A recent report by Vodafone Business found that COVID-19 has ignited a surge in Internet of Things (IoT) adoption, with 79 percent of U.S. businesses saying they’ve launched new IoT projects as a result of the ongoing health crisis. However, as IoT continues to digitally transform enterprises and their operations, business leaders must understand how their workers interact with IoT and equip their workforces with the training needed to deploy this disruptive technology effectively.

Unfortunately, despite the unique skill sets and processes that IoT demands, worker training is a consistently overlooked element of IoT. This is especially true for industrial IoT (IIoT) sensors and other devices used across the utilities and manufacturing sectors. Numerous industrial organizations are realizing the value of IIoT, but many of these businesses tend to over-rely on automation and fail to support their workers on the front lines whose jobs are immensely impacted by IIoT adoption, and the vast troves of data it creates.

As IIoT emerges as a go-to vehicle for driving operational visibility, efficiency and bottom-line savings, IIoT frontrunners are increasingly extending IoT data to the edges of the enterprise, where workers in the field interact with the physical equipment and the world around them. This means more and more industrial workers are using mobile apps, wearable technology and augmented reality solutions to carry out day-to-day tasks.

For oil and gas companies, for example, IIoT increases the real-time data monitoring of rigs and optimizes productivity, but this data still needs to be communicated to workers on rig sites to allow them to update processes accordingly. Similarly, across manufacturing, sensors on production lines can create efficiencies, yet there are often still field workers doing the heavy lifting, and the data created on these lines will prove critical to how they do their jobs. Therefore, if these essential workers are not properly trained on new IoT-related tasks, the skills gap across the industrial sector will widen dramatically and these organizations will likely face costly repercussions.

While most organizations have training programs in place that cover basic processes of each role, traditional training methods simply do not apply when providing digital training. More often than not, the training and ongoing support required to build a well-equipped, engaged industrial workforce is not in place. 

It’s important to train workers in the environment in which they will be working. For IoT tools used on the job, these digital trainings should be hands-on, self-guided simulations that occur within the IoT applications themselves. However, to carry out a strategic, full-scale IIoT transformation, organizations cannot view training as a “one and done” event.

Rather, it’s important to implement a digital training solution that delivers ongoing employee education and real-time support that proactively reinforces learning. In addition, as IIoT rapidly evolves, these ongoing trainings will allow industrial organizations and their workforces to adapt accordingly and accommodate new trends, cultural expectations, best practices and security and compliance procedures.

IIoT is now a vital component of digital transformation in industry, especially as these organizations continue to grapple with the unique circumstances surrounding the COVID-19 pandemic. IoT technology holds great promise, but at the end of the day it’s the humans who are ultimately interacting with IoT systems and leveraging the actionable insights that they provide who business leaders cannot afford to lose sight of in their technology deployments.

Before overhauling traditional infrastructure in favor of IIoT technologies, organizations must ask themselves how they will upskill their workforce and empower them to retain these newly learned skills. Those that best prepare their employees with the initial and ongoing education needed to take on this new industrial revolution will be best prepared to surpass competitors and break into new markets. 

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“Hey Siri, what is IIoT?” Every time you ask Siri, you get an accurate answer. Wonder how your virtual assistant suggests, thinks and talks like a human being? Because it has been taught that way through machine learning.

Finance, retail and healthcare are just a few of the many industries that are benefitted by machine learning. So now that Siri has given you a whole lot of information about what Industrial IoT (IIoT) is, let’s delve a little deeper into how machine learning and AI can enhance IIoT.

Redefining The Future Of Business

What IoT does to consumer personal devices, IIoT does it on an industrial scale by helping the machines and equipment communicate and relay important information. When machines have the capability to communicate with each other through sensors, it increases efficiency, saves cost and streamlines the entire workflow.

For instance, Airbus has launched ‘Factory of the Future’, a digital initiative to restructure their manufacturing process. By integrating sensors with tools and providing employees with wearable technology such as smart glasses, Airbus improved productivity while experiencing a significant reduction in errors.

Let’s look at another popular example which is IBM Watson. The AI tool is being used for oncology research. It analyses patient data, their medical records, and various other factors to assist oncologists in informed decision-making.

All this and more adds up to an Accenture survey which states that the Industrial Internet of Things (IIoT) could easily add $14.2 Tn to the global economy by 2030.

While IIoT is steadily creating a lasting impact on industries, there is still a huge area that can be unlocked with the help of AI and machine learning to increase cost-saving, improve security, augment performance and boost resources.

Leveraging Data For Enhanced Business

IIoT translates to creation of large amount of data. Generation of data is good. But how are companies using that data to their advantage? Not every industry that uses IIoT takes advantage of machine learning and AI in their IIoT platform.

Big data is not a novel concept for many business domains. But sorting out useful data from a big chunk of data pile is not an easy task. For instance, a Boeing 787 creates more than half a terabyte of data every flight.

A Bombardier ‘C’ series jetliner equipped with Pratt & Whitney engines with around 5000 sensors produces 5000 GB of data. And we are still at individual aircrafts from one industry. Imagine the volume of data generated every flight by thousands of aircrafts around the world.

What about the plethora of data generated from the retail industry or even healthcare for that matter? Ecommerce websites use machine learning to understand consumer buying patterns and offer recommendations. It is beyond an individual’s capability to filter valuable information from the amount of data being collected from every machine that is connected in an IIoT network.

This is where smart technologies like machine learning and AI step in. The data generated by these engines use AI to predict the power needed and helps in improving performance by reducing fuel consumption. Using machine learning and AI in IIoT can predict the behavior of machines using previously collected datasets.

This can prevent accidents, incidents and other damages that could cost an organization dearly. A good  example is the Indian Railways that has deployed AI to ensure safety of trains through remote monitoring and real-time detection of failure in the signal systems.

Machine learning and AI can be applied to IIoT network in any domain. If you take healthcare, the huge amount of money pumped into Artificial Intelligence researches and start-ups in healthcare shows how well machine learning and AI can aid in offering enhanced healthcare to patients.

The quantifiable data generated from wearable devices or biosensors can be carefully analyzed and treatments can be altered accordingly. A meticulous analysis of data also means a more accurate diagnosis.

The Road Ahead

Machine learning is a step ahead of predictive analytics. It will not only offer an answer to a proposed situation, but will evaluate the outcome of the situation and communicate to the computer the various permutations and combination of factors that can make the outcome possible.

Incorporating machine learning and AI in IIoT can easily identify the potential failure of machinery at an early stage. This will save companies huge costs they would have to incur as a result of unexpected downtime, equipment failure, and cost of repair, production losses and damage caused to personnel.

With the help of machine learning and AI in IIoT, a large energy and utility organizations can predict consumer demand and make timely adjustments to their supply. Machine learning and AI in IIoT helps companies ensure reduced overhead costs as all the analysis and forecasts are completely automated. It doesn’t need a workforce to constantly monitor systems, evaluate data generation and predict possible outcomes.

Machine learning a subset of AI and IIoT cannot be looked as separate entities any longer. They have to go hand-in-hand for organizations to reap a number of benefits as well as gain a competitive edge.

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