The Road Ahead for Australian Start-up MOVUS for AI and Predictive Maintenance

S. Soh

Summary Bullets:

• Reactive, Preventive, and Predictive Maintenance regimes will continue to be major use cases for AI-assisted industrial IoT deployments

• As a start-up, MOVUS has a strong first mover local advantage, channels, an end-to-end platform and OT approach which are unique.

Artificial intelligence (AI) – the science of making computers mimic humans using logic, decision trees, deep learning, and machine learning – is fast approaching the market opportunity around preventive and predictive maintenance. According to a recent GlobalData survey, the top two business challenges in Australia are in improving operational efficiency and reducing costs. Many businesses, such as manufacturers, producers of natural resources, through to the agriculture and health sectors, need ongoing reliability of machines and their constituent parts to keep the lights on in the business. Unplanned outages, for example, can cost an oil and gas company, on average $50 million dollars annually. In the case of a windfarm, in the event of one single fail, an entire turbine needs to come down, a technical crew with a crane needs to be on site costing $100,000 or more for each time a part fails. There are many cases of overheated servers in data centers that caused major outages. The myriad of examples create compelling business cases.

MOVUS offers an end-to-end solution comprised of sensor, gateway, machine learning, cloud infrastructure, desktop dashboard, and mobile application. The sensor is magnetically attached to the equipment (e.g., pumps, motor, blower, fan, compressor, gearbox, cooling tower, generator, etc.) and learns its baseline performance based on signals such as vibration, temperature, and acoustics, combined with external data such as temperature and humidity. The moment an anomaly is detected, an alert is sent to the maintenance team for further investigation. An operator may know a part is going to fail, but can arrange a replacement part and schedule downtime ahead of a catastrophic failure. While this is not a new concept, MOVUS is positioned from a number of vantage points:

Cross-Industry Presence: As opposed to blanket approach for AI-enabled preventive maintenance, MOVUS focuses on fixed asset intensive sectors such as energy, utilities, manufacturing, natural resources, and agriculture.

Product: Global suppliers will offer, or plan to offer an AI capability associated with an equipment sale. MOVUS offers the full end-to-end solution on a monitoring-as-a-service model – $70 per month per equipment monitored makes for a cost effective offer.

Platform: MOVUS has created an open platform as the basis for its service offering and this platform can be easily integrated into a customer’s technology suite. FitMachine data, metadata, and event information can be accessed directly from the platform and absorbed into the customer’s existing business processes. In addition, the modular and open nature of the platform means that it can be used with standard offerings like SAP or Oracle rather than being positioned as a competitor offering.

Position: The company targets use cases that are more on the operational technology side of the market, not traditional IT. This market is less developed. Many disparate parts, industrial control systems, to HVACs are not being exposed to any connected IT systems. MOVUS points out that of the 2.3 billion electric motors in operation globally, only 3% are monitored (estimates) which shows a strong growth potential.

Partners: Telstra Ventures has taken an equity stake in MOVUS which will open up broader channel possibilities.

With 90% of the data created in last two years, predictive maintenance is just one use case of AI. Traditional predictive maintenance solutions are still cost-prohibitive as they are bespoke and takes a costly professional services angle. They are often sold by equipment vendors as an add-on. The MOVUS OTT approach shows a better way to scale up, support multi-vendor environments and over time fine tune a platform with more data and powerful algorithms. There are other models emerging on how such a company can monetize data and using AI in a digital supply chain.

 

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