One of the drivers for IIoT and Big Data/Predictive Analytics investments is the shortage of skilled labor in many industries. The natural resources sector, particularly oil & gas and mining, seems to be most-challenged. This is when it comes to the volume of retiring workers and the difficulty of replacing them. A variety of factors play into this, including:
- Skills required to operate and maintain the equipment
- The inherent danger
- The unattractive work locations.
The problem I have witnessed in my continuing career of 45 years in the industry is it seems to be occurring faster than the workforce is aging. So will this latest “great shift change” be easier to resolve due to technology so that labor migrations can be continual instead of lumpy?
The Great Shift or Crew Change is Real
The great shift (or crew) change is the term used to describe the waves of retirement that happen in an industry due to generational maturing. Since WWII people have entered the work force in waves, often stimulated by waves of economic investment. The problem becomes severe when the economic cycles combine with an aging workforce to create a need to downsize an industry due to tight margins; just as a group of workers concurrently become eligible for retirement.
These workers frequently have accumulated extensive domain expertise and specific knowledge that is usually not documented or captured, and is not easily transferrable. Compounding the problem is the changing demographics of the workforce where millennials are becoming more urbanized, while work locations remain more remotely located. Also these new workers are more likely to prefer work environments that are cleaner, safer and more comfortable that previous generations were willing to tolerate. For industries, like oil & gas and mining, where production facilities might be very remote as that is where the resources are located this exacerbates the problem. Even processing facilities in industries like chemicals and oil may be relatively remote as plants with dangerous processes, and the potential for disastrous accidents are not likely to be sited in congested urban areas.
The IIoT and Big Data with Associated Predictive Analytics Provide Relief
Current workers are going to leave the workforce and new workers are not predisposed to replace them in a like-for-like manner. This means the industry must turn to technology to solve the issue in a way that allows for not only addressing the immediate issue, but to redefine the way workers are utilized going forward. We need to address not only the immediate need, but to change the model of how labor is sourced in the manufacturing and natural resources sector going forward.
The Industrial Internet of Things (IIoT) is one of the key technologies underlying Smart Connected Assets. One of the earliest uses of the IIoT will be growth in remote operations. The need for workers to actually be present in remote, dangerous and undesirable locations to perform their job functions is eliminated with extensive telematics. This is doable today and the mining industry is already finding that having equipment operators in control rooms is changing their ability to recruit new employees. There are still challenges in that biofeedback is missing when an operator is remote from the machinery being operated but the IIoT enables greater capture of performance data so that additional information can compensate. With predictive analytics some of the “gut feel” that operators gain over years of experience can be replaced with augmented reality displays for newer workers.
The ultimate end-game for Smart Connected Assets are more autonomous systems that need fewer humans to operate and manage them. This is the long-term reason to invest in Smart Connected Assets. The industry needs to reduce the reliance on human experts to operate processes and leverage technology, particularly big data and predictive analytics. This is to redesign processes so that companies need fewer operators. Of course maintenance technicians will still be required, but with Smart Connected Assets maintenance can be provided by the equipment OEM. Also it can be provided with improved reliability as a result of deploying asset performance management (APM) solutions. This way visits to actual work sites can be minimized both in frequency and duration.
Cloud technology coupled with Big Data and Predictive Analytics are game changers, but only if the industry begins to leverage them today. If the industry just invests minimally in remote location technology and stops there it will face another “great shift change” in another 15 to 20 years. The industry can kick the can down the road, or it can finally change the way it does business. The choice is there – businesses just need to make it.
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