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.