How does product development, manufacturing, the end user and after sales feed into a product’s lifecycle? What data is created at each stage, and how can that data be used along the way?

When a product is being manufactured, it’s a combination of mechanical parts, software, product information and other parameters. All these factors will determine the behavior of the device. Product development aims to come up with a piece of software that can be installed into different variants of the same product. At the same time, the configuration of a single device is determined by the customer’s order.

At the manufacturing stage everything created by product development, mechanical parts, software components, and parameters come together in the just the way the customer wanted. Through configuration management the production can choose exactly how they want to combine these from all the numerous options, and then finally the tailor-made device leaves production and is ready for use, its lifecycle begins with the customer.

By utilizing different IOT systems the customer, as well as the device manufacturer, can get information about the device while it’s being used. Servicing the device is as important for the manufacturer as for the client, as it is not only an opportunity to get specific information about how the device is used and its condition, but it’s also a chance to update information about the device’s lifecycle.

Product development simulates the device

Product development creates the mechanical parts (3D models) for the device and they are analysed for strength (finite element models). Product development also creates the software needed, and the sets of parameters that configure the software to operate as intended. Sometimes the product development also simulates the functionality of the device or a part of it (mathematical models). Simulation can also be used to test software in a simulated environment (HIL testing).

The task of product development is to define the interdependencies between the parts and pieces of software. For various reasons, not every combination is possible or allowed; some function might require specific mechanical parts and particular software or parameters, but bearing these prerequisites in mind, the customer can then order their product.

Production gives it a birth certificate

Once the customer has ordered the device, the production gets the go-ahead to manufacture the device. Production needs to find out which of the combinations product development created will best fill the customer’s need. This is best done with automated configuration management.

When the individual device is complete, it will also receive a ‘birth certificate’, which includes all the information that went into creating the device. This includes, for example, structural information, parameters, information on the versions of those parameters and software, calibration values, and the results for possible end of line tests that were made in production. This information creates a data model for the device’s digital twin, and data can be added to this model later during use.

Service and after sales help the customer and create additional value

With service software the service personnel are always up to date with what kind of device they are servicing. Servicing software tools are connected to the device being serviced and can read the data for the current status of the device. It is then possible to compare this data to the birth certificate data model, and to see changes that have occurred, for example, in the device’s parameters. The data obtained in this way can, for instance, be used to verify guarantee services – in case the product has been “tuned” outside the permitted values. The servicing software tool sends the information to an IoT system that will then attach them to the device’s lifecycle data.

IoT systems also make it possible for information to be gathered from the device during operation. If the device is manufactured in large numbers, the background system can analyse data and produce, for example, predictive service solutions. In predictive service the device can be automatically serviced if the data analytics show it is likely to fail within a certain time-period.

When service and after sales are fully up-to-date with the current state of the device in use, genuinely useful services that add value to the device can be offered to its owners and users.

The digital twin

When all of the above is done, the ‘next level’ is reached; the mathematical models created in product development can be combined with information and data gathered from the device in use. This so-called ‘digital twin’ is one of the most hyped terms at the moment. Some of the terminology and lingo related to modelling and digital twins are very similar to each other, but the context is of course often needed to understand the usage of terms correctly – the context being often related to the phase of business the term is being used in. Different kinds of mathematical models can, however, be used to simulate the physical changes that have occurred, especially when some measurement data about the actual physical device is provided by an IOT system.

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Harri Laukkanen

Vice President and CTO (Smart machines)

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