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Notable IT experts including Isaac Brown of Lux Research claim the future of the industrial IoT (or IIoT) lies in predictive analytics – and we couldn’t agree more. With the annual equipment management costs ranging from 12 to 32% of its value, enterprises need a whole new way to optimize their maintenance budgets. And that’s where predictive equipment maintenance technologies and the “as-a-service” model come in handy.
What is Equipment as a Service & why should you care?
Remember where the Software as a Service approach came from?
IT corporations couldn’t figure out how to make their software products more affordable and took a few lessons from leasing companies. In 2014, over 1 million businessmen around the world switched to subscription-based MS Office licensing – and it definitely makes sense. Why purchase MS Office 2016 for nearly $ 150 when you can always have the latest version of the program for only $ 8.50 per month?
Same with equipment. We are on the verge of the fourth industrial revolution. In less than 6 months there will be 6.4 billion connected devices worldwide. How do you know the fancy heavy truck you’ve bought today won’t be labelled as outdated tomorrow?
High maintenance costs only make things worse.
If your annual maintenance costs consume 5% of the equipment value, your business is headed for bankruptcy. Basically, you have three options:
- Splurge millions on machinery that never fails (but even Homer sometimes nods);
- Invest in Predictive Maintenance Internet of Things solutions;
- Partner with an EaaS provider (in case there are such companies in your industry).
Equipment as a Service is the future of the industrial IoT
Equipment as a Service is a brand-new business model adopted by the leading original equipment manufacturers (OEMs). EaaS providers do not simply sell machinery to end customers; instead, they offer a wide range of services including remote diagnostics and timely repair.
Wait a minute; isn’t it something Caterpillar’s been doing for years?
Well, almost. The only difference between traditional and predictive maintenance (PdM) is that the latter approach enables companies to prevent failures (and not just fix them).
Here’s an example of predictive maintenance in the context of the EaaS model.
Suppose you’re an EaaS provider and sell electrical equipment to several companies across the country. Your transformers are enhanced with smart sensors that measure temperature, voltage and other parameters. The Internet of Things operates on three levels, so you also have a cloud- or server-based platform (a computer program that stores and analyzes smart sensor data) and complementary mobile/desktop applications.
Smart gadgets detect one of the transformers behaves abnormally, and the system automatically delivers an alert notification to your smartphone. You open the admin console, access the transformer’s profile and check sensor readings in real time to understand what’s going on.
What are the main advantages of predictive maintenance solutions?
- You can generate a service request, send a technician to field and promptly order equipment parts right from your admin console;
- PdM software usually contains a Utilization tab where you can preset various parameters including the frequency of maintenance, performance KPI and conditions under which the alert notifications are generated;
- Equipment typically has a maintenance budget allocated to it over its life-time. If you see you won’t be able to stick to it, you can timely replace the machine.
Although the development and implementation of comprehensive PdM systems involve considerable investments, the new model offers numerous benefits to both OEMs and their customers:
- Small and medium-sized businesses that purchase equipment under an EaaS license avoid the capital investment stage and start saving straight away;
- EaaS providers, in their turn, improve the absorption rate (a parameter that shows how long a company can get going if it never sells a single piece of equipment anymore), increase competitiveness and drive customer satisfaction.
Predictive maintenance and the Internet of Things drive the industrial revolution
According to Morgan Stanley, the global spending on the industrial Internet of Things solutions will grow by 8-18% within five years. Some prominent market players including General Electric, Caterpillar and Tennant prove the forecast is fairly well-grounded.
GE embarked on their first IoT project in 2010. The company set up an R&D center in California and now annually generates $ 5 billion in revenues selling analytics software. A few months ago GE Oil & Gas, General Electric’s subsidiary, partnered with Diamond Offshore Drilling under the first-of-a-kind service agreement. According to the contract, GE will buy Diamond’s blowout-preventer systems located in the gulf of Mexico, repair the equipment and provide maintenance services throughout the BOP entire lifecycle. The new service model also involves data monitoring & analysis, certification and personnel management.
The GE-Diamond example of an EaaS and predictive maintenance solution is unique for the Oil and Gas industry, so it might be too early to say whether the model proves to be cost-effective in the long run. However, we’ve got several EaaS use cases from other industries for you to draw inspiration from:
- Monsanto. The largest seed company in the world launched the Climate Corporation platform that uses seed science, agronomy and data analysis tools to make field-by-field recommendations for maximizing yield. The platform is currently available in two versions: Climate Basic and Climate Pro. The former is a free gateway tool for US farmers, while the latter is offered on a subscription basis. The company also equipped its trucks with temperature and geolocation sensors to prevent seed loss. So far, Monsanto’s combined IoT efforts resulted in $ 1 million savings and 13% increase in crop yields (while the Climate platform is used by 22% of US farmers);
- Kaeser. The famous German manufacturer of air compressing systems started an IoT project in order to streamline supply chain, reduce equipment maintenance costs and improve customer experience. Kaeser’s Sigma Air Manager solution connects machines at a compressed air station, collects the data generated by smart sensors and sends it to the company’s server facilities. Sigma monitors such parameters as temperature, pressure, vibration and total energy consumption. The system predicts machine failure 24 hours in advance and can be integrated with a company’s existing enterprise applications;
- Caterpillar. Following the successful launch of its CAT fleet monitoring system, the US manufacturer and distributor of machinery, engines and financial products made a minor investment in Uptake – a promising company that runs a dynamic analytics platform. The two intend to combine Caterpillar’s product engineering expertise with Uptake’s innovative approach to software development to use smart sensor data to its fullest. Further plans include the rollout of a predictive analytics system to the market.
The advantages of using predictive maintenance solutions and the EaaS model include the opportunity to reduce operating costs, prevent machinery downtime and upgrade equipment at a lower price. However, developing apps and embedded systems for the industrial IoT requires great expertise. Provided you address a reliable vendor who has a modern R&D center, employs high-profile developers and turns to IoT security best practices, you can significantly reduce equipment maintenance costs or even become the first EaaS provider in your market.