Maintenance is a serious concern when developing and manufacturing a product – and for good reason.

For machine operators and factory managers, preventative maintenance and asset repairs consume unnecessary resources, eat deeply into operational costs, and present a serious impediment to efficient operations. A single hour of downtime alone can cost a large enterprise over $100,000 in lost productivity, and can be a hard hit to customer satisfaction.

In fact, a third of all maintenance activities are carried out too frequently – and, according to IBM, nearly half are ineffective.

Similarly, homeowners and consumer electronics users can find their purchase cost double or triple over the years by having to call in professionals to fix or replace faulty electronic products.

Manufacturers and asset managers are looking for a better approach to maintenance.  The answer lies in integrating the Internet of Things (IoT) with predictive analytics, to deliver a predictive maintenance solution.

Given this, it’s no surprise that the market for Internet of Things (IoT) predictive maintenance applications is growing rapidly, predicted by one report to hit $10.9B by 2022.

Read on to learn how IoT predictive maintenance replaces both calendar-based and reactive maintenance for better operational efficiency and more robust assets.

Why predictive maintenance is a game changer

IoT predictive maintenance replaces this type of preventative maintenance.
Reducing the need for manual inspections saves time, resources and money.

Imagine if you received an alert from a mobile app ahead of any fault occuring. Instead of having to guestimate when the part will be obsolete based on past observation, or hope to catch it through regular monitoring, predictive analytics and sensor-triggered alerts tell you when to replace the part, reducing even planned downtime and keeping the product running for an optimum amount of time.

Predictive maintenance also eliminates repair costs, a large unknown for both manufacturer and end user. When an electronic component in a product fails, identifying the problem may take 5 minutes – or 5 hours. The same holds true for replacing broken or worn-down parts.

Major breakdowns are expensive, both because of this lost operating time as well as secondary financial losses – for example, if a commercial or home refrigerator breaks down, the loss of goods can run into the thousands. And the larger or more complex the machinery, the greater impact maintenance has on production and runtime costs. Even a small flaw in the system, if not caught early, can lead to unexpected and costly downtime.

With IoT-driven predictive analytics, you can accurately forecast when when assets will need an overhaul. You can even have alerts sent to a mobile app or web dashboard whenever a part needs to be replaced, ahead of the entire system failing.  This holds true whether the product is an expensive blender, a dialysis machine or a production line.

How it works

During the IoT product design phase, manufacturers can identify the parameters for failure in their machines and equipment.

OEMs and consumer goods manufacturers can then design an IoT Model  – a blueprint of the connected system of data-collecting and transmitting sensors, applications, cloud, gateway and other system parts. They can configure a set of ‘rules’  that will identify maintenance issues and send alerts when machinery will need to be repaired or replaced.

Once the system is in market, analytics derived from the IoT system’s data will analyze relevant historic event information and compare it to the IoT Model, the reference of ‘what should be’, in order to predict an event failure. A predictive analytics dashboard also summarizes the operational data, enabling the user to see how the system is operating at all times.

Once in market, every ‘thing’ can collect aggregated data and communicate its status back to a cloud or external system. This creates a closed loop of insights that run back into the manufacturing process.

These insights are the heart of predictive maintenance – and they do far more than reducing downtime.

Benefits of utilizing predictive maintenance from IoT

Having in-market data not only minimizes product downtime, but it can also impact your company’s top line:

  • Extend asset life – IoT behavior analytics enables OEMs to perform root cause analysis and find the issues before they can spiral or prevent a machine or factory floor from operating.
  • Monetize Predictive Maintenance – increase asset value and turn products into services. When manufacturers can prove they have increased uptime and lowered maintenance costs, they can deliver a measure of predictability to their customers that can increase purchase price and be leveraged as a strategic competitive edge.

The opportunity to introduce digital services to customers based on data analytics can also generate a recurring revenue stream and breakthrough growth for the company.

Respondents in a PWC survey expected to reduce operational costs by 3.6% annually through implementing Industry 4.0 initiatives. source : PWC
  • Reduce downtime and improve production yield – Reduce unplanned downtime by catching issues before they can make a whole system fail. And reducing planned manual inspections also boosts productivity and production yield.
  • Improve customer satisfaction – Automated alerts that remind customers when it’s time to replace parts and recommend maintenance services at specific times will both differentiate your product from others in market and keep customers happy.  
  • Reduce room for human error – Servicing mechanical equipment requires an in-depth understanding of its machinery, engineering and operations. Add to that an entire system, including connectivity to a cloud, apps, software and firmware, and you have a host of things that need to be maintained. Predictive maintenance identifies ‘fault lines’ in IoT systems before they become major issues – and reduces the chances for human error.

Predictive maintenance can never completely replace manual supervision, and there will always be a need for some level of human intervention. But the reduction of downtime, and the subsequent reduction in operating costs and increase in product robustness and customer satisfaction, are prime reasons why manufacturers from every industry sector are turning to IoT.  

Zahava Dalin-Kaptzan
Zahava is a Content and Marketing Manager at Seebo and a writer who loves exploring new technologies through the written world.