Predictive Analytics: Giving Smart Manufacturers an Edge

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predictive analytics“The quality of service a company can deliver after the initial sale is important to the overall long-term financial health of the company, and Predictive Analytics can fundamentally improve the way a company can deliver service,” said Gary Brooks, Chief Marketing Officer at Syncron in a recent DATAVERSITY® interview.

Syncron specializes in using Predictive Analytics to help manufacturers of durable goods maximize uptime and deliver exceptional after-sale service experiences.

“We serve a growing list of customers like Hitachi Construction, Volvo, Deutsche Bahn, JCB, Motor Coach Industries,” he said. “If you think about big equipment manufacturers around the world, there’s a high probability that we’re helping them to improve their after-sales service.”

Durable Goods

Aircraft, appliances, mining equipment, industrial equipment, and cars are examples of durable goods: heavy equipment designed to last more than five years. Durable goods are repaired rather than thrown away, so manufacturers also offer parts needed for the service and repair of those machines. “Manufacturers are realizing that what they do after the sale is as important as what they do before the sale – and it has such an impact on the financial performance of the company,” he said.

Typically, manufacturers have focused on improving the product side of the business by optimizing the manufacturing floor, engineering products better, or maximizing sales. Brooks believes that’s no longer the smart choice and now, “The margins are growing in the wrong direction. They’re very attractive on the service side,” yet the manufacturing side is lagging behind.

“The Service Council study revealed that upwards of 27 percent of manufacturing companies’ total revenues come from service, while a separate Bain and Co. report suggest service averages a gross margin of 39 percent—significantly higher than margins on most manufactured products. The Bain and Co. study also revealed that manufacturing companies’ service business grew by 9 percent annually compared to the 5 percent growth rate captured on the product side of the business, and many manufacturers expect this trajectory to continue.”

With service becoming more important to success, manufacturers are looking for new ways to better compete, and Brooks remarked that Predictive Analytics can provide that competitive edge.

Business Intelligence vs. Predictive Analytics

The Dama International Data Management Book of Knowledge describes Predictive Analytics as the development of probability models based on variables, including historical data related to possible events. Based on user-defined criteria, a model triggers a reaction by the organization. The triggering factor may be an event, such as the introduction of a certain type of sound, which could indicate that a part was nearing its end-of-life.

According to Dell/DMC, Business Intelligence is descriptive, reporting on what happened in the past and what is happening now. Predictive Analytics looks forward, considering the possibility and probability of what could happen. BI uses reports; PA uses the information supplied by BI to create probability models. Business Intelligence is uses a reporting tool to interpret historical data. Predictive Analytics uses historical data to predict the likelihood of future events. Each has a different role to play.

Predictive Maintenance Creates a New Model

Companies have historically operated on a ‘break-fix’ model, where something breaks and the company reacts by initiating a fix. If, for example, an HVAC system breaks on a hot day, the break-fix model leaves the waiting customer unhappy and uncomfortable while someone is dispatched with parts to go out and fix it. Brooks sees a trend toward a more proactive model:

“Imagine a world where the parts in your HVAC system are smart parts, where there are sensors built into that piece of equipment that are constantly sending signals,” he said, “and it’s letting us know when a part may fail.”

It’s not the reactive scenario where a part fails, “your system’s down and you’re dying in heat.” This proactive model allows a technician to do a preventive maintenance call and repair the part based on the signal it sends before it fails, thus preventing down time.

Brooks said Syncron is unique because they’re “maniacally focused on one thing, and that’s helping customers deliver the best service experience possible,” and predictive maintenance is how they do it.

“When you think about whether it’s your air conditioning unit in your house, or it’s your car, or whatever you may have that’s a durable good – if it breaks and that repair experience is seamless, or if it doesn’t break because of the use of Predictive Analytics – there’s a high probability that you will buy that product again.”

Changing Expectations about Service

Economic pressure, demographic pressure, and the proliferation of social media on a global level are resulting in a power shift from the manufacturer to the consumer, remarked Brooks:

“Ten years ago, the manufacturer would say, ‘Okay, I’m going to come and fix it on such and such day at such at such time,’ and now the shift is to the consumer saying, ‘No, I only have a window of this specific time and you need to come and fix it then.’ The companies that are getting this – and truly becoming customer-centric – are the ones that will win in the long term because they understand that their long term financial health is really dependent on making these customers ecstatic with the level of service that they’re receiving.”

Transformational Awareness of the Impact of Demographics

In addition to a change toward proactive maintenance, another trend shaping manufacturers’ interest in Predictive Analytics is an increased awareness about the impact of changing demographics. “It’s no surprise to anyone that we’re in the middle of one of the largest demographic shifts that we’ll see in our lifetime,” he said. “By 2025, 75 percent of the US workforce will be composed of millennials, and by 2030, a third of the global workforce will be millennials.”

As a result, manufacturers are becoming more interested in understanding the service expectations of those customers in the millennial age bracket. “If I’m a millennial and my car is not serviced the way that I expected it to be serviced,” he said, “I’m going to social media to share [my experience with] that manufacturer,” which has the potential to be “devastating to the brand.” As a result, manufacturers are seeing the benefits of delivering superior service.

“I’m far from a millennial, but I even see my own expectations changing. I travel around the world and I use Uber whenever I can. I like being able to instantly order up a driver, and I expect them to be there within minutes, and I expect to be able to see when they’re coming around the corner in real-time,” he said. “Well, when someone’s coming to do a repair at my house, I want that same level of service.”

Companies like Amazon are making same-day service a reality:

“So everything’s instantaneous, and even for me, not being a millennial, it’s shifting my expectations about service. I want to schedule my appointment from my car, on an app. I want the app to tell me when my car is having a problem so I don’t experience any downtime.”

Just in Case vs. Just in Time

Service providers typically need to keep a large variety of parts in inventory so their customers don’t have to wait while they order a part. “Having it ‘just in case’ requires lots of inventory in lots of locations if you’re a manufacturer around the world, but if I now know when something’s about to break, I can have it there ‘just in time.’”

If a customer is bringing a car in next week and analytics have determined the part has another 30 days of life before it fails, “We now have enough time to move that part to the right location at the right time,” no matter where in the world the part is, he said. “So the overall amount of inventory and cost that’s needed will be significantly reduced.”

Brooks said that an important metric for durable goods is over-the-counter fill rates, which is an indication of how often any one part is available when a customer needs it. Predictive Analytics can:

“Enable us to maximize those fill rates in the upper 90 percent range, where every time that that vehicle needs a repair, the part will be available – [as opposed to today, where] 50 percent of service attempts fail because of the lack of a part,” he said. “That’s in the current inefficient break-fix world, but in the new world, using IOT and predictive analytics, we can drive those fill rates up to near 100 percent.”

Uptime is Everything

The new sharing economy is providing opportunities for changing the concept of equipment ownership, as well. “We’re seeing that across a bunch of other industries from motorsports to heavy equipment, where if I’m a large construction company, rather than buying a large piece of heavy equipment, I’m buying access to that vehicle,” he said. “I get access to it so many hours a month, and I’m paying for the service that that vehicle delivers, rather than buying a big expensive piece of capital equipment that needs to be maintained.” This transfers the responsibility for the manufacturer to guarantee near 100 percent uptime because if the fleet is not running, it’s not making money.

Brooks predicts that this shared economy will continue to expand across industries:

“If I’m the construction company and I own that crane and it fails, it’s no longer a revenue-generating asset for me on that day, but if I’m buying access to that piece of equipment from a fleet owner, they would have to quickly bring in another replacement vehicle so I can continue to generate revenue with that piece of equipment, so it’s a fundamental shift in the model.”

Brooks and his colleagues see the industry continuing to evolve through the use of Predictive Analytics.

“I was chatting with one of our partners who’s been in this industry for quite a while, and he feels that the transformation that we’re about to go through in the durable goods industry over the next decade will just be amazing, where not only our car is talking to the manufacturer, cars will talk to each other, and the world that we know of durable goods will look extremely different in just a few years [from how] it did a decade ago.”

Syncron’s solutions continue to help a growing number of companies optimize the performance of their service parts business. “By having the right part at the right time, at the right place, at the right price, the manufacturer can deliver a very high-quality level of service while maximizing their revenue and their profits,” he said. When pricing is optimized and inventory levels are reduced, less capital is tied up in parts, which can improve gross profit margins in the range of 10 percent to 40 percent, “And that delivers value right to the bottom line.”


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