On the way to servitization, real-time IIoT data can enable the critical first step
For the manufacturers and users of complex machinery and other industrial products, the servitization business model promises increased value and competitiveness, more stable revenues, controlled costs, and minimized risks. In the servitization journey, live IIoT data can facilitate the all-important step of enabling predictive maintenance services.
Refocusing on the value of industrial assets
Both manufacturers and their customers share an interest in making the industrial assets they make or use as productive as possible. They are typically also looking to generate a return on their investments sooner than later. Companies that use industrial assets will often look to their vendors for product enhancements or consulting expertise that can help them meet their own customers’ needs in a more competitive manner.
In a volatile business climate where digitally savvy competitors can disrupt your markets and supply chains any time, the makers and the users of industrial assets become more conscious of the unique value they can deliver and look for new ways to translate it into a business advantage. In doing so, they have to rethink what it means to manufacture and use industrial machine products.
Servitization as a framework to create and deliver value
One of the more radical transformations underway in the industrial world, servitization, entails a complete transition from products to services. Instead of delivering and receiving machine products, companies focus on the value and enablement these products make possible. That enablement may best be delivered in form of a service offering with firm guarantees. Training, maintenance, support, and upgrades could be as important in providing the service as the manufacture and installation of the machine.
In a fully servitized environment, ownership of the devices and industrial assets remains with the manufacturer. The customer and the manufacturer execute a service contract based on their shared commitment to produce a certain outcome. For both of them, the expenses or revenues are predictable and the risks are manageable or minimal.
A predictable, results-driven servitization journey
Companies find it challenging to transition from traditional, product-centric manufacturing to servitization. For one thing, you can’t just suddenly stop all revenue generation from product purchases. Maybe you need to offer complementary support or maintenance services first and help customers adjust their mind set before you they are ready to make the switch with you.
You also will need to make changes in how you manage and staff sales, logistics, maintenance, engineering, and customer support. Some customers may be more ready than others to move into a service-based business relationship with you.
We have seen how companies move into servitization in an incremental process that avoids financial penalties and keeps the risks low. Most commonly, they ensure customers’ buy-in by introducing reasonably priced services with a clear value proposition, such as big-data business analytics that helps customers become more competitive. Once that is successful, it becomes much easier to realize full-fledged servitization to everybody’s advantage.
If the main benefit is reliable transportation of people in a public transport system, the value-driven manufacturer will want to meet the schedules and service-level commitments that the transport utility makes to its traveling constituents. The manufacturer’s additional services can surround the development and delivery of mass-transit vehicles with operator training, performance tracking, field maintenance, and other essential service elements.
Predictive maintenance enabled by IIoT data as the first building block of servitization
For many machine manufacturers, predictive maintenance services are a natural first step toward servitization. To ensure timely action, they rely on machine condition monitoring from the industrial internet of things (IIoT). Using live data, they see how the machinery performs against benchmarks and whether there are data patterns that might indicate a possible breakdown.
Manufacturers set up metrics such as Overall Equipment Effectiveness (OEE) to assess and improve the performance, availability, and output quality of their machines. Reliable machine data that gives you a full view of current performance is essential in implementing predictive maintenance, or you never get out of the reactive mode.
Your first successful service can provide a model for others. Your customers’ needs will help set the direction. Manufacturers of industrial assets and specialty vehicles, for instance, might expand their service portfolio from maintenance to operator and driver training, facilities planning, customized innovation, and market analytics.