Marks outlined a roadmap for mid-market manufacturing companies in advance of Chief Executive’s forthcoming Smart Manufacturing Summit to be held in partnership with Boeing to be held on May 15th through 17th.
Manufacturing is at an inflection point, thinks Marks, where the application of artificial intelligence is being used to understand how to interpret the demand signals. “Half of all the world’s data was created last year, in 2016,” she maintains, “because we now have so many sensors out there collecting information. This doesn’t mean we understand what is being generated. The challenge for manufacturers—whether they are single plant facilities or huge multi-billion global operations—is to make sense of the vast amounts of this data and apply it to their systems.”
The assembly line of yesterday is not the differentiator it may have once been. Industrial controllers and sensors are now ubiquitous among manufacturing facilities. Siemens alone has 30 million sensors in its own facilities. Add those by competitors such GE, IBM, Honeywell and all the Japanese and Korean providers and the universe data-generation is vast. The use of such data, for example, now allows Siemens to use 3D in the manufacturing of gas turbine blades that turn at 13,000 RPMs.
Over the course of the next 15 to 20 years, the data revolution is expected to create a fourth industrial revolution that will allow manufacturing companies to reduce their throughput times and greatly increase their flexibility. The reason for this is the growing movement toward customized mass production coupled with the need to reduce the use of raw materials and energy.
Part of this revolution is represented by connected controllers that bring together the physical and digital worlds at the farthest edge of manufacturing enabling the surge of the industrial app economy. Like connected people, they can take and enable more intelligent action. Connected controllers are to machines what the iPhone is to a person.
In this new paradigm, control systems are no longer passive devices, disconnected from outside data and business outcomes. They’re changing how machines operate and communicate, accelerating the pace at which the digital world influences physical systems. These new controllers are bringing apps to the machine’s level for the first time, augmenting the real time control loops and creating economic efficiency without requiring a rip and replace of existing control systems.
For example, GE launched its Control System Health App. The app reduces machine downtime by enabling preventive maintenance, providing simple descriptions of faults with recommended corrective actions. Early adopters of connected controls have seen a 40% reduction in maintenance costs as a result of anticipating and addressing problems before they occur. This health check app is the first of many Predix-based (a GE-based system) apps that collect real-time data and leverage analytics to provide better outcomes.
What this movement means to your company
How should mid-market or smaller manufacturers prepare themselves for this emerging world where networks determine a company’s ability to be the best cost producer? “The first thing a company needs to do is to set a vision of where they see their company going,” Marks says.
“They need to understand not only their capex but also their opex—their operation expenses—what are they spending today that if they applied technology could reduce that for the life cycle? To some degree, they are spending this on maintenance or other things that really represent a lost opportunity to make their processes better. This should be better redeployed. Just because one has legacy systems doesn’t mean you have to continue to run your processes this way.”
“The future of manufacturing,” Marks maintains, “will be less assembly oriented and more decision oriented. It will be less of a step-by-step process and become more of a dynamic oriented, customized mass-produced product line. Companies will be more able to make and configure to order. In the future, we will see artificial intelligence driving some decisions of how