Introduction
The great crew change is a common term used across the industrial sector to describe the ongoing retirement of a large body of experienced workers in factories and construction, although it started with the oil & gas and mining industries. It is usually linked to the lack of replacements to adequately absorb the skills and experience required for continued operation. IoT has a broad impact on the workforce across all sectors, as we recently wrote, in implications for the future of workforce productivity. This report looks at Internet of Things (IoT), artificial intelligence (AI) and augmented reality (AR) adoption in the industrial sectors and why it should be considered a pressing matter to engage in this wave of digital transformation.
The 451 Take
Industrial enterprises already face the challenge of dealing with the physical world and the burdens and restrictions that creates. Universal entropy means everything is in a constant state of wear or decay that has to be battled against. Demographics of the workforce present a similar challenge – people age and wear out. Technology such as IoT, AI and AR can help, but doesn't always fully replace those people in most circumstances. The more complex the environment, the more it needs high-end skills and experience to keep it running. Where industry has sought to reduce costs, running as lean as possible, it is also up against the potentially more appealing worlds of the tech giants for new hires, and it has often failed to refresh the workforce, causing this errant bubble. It is likely to be saved by the application of new technology, such as IoT, that helps get a handle on that inherent entropy more efficiently, combined with AI techniques to understand the data and remote advice via AR tools from those remaining experts to direct the new workforce. Sitting and waiting for resolution is not going to be an option – those that adopt not only become more efficient and produce better products, they also attract the new wave of workforce that has grown up digital.
Context
The initial great crew change references date back to the mid 1980s, when massive layoffs caused a major age gap in the oil & gas industry. The term was first used by the Society of Petroleum Engineers to point out the dangers of loss of knowledge due to retirement in a time when half of the crews were over 55 and the other half under 35. Throughout the 1990s, the oil & gas industry had difficulties finding technically skilled workers as the new and booming IT industry took away most of the talent. Despite the industry's efforts to close the gap, it was fueled again by the 2014 oil crash, which again saw massive layoffs in the industry. The industry service providers and operators were hit especially hard, with margins and rates under heavy pressure. Indirectly, it helped spark a revolution that could be described as the industry 4.0 moment for oil & gas – this was when these companies started looking toward IoT for process optimization and reducing waste.
Edge First
It is easy to see where the workforce focus has shifted to over the past few decades – in particular in science, technology engineering and math (STEM) professions, despite The World Bank showing the value add of industry (including construction) for the US as $3.5 trillion in 2017 and $24 trillion for the world, up from $8 trillion in 1994. The high tech providers and those enterprises built upon the new technology have the lion's share of the biggest companies on the S&P 500.
Just considering the US, the Bureau of Labor Statistics publishes numbers on employment for the largest STEM occupations. In May 2018, software developers of applications led with 903,000 people. These were followed by computer user support specialists at 630,000, computer system analysts at 588,000 and software developers of system software at 405,000, with other similar roles further adding to the total. Sales reps for manufacturing, technical and scientific products account for 312,000 people. In non-IT-specific roles, civil engineers and mechanical engineers accounted for just 306,000 and 303,000, respectively. Removing sales roles from the total, this equates to 3,666,220 jobs in high-end IT, against 609,470 in civil and mechanical engineering at only 14% of the STEM workforce. Data for annual mean wage for STEM roles in the same period shows the highest paying annual mean wage was a petroleum engineer at $156,000, with computer and systems managers at $153,000, and architectural and engineering managers at $149,000.
The Need for Digital Transformation
We have previously discussed the need for most forms of industry to engage in some degree of digital transformation in order to remain competitive, including in our
2020 Trends in the Internet of Things. In manufacturing, a key performance indicator for a factory is overall equipment effectiveness (OEE), and that measure in current manufacturing sees 65% as good and 85% as world-class. Increasing the frequency and accuracy of plant instrumentation through Industrial IoT offers a path to improve this KPI. Knowing what is happening is the first challenge. Once data is rationalized and verified as useful, it can then be used by the next wave of technology, with AI and machine learning (ML) used to not only tell what is wrong, but for predictive maintenance to stop problems before they happen. These both help to mitigate the potential loss of shop floor expertise in spotting problems. The final piece of the puzzle is to provide workers with expert guidance in how to engage with machinery. It is here that techniques such as augmented reality (as the user interface for IoT) can be used to pull lots of information sources together – from the human workforce (via experts able to assist more junior workers remotely or by providing prerecorded detailed actions to them) and from AI (the ability to ingest documentation and previous work orders, to provide helpful suggestions and guidance to the workforce through both voice and AR interfaces). It should also be noted that the use of new and interesting technology itself is being used as a recruitment tool to attract the next generation of industrial engineers.
The use of AR in industrial use cases is one of the major drivers for the technology, although many more use cases for AR and sibling virtual reality (VR) can be found in our long-form report
Augmented and Virtual Reality: Which Use Cases Are Gaining Traction and What's Next.
We track the increase in the use of IoT data, in this case in manufacturing, below, into applications of that data now and in the next two years. We are seeing a steady increase into predictive maintenance, followed by smart robotics and connected workers.
Energy Transformation
Workers in the oil & gas industry have seen booms and busts many times over the past decades, with oil prices rising to all-time highs and crashing overnight. They have seen their skills swing between wanted, hot and even undesired over the course of just a few years. After the last oil crash, in 2014, as many as 400,000 lost their jobs, according to some industry analysts. When the UK government published its
Oil and Gas Workforce Plan in 2016, Anna Soubry, then minister of state for small business, industry and enterprise, and Andrea Leadsom, then minister of state at the department of energy and climate change, wrote the foreword and stated that, "At the time of writing, the oil & gas industry is reducing its workforce, and the uncertainty created by the low oil price may cause both current and future engineers to seek employment in different areas, potentially losing this valuable skillset altogether."
The industry has seen fierce competition during the last decade, with the rise of renewable and sustainable energy sources. According to the International Energy Agency, renewable energy supplied 25% of global energy generation in 2017, and is closing in on 50% in several countries. The change is led by many new and young high tech companies asking for a different skillset than the traditional energy industry.
Agriculture
An aging population also is a strong driver for agtech, or smart farming, especially in western countries with high urbanization. Where labor is hard to find, unmanned robotic tractors will get the work done. US-based Deere & Company is investing heavily in the development of URTs as the world-leading company in agricultural machines, but Japanese Yanmar is catching up quickly and has recently introduced a new series of URTs, ranging from Level 4 autonomous tractors to crop-specific machines like the rice planter. While Yanmar is leading the Asian race to URTs, Iseki and Kubota are not far behind. Chinese manufacturers are making big strides, but often fail to meet western requirements and lack the proper dealer networks to enter the western markets.
Rise of the Robots
Automation has often been used to remove the human workforce – the industrial revolution devastated the craft-based industries and trades, but it still needed people to keep the machines running, and this remains true today. We are continuing to explore the potential of autonomous robotics, but the complexity of industrial operations and machinery still requires human intervention in the majority of cases. While robotics can automate the dangerous and repetitive jobs, it is the use cases outside the norm that need the flexibility of a person. It is here, just as with the information-based augmentation described previously, that augmentation of human physical ability with autonomous robots is likely to have a significant impact through the use of collaborative robotics. We have described this in the definition of our levels for industrial autonomous robots. A device with the ability to adjust to the skills of the person it is working with helps deal with the challenges companies are facing with overall skills.
The Great Crew Change Leading to Self-Driving Trucks
In commercial trucking, the great crew change is manifesting itself in a way that is transforming the industry. Aging truck drivers, combined with increasing freight volumes, have led to transportation companies seeking autonomy to mitigate the impending issue.
In the US, commercial trucks transported 11.1 million tons of freight daily in 2018. That number is projected to climb to 11.4 million tons in 2020 and 12.5 million tons in 2030, according to the federal Department of Transportation. Meanwhile, information from the American Trucking Associations (ATA) trade group showed that the average age of long-haul truckers was in the late 40s in 2018, and the shortage of drivers increased 20% to more than 60,000 that year. Employing truckers has also become more expensive, with trucker pay increasing 10% in 2018, according to the National Transportation Institute.
Transportation companies are aware of the issue and are adopting autonomy at increasing rates. Nearly one-third say they plan to have autonomous systems in place within two years, according to 451 Research's recent Voice of the Enterprise: IoT, the OT Perspective enterprise survey.
Major trucking manufacturers are developing their own self-driving technologies or partnering with technology companies to bring the features to market. The world's largest manufacturer, Daimler Trucks,
already has partially automated long-haul trucks in production, and plans to switch to highly automated trucks (SAE Level 4) within the next decade. Technology startups like TuSimple and Plus.ai are partnering with other manufacturers, such as PACCAR and Navistar, and have tested successful runs on US interstate highways with Level 4 autonomous trucks.
TuSimple has completed several round-trip deliveries from Phoenix to Dallas for the US Postal Service. While the trips included someone in the driver seat, they have been more than 90% autonomous, according to the company. Another startup,
Plus.ai, recently completed a cross-country trip on a Level 4 truck, delivering a shipment of butter from California to Pennsylvania for Land O'Lakes. The trip covered 2,800 miles and took less than three days. Similar to TuSimple's test runs, the ride included a driver and engineer to take over control if needed, but the trip was primarily in autonomous mode.
Conclusion
The great crew change is an undeniable demographic shift that is driving technology adoption. In some cases, this is in support of a new workforce, in others it is seeking to replace them. In this shift, there will be enterprise casualties that either fail to change quickly enough or that implement systems without adequate backup or security. People with experience know both how a system should work and how to bypass it when trouble strikes. Physical machinery may need to be switched to a manual operation mode if a system is compromised by hackers or has an unexpected malfunction. Plans need to be in place for situations such as these, too. Hence, is it still important to capture the knowledge and skills of the retiring crew, regardless of the levels of automation being applied.
