An increasing number of organisations across nearly every industry sector are embracing IoT (internet of things) solutions to improve operational efficiency, maximise productivity and generally change the way they do business for the better.
As engineering moves further into the world of Industry 4.0, it’s essential that aftermarket is not an afterthought. Providing a distinct level of tailored support and services will increase asset utilisation for the customer.
Implementing a service-led strategy can take many forms, from providing additional advanced services through to ensuring the outcome of a specific business process. The latter – especially when based on connecting equipment to the Internet of Things (IoT) – impacts on the entire business. It transforms the value creation chain of planning, research, development, manufacturing, marketing, sales, and service, future-proofing businesses and empowering them to embrace industry 4.0.
EaaS describes the process in which production systems or machinery are not purchased but provided with bundled services, often with a performance guarantee by a supplier, for a regular fee. The responsibility for maintenance, service, repairs, and replacement remains with the provider. In comparison to the classic leasing model, EaaS is more comprehensive and focuses on enabling the utility of the output of a machine.
However, the successful transition of a manufacturer providing a value-added service to a traditional product is dependent on its engineers and other employees embracing the new operating environment and understanding the servitisation-related variables. Developing an IoT enabled service requires expertise in embedded technologies, electrical engineering, DevOps (software development and IT operations) and the server model.
Thinking more in terms of service and holistic solutions requires a shift of attitude. By embracing the process of building revenue streams by using the digital capabilities of equipment, operations can be transformed.
The implementation of servitisation models, paired with smart manufacturing, can also provide a positive impact for sectors crying out for skilled workers. Adding services to products, or even replacing a product with a service, combined with the use of manufacturing data and applications, also allows employees to recognise new abilities and to increase productivity and employment levels.
Whilst some manufacturers are already using data analytics from digitised operations to increase asset utilisation, few have made the leap from a traditional business model to a service-led model. Bringing a third-party IoT specialist on board could provide the roadmap for this change of culture.
Since servitisation is a relatively new concept, manufacturers might say they can’t increase their equipment portfolio because they don’t have the systems or resources in place. Still, it has never been so critical to develop new skills for managing key business processes for aftermarket services that offer a genuine revenue-making asset, while avoiding the issue of purchasing stock that ends up as surplus to requirements.
Delivering a servitisation model to its customers is also behind relayr’s partnership with a global provider of engineered solutions for the diamond industry. Coborn Engineering Company Limited is adding new digital services to its diamond grinding machines which will improve product performance and therefore increase asset utilisation for the customer.
The purpose of this alliance is to develop Coborn’s diamond grinding machines into smart equipment by installing digital services in its products. This will enable Coborn to offer machines with guaranteed performance and availability targets. As an additional feature, Coborn will be offering a pay-per-use business model for its equipment (EaaS). Partnering with relayr will enable Coborn to deliver business solutions to their customers through a blend of technology, finance, and insurance offerings.
It is essential to have a solid strategy. Manufacturers need to understand the positive implications of the technology for their specific business case. This means knowing what operational data and digital footprint a device has, in order to effectively manage such an offering.
Adopting an EaaS model should be first and foremost aligned with the company’s vision and strategy. In addition to that, the organisation needs to find a sustainable way to upgrade legacy machines and allow for predictive maintenance. In so doing, manufacturers can expand their services, reach new markets and increase revenue streams.
Subscription models ensure recurring revenues, which offer the company greater financial resilience in comparison to transactional one-off business. In times of economic downturn and rapid change, it is critical to rethink business platforms and find a flexible solution.
BOX: SKF’s servitisation story
The Swedish bearing manufacturer has committed to this approach, which the company says reflects wider changes in society, with industries from music retail to mobility all moving to models where customers pay only for the services they consume.
“Our business in the midst of a massive change: from a product-selling company to a function provider,” says Victoria van Camp, CTO and president, innovation and business development at SKF. “We want to provide our customers with what they actually need. That isn’t lots of bearings, it’s machines that run and run.”
Becoming a service provider rather than a product manufacturer requires 'a completely different mind-set in R&D' she says. “If you are responsible for the performance of a customer’s machine over its lifetime, you need a much deeper understanding of how your products are being used in that machine. Are they continually exposed to hot, corrosive chemicals? Are they being hit with a hammer during installation?”
Another source of information is the huge quantities of data generated by modern industrial products.
Connected machines and digital management tools can generate detailed records of the way equipment is used and maintained. Bearings can be equipped with sensors that measure temperature and vibration. But how to manage such a vast amount of content?
“Our engineers are experts at analysing performance data, but SKF produces billions of bearings every year,” says van Camp. “When we started to think about applying those approaches to everything we make, it quickly became obvious that the task was too big for even an army of human analysts. The only way you can do it is with artificial intelligence.”
That realisation quickly led to another. “We had people inside the organization working on AI systems, but we recognised that building the capabilities we needed, at the speed we needed to do it, would require outside expertise,” she says. SKF initially partnered with, and then bought, Israeli machine learning Presenso in October 2019.
SKF industrial customers are already benefitting directly from Presenso technology, which can automatically sift through the data generated by a factory, spotting issues and improvement opportunities.
The technology is particularly powerful when connected to other datasets collected by SKF. Van Camp explains: “In a paper mill, for example, you may have hundreds of bearings in rollers and conveyors, all working in a hot, wet environment and in the presence of aggressive chemicals. Corrosion is a potential problem for those bearings, and we have invested a lot of time developing special coatings and barriers to prolong the life of our products. Now, using machine learning and AI, we can look much more deeply at what is going on in those plants, and differentiate the places where a standard product will work well from the few cases where you need a more specialised solution.”