GAMS preview: The limitations of Big Data
In preparation for the 2016 GAMS Conference on Sept. 14 in Chicago, CFE Media asked our panelists to discuss some of the key issues facing manufacturing. This is one in a daily series of articles.
The 2016 Global Automation and Manufacturing Summit (GAMS), presented by CFE Media, will bring together experts from all areas of the Industrial Internet of Things (IIoT) to look at not just the current state of IIoT but also at the potential benefits of deployment for the manufacturing industry.
The third GAMS conference takes place Wednesday, Sept. 14, beginning at noon. It is held in conjunction with the Industrial Automation North America (IANA) pavilion at the 2016 International Manufacturing Technology Show at McCormick Place in Chicago. The event is co-presented by Hannover Fairs USA.
In preparation for the 2016 GAMS Conference, CFE Media asked our panelists to discuss some of the key issues facing manufacturing. This is one in a daily series of articles leading up to this year’s conference:
CFE Media: What do you see as the limitations of Big Data in maintenance? Are we expecting too much from IIoT?
Spada: In the machine tool industry for example, the biggest limitation to "big data" in maintenance is isolating the source of the problem when you have many variables that can’t be monitored. Tooling for example has many variables that can ultimately affect the machine. The workpiece in many instances has variability that can go undetected due to upstream processing. Isolating whether it was the machine, materials, or tooling to determine best practices in maintenance could be a limiting factor unless more sensors are incorporated on these external components.
Gruber: The sky is the limit: A current phenomenon is that we invest little trust in digital technology with the result that not much ROI is gained.
Kimberley Hagerty is the driving force at Pratt & Whitney, the world leader in aircraft engines behind their digital transformation. She states that some of her machines are 20 years old and provide little data, and there are some that provide more information.
As Kimberley Hagerty puts it: "One miss on one machine in one state upsets the entire value stream. So I need real-time, what-if scenarios for what happens if something doesn’t arrive or something doesn’t move within my value stream, regardless of the state the machine is in. My entire value stream needs to adjust in real-time."
Banda: Depending on the application, data analytics could be the weak link in the big data chain. Unfortunately, data is essentially worthless without the appropriate analysis tools and a plan to improve processes. While not a limitation, it is also of the utmost importance to develop an understanding of the possible ROI of new IIoT systems. This ROI is highly dependent on how well you drive value out of Big Data. Beyond the more obvious benefits in terms of predictive maintenance, the data can be turned into a new revenue stream by spinning off new services that are offered to customers. Maintenance service for manufacturing equipment is a perfect example of this, and it’s a great way to maximize ROI on new technology.
LeBeau: Technology is only the answer if you have asked the right questions and understand the role of technology in the answers. The ability to collect data is only relevant if it can be processed and presented in a way that is useful to the operation. Because you can do something does not necessarily mean there is a value in it. The most important part of any IIoT effort should be the identification of the value different types of data can provide to the current operation.
See related stories on GAMS and IMTS linked below.
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