Posted by Michael Douglas
Manufacturing and factories are predicted to be the biggest area for growth for IoT by 2025, according to leading industry analysts. In fact, Mckinsey predicts that factories alone will produce between $1.2T - $3.7T in revenue by 2025.
These are some sizeable goals, so I was curious to hear how the industry was advancing in real-life examples and to see if there was substance behind the hype. I attended a number of industry sessions at IoT World in Santa Clara that helped give some insight into specific use cases as well as challenges and triumphs the industry is facing and overcome.
Due to the opportunity and challenges IoT presents, the manufacturing industry is being bombarded with new technology frameworks, platforms and ideas that manufacturers are struggling to get their arms around. The majority of IoT deployments are focused around “brown field” sites, which are legacy facilities and machines that have been there for some time.
John Dyck, Director of Software Business Development at Rockwell Automation stated “There's currently two types of brown field assets that are being instrumented as new smart assets. The first are dispersed items that are spread around remote locations that are not currently connected to a main manufacturing site. E.g. Oil Fields, Oil Rigs, Pumping Stations, Utilities Industries etc. The second type of assets are physical machines that are in the four walls of the plant.”
Dyck went on to say that each asset type requires a different approach to instrumenting i.e. have integrated sensors that collect data related to machine operations, interactions with other machines, machine-process data, and product quality data. Each asset also has a different value proposition e.g. you might not be concerned with power consumption for an in the four walls asset v.s. a remote asset therefore your end goal might be different depending on the assets environment and location.
The dramatic drop in data storage costs is one of the most significant revolutions that has helped accelerate innovation in the IoT industry within a manufacturing setting. According to industry experts, in 2010 it cost approximately $80,000 to store one petabyte of data which is 1024 terabytes - or in layman's terms nearly 10 billion photos on Facebook. By 2020, the cost to store one petabyte of data will be $4. Within manufacturing the days of maintaining a PC on every machine are gone - now you can maintain hundreds if not thousands of machines from one user interface.
However, we must be cautious about assuming that cloud service solves all storage problems. There are a number of variables to consider:
One of the key benefits that come from connecting manufacturing machines is unlocking the data that previously went untapped and uncollected. With the dramatic reduction in the cost of data collection and storage, there’s opportunity for low cost and high value information.
The best example of this opportunity in action comes from Rick Lisa, Director of Worldwide IoT Business development at Intel. Lisa explained that after instrumenting a machine, they monitored the conditions of its output productivity and tuned the internal workings of the machine based on the output and were able to save over $9 million in one year as a result. This is what’s called “the feedback loop” which refers to the learnings of tuning a machine or device and receiving data from that device, once the engineer or user reviews the data they can then change the tuning to improve productivity output.
This method of tuning and connecting machines is having a dramatic impact on the manufacturing industry, as Lisa went on to say “If you consider the scale of a company like Intel that has thousands of machines across dozens of factories across the world that creates 500 - 600 million 'things' a year, then you can see the ROI potential there.”
Unlike some sections of the IoT industry where the potential is overhyped, connected manufacturing has the ability to truly deliver on its promises. With examples from Rockwell Automation and Intel, it shows how much value is locked within aging brown field assets. If organizations can expose this data to the right people at the right time and place, organizations will have a much more efficient, real-time manufacturing operation and supply chain.