In the past few months, we have seen a push from tech giants to fill our homes with “connected” devices, therefore increasing public awareness of the internet of things or IoT. However, this isn’t such a new technology and its adoption is already much more advanced in industrial environments than in consumer markets. In fact, it is considered as a critical tech enabler in the industrial revolution we are currently experiencing, also referred to as Industry 4.0.

The market data is indeed confirming this phenomenon as a recent study by Zion Market Research estimated the size of the global industrial internet of things market at roughly 145.81 billion dollars in 2017 and predicted it to reach 232.15 billion dollars by 2023, growing at a CAGR of around 8.06% between 2018 and 2023.

At Early Metrics, we have seen a steep rise in the number of newcomers in this sector in the past four years and have had the chance to observe the key trends that affect the market. In essence, IoT is the business of collecting, aggregating and analysing data from several connected objects or devices. This means the technology lends itself to a plethora of applications. We look at the five most important trends that are affecting the current and future adoption of IoT in industrial processes.

1. Predictive maintenance and asset tracking are key areas of application

In an industrial setting where processes are increasingly automated, one of the key advantages of IoT is that it could allow for the tracking of all the machinery at a large scale. By collecting factory-wide data, say in a car manufacturing facility, algorithms could then use the information to spot anomalies faster and allow for the automation of industrial process optimisation. In turn, this data could help predict whether the machinery will experience malfunctions and indicate when to carry out maintenance work to prevent these. This is generally referred to as predictive maintenance.  Asset tracking is also a popular application at the moment. Chips can be used to track the production and delivery of items, a desirable function for luxury good producers in particular. Rated startup Juconn is a great example of a newcomer providing incumbents in the transport and agricultural industry with new ways of optimising and tracking their operations.

2. Automated fleets represent a big opportunity for future IoT adoption

In the foreseeable future, we can very well imagine that industrial giants will turn to automated fleets of trucks, cargo ships and drones to optimise their delivery of goods or the internal operations of their factories. In fact, it’s already happening: Rio Tinto’s automated haul trucks have already been in use since 2008 in mines in Australia and around the world. Along with the opportunities that automated vehicles represent, there are also new risks and challenges to be managed. That is where IoT solutions come in handy to track vehicles, manage maintenance, optimise traffic and routes as well as increase the safety of the workers.

3. Data fragmentation and cybersecurity are the main obstacles to adoption

Despite its rising popularity, industrial IoT is still not the norm in most industrial companies. One of the key obstacles to its adoption is the risk of cybersecurity. Indeed, there have been cases of hackers targeting specifically IoT devices via DDoS, leading to data leaks, drop in network and disruption of the devices’ function. This is a major concern in the case of automated fleets as hacks of this nature could not only put operations in jeopardy but also endanger lives. Startups such as Ioetec, which received a high rating from Early Metrics, are already providing solutions to protect IoT enabled devices so there is hope in this regard. Another hurdle is data fragmentation, meaning big conglomerates have so far been using non-standardized and “messy” ways of collecting and storing data which makes it difficult to implement IoT software. However, IBM, Amazon, Cisco, GE, and other giants are working on multi-tier solutions that simplify the design, integration, and management of IoT infrastructure at scale.

4. AI and IoT go hand in hand to make the data actionable

Beyond collecting data, the true value of IoT lies in the ability to analyse the data and extract actionable insight. Therefore, a layer of artificial intelligence and machine learning is needed to realise the full potential of industrial IoT. For instance, we mentioned earlier that predictive maintenance is one of the most mature applications of connected devices. Predicting industrial malfunction or simulating process changes on a wide scale depends on the availability of powerful enough software that can learn from the data collected and make recommendations accordingly. On the other hand, the efficiency of an industrial AI algorithm does rely on the quality of the data it is provided with, i.e. on the quality of the IoT infrastructure. This explains why these two types of technology are bound to co-develop and influence each others’ integration in industrial environments.

5. Edge computing might take over cloud technology

So far, the bulk of IoT innovation has relied on cloud technology to transfer and store data and as IBM’s 34 billion dollar acquisition of Red Hat shows, cloud is still very relevant. However, it’s not without its limits as storage costs are high, connectivity can suffer in remote areas and lags in data transfers can slow down operations. Edge computing is, therefore, arising as a more adequate alternative for IoT infrastructures. Placing storage and processing units closer to the sensors and in the periphery (i.e. “edge”) of the network can indeed increase the efficiency of industrial IoT systems. Moreover, “smarter” chips are being developed so that devices can do part of the data processing and communicate with each other to optimise their functioning in situ rather than through a centralised processing unit. Edge computing is still in its early days but it could very well be the preferred set up for future IoT structures.

There are therefore many ways in which the internet of things is shaping the Industry 4.0 and we can expect that the use of IoT solutions will be widespread within the next five years. As the IoT market matures, it will be interesting to see how the development of other related tech sectors such as cybersecurity, automated vehicles and most importantly machine learning will impact the application of IoT in our factories but also in our homes.