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Three Questions to Ask Before Developing an IoT Predictive Maintenance or Equipment-as-a-Service Solution

Industrial equipment manufacturers are justifiably excited about the opportunities that the IoT offers them to digitally transform their business. Though many equipment manufacturers currently offer customers some maintenance or other services, for the most part their relationships with their customers end after they deliver their equipment to them. This limits their ability to keep track of how well their products are working and how their customers are actually using them, information they could then use to improve their offerings. It also makes it harder to build strong customer relationships, enabling competitors to sweep in and develop their own relationships with customers. This means the manufacturers are relying mainly on periodic, hard-to-predict equipment sales revenue, rather than steady, long-term service revenue. Perhaps most important, at a fundamental level, services can differentiate products and make them more useful for customers. The closer equipment manufacturers can move to a business model focused on the underlying service  performed by their equipment—that is, to an equipment-as-a-service business model—the closer they can move to providing their customers with the actual value they desire.  

Equipment sensors, network connectivity, cloud-based management platforms, backend analytics, data orchestration platform and other IoT technologies provide a way for equipment manufacturers to secure data on how their equipment is working and update or improve it after they have delivered it to the customer. This offers equipment manufacturers an opportunity to move beyond just selling products to selling products and services, or even just services exclusively. This transformation to an IoT predictive maintenance model can be incremental, with new solutions that track operational data and then transmit it to a cloud-based platform able to use condition-based algorithms and predictive maintenance analytics to monitor the equipment’s health and determine, with a high degree of accuracy, when repairs need to be made. The benefits from such IoT predictive maintenance transformation are significant—equipment downtime is reduced and on-site service calls can be both scheduled more efficiently and made more productive. In fact, according to a 2016 report from the Aberdeen Group, IoT-driven maintenance can improve overall equipment effectiveness by up to 89 percent, with a reduction in maintenance costs of up to 13 percent year-over-year and an increased return on assets (ROA) of up to 24 percent. 
However, the IoT also allows equipment manufacturers to go beyond predictive maintenance with other value-added services that move them closer to becoming true equipment-as-a-service providers. These services can range from automated alerts on machine status or automatic deliveries when equipment supplies need replenishing, to even moving from selling customers equipment to pay-per-use business models that charge customers for equipment utilization based on field operating data. Such equipment-as-a-service business models allow customers to avoid large, upfront capital investments and instead pay for actual use of the equipment. In addition, such business models enable manufacturers to tailor their equipment-as-a-service offering to the customer’s specific needs and use data analytics to optimize equipment utilization over time, increasing efficiency and providing additional savings to the customer.

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While new IoT technologies and a broad ecosystem of Industrial IoT (IIoT) developers, manufacturers, and suppliers may provide equipment manufacturers with new IoT predictive maintenance and equipment-as-a-service solutions, there are still several important questions they should ask themselves before developing or deploying such solutions. Specifically, equipment manufacturers should carefully consider 1) What is the customer value and ROI delivered by the solution? 2) Are there any “hidden costs” involved in the solution? 3) Who are the partners who will best enable them to efficiently build and launch their solution? By carefully considering these three questions, equipment manufacturers can better position themselves to develop IoT predictive maintenance and equipment-as-a-service solutions that create value for both themselves and their customers.

What is the customer value and ROI delivered by the solution?

In determining the amount of customer value and ROI delivered by a predictive maintenance solution, equipment manufacturers first need to consider the role their equipment plays in their customers’ operations. For example, if it is a mission-critical piece of equipment, where any downtime ends up shutting down production, predictive maintenance is likely to deliver significant value to the client. However, if the equipment is used sporadically for non-mission-critical operations, or there are other machines the customer can use if the equipment is out of service, the value of IoT predictive maintenance will be lower. In addition, high-value assets—which require significant CapEx and usually have high replacement and annual maintenance costs—are good targets for IoT preventive maintenance or equipment-as-a-service solutions. However, in some cases even less valuable equipment might benefit from an IoT solution. For example, if a low-cost blade that stamps and cuts metal reduces downstream productivity when it is dull, an IoT predictive maintenance solution that tracks blade degradation might deliver the customer significant value by increasing output. Adding new type of sensors to their equipment could also add incremental values to manufacturer that they could not have done without this strategy and flexibility in place.

Are there any “hidden costs” involved in the solution?

When considering the hidden costs of any IoT predictive maintenance or equipment-as-a-service solution, the first area to examine is the labor involved. Labor is likely to be one of the biggest costs in any IoT deployment, and it can be difficult to estimate. Equipment manufacturers need to be realistic as they determine how much time they will need to do everything needed to transform their IoT idea into an actual working solution. This includes time spent building the IoT solution development and deployment strategy, including identifying the IoT solution use case to enable and developing a plan for what equipment data to collect and how best to process and interpret that data. There is also the time spent actually coding and testing the application in-house, or the costs of outsourcing this work. Another labor cost is the time needed to integrate the IoT device into both the physical equipment and the cloud services that will be used to monitor the data, analyze it and, in some cases, control the equipment itself. If the IoT connectivity device selected for the solution is not designed for quick connectivity and fast integration with both equipment and cloud services, these costs can be quite significant. 

Another hidden cost is security. Security breaches can be quite costly when they lead to equipment downtime or require the equipment manufacturer to develop short-term security work-arounds and/or long-term security fixes. They can also damage the customer or the equipment manufacturer’s reputation and even lead to litigation. However, building very high-end security into a solution can be expensive, not just to develop but also to deploy and maintain. Equipment manufacturers should therefore try to match the level of security they need with the solution. However, whatever level of security they choose, they should make sure that the security covers the IoT solution end-to-end—from the device to the cloud. 

Data collection and cleaning can also result in hidden costs. For example, collecting extraneous data can lead to high data transmission costs, increase data storage expenses, and require the equipment manufacturer to invest resources into cleaning this data after it has been transmitted. One way equipment manufacturers can reduce these costs is with new edge-computing and data orchestration technologies. These technologies enable filtering and processing of more data at the edge, reducing the amount of data that needs to be transmitted and stored and making it easier to integrate the data into predictive maintenance analytics or other data analysis tools. Some tools exist so manufacturers don’t have to become experts in coding at the edge to be able to push those rules in a lightweight and predictable manner, limiting risks and cost of the deployment.

Who are the partners that will best enable them to efficiently build and launch their solution?

Equipment manufacturers are not going to be able to build these IoT solutions themselves; they will need application development, edge device, networking and other partners. In choosing their partners, equipment manufacturers need to evaluate a potential partner’s experience and expertise in developing, deploying IIoT solutions and simplifying the process so manufacturers don’t have to reinvent the wheel. For example, has the partner already done something similar to what the equipment manufacturer wants to do, providing the manufacturer with knowledge and advice that can reduce IoT solution development and deployment complexity and maximize the value of the final solution? Partners who can work through all the steps involved in planning, developing, deploying and operating the IoT solution will allow equipment manufacturers to avoid common mistakes and accelerate IoT solution time-to-market. In addition, partners that offer end-to-end logistics, with clear responsibilities, enable equipment manufacturer to remain more focused on their core business. Partners with well-integrated IoT technologies and services also allow equipment manufacturers to minimize the number of suppliers creating and managing the links between connected devices, the network, data orchestration and the cloud-based management platform, without compromising on functionality. Finally, partners who can adapt their business models (Opex vs. Capex, pay per use, etc) to the manufacturer’s transformation into service providers would be critical to the success of such endeavour.

For many equipment manufacturers, moving from just building and selling machines into the services business with IoT predictive maintenance or equipment-as-a-service solutions is a big step, requiring them to start or expand the digital transformation of their business. Yet the sustainable rewards of such a transformation—lower machine down-time, improved innovation, stronger customer relationships, ongoing service revenues—are leading equipment manufacturers around the world to begin developing IoT solution strategies, if they have not done so already. By answering the three questions above, these equipment manufacturers can secure the knowledge they need to ensure these new IoT solutions generate enough value to justify their development, accurately assess the hidden costs of building and deploying these solutions, and find the right partners to support their IoT solution initiatives. As a result, they will be able to create a stronger IoT solution strategy, one that allows them to re-imagine their business and realize the opportunities of today’s increasingly connected economy.

Read the Sierra Wireless white paper, Industrial IoT Opportunities: Predictive Maintenance & Equipment-as-a-Service and watch this on-demand webinar. Start with Sierra to find out more about how Sierra Wireless’ Device to Cloud platform can you to develop and deploy IoT predictive maintenance and equipment-as-a-service solutions that create new competitive advantages.