A combination of digital technologies and advanced analytics is going to have a major impact on measuring manufacturing.
So say the consultants at McKiney, who in a new article that say there are opportunities to achieve much more granular and real-time manufacturig performance management.
"At the end of the month or reporting period, they [manufacturers] 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," McKinsey says.
McKinsey says that by using data captured from sensors, along with advanced-analytics tools, industrial companies can deploy self-learning models that simulate the expected value and cost of individual processes and even entire factories on a continuous basis. This will eventually drive the new (or at least revised) metric of "profit per hour" in manufacturing.
The graphic below from McKinsey showes how sensors and more combined with analytics will provide real-time control based on target hourly profitability goals to quickly respond to variations.
Is this some future vision, or something available today?
Much of the technolology is available today, though we are at the early stages of the analytics to completely support all of this, McKinsey says. It cites the case of one steel manufacturer that adopted the profit per hour metric, with the goal "to give plant-level managers and frontline operators greater visibility into production variability, as well as to offer financial executives a surer sense of the facility’s performance."
It used technology very similar to that shown in the graphic here as part of the program.
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