Here’s a figure that might give manufacturers the chills: 70%. That’s the percentage of manufacturing companies listed on the Fortune 1000 in 2008 that have since vanished.
How did the other 30% manage to stay alive? By adjusting to radical market shifts, investing in new technology, adapting their business models to stay in the game, and, more recently, by leaning into artificial intelligence (AI) and the Internet of Things (IoT).
One of the survivors is Rolls-Royce, the industrial giant that makes jet engines, submarines, oil and gas turbines, and the massive engines that power container ships. One secret of the company’s recent success: It has leveraged the power of predictive analytics, IoT and machine learning and applied it in the service of predictive maintenance.
Predictive maintenance makes traditional preventive maintenance look downright prehistoric—and it’s turning the discipline of field service from a traditional cost center into a chief revenue officer’s new best friend. Power plant operators who rely on IoT-connected equipment, for instance, can now use AI platforms such as GE’s Predix to forecast precisely when microscopic fissures will begin to appear in their turbines and generators, which would then need repair.
These predictive capabilities have helped drive growth of what’s called “servitization,” a business model that’s turning operational silos of service and maintenance into high-margin businesses driven by AI. Here’s the model in a nutshell: Rather than simply selling a piece of industrial hardware, manufacturers can sell customers a contract to provide highly streamlined, AI-powered maintenance and repair services for that specific product. The upside for customers? Less downtime due to machine failure and fewer burdensome repair costs. The upside for manufacturers? They’re now able to leverage the data generated by IoT sensors placed within their devices into revenue that’s generated over the lifecycle of their product.
To discuss the pros and cons of this shift in the industrial sector, we(Forbes) sat down with Lubor Ptacek, VP of product marketing at ServiceMax, one of several companies owned by GE Digital that specializes in service technology.
Has technology like IoT and AI forever upended traditional business models for manufacturers?
Not yet, but it will. We have a long way to go in terms of adoption. Everyone’s trying to leverage IoT to upend their business model, but the real value of IoT hasn’t been realized yet. We have to learn how to take up the data we’re collecting, analyze it and make it useful. The challenge is to determine when and what type of service is required from the large volumes of IoT data.
That’s where AI can really help—by analyzing the data for anomalies and correlating it with the desired equipment strategies. IoT can also leverage AI to enable manufacturers to offer the service outcome rather than just sell equipment.
Do you think servitization makes more sense for a larger company than a smaller one? How does scale change the equation?
I don’t think the size of the company matters as much as the equipment they manufacture. Servitization makes the most sense when the cost of service is a significant part of the greater cost of ownership. If the cost of service is negligible, then I don’t think it makes sense. But in some industries, the cost of service can far exceed the cost of ownership. Take GE Aviation. There it makes sense. A jet engine needs service regularly. For a medical device, it makes sense, too. Those need to be calibrated regularly because you have to meet regulatory requirements, and so on.
Do manufacturers need to adopt a servitization business model in order to be competitive in today’s marketplace?
Very much so. A lot of manufacturers deal with very low profit margins on a product. They’re competing with cheaper imports from overseas. How do you compete in that situation? Well, one thing you can’t import from China or Brazil is service. If you bundle the sales with service, you’re at a competitive advantage.
To make the most of the data generated by IoT, is it smarter to hire AI specialists internally or to outsource AI work to third-party vendors who specialize in it?
Every company needs to ask themselves what makes the most sense. Ultimately, in manufacturing, the greatest capability is the IoT. Once you start putting sensors in the products, you need to make sense of how to use that data, whether you do it yourself or with outside help.
In general, what strategic advice do you have for traditional manufacturers making the transition to servitization?
Start small. Identify a small sensible project with a clear benefit and use that to get going. Say you’re manufacturing home appliances, you have multiple products and multiple lines. So start with one product. Or start with one particular area where you roll it out. Focus on something small and tangible.
How should you approach a customer about making the shift from buying a product from you to buying a service?
The one question that needs to be asked is: Is it worth it? Discuss with customers how the pricing structure is going to change and what that means for them. For the customer, the decision in the end will be very pragmatic. When you buy a car, you decide whether or not to buy a service plan for it. You do the calculations and make your decision based on how much you love the car.
Technology is evolving at such a fast pace. How do you reassure a client that a product is not going to be mothballed within a few years?
It varies from industry to industry. Have you designed the product so that certain parts can be swapped easily? Chances are, yes. In other scenarios, it depends on the expected lifespan of the equipment. If it’s a jet engine, you expect it to last 20 years, so you will want to invest in long-term service.
Customers hate when things stop working. Downtime, expensive repairs—they hate that. Servitization is the most impactful way to take the risk away from the customer and put it all on the manufacturer.
(Forbes)
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