision support, and Industry 4.0—can be a daunting task. Because these terms are often used synonymously, it is challenging for leaders to determine how they can employ each of these techniques to the best advantage. Indeed, many businesses are losing potential value because they cannot spot the opportunities to make the most of advanced analytics.
Building comprehensive expertise in the available analytics techniques is beyond the call of duty for most operations leaders. However, it is essential to gain a better understanding of how to use advanced analytics to inform business decisions.
We recommend thinking about analytics in terms of three categories: analysis, modeling, and optimization. These categories follow the application of analytics from performance measurement to predictive modeling to optimal decision making. (See Exhibit 1.)