Listen to the audio version of this article!
In my previous #askIoT post, IoT Will Transform Our World, we saw that the Internet of Things (IoT) adds value in three major areas: increasing efficiency, improving health/safety, and creating better experiences. But you may have also heard of the Industrial Internet of Things (IIoT) and wondered, what’s the difference between IoT and IIoT?
“Worldwide…a 1% improvement in industrial productivity could add $10 trillion to $15 trillion to worldwide GDP over the next 15 years.” — Chunka Mui, Forbes
The difference between IoT and IIoT
The Industrial Internet of Things deals with the first two areas, increasing efficiency and improving health/safety.
IIoT refers to a subcategory of the broader Internet of Things. IoT includes IIoT plus things like wearables, smart ovens, or smart consumer products. IIoT focuses specifically on industrial applications such as manufacturing or agriculture.
The Massive Potential of IIoT
“The Industrial Internet [of Things] will transform many industries, including manufacturing, oil and gas, agriculture, mining, transportation and healthcare. Collectively, these account for nearly two-thirds of the world economy.” — World Economic Forum, Industrial Internet of Things Report
In recent years, innovations in hardware, connectivity, big data analytics, and machine-learning have converged to generate huge opportunities for industries. Hardware innovations mean that sensors are cheaper, more powerful, and run longer on battery life. Connectivity innovations mean that it’s cheaper and easier to send the data from these sensors to the cloud. Big data analytics and machine-learning innovations mean that, once sensor data is collected, it’s possible to gain incredible insight into manufacturing processes.
These insights can lead to massive increases in productivity and drastic reductions in cost. Whatever is being manufactured, it can be done faster, with fewer resources, and at lower cost.
An example of the potential of IIoT is predictive maintenance. A broken machine in a manufacturing process can mean millions of dollars in lost productivity while production halts to fix the issue.
The past solution was to regularly scheduled maintenance, but this has a few issues. What if the machine breaks before the maintenance? This leads to huge loss of productivity as described above. And what if the machine doesn’t need maintenance? Time, effort, and money is wasted that could be better spent elsewhere.
Predictive maintenance means using more sensors to collect better data on machines, and then using data analytics and machine-learning to determine exactly when a machine will need maintenance. Not too late, which leads to broken machines, and not too early, which leads to misallocated resources.
Predictive maintenance is just one example, and it’s already a reality.
As adoption and advancement of IIoT accelerates, the changes will be profound. Eventually we can achieve an autonomous economy in which supply exactly meets demand, completely optimizing the production process and leading to zero-waste.
And there’s every reason to think that IIoT will accelerate in the near-term…
Adoption of IIoT
In many ways, IIoT is ahead of IoT, and will continue to see faster adoption. Why? A key difference between IoT and IIoT is that, unlike consumer IoT applications, incentives for adopting IIoT technologies are much greater:
“[IoT and IIoT have] two distinctly separate areas of interest. The Industrial IoT connects critical machines and sensors in high-stakes industries such as aerospace and defense, healthcare and energy. These are systems in which failure often results in life-threatening or other emergency situations. On the other hand, IoT systems tend to be consumer-level devices such as wearable fitness tools, smart home thermometers and automatic pet feeders. They are important and convenient, but breakdowns do not immediately create emergency situations.” — RTI
Another difference between IoT and IIoT is that there are clearer near-term benefits for IIoT vs IoT. Manufacturing companies can reduce costs and increase productivity, meaning more tangible return-on-investment for adopting IIoT solutions. Companies like ThyssenKrupp, Caterpillar, and Thames Water are already reaping benefits from being early IIoT adopters.
But IIoT isn’t without it’s challenges…
Barriers to IIoT
Two of the biggest hurdles are security and interoperability.
Bringing physical systems online generates substantial benefits, but also means that those systems can be potentially compromised. Cyberattacks become scary when they can enable remote control of or damage to physical systems; huge financial losses at best and serious injuries or death at worst. Security is a major concern for IoT in general, and needs to be a big part of the conversation in the coming years.
To collect the data from sensors and make that data useful, everything in the system needs to work together. Lack of interoperability and lack of standards between IoT sensors, devices, connectivity, and communication protocols can hinder the process of connecting everything. This is also a problem for IoT in general.
Considering the Implications of IIoT
The above graph shows an incredible increase in U.S. productivity over the last few decades.
“In 1980, it took 25 jobs to generate $1 million in manufacturing output in the U.S. Today, it takes just 6.5 jobs to generate that amount ” — Brookings
As we head into the future and see accelerated IIoT adoption, the increases in productivity will be even more pronounced. Tesla’s Gigafactory will be highly automated, promising a staggering $100 billion in output with only 6,500 workers. That’s only 1.3 jobs to generate $1 million in manufacturing output.
So what does this mean for U.S. jobs?
On the positive side, this will likely help bring manufacturing back into the U.S. from abroad. Manufacturing moved outside of the U.S. because labor was cheaper in foreign countries, but IIoT solutions will create machines and systems that outcompete this cheap manual labor.
IIoT will also create entirely new industries and categories of jobs to support these high-tech systems. Medical robot designers, grid modernization managers, intermodal transportation network engineers, and more.
However, we should be wary that there may be fewer jobs created than destroyed. As shown above, increases in productivity mean fewer jobs are needed to create the same value, potentially meaning fewer jobs overall.
And even if there is no net job-loss or even a net job-gain, we also need to consider the kinds of jobs being created and destroyed. The new job categories will demand interdisciplinary skills; deep knowledge about specific industries coupled with skills and expertise in new technologies, software, data analytics, system integration, and cybersecurity.
These jobs are not blue collar, the skills will take high-level training and education. How will this training and education be provided? Who’s going to pay for it? I don’t have answers, but these questions are critical to consider as we head into our next Industrial Revolution.