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predictive-maintenance-en-1 Predictive Maintenance: The greatest benefits and smart use cases

Competitive pressure from Asia is on the rise. This is also true for machine and plant engineering. As a result, suppliers are looking for new services designed to strengthen customer loyalty and to support their unique position. 

The Industrial Internet of Things (IIoT) and machine-to-machine communication are two most promising approaches to tackle this.Maintenance of machines at exactly the right time is cost-effective: Wear components are used as long as possible, and are replaced (only) when needed to. This ensures machine availability and avoids downtimes. The concept for this: Predictive maintenance. Here, digitized machines deliver millions of data via sensors to a decentralized network (edge computing) or directly to an IIoT hub for further processing. This data will then be analyzed by the manufacturer and evaluated using machine learning technologies. That’s what makes it "predictive": The evaluation determines the probability of failure of certain components within a defined period ahead. Accordingly, they are replaced at the right time, thereby reducing costs.

Predictive maintenance compared to other maintenance approaches

When compared to other maintenance approaches, predictive maintenance offers some advantages. For example, to reactive maintenance: Here, downtimes are the trigger for a chain reaction to replace parts. Contrasting this, preventive maintenance addresses issues well ahead: Here, "trouble makers" are replaced on suspicion at fixed intervals. This is often done prophylactically and earlier than necessary. Predictive maintenance is the way to avoid the mentioned disadvantages by calculating the optimum point in time for the job.

The advantages of predictive maintenance:

- Downtime is kept to a minimum

- Expensive, specific individual parts do not have to be kept in stock

- Production efficiency is increased

The decisive advantage for suppliers is the interaction between predictive maintenance and the machine: Events such as a malfunctioning warning are generated from the machine and collected by sensor technology. These events can then be used as the basis for new services and business models. They provide the customer with a considerable benefit - and the machine manufacturer with a competitive advantage. Monitoring takes place by displaying the machine as a digital twin in a dashboard. The user can take a look at general status information, so to speak the "heartbeat" of the system and easily gather information on potentially problematic components.

Predictive maintenance in the customer portal

The dashboard is the starting point for after-sales service. Events are picked up in a customer portal and linked to the services of a commerce suite in the context of electronic procurement.

An example: Your machine is digitized via IIoT. Sensors measure vibrations, temperature and rotation speed.  You collect these data in your IIoT hub. Based on your knowledge of system-critical components and a machine learning algorithm, you are constantly aware of the system’s stability and can determine at which point in time a certain component could fail in what probability.

Then, you push this information to your customer via a connected after-sales portal. By linking to e-procurement functionalities, customer employees can immediately reorder the critical components. Your subsequent service offers are also integrated in the portal. For example, machine operators can book an appointment for maintenance with the manufacturer and import it automatically into their calendar. By this, you create an all-round package of services adding value to the machine or system as such. This way you don´t just deliver a machine to your customers, but a digital organism that will be cost-efficient and reliable for many years to come.

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