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The Internet of Things (IoT) has dramatically altered the landscape for data collection and associated business processes. The systems needed to ingest, store, and process data used to reside within the walls of the corporate IT data center. Mobile devices started to break down these walls. Now, “things” share data regardless of location and IoT has become a primary application for cloud computing. Industrial IoT (IIoT) provides original equipment manufacturers (OEMs) with new visibility that enables value-added services, competitive advantage for product design, and revenue growth. Adoption is no longer an option.
Industry Dynamics Driving Need for Industrial IoT
Today’s equipment manufacturers can’t afford to take a “wait and see” approach relative to IIoT adoption. It you’ve been waiting to see how this shakes out, it may already be too late. In a networked environment, change occurs quickly; making the gap between “first” and “everyone else” grow rapidly i.e., kind of like being #2 behind Facebook or Twitter. Compared to internet applications, the speed of change in the hardware side of an OEM's business is not quite as rapid, but the industrial segments are ramping up IIoT use and laggards are being left behind.
Business as Usual No Longer a Viable Option
Changes in technology and the workforce are putting OEMs in a bind. As an OEM improves its products, the technology in the equipment moves beyond the capabilities of local, general-purpose technicians to maintain and repair it. Equipment becomes increasingly complex with synchronized mechanics, electronics and software. The aging and retiring workforce compounds the difficulties with user’s on-site support resources.
At the same time, the user's KPIs continue to move higher, especially those related to production uptime and product quality. Unplanned downtime becomes particularly painful in many industries with interrupted production schedules and work in process material losses. To meet production schedules and obtain revenue objectives, the highly automated systems must perform at a high level and without interruptions.
An OEM may offer a technically superb machine. However if its customers cannot keep the OEM’s equipment running, it will lose future orders. It doesn't really matter who is at fault.
Revenue Growth for OEMs
Adversity often presents opportunity for OEMs to review service performance and consider the benefits of IIoT-enabled remote collection of operating data and equipment condition monitoring. Three approaches provide an OEM with avenues for revenue growth.
Predictive Maintenance: No one knows the equipment better than the OEM. The manufacturer can develop analytics that use data to assess condition, and provide value-added predictive maintenance (PdM) services that can inform users of a pending issue before they experience the problem. The OEM can sell PdM services and technical support to achieve near-zero unplanned downtime.
Closed-loop PLM: Most equipment manufacturers ship products and never obtain feedback on performance - until something goes wrong and the customer complains. Other than testing some prototypes, product development occurs "open loop," without customer feedback. With IIoT, "closed-loop" product lifecycle management (PLM) provides data from a variety of operating environments. Analysis of the data by product development provides opportunities to optimize the design, gain a competitive advantage, and grow market share.
Consumables: Sometimes the equipment contains materials that are consumed with use - like with vending machines, printer ink, and the like. With an IIoT solution, the OEM can monitor consumption, manage inventory, and sell replacements as needed. The user avoids stock-outs, gains incremental revenue, and/or avoids unplanned downtime. The OEM has much better assurance that its consumables are used, and often gains higher margins.
Segmentation of an Industrial IoT Solution
Smartphones provide a useful analogy for the major portions of an IIoT solution. The smartphone contains sensors, a computer, and small applications. It communicates over a network - usually cellular or Wi-Fi. The major portion of the application resides on the cloud in a data center. To understand IIoT, just replace the smartphone with intelligent equipment that can monitor and communicate its own health to alert a support center or the end user when conditions warrant.
Analytics should be in both the intelligent equipment and the cloud. Each OEM has an intimate knowledge of the equipment and determines an optimum approach.
- Edge Analytics refers to analytics that performs some self-assessment in the piece of equipment in real-time. Something that may require more immediate attention (“I’m hot and using excessive current”) to generate an alert that is routed to operations and/or maintenance.
- Cloud Analytics looks at multiple inputs over time from a machine to predict failure that requires attention in the near future. Also, comparisons across multiple machines provides more refinement of existing condition monitoring algorithms or new insights for design improvements.
Low Barrier to Entry for IIoT Solutions
Many equipment designs already have intelligence – like the engine control module (ECM) in your car – with little or no additional recurring cost need for IIoT. For dumb devices, ICs and electronics have moved down the learning curve so that intelligence and communications can be added with a recurring cost below US$ 50.
For cloud applications, current deployment approaches avoid large up-front investment for infrastructure. Cloud and data services like IBM’s cloud infrastructure start with no charge and increase in cost based on actual usage of resources (processing and storage). This allows the equipment supplier to focus its investment on the software applications.
OEM’s Operational Business Process Improvement
When a customer has an equipment problem, the workflow for service and repair significantly improves with IIoT and predictive maintenance. The old method involved a complaint call with high urgency to avoid or minimized unplanned downtime and production interruptions. The repair process needed two passes – one to determine the source of the problem and another to execute the repair. Getting the machine back online could take several days, which is often unacceptable to the customer. Avoiding the production and associated revenue loss provides a key reason for internal maintenance staff.
With IIoT, everyone gets advanced warning of a pending failure. Assessment occurs via the web and the repair can be planned with the customer. Often, the repair occurs via the web. At the ARC Industry Forum in February 2015, an OEM stated, “80 percent of breakdowns are resolved online.” This results in a far smoother and more client-friendly process for scheduling maintenance and repair. With IIoT and PdM, outsourcing maintenance to the OEM involves less risk and improves first-time repair.
About IBM Analytics and IoT
As ARC Advisory Group learned in a recent briefing, the IBM IoT Foundation offers a comprehensive, managed, cloud-hosted service with capabilities to harness devices and data via the Internet. It provides connectivity, control, device registration, rapid visualization and storage of data derived from IoT. When combined with IBM's Maximo Asset Management software and the IBM Bluemix platform, IBM Internet of Things Foundation can provide a powerful application platform for devices and data. According to the company, it allows line of business users to rapidly compose applications, mobile apps, visualization dashboards, and adopt predictive analytics. These elements can feed an enterprise’s back end with IIoT data to generate new insights and enable new business models.
Conclusion
Remote monitoring has been around for a while. With IoT, implementation cost has been significantly reduced while driving expanded acceptance. Adoption has tracked with the value of the equipment. The semiconductor industry has been doing remote PdM for more than a decade with “tools” typically costing $10 million or more. This has recently moved into $1 million mining and agriculture equipment, and it is now entering $100k machines. As the IIoT infrastructure continues to become less expensive to deploy and operate, it will soon make sense for $10k equipment. And, eventually $1k things.
End users are increasingly willing to consider alternatives with increasing equipment complexity, new people replacing experienced retirees, and rising KPIs. A credible IIoT solution helps users become more willing to outsource maintenance to the OEM.
For OEMs, IIoT can provide significant improvements in customer experience, sources of new revenue, and product design. Adoption by OEMs is no longer an option.
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Keywords: OEM, Industrial IoT, Uptime, Predictive Maintenance, IBM