Early in the internet of things (IOT) adoption cycle, it's often assumed the technologies will permeate industries in roughly the same way.
But there will be differences across major sectors of the economy -- and it's helpful to understand how those differences will influence IOT adoption.
IOT adoption assumptions
Many believe that the core value proposition applies to different industries in the same way -- more data from more sensors (things) combined with machine learning and predictive analytics will unlock significant value to tune operations. The industry-specific data is a detail that can be addressed with user interface tweaks.
There are many firms building multi-industry IOT platforms. These companies typically invest in a variety of platform components such as data sensors and hardware, data science algorithms, and a front-end user interface. This model has been successfully implemented in the past: Sales and marketing automation software solutions, among others, are designed for nearly all industries.
But there is an open question for IOT in general, and IOT for buildings specifically: Will platforms be industry-agnostic? With the IOT adoption curve in buildings mimic other industries?
Looking at the opportunity for IOT solutions within factories, agricultural facilities, and buildings indicates that the adoption curves will be significantly different. On one hand, all three of these industries have assets that are geographically distributed, a fragmented vendor landscape, sales and customer support that is dependent on relationship-driven networks, and a reliance on third-party service contractors to deliver the full solution to end users.
An IOT solution can cut out the inefficiency and transform how end users perform their jobs in a new, more data-driven way, right?
Maybe not. There are significant differences between these industries.
Understanding the differences between buildings, agriculture and manufacturing
Buildings, even without an IOT solution, already have a high level of current data about operations, based on extensive existing sensor networks (typically the building automation system, plus lighting control, energy metering, and others). Additionally, many buildings do not have clearly quantifiable and compelling value propositions for IOT solutions.
This is significantly different than agricultural sites, which typically have very little data about their operations. It also is different than factories, which can easily quantify a reduction in defects or an increase in assembly line productivity. Looking at these two metrics -- current operational data visibility and quantifiable value -- is a good way to understand the challenges that IOT solutions will have in buildings.
Certain metrics make buildings look like a home run for IOT solutions. Most commercial buildings already have many sensors and systems that are disconnected from the cloud. Additionally, 30 percent of energy in buildings is wasted, which adds up for portfolio owners and enterprises with many locations. A recent McKinsey report on IOT estimates that connectivity can reduce energy in buildings by 20 percent and can lead to a nearly 20 percent increase in productivity.
The 20 percent energy savings is a big number, but it is likely only a few percentage points of the total operating budget, given that energy typically is a smaller cost compared to total facility spend and workforce costs.
Productivity increases are compelling, but how can they be measured? If an employee can do 8 hours of work in 7 hours, what does that mean to his or her employer? There are good data to indicate that green buildings are more productive buildings and cut down on employee sick days, but it is hard to quantify this on a building-by-building basis.
Additionally, buildings already have very comprehensive data networks, called building automation systems (among other control and data networks). A complex domestic office building may have up to 10,000 BAS points. Over a year, if the BAS is trending (saving to a disk) the data every 15 minutes, there will be a total of 350 million time-stamped pieces of data that can be accessed by facility management professionals. Adding submeters as an IOT solution deployment will increase the level of data, but even 5 meters per floor of a 50-story building is a fractional increase in data.
Connecting the data -- BAS, submeter, or other -- to the cloud, serving it up in a dashboard, and providing some analytics on this information does have value. But it is not a 10x improvement when much of the data already can be found in the on-premise BAS.
The IOT opportunity in factories tells a different story. Many industrial facilities do have advanced programmable logic controller (PLC) systems that provide good data visibility. But there is wide interest in improving the level of data collection.
A recent survey from PwC on IOT in manufacturing found that 35 percent of firms have implemented smart sensors to collect detailed operational data. Another 40 percent of firms plan to implement IOT solutions in the future. The paper also highlights specific factories that have installed up to 10,000 new sensors as part of an IOT deployment, comparable to a standard commercial building BAS. Similar to buildings, a lot of the data collected is not used. A recent Industrial Internet Consortium report found that 99 percent of factory data is discarded without being used to provide any insight.
As factories begin to use the data they have, or collect more from new sensors, they will realize a significant increase in data visibility. This has a compelling and quantifiable benefit.
The organization IoT Analytics reports that the average factory runs at 60-70 percent overall equipment effectiveness (OEE) and “world class” factories are just 85 percent OEE. Increasing equipment use has a direct impact on the top line of manufacturing facilities.
The McKinsey report notes that while the total economic impact of IOT by 2025 in offices is between $70 and $150 billion, it is between $1.2 and $3.7 trillion in factories. (This is the largest sector by total economic impact in the McKinsey report.)
Agricultural sites like farms also have been focused on IOT solutions, because the value can be quantified clearly and in a compelling way. Additionally, many farms are starting at a very “data-light” position. Specifically, OnFarm, an agtech vendor, estimates that the average farm will generate 4.1 million data points per day by 2050, up from 190,000 in 2014.
Additionally, farms can quantify the value of agtech solutions because they lead to higher yields, another top-line benefit. A 2016 Deloitte report on agriculture quantified two areas of growth driven by agtech -- a 30 percent potential increase in yield and a 33 percent reduction in value chain losses. The report conveys a sense that the vendor landscape and range of technology and service offerings will only grow in the future.
The agriculture industry of the future may look very much like the robust set of technologies, services and vendors that currently serve buildings. This growth will support a 10x benefit in the way farms are run.
A 2015 Andreessen Horowitz podcast about agtech noted that many people working in farming have little business experience -- most are family-run businesses. A data-driven view of operations can have dramatic positive impact. For example, sensing the water flow in plants (called the plant’s “blood flow” by the podcast guests) can help optimize harvest dates. This enables a farm to harvest everything on one particular day, rather than over multiple days (which raises costs). The value proposition of using data to optimize operations is particularly clear when, as a podcast guest noted, “You have one chance a year to make a profit."
Buildings, factories and agricultural sites present different IOT opportunities. Buildings appear to lag behind in two metrics: 1) the increase in operational data that IOT enables; and 2) the compelling and quantifiable benefit of this data.
However, the overall size of each market also will dictate the future prospects of IOT solutions. The more advanced technology posture of many facilities may make scaling IOT solutions more streamlined than in factories and agricultural sites.
Joseph Aamidor is a senior product management consultant focused on smart buildings, IOT and energy. He helps startups and established industry players understand the smart buildings market, develop competitive strategy and forge partnerships. He previously served in senior product management roles at Lucid and Johnson Controls.