IoT enabled predictive maintenance is just the tip of the iceberg

IoT enabled extended warranties and predictive maintenance use cases can open the door to new revenue models but for many getting through PoC (proof-of-concept) is still a stumbling block.

The Internet of Things is deemed by many as a nebulous concept, difficult for an enterprise to actually work out exactly how to capitalize on ‘connected device technology.’.

Saving money through optimization of business processes or human overhead is the most popular business driver for connecting devices to the cloud, asset tracking (to prevent loss), just-in-time logistics (reducing inventory risk), and predictive maintenance (reducing loss of productivity) being the most common use cases.

Predictive maintenance and beyond

Usually, once a company begins prototyping a proof-of-concept, a secondary business case emerges: the ability to sell value-added services through the insight being gathered from the connected device.

Manufacturers of industrial equipment such as refrigeration, construction, and other machinery are in a fortunate position to generate previously untapped revenue from new aftermarket services. This is achieved by leveraging connected monitoring devices that can predict when a machine is going to fail.

A key challenge in implementing a predictive maintenance service offering is identifying the right mix of technologies to enable it. This is where u-blox is helping companies; getting Proofs -of -Concepts up and running quickly and then seamlessly scaling the service to production with a comprehensive MQTT-based (MQ Telemetry Transport) application that works right out of the box.

With a range of devices that can be easily connected to machines, such as Digital Twin’s Industrial Controller, coupled with the u-blox IoT Communication-as-a-Service offering and cloud-based predictive software tools such as SmartFlo, it is relatively easy to begin generating new revenues from selling extended warranties or service packages to existing legacy customers.

There are many obvious use cases but these are just the tip of the iceberg. Once a company embarks on deploying this style of solution, new business models, products, and services begin to emerge from a deeper understanding of the benefits of augmenting the data from machines into digital twins.

If you are evaluating technologies to enable a predictive maintenance use case, please contact us. We can suggest a range of 3rd party options depending on the types of machines and equipment you are working with and the geographies within which they operate.