The adoption of the Internet of Things (IoT) can be seen across almost every industry. From cars and medicine to energy, agriculture, cars and ships, all are being impacted by this transformational set of technologies. The economic effect will be huge, with estimates placing spending on the IoT worldwide to reach $500 billion by 2020.
More optimistic forecasts peg the market as high as $15 trillion of global GDP by 2030i. In China alone, it could deliver up to $1.8 trillion in cumulative Gross Domestic Product. It is clear that businesses need to be forming IoT investment strategies as they maximize the way IoT is transforming interactions between humans and machines.
While consumer adoption of connected technology has been gradual, we will see more pervasive adoption among this group over the next decade. However industrial adoption of IoT, the Industrial Internet of Things (IIoT), is advancing at a faster rate. During the past three years, the number of sensors shipped globally increased more than five-fold from 4.2 billion units in 2012 to 23.6 billion units in 2014ii.
Operational efficiency is one of the key attractions of the IIoT, and early adopters are focusing on harnessing these benefits. Deploying automation and using IIoT to make production techniques more flexible could boost productivity by as much as 30 percent. Predictive maintenance of assets through this technique could result in savings up to 12 percent over scheduled repairs. Overall maintenance costs can be reduced by up to 30 percent and up to 70 percent of breakdowns can be eliminatediii.
However, there is more to the story.. It is not only a source of operational efficiency, but also one for breakthrough innovation and new growth opportunities. Accenture research shows that 73 percent of companies are already investing more than 20 percent of their overall technology budget on big data analyticsiv.
Adding sensors and data collection mechanisms to industrial equipment has led to exponential growth in data volumes. Add to that the growing technology capabilities in the area of analytics — radically enhancing the ability to mine and analyze data for future-looking insights.
For example, a major car manufacturer is pursuing a unique approach to increase value for customers: a flexible, convenient pay-per-use model for city dwellers needing cars. Customers can use an app to find the car that is parked nearest to them. They open the door with a membership card, drive to their destination, and simply park the car on the street and lock it up.
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This service competes with conventional taxis and hourly car rental services. Customers can choose to pay by the mile, the hour or the day. The rates are lower than for taxis, and there is no need to reserve, return or order a car; the cars can be parked and found anywhere using location sensors.
The fact that the Board of Directors are the primary influencers of big data analytics strategy (53 percent as compared to 28 percent of COOs as primary influencers)v, proves that businesses are increasingly viewing IIoT as a new source of innovation-driven revenue. (See chart below)
Source: Industrial Internet of Things: Unleashing the Potential of Connected Products and Services, WEF, January 2015
i, iii, iv, v Industrial Internet Insights Report for 2015, Accenture
ii Elfrink, Wim. "The Internet of Things: Capturing the Accelerated Opportunity". Cisco Blog, October 15, 2014
But are most companies up for the challenge? Survey results indicate, that only 40 percent can predict based on existing data, and fewer still (36 percent) can optimize operations from that data.
Understandably, initial investments are focused in areas for improving the ability to react and repair and move toward zero unplanned downtime. However, when asked about future plans, respondents stated their priorities in more ambitious plans for analytics: increasing profitability (60 percent), gaining a competitive advantage (57 percent) and improving environmental safety and emissions (55 percent) represent more pervasive, cross-functional and sophisticated use of analytics.
At Accenture we believe that the transition from efficiency to value-enhanced growth, is likely to follow four distinct phases. (See chart below) Phases 1 and 2 will represent immediate opportunities that drive the near-term adoption, starting with operational efficiency.
These are the present, and will only accelerate in the next couple of years. Phases 3 and 4 will include long-term structural changes that are roughly three years away from mainstream adoption. These last two phases will lead to disruptive changes in businesses and industries. They will manifest themselves in the form of the outcome economy and an integrated human-machine workforce.
The outcome economy will be built on the automated quantification capabilities of the Industrial Internet. The large-scale shift from selling products or services to selling measurable outcomes is a significant change that will redefine the base of competition and industry structures. As the Industrial Internet becomes more ingrained in every industry, it will ultimately lead to a pull-based economy characterized by real-time demand sensing and highly automated, flexible production and fulfilment networks.
This development will call for a pervasive use of automation and intelligent machines to complement human labour (machine augmentation). As a result, the face of the future workforce will change dramatically, along with the skill sets required to succeed in a much more automated economy.
Yet, it is still early. Numerous technology challenges and important hurdles remain to be overcome. Not all products can or need to be connected. But amid the new, an old truth remains: business customers need products and services that create more value for them than those on offer today. The time to push is now.
