Industrial operations are getting more productive, but also more complex as the Industrial Internet of Things connects people, process and technology together. But with massive knowledge attrition pending due to generational workforce changes, utilities must look beyond just integrating IT and OT and look to reduce complexity and enable knowledge transfer in order to continue to improve efficiencies. The answer to solving this challenge and to deliver unprecedented intelligence, productivity and reliability utilities need to look to transition from just the initial IoT efforts, to move to true Software Defined Operations.
To get to the current level of operational insight, utilities have had to overcome hurdles due to organizational structural, while also focusing on collaboration and the integration of collective skills throughout the organization, to contribute to the success of the business. As part of this operational maturity, some utilities have also implemented a consistent process model allowing businesses to create proper handoffs and responses to issues that may span multiple areas. For example, when it comes to the Industrial Internet, security plays a key role. The operator of an OT system is likely to be unfamiliar with IT security procedures and protocols, so the IT team needs to develop protocols that encompass security because it is the operator who needs to know if there is an actual problem with the sensor or if the issue is simply with a security key.
This awareness is at the core of Software Defined Operations, and is the key to future smart grids and beyond that, smart cities. In the midst of this major sea change in technology there lies a greater transformation driving the next generation of energy performance and reliability, and with it, the ability to never lose tribal knowledge.
The pending knowledge migration
Today, utilities are struggling to find a way to codify tribal knowledge and experience so it can be passed on to new employees to get them up to speed quickly. According to the US Bureau of Labor Statistics, by 2016 millennials will account for 36% of the US workforce and by 2025 they will account for 75% of the global workplace. Millennials surpassed baby boomers in the workforce in 2014 and generation X in 2015, and this will only increase as boomers move toward retirement. This pending tidal wave of retirements and influx of new, lesser experienced employees can dramatically impair the way an industrial organization operates, and can mean a massive migration of experience and knowledge from both operational and informational roles.
Another important aspect we must consider about this generational shift is the expectations of the generation replacing those who are retiring. For baby boomers it was common for them to stay with a company for 20 years or more. However, today's workforce does not have that same expectation of longevity with a single company. They expect to work at a company for maybe five years to develop skills and professional acumen, and then they look for the next company to progress their career. This change in mindset towards work tenure is weighing a heavy toll on the training budgets of many organizations.
In light of this, utilities must begin to approach operations and employee training in a new manner. Organizations once took the experience and knowledge of their long-time workers and built it into the training programmes they provided new employees. Now we can use machine learning, predictive analytics and Software Defined Operations to build that experience and knowledge into the OT and IT systems themselves.
Bridging the knowledge gap
By translating tribal knowledge of longtime workers into documented processes that are enabled and directed by technology, utilities can bridge the knowledge gap between longtime workers and newer employees who may only stay with the company for a few years. This process documentation also protects organizational knowledge that senior management may have only relied upon to be in the head of their more tenured workers.
To make the best usage of these new training processes we have to look at how a new employee learns on the job.
Actions become instinctive
Over time, individuals are trained to recognize a specific occurrence, set of metrics or indicators and perform a task in response to those indicators. In other words, a situation occurs in a specific condition and requirements are met through the actions of the individual. This is a rules-based process. The more we train and practice doing the proper response, the more instinctive the reaction becomes. Your alarm clock rings in the morning and you instantly reach over to shut it off without thinking… that's an instinctive reaction that has been built through years of conditioned response to a specific situation. It’s done without thinking and the action is completed.
This instinct is what we lose most when we have workforce turnover. It is this instinct that equates to experience. What makes instinct and experience so important is not the Pavlovian response of an alert lights up, so I press this button, but when an employee instinctively knows how solve a problem that has never been encountered before. A robot can press a button when an alert lights up, but what happens when the light doesn’t come on when it should? Or, how can we tell if it’s simply a problem with the electrical circuit connected to that light, or a true operational event that must be dealt with?
These are the types of situations that cannot be solved with robotic conditioning. Instead, these actions are informed by instinctively understanding and assessing the circumstances around you, the environment in which you are operating and the current conditions that are dictating your actions. The challenge is that it takes years of experience, training and practice to gain that instinct – an investment of time that we don’t necessarily have if we want to capture immediate value from new employees.
Training technology and systems
Instead of training individuals to gain these instincts over decades, we can train the technology and operational systems to become instinctive. Using artificial intelligence, machine learning and rule-based analysis of events, we can train the software to respond automatically to certain triggers and automate most manual workflows. This essentially creates an instinctive virtual operator that you do not have to be concerned about employee turnover that knows how to consistently assess and handle most situations that occur.
The right technology can give the current and future generations the skills to perform as though they have many years of experience on the job.
Moving to the edge
Most of what we just described generally takes place within the network operation centre. To truly take advantage of Software Defined Operations we must move the instinctive experience of those long-term employees to the edge of the operations. With a technology platform that can operate from the central infrastructure, to the cloud, to the edge of the network, an organization can push the instinctive to the edge and focus on the intuitive.
Let’s look at an example: a situation occurring at the edge of the network would require a service person to go out to the field to assess the situation, then travel back to the command centre to gain the data to respond to the circumstance. This is purely a responsive function that doesn’t take full advantage of the capabilities of the worker at the edge, and is an ineffective model for operations.
If the operator can access that intelligence anytime and anywhere, they can begin to focus their intuitive and instinctive skills informed by the rule-based decisions of the technology at the point of service. That new worker can perform similar to a seasoned veteran making all the necessary adjustments and informed decisions based on the analytics at the very edge of the network.
Only with Software Defined Operations can utilities bridge the tribal knowledge gap between the senior worker and the next generation coming in while also providing those new workers with the conditioning, training, acumen, skill set professionalism and information that is typically only found in a long time service employee. This is what the future of smart utilities looks like.
To hear more about this topic, join Kevin Collins, CEO at Bit Stew, and other industry leaders in the panel discussion, Big Data & Analytics and IoT, at European Utility Week 2015. Topics will range from how to approach new business start-ups from an incumbent utility to dealing with competition from new entrants in the energy market and how IoT impacts the value chain.
Mike Varney spent over 20 years in the US Navy, where his experience included commanding the most advanced nuclear-powered submarines in complex operations around the globe, leading a special operations team in reconstruction efforts in Afghanistan, and directing a Naval Operations Centre. He has also served as a strategic advisor for the US Department of Defense, a senior evaluation officer at nuclear power plants, and an advisor to companies providing smart grid technologies to utilities. Mike holds Bachelor of Science degrees in Nuclear and Marine Engineering as well as Engineering Management. He also holds a Master of Science degree in National Security Strategy. Today, Mike is the Executive Director, Strategic Initiatives where he leads the strategy for Bit Stew Systems MIx Developer Network and Bit Stew University.