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The Outcome Economy, Powered by IoT

The Outcome Economy, Powered by IoT

Twenty-five years ago, I was in business school when a professor gave us the assignment of forecasting the global demand for drill bits fifty years into the future. My fellow students and I approached the problem in pretty much the same way, by making assumptions about how the world would be in that time, and what the impact would be on the drill bit marketplace. We’ll be off fossil fuels by then, so no more need for those kinds of drill bits. The population will be much larger, and that will drive demand for hand tools that rely on drill bits. After everyone took their turns providing a number and the rationale behind it, the professor informed us that we were all wrong. The answer, he explained, was that “fifty years from now, the world-wide demand for drill bits will be zero. However, the world-wide demand for holes will be enormous!”

The point of this lesson was twofold. First, that it is myopic to think that people need certain assets; rather what they need is the outcome of that asset. People don’t need cars, they need mobility. Cities don’t need street lights, they need streets that are safe to drive on and walk down at night. People don’t need drill bits, they need holes. 

Secondly, that this shift from buying products to buying outcomes would require emerging digital capabilities that we were just beginning to catch glimpses of 25 years ago. These digital capabilities would enable companies to measure, analyze, and adjust their offerings in near real time in order to deliver and quantify their value. Such outcomes may range from guaranteed machine uptimes on factory floors, to actual amounts of energy savings in commercial buildings, to guaranteed crop yields from a specific parcel of farmland.

Half way in, and we certainly appear to be well on our way to realizing that prophecy. Enabled by increasingly rugged, low cost sensors, the physical world is becoming digitized. Over the last 10 years, the digital exhaust from these sensor readings has enabled greater efficiencies, safety, and revenue opportunities.

Companies like Union Pacific were early beneficiaries by analyzing 20 million daily sensor readings that described the temperature and sounds from train wheel bearings. Union Pacific can now predict a derailment with a high degree of confidence more than a week out, which has cut bearing-related derailments by 75 percent and reduced unscheduled maintenance-related delays. Quite an achievement considering that a train derailment can cost upwards of $40M and put lives at risk.

Valmet has traditionally been a manufacturer of pulp grinding machines that produce tissues, glossy paper, cardboard and other paper products. Valmet began instrumenting these machines - which are the size of a football field - to better understand what leads to unplanned downtime and inefficient consumption of machine consumables, such as belts, felts and chemicals. The resulting data and analytics have led to two new revenue opportunities for Valmet. First, they can deliver a service to clients on how to best optimize the machine for maintenance, which leads to higher uptime for their clients. Second, they are able to quantify the value of their higher priced (and higher quality, as a matter of fact) consumables with respect to life expectancy under actual client operating conditions.

What we are starting to see now is that the industrial IoT leaders are establishing board level goals that go beyond operational efficiencies, safety, and add-on revenue streams to something much more disruptive and fundamentally game changing by selling outcomes.

Companies like Monsanto are moving from selling products like seeds and fertilizers, to precision agriculture where crop yields are maximized. By connecting smart farm equipment such as tractors, tillers and seeders with data on weather, soil conditions, and crop health, Monsanto can measure, analyze, and adjust activities like when and how a farmer ploughs his field, how deep to plant the seed, and spacing of plants in a row. Crops have their best chance to reach their highest potential when data from billions of events, coupled with combinations of analytic techniques involving statistics, machine learning, and graph analysis aid farm management practices.

Spanish train operator Renfe was looking to take market share from airlines on the route between Barcelona and Madrid.  Airlines at that time had 80% market share, due in most part to business travelers valuing the on-time performance of airlines compared to trains.  Enter Siemens, which didn’t just sell Renfe a train and a warranty; rather they continually monitor and resolve issues before they happen in order to deliver on the promise of reliable mobility.  A train developing abnormal patterns is dispatched for an inspection service to prevent failure on the track. This has resulted in only one out of 2,300 journeys being delayed by more than five minutes.  

“That happens because we have data, we have analytics models, and we can actually predict certain failures,” said Gerhard Kress, Director of Mobility Services at Siemens. “There’s gearboxes, for example, on high-speed trains, it’s one of the things that is most tricky to monitor. We had a couple of cases where you could predict those things would be breaking in a few weeks. We had ample time to provide the spare parts, do the right thing, repair it, take the train out of normal circulation without harming the schedule, and work with the customer without having any problems for them.”  

Now, the airlines are down to 30% market share.  Siemens is increasingly selling more outcomes because they have the capabilities to measure, analyze, and adjust in order to deliver on the promise of that outcome.

Far from isolated case studies, we are seeing similar transformations based on outcomes at Boeing, VolvoMaersk, and many others. What they all have in common is they are industries that rely on heavy, complex assets where those assets are used by others to play a part in a much larger outcome. That leads me to think that while my professor was prescient, Home Depot and Lowes will still sell simple hand held drills 25 years from now.
Portrait of Chad Meley

Chad Meley

Chad Meley is Vice President of Solutions Marketing at Teradata, responsible for Teradata’s Artificial Intelligence, IoT, and CX solutions.

Chad understands trends in machine & deep learning, and leads a team of technology specialists who interpret the needs and expectations of customers while also working with Teradata engineers, consulting teams and technology partners.
Prior to joining Teradata, he led Electronic Arts’ Data Platform organization. Chad has held a variety of other leadership roles centered around data and analytics while at Dell and FedEx.
Chad holds a BA in economics from The University of Texas, an MBA from Texas Tech University, and performed post graduate work at The University of Texas.
Professional awards include Best Practice Award for Driving Business Results in Data Warehousing from The Data Warehouse Institute and the Marketing Excellence Award from the Direct Marketing Association. He is a regular speaker at conferences, including O’Reilly’s AI Conference, Strata, DataWorks, and Analytics Universe. Chad is the coauthor of the book Achieving Real Business Outcomes From Artificial Intelligence published by O'Reilly Media, and a frequent contributor to publications such as Forbes, CIO Magazine, and Datanami.

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