Many companies do their best to optimize production processes using established rules of thumb or incomplete data. But at the end of the month or reporting period, they often discover sizeable gaps between actual profits and what they had expected. In our experience, that is because they typically lack precise-enough measures to understand the small, real-time variations in process flows and manufacturing steps that cumulatively erode returns at facilities such as mines, steel mills, or other manufacturing plants. This information, moreover, is rarely shared quickly enough for managers to respond in the tight time frames required.
McKinsey’s work across a number of industries suggests that companies can eliminate these profit-draining variations, as well as speed up reaction times by using advanced data analytics combined with upward cascades of data to manage performance. A metric they have termed profit per hour—which in an earlier article we described as a way to improve resource productivity—provides a much more exact view of fluctuations in the operating environment and a much better means of communicating the implications to top managers.