GAMS preview: IIoT and the state of manufacturing

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 issues
By CFE Media August 21, 2016

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 issues.

CFE Media: We’ve been actively talking about the Industrial Internet of Things (IIoT) for the past two years, and it’s been looming on the horizon for years before that. Assess where manufacturing is today in both its understanding of and implementation of IIoT.

Rich Carpenter, GE: There are two main advancements:

1. With various systems having been moved to cloud infrastructures, customers are less concerned and more open than they were when IIoT discussions first started.

2. As technology has improved, it is not possible to use plant floor data for Big Data/analytics initiatives without having to disturb the plant floor control systems. This has broadened the audience within the manufacturing customer base for the data.

Rob McGreevy, Schneider Electric: Manufacturers have long been collecting production data from across operations. The number of low cost data generating sensors enabled by the Industrial Internet of Things has made it even easier to generate massive amounts of industrial data. However, data alone is not where manufacturers get value. The real value of IIoT comes converting that data and turning it into actionable information. Enterprise Asset Performance Management leveraging predictive analytics technology is one example of an application where significant improvements and savings have been achieved. Predictive analytics based on advanced pattern recognition and machine learning is uncovering opportunities to extend asset life, reduce unplanned downtime and improve reliability and performance. Our customers have seen savings up to $7 million in a single early warning catch. These types of initiatives are having a direct impact on the bottom line, helping to justify continuing investment in an IIoT strategy.

Rick Vanden Boom, Applied Manufacturing Technologies (AMT): I think manufacturing is just getting started to consider the possibilities and benefits of IIoT. Manufacturing systems typically have a high degree of connectivity, but more on a local level (within the system itself or within a plant or company) and most often geared towards enabling system functionality and basic data reporting. We are just starting to see the possibility of self-monitoring machines and systems, self-optimization, preemptive maintenance calls, and even ordering spare parts. Jose Rivera, CSIA: On one end of the spectrum you have some companies being very concerned about security and viewing IIoT as a threat. These companies have basically put a lock on their doors.

On the other end of the spectrum you have companies fully embracing it. An example often cited is ThyssenKrupp and their deployment of IIoT for doing predictive maintenance—technology guidance to technician going to the site, etc. This was an impressive development with Microsoft, with full commitment from both organizations to make it happen. ThyssenKrupp has shown how this deployment helped them in mature economies to make their experts more productive, and in emerging economies it has helped them guide less experienced technicians in their assignments.

In my opinion, when it comes to IIoT the proven model has been around asset management. Here is where you have a quickly growing number companies getting on the bandwagon. The model had been around decades but only for large expensive capital equipment (e.g., a turbine), long before IIoT. Vendors of these equipment were requiring service contracts in order to honor product warranties. This probably laid the foundation for the deployment of asset management to less expensive capital equipment using IIoT.

In between the two ends of the spectrum of IIoT deployment you have companies deploying IIoT, but not necessarily realizing that what they are doing falls under the IIoT umbrella.

There are good examples going beyond the simple asset management of a machine to reach into operations improvement. I’ve seen the presentation by Mazak, a builder of machines. They show how they upgraded their own plants and how they have invested in an extensive internal network to collect, analyze, and share data from the machines. The data includes not only asset condition information but is also extensive on the operational data that is relevant to the operator of the machine. Leveraging this data allows the operators to make better decisions.

As prices for IIoT equipment have been coming down, experimentation is taking place in plants by curious and innovative personnel. Several suppliers have been encouraging this with interesting entry-level "experimentation" packages. We have just started to see cases where capital goods (e.g., big machines) have been offered on a subscription basis, thus forcing the associated automation to be offered in this mode as well. This of course has big implications on the business models by the providers: equipment and integration services.