The OEM landscape is continuously transforming. In a service industry that once focused on the tactical side of product function, the new shift now emphasizes on how to become the strategic revenue generator and competitive differentiator through the utilization of modern technological systems, such as IoT.
This pressure enhances the competitive environment, putting OEMs on a constant mission to expand their offerings and provide the best in class equipment to their customers to build new partnerships and enhance their current relationships. The key metric of this strategy is ensuring customer satisfaction. But we cannot simply neglect the importance of increased productivity, efficiency, and achieving appropriate returns on investment (ROI).
According to Bain’s projections, 6.8 million connected heavy construction machines will be shipped between 2018 and 2025. It is clear that the future largely depends on the Internet of Things (IoT) to power further data collection and analysis and overall innovation in the field. Yet this investment into IoT, like any strategic investment, must be assessed and the ROI is a critical measure before determining whether your organization should invest.
The top performance indicators for measuring the success of IoT for heavy equipment OEMs are:
Let’s take a look at how IoT can enhance an OEM's performance in these specific areas and how they connect back to the ROI attainment.
In today’s day and age, one of the most crucial aspects of obtaining an exceptional customer experience is to make sure that you are valuing and prioritizing the customers’ time. Predictive service helps address this problem. By utilizing IoT to forecast when a piece of heavy equipment may need repairs or service, even under unexpected circumstances, OEMs can better manage and accurately schedule downtime on machinery and equipment, ultimately delivering a better service experience to customers.
Better servicing not only leads to more satisfied customers, but it also provides operational gains. The benefit of an IoT solution for heavy equipment is that it helps the user better understand exactly what the problem is that needs to be examined from a remote location. This gives the technician a heads up and ensures they will have the right equipment, parts and skills to solve the issue and get things up and running again faster.
Providing accurate updates to service technicians also smooths over the process of meeting compliance requirements for companies with service level agreements. The embedded IoT sensors not only help with the predictive servicing on machinery, but they also enable automatic reporting on asset health and communicate when thresholds have been reached. This feature dramatically improves the chances of meeting SLAs.
In addition to remote monitoring, predictive maintenance detects possible failures ahead of time, so an OEM can take corrective action at the right time to avoid unscheduled maintenance and unplanned downtime, thus mitigating project risks and reducing costs. For an OEM, predictive maintenance data is useful in terms of quality issues. An OEM can track historical failure data so corrective action can be put in place to avoid unnecessary downtime for additional customers due to unforeseen quality issue.
Combining, storing, and analyzing heavy equipment data is the kind of ‘deep learning’ required to take predictive maintenance to the next level. Data gathered on a piece of heavy machinery allows OEM to go from predicting when a known failure mode might occur to preventing it. This new learning is then incorporated to improve engineering knowledge that in turn helps build better models. Taking this to the next level, as we better understand failure modes and their predictors, and collecting even more precious data on a per machine basis, can enable OEMs to model performance for individual machines in terms of operator utilization and environmental impact.
In addition to improving engineering and design concepts, OEMs can leverage data from an IoT solution for heavy equipment to create different types of equipment, with less simulation and modeling required. Using the real data from existing infrastructure, the options for finding new designs or even new uses for equipment can lead to new markets and new product lines for an OEM. This presents a massive opportunity for the OEM to ensure that they stay ahead of the competition.
OEMs realize that connected off-road equipment can help their customers reduce costs, increase productivity, and improve safety. These services not only add value for an OEM customer, they also increase brand loyalty, as other brands of equipment are not integrated with an existing IoT solution. Parker offers a customer-centric IoT solution, Mobile IoT, to meet the specific requirements of heavy equipment OEMs. As a result of implementing Parker’s Mobile IoT, OEMs are able to generate additional revenues not only from its data-driven offerings, but also from its core business through increased equipment sales and aftermarket services.
For OEMs, building an IoT platform in-house can be costly as well as require years of development. Working directly with Parker enables OEMs to benefit from comprehensive technology integration and data analytics expertise to create valuable machine designs for customers without the cost or risk of building out a solution in-house. Click here to learn more about how Parker is positively impacting OEMs around the world from an IoT perspective.
Article contributed by Clint Quanstrom, IoT general manager, and Kyri McDonough, marketing communications manager, Parker Hannifin Corporation.
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