The Internet of Things (IoT) has its roots in manufacturing and industrial companies. As consumers are just beginning to look for IoT-driven thermostats to turn down the heat when away from home, manufacturers are already moving IoT off the plant floor and into other settings where it can transform their business.
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When referring to IoT in a manufacturing context, it often means technology that has been heavily used since the 1970s. Electrical engineer Dick Morley led the revolution toward industrial automation by introducing the programmable logic controller, the core technology on which most modern manufacturing technology is built.
Next Step: Using That Data
Programmable logic controllers (PLCs) and more modern alternatives, such as Ethernet I/O modules or proportional control devices, enable centralized control over machines and communication between one machine and another. This machine-to-machine (M2M) communication is the driving force behind industrial automation. These networked machines are often connected to a centralized system, like supervisory control and data acquisition (SCADA). Apart from sending instructions to the machines, SCADA and other centralized technologies may gather data on machine performance and process faults to enable more proactive maintenance, diagnose problems and drive business intelligence dashboards.
However, with technologies, IoT data is still largely consumed on the plant floor. But, in order to truly transform business, IoT data must be shared outwards beyond just M2M or technicians on the plant floor or in the field. It must be made available to executives to support decisions and trigger business processes in transactional systems, such as enterprise resource planning (ERP), driving automation and efficiency in key processes.
"Machine-to-machine (M2M) communication is the driving force behind industrial automation"
Lifting IoT From the Plant Floor
According to an IFS primary research study1 of 200 IoT decision makers within industrial companies, about 85 percent of them collect data from sensors on their equipment or equipment they install at customers’ sites. Most use this data for condition-based maintenance (CBM) or industrial automation, but only 16 percent have integrated data from these connected devices with ERP software, the transactional backbone of the company. Only a few more can view IoT data in asset performance management software, which helps executives manage the total value produced by and cost of operating capital assets. The transformative value of IoT is, in many cases, still restricted to the plant floor.
Closing the IoT Enterprise Gap
While industrial automation and smarter maintenance are desirable, the people making decisions about the direction of the company are cut off from real-time data and, therefore, have no ability to use IoT to operationalize their decisions. So, companies that are intent on using IoT data to influence their operations at a fundamental level, driving significant change through digital transformation, will need to figure out a way to close this IoT enterprise gap.
IoT CBM Already Closing the Gap
Condition-based maintenance, when done right with IoT, has the potential to deliver digital transformation benefits.
According to Ralph Rio, vice president of enterprise software at ARC Advisory Group, “study data suggests that the most common use case for IoT in these industrial settings is condition-based maintenance. The benefits go beyond operational improvements and maintenance cost avoidance. It increases uptime that provides additional capacity for increased revenue. It also avoids unplanned downtime that interrupts production schedules and causes missed shipment dates and customer satisfaction issues. When married to demand and scheduling systems in ERP, IoT becomes a revenue enhancement tool improving the top line.”
Taking CBM to the Next Level: Analysis Going Forward
In addition to monitoring the performance of individual pieces of IoT-enabled equipment, manufacturers can collect data from all their equipment to produce large data sets that can be aggregated, analyzed and modeled. This accumulation of performance and reliability data enables comparisons of individual pieces of equipment with others of the same model and type.
Comparative data helps service providers identify individual units that are operating outside the norm so corrective action can be taken proactively. The accumulation of this data allows engineering, manufacturing and service providers to identify product quality issues that can be corrected in future versions of the product or corrected for existing equipment through the creation of field upgrades and engineering changes.
A predictive maintenance strategy needs software that facilitates the analytics and modeling of all the data gathered from IoT devices. The new generation of ERP software solutions are designed with this level of device connectivity and future technology in mind, making it possible for organizations to implement predictive maintenance and improve efficiency – ultimately benefiting a manufacturer’s bottom line.
Taking Transformational Steps: Witness IoT and Field Service Management
The IFS survey had a weighty sample of industrial manufacturers, HVAC contractors and automation companies, some of whom are monitoring equipment they sell to their customers to support aftermarket services. According to the study, they are focusing these efforts on individual components in a machine that may be mission critical or prone to failure, or the equipment health of an individual machine rather than an entire installed system or asset.
Relatively few used IoT to capture data from service vehicles or devices carried by technicians, suggesting a disconnect between condition monitoring and service execution. Only by taking the next step and integrating with enterprise software will these service organizations be able to reap the real benefits of IoT and automate response to equipment fault data or provide to customers an analysis of system performance against service level agreements (SLAs).
Gaining the Competitive Edge With Digital Transformation
In the IFS survey, between 30 to 40 percent of digital transformation and IoT leaders reported that IoT data was already making it into supporting business software, compared to only 10 percent of digital transformation laggards.
IoT is moving up the bell curve and away from the early adoption stage, but only by elevating IoT from just a transactional tool to a strategic enabler will manufacturers be able to start reaping the rewards of the latest industrial revolution.
