The distributed nature of flow movement, measurement and control systems lends itself to the benefits of an Industrial Internet of Things- (IIoT) based solution. Current basic deployment scenarios of the IIoT solutions implemented for process manufacturers and related companies include:
- Greenfield deployments, primarily smart solutions related to advanced monitoring and visibility
- Brownfield upgrades, the introduction of IIoT technologies and approaches to existing facilities to expand asset and process visibility and analytics
- New asset-monitoring services, or “servicization,” for vendors that provide remote predictive analytics and monitoring assets
The common benefit of these solutions is visibility and insight into assets and processes in real time, regardless of location. IIoT solutions collect, integrate and organize data for engineers and scientists to analyze and improve production outcomes. Benefits of these solutions include improved maintenance programs for higher reliability at lower cost, optimized resource usage and predictive analytics to detect and deal with problems before they occur.
These benefits are why the enthusiasm and growth in IIoT momentum over the last year has been so strong. This enthusiasm does not mean the industry has agreed on its naming conventions, so the industry continues to refer to “digital transformation,” “smart manufacturing,” the “fourth industrial revolution” and “Industry 4.0” in addition to IIoT.
Regardless of what a sensored, connected and integrated plant or facility is called, the IIoT continues its march forward as the expected architecture. The competitive advantages enabled by increased and consistent visibility, accuracy and data-driven insights on production results are too important to ignore. A $50 billion opportunity exists from IIoT implementations in oil and gas alone, with other sectors of industry expected to see similar, if not better, results.
If an organization has not begun its IIoT journey, it is time to play catch-up.
Seize the day
Research shows a large gap between the expectations and advantages of IIoT, and the state of deployment efforts. Further, IIoT deployments are generally piecemeal rather than broadly applied. IIoT benefits are therefore not realized by many end users because the opportunities are still future possibilities instead of being already recognized in bottom-line results.
Part of this gap may be caused by the often-cited advice that companies should start small and focus on IIoT deployments to prove value and impact before moving on to more widespread implementations. This advice is hard to argue with and aligns with the limited number of large IIoT deployments within process industry companies.
Early success will bode well for organizational enthusiasm and continued IIoT investments, but the time between starting small and deploying widely must be kept to a minimum, preferably months instead of years. This ensures a company will not fall further behind more forward-thinking competitors.
Focus on business impact, not technology
In many IIoT discussions, the starting point is the sensor, the origination point for data flowing through a communications network to a centralized application with data storage, integration with other data and finally, analytics. It is easy and fun to focus on sensors and other hardware and software given the prevalence of interesting new technologies like microsensors; Arduino, Raspberry Pi and Intel Galileo platforms; long-life batteries; low-power wireless systems; and builder kits from Microsoft, IBM and Amazon. However, technology is just a means to an end.
Unfortunately, these technologies often do not serve the business end. The right question is not what is possible with new technologies, but what is important to a business: a quantifiable, positive impact on production and business outcomes.
This impact can be realized in many ways such as increased uptime, improved quality, higher yields and better asset utilization. Positive results could also include motivating and incentivizing employee behavior through visibility of their actions and effects on production.
The best IIoT deployment stories are those that demonstrate positive impacts on the bottom line. These stories stress impacts and work backward to the technologies making these benefits possible.
How to add value
Servicization, or remote monitoring services, represents the transition from an asset view to a capability view. This opportunity is typically framed from the point of view of the asset vendor, but probably should instead be framed in terms of end-user benefits. GE, for example, talks about moving from selling turbines to selling services, and many major pump manufacturers use similar rhetoric.
Organizations often ask what should be outsourced in IIoT. Pump vendors may have expertise that is difficult to duplicate in-house, particularly because most vendors monitor hundreds or thousands of pumps in other industrial applications.
The savings in scheduled pump maintenance alone could justify the cost of an outsourced monitoring service in some cases. In other cases, however, remote monitoring services can be a disruptive issue for employees and processes, so care must be taken when deciding what to outsource and what to keep in-house.
By tapping expertise on assets from vendors, end users can focus more on results from the value created by asset integration than on the status of one link in the process. If the end-user company’s differentiating expertise is in asset optimization, then this may be less important, but the company should ask where the organization creates the most value.
Recognize analytics requirements
IIoT solutions typically rely on an assumption that at some point the “magic happens here” to close the gap between raw data and actionable insight. Typically, this magic is buried under a banner of machine learning, big data or advanced analytics. Where and how these key insights are revealed is often given insufficient consideration, though.
Looking at end-user examples, the real work of analytics includes “data wrangling” or the aggregation, cleansing and contextualization of business and process data. Typically, 70 to 80 percent of analytics is simply getting the data right before the analysis can be performed, and this will only become more complicated as end users install more connected products that provide more streams of data for integration (see Figure 1).
The issues of engineer productivity, team collaboration and process industry capabilities must also be dealt with in the context of analytics offerings. These are hard issues, and therefore the specifics of analytics requirements deserve the same attention as the business cases.
The right path likely addresses these needs — in terms of data and in teamwork — by providing end-user engineers and production experts with applications to quickly create actionable information from raw data. These tools rely on visualization of data from which insights can be derived.
Any new project or proposal requires effort to overcome the inertia of doing nothing. IIoT projects are in the balance between the availability of necessary technology and the risks and rewards of execution, a difficult juncture. And for IIoT, the two reasons — some would say excuses — for lack of forward progress are often standards and cybersecurity.
With standards, for example, this or next year will not bring a world of functional, compatible, Lego blocks an end user can use to create a mixed-vendor, distributed IIoT solution. Customers must jump in at some level and get started instead of waiting for winning standards.
Meanwhile, cybersecurity is consistently a leader on lists of IIoT requirements, concerns and issues. Heating, ventilation and air conditioning, supervisory control and data acquisition and other systems have been hacked. Unfortunately, no easy answer to these issues exists. What is needed is hard work, best efforts and solutions with limited exposure to the outside world.
But using a lack of standards or fear of cybersecurity issues as an excuse to not move ahead on IIoT implementations pushes the required learning and experience further down the road, and puts end users further behind in the IIoT race.
A new generation of sensors means data can be economically generated and gathered from flow control and related systems in quantities previously unavailable. Data can then be sent to process control and monitoring systems.
This data can be used to improve automated real-time control and help plant engineers and operators make better decisions. Personnel can use it can use to increase efficiency, diagnose equipment problems and improve safety.
The IIoT is here, and whether organizations implement it directly or leverage offers from asset vendors, it is the model and opportunity for the next generation of plant and facility infrastructure.