Big Data Gives Manufacturers a New Revenue Source

Jun 05, 2015

Thanks to big data, some equipment manufacturers have never been more nimble.

Consider, for instance, that a maker of mining equipment can tell when one of its machines in the field isn’t working properly before the miners themselves know. Or that a manufacturer of air-conditioning systems could maximize a high-rise building’s energy efficiency without being on the premises.

Advances in sensors and low-cost wireless communications are allowing manufacturers to collect massive amounts of data about their equipment for years after it leaves their assembly lines. This big-data harvest is being analyzed, parsed and packaged into information that can predict when an office building’s air chiller will break down; explain why some excavators do more work than others; or identify when the roof of a coal mine is about to collapse.

Companies facing anemic demand for new equipment are finding veins of new revenue in selling unique information that they can leverage into sales of replacement parts and consulting services that keep existing machines running longer and more efficiently.

“All of a sudden, we have a whole new way of making money that doesn’t rest on a customer throwing something out and buying new,” says Michael Porter, a Harvard University business school professor. “You can fix it before it fails and get paid for that.”

This service-based business rests on information pathways collectively known as the industrial Internet of Things. At its foundation are sensors attached to all manner of things that can be programmed to collect myriad streams of data from temperature, speed and vibration levels to electricity consumption and capacity utilization. This data gets uploaded by wireless networks to cloud-based server centers where it’s stored or filtered through industry-specific software that analyzes it and transforms it into usable information. That information is then delivered to factory engineers, construction site managers, building managers—or to other equipment responsible for controlling operations.

Internet of Things

Companies world-wide are expected to spend an estimated $120 billion this year to connect factory operations, building systems and mobile equipment in the field to the Internet of Things, up 18% from 2014, according to International Data Corp., a technology market consultancy.

Last year, 278 million factory machines, construction vehicles and other pieces of industrial equipment were connected to the Internet of Things, 10.2% more than in 2013, according to technology research consultant Gartner Inc. By 2020 Gartner expects 526 million pieces of manufacturing equipment will be connected. Meanwhile, in the mining industry the number of connected products is expected to rise to 90 million in 2020 from 24 million in 2014. The 25% annual growth rate in mining connectivity is among the fastest among the industry sectors tracked by Gartner.

Just 10% of Joy Global Inc.’s mining equipment now in service is connected to the manufacturer’s Smart Services monitoring program. Nevertheless, Joy executives consider it a growth business for a company where 65% of sales now come from service and replacement parts, after soft markets for mined commodities choked off demand for new equipment.

Gary Sinclair, mine manager of a Compass Minerals International Inc. salt mine near Manchester, England, says his initial doubts about the value of Joy’s predictive analytics and monitoring were dispelled shortly after two Joy-built mining machines were connected about three years ago. Each mining machine features a large rotary drum in front with metal teeth that rip salt away from the face of underground salt beds.

Mr. Sinclair recalls that an electric traction motor used to propel one of the machines kept overheating, causing engineers to conclude that the power-and-speed adjustment control on the motor had failed. But Joy Global’s Smart Services’ sensors had detected that a heat-exchanger device—a much smaller repair job—had failed instead, causing the motor to heat up.

“They pinpointed the problem and saved us a lot of money,” Mr. Sinclair says.

Joy ride

Joy Global says about two-thirds of the information it supplies to connected customers is related to whether machines are yielding optimal production. Joy’s long-wall mining machines that shear coal from underground coal seams are up to 1,300 feet long, and can become misaligned—which reduces output—if one end of the machine advances more than the other. Sensors embedded on the machine can correct that drift multiple times during a day, allowing it to strip more coal.

“If you’re going from 800 tons an hour to 1,000 tons an hour, that’s huge,” says Ben Snyman, vice president of product management for Milwaukee-based Joy Global.

As a long-wall machine advances into a coal seam, the mine roof behind is allowed to collapse. Most such machines are equipped with fiber-optic, phone or power lines that keep them connected to Joy’s monitoring network, through which Joy’s Smart Services watches their progress. More than 200 mobile hydraulic roof supports on the long-wall machine itself also have Smart Services’ sensors that can detect pressure changes in the rock above, especially when there are cavities inside the rock that could cause the roof to collapse prematurely, burying the machine and stopping production. Depending on the mine, such cave-ins can happen as frequently as once a month.

When collapses happen, “it takes about a week to clear off that machine, and a week is about 250,000 tons of lost production,” says Mr. Snyman. With predictive analytics, Mr. Snyman says long-wall operators can take preventive action by slowing the production rate of the machine—but not stopping it—and increasing the hydraulic pressure from the roof supports.

As remote monitoring becomes more precise, equipment makers are taking on the added responsibility of maintaining peak performance levels from their products in the field. Ingersoll-RandPLC, the maker of Trane commercial heating and air-conditioning gear, supplies remote monitoring of HVAC systems and data analysis through its Trane Intelligent Services. Like other equipment makers, Ingersoll is able to accumulate long data logs on equipment and buildings that can reveal changes in temperature and the wear patterns of HVAC components that can lead to system failures.

Ingersoll develops operating strategies for its customers that balance the need to keep buildings lighted and cool with the need to meet increasingly rigorous goals for lowering energy consumption, including sometimes reducing usage on short notice to qualify for special utility payments.

Trane coming

“Users are becoming more sophisticated when it comes to energy and energy-optimization systems,” says Dave Regnery, president of Trane’s commercial business in North America and Europe. “We’re seeing a migration from [customers wanting] monthly checkups to continuous monitoring.”

Ingersoll competes in an increasingly crowded market for remote monitoring of building systems that includes HVAC rivals Johnson Controls Inc.,the maker of York equipment, and United Technologies Corp.’s Carrier brand. As connected services gain more acceptance, data and service providers beyond just equipment manufacturers are likely to jump into the market.

“This throws a lot of balls in the air,” says Harvard’s Prof. Porter.

In the wake of the evolving market dynamics, Caterpillar Inc.is fortifying its analytics business. The Peoria, Ill., company in April consolidated its suite of data-driven services under a new analytics and innovation division that includes a joint venture with Chicago-based data-analytics firm Uptake LLC.

Uptake gives Caterpillar access to young data scientists with new fresh approaches for reducing equipment breakdowns or eliminating idle time for machinery at job sites.

“We’re not data scientists, but we’re leveraging people who are,” says Caterpillar’s Greg Folley, vice president in charge of the analytics division. “With the mountain of data we’re getting, if we manage it property, we can really turbocharge [machine] productivity.”

Source: THE WALL STREET JOURNAL